https://videos.concepttocloud.com/v/guardrails-your-ai-cant-talk-its-way-past Guardrails your AI cant talk its way past Free access plus LLM-written SQL might give decent results, but not every time. Scoped permissions, approvals and limits, enforced by the system rather than the prompt. Full video: https://youtu.be/JezW4GI1v04 Shorts AI agents datagovernance https://video-meta.open.video/poster/y-CWAbmRtD35/AsoAbVU5iR2_FeQjxF.jpg https://streaming.open.video/contents/y-CWAbmRtD35/1790809495/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AsoAbVU5iR2 46 https://videos.concepttocloud.com/v/where-ai-guardrails-actually-belong Where AI guardrails actually belong Where do AI guardrails belong? In the data model: PII-safe pipelines that keep sensitive data inside the boundary, and decision logging on by default. Full video: https://youtu.be/7rcCUeXe1ZM Shorts AI dataprivacy compliance https://video-meta.open.video/poster/6pr8Eh8R7nLY/AYUBaUUPiQ2_sIjGbO.jpg https://streaming.open.video/contents/6pr8Eh8R7nLY/1790809621/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AYUBaUUPiQ2 26 https://videos.concepttocloud.com/v/dont-just-give-your-llm-the-database Dont just give your LLM the database The easy way to put AI over your data: give an LLM the database and say have at it. Sometimes it infers the schema right. Sometimes is the problem. Full video: https://youtu.be/JezW4GI1v04 Shorts AI LLM agents https://video-meta.open.video/poster/-FvicxSJFvoA/AIUkH-UiPR2_GeqqoY.jpg https://streaming.open.video/contents/-FvicxSJFvoA/1790809622/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AIUkH-UiPR2 44 https://videos.concepttocloud.com/v/wire-an-agent-to-real-data-without-it-going-rogue Wire an Agent to Real Data Without It Going Rogue An agent that cant touch anything is useless. An agent that can touch everything is dangerous. The answer is in the middle, and it isnt a better prompt. The easy way to put AI over your data is to hand an LLM the database and say have at it. Sometimes the SQL it writes is right. The problem is sometimes. In this one I walk through the alternative: a governed layer over your data, with scoped permissions, approvals and limits enforced by the system, so the same question gets the same, correct answer every time. Building with agents? https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit - The tension: useful access versus safe access - Why free access and LLM-written SQL isnt good enough - Scoped permissions, approvals and limits enforced by the system - Semantic models (Apache OSI, Snowflake, Malloy) so a metric means one thing 0:00 Useful and safe at the same time 0:42 How do I deploy AI over my data? 1:26 Should everyone LLM their own reports? 2:04 The wrong way: free access, LLM-written SQL 2:38 The right way: scoped permissions, approvals and limits 3:32 The security cost: training on and leaking your data 3:54 Most leaks start inside the building 4:34 Semantic models: teach the LLM how your data fits together 5:10 Gross profit, calculated two ways 5:49 Sensible security defaults, not lockdown Concept To Cloud builds agentic AI that is safe to run in production. Run the AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI agents MCP aisafety https://video-meta.open.video/poster/8lzGArjJAfh6/AsURaVoiP72_vGCqTa.jpg https://streaming.open.video/contents/8lzGArjJAfh6/1790809639/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AsURaVoiP72 415 https://videos.concepttocloud.com/v/guardrails-in-the-prompt-arent-guardrails Guardrails in the prompt arent guardrails Writing guardrails into the prompt doesnt hurt, but it isnt a control. Tell an auditor thats how you stop the model misbehaving and theyll send you back for a better answer. Full video: https://youtu.be/7rcCUeXe1ZM Shorts AI LLM compliance https://video-meta.open.video/poster/8Bf0ltT6Ffo6/6solb-VzOQ2_kyZTbe.jpg https://streaming.open.video/contents/8Bf0ltT6Ffo6/1790809622/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6solb-VzOQ2 35 https://videos.concepttocloud.com/v/how-do-you-cut-into-a-system-nobody-understands How Do You Cut Into a System Nobody Understands? Can you use safe seams how do you cut into a system that no one fully understands without breaking it? That question drives everything else in a modernisation project. Full talk https://youtu.be/Tge0qCkKUn4 softwarearchitecture legacymodernisation engineering shorts https://video-meta.open.video/poster/z1qKgnetxnTA/7ZU6aooyP72_bQtmWU.jpg https://streaming.open.video/contents/z1qKgnetxnTA/1790809990/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7ZU6aooyP72 12 https://videos.concepttocloud.com/v/the-strangler-fig-approach-to-legacy-rewrites The Strangler-Fig Approach to Legacy Rewrites The strangler fig approach: can you take a small part, modernize it, deploy it, get it running in production, and then move on to the next one? Replace it piece by piece, not all at once. Full talk https://youtu.be/Tge0qCkKUn4 stranglerfig softwarearchitecture legacymodernisation shorts https://video-meta.open.video/poster/8ImitlvcsfoY/BZUkbVpjOR2_OkOARs.jpg https://streaming.open.video/contents/8ImitlvcsfoY/1790809989/index.m3u8 https://videos.concepttocloud.com/embed?contentId=BZUkbVpjOR2 14 https://videos.concepttocloud.com/v/one-free-fix-for-your-whole-supply-chain One Free Fix for Your Whole Supply Chain If you take one operational change away from this: pin deployments to digests, not tags. Its free, its an afternoon of work, and it makes everything else in a provable supply chain possible. Full talk https://youtu.be/9B7TNdiCWuU devsecops softwaresupplychain sigstore shorts https://video-meta.open.video/poster/D2e8tz0BZDhz/7tURGEpjPQ2_EhRTHG.jpg https://streaming.open.video/contents/D2e8tz0BZDhz/1790809995/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7tURGEpjPQ2 13 https://videos.concepttocloud.com/v/four-or-five-databases-when-one-would-do Four or Five Databases When One Would Do A lot of teams run four or five databases when one would do. Let me make the case for keeping it boring. Full video https://youtu.be/rLCGAf2JGj4 postgres postgresql database dataengineering shorts https://video-meta.open.video/poster/zYmall1cYnn5/7tpkbFojO72_qYcATv.jpg https://streaming.open.video/contents/zYmall1cYnn5/1790809988/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7tpkbFojO72 10 https://videos.concepttocloud.com/v/ask-your-platform-where-the-test-set-is Ask Your Platform Where the Test Set Is If you take one thing from this: go and ask your platform where your best models test set is, and see how long it takes to find that out. Full talk https://youtu.be/v0lUudGbwQ mlops dataengineering machinelearning dataprovenance shorts https://video-meta.open.video/poster/B2iakDqAtbMY/BJplbooyP62_tNEMeu.jpg https://streaming.open.video/contents/B2iakDqAtbMY/1790809988/index.m3u8 https://videos.concepttocloud.com/embed?contentId=BJplbooyP62 9 https://videos.concepttocloud.com/v/it-works-isnt-the-finish-line-for-regulated-ai It works isnt the finish line for regulated AI In a regulated business, the finish line isnt it works. Its it survives an audit. And if the guardrail isnt in the data model, it doesnt exist. Full video: https://youtu.be/7rcCUeXe1ZM Shorts AI compliance regulatedAI https://video-meta.open.video/poster/z3vmBjudYbD6/Y6pkGUViPl2_XAaJqW.jpg https://streaming.open.video/contents/z3vmBjudYbD6/1790810116/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Y6pkGUViPl2 17 https://videos.concepttocloud.com/v/a-very-expensive-compute-bill A Very Expensive Compute Bill If you use Delta Live tables as your underlying compute and then serve a web app off the back of it, youre going to have some very sad customers and a very expensive compute bill. Use the right tool for the reasons it was actually built. Full video https://youtu.be/rLCGAf2JGj4 postgres dataengineering backend shorts https://video-meta.open.video/poster/-uaWxzXl6rMW/YkVBbpozOk2_LcKOmO.jpg https://streaming.open.video/contents/-uaWxzXl6rMW/1790810118/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YkVBbpozOk2 16 https://videos.concepttocloud.com/v/a-good-assessment-can-tell-you-not-to-buy A Good Assessment Can Tell You Not to Buy A good AI readiness assessment can tell you not to buy. Our job isnt to say yes, go ahead with AI no matter what. It doesnt make a difference to us whether or not the project goes ahead but the honesty does matter. Full video https://youtu.be/1vrKRjJr2Jg ai aireadiness machinelearning shorts https://video-meta.open.video/poster/zua0xB0d6bK7/YQV7aVVzOA2_kpeHGX.jpg https://streaming.open.video/contents/zua0xB0d6bK7/1790810116/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YQV7aVVzOA2 17 https://videos.concepttocloud.com/v/a-fractional-cto-right-for-some-a-waste-of-money-for-others A Fractional CTO: Right for Some, a Waste of Money for Others A fractional CTO is senior technical leadership without the full-time salary. For some companies thats exactly right. For others its a waste of time and money. Full video https://youtu.be/8evt9kOEeXM fractionalcto startups cto techleadership shorts https://video-meta.open.video/poster/43ziBFWAZn_W/YkU6G-pPOA2_ucMabZ.jpg https://streaming.open.video/contents/43ziBFWAZn_W/1790810116/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YkU6G-pPOA2 18 https://videos.concepttocloud.com/v/what-happens-when-your-llm-goes-offline What Happens When Your LLM Goes Offline? If youre baking an LLM into a product, what happens when it goes offline? Because these things have a habit of going offline. Does your product continue to work? What do those failure modes look like, and what can you do to mitigate the failure of an LLM service? Free AI-Readiness Audit https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI MLOps reliability dataengineering shorts https://video-meta.open.video/poster/7uqGgpWdBbL6/YkUQaUU4OB2_BObkXz.jpg https://streaming.open.video/contents/7uqGgpWdBbL6/1790810118/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YkUQaUU4OB2 17 https://videos.concepttocloud.com/v/a-chromebook-but-for-linux A Chromebook, but for Linux Chromebook reliability with a GNOME desktop underneath. Give your granddad one and hell have a tough time breaking it and if he does, you flash it and start again. Full build-along https://youtu.be/nhx27jZDCL4 Shorts Linux Bluefin GNOME https://video-meta.open.video/poster/74eSdjGc3DvW/YRVBGUVPOB2_MICzvx.jpg https://streaming.open.video/contents/74eSdjGc3DvW/1790810617/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YRVBGUVPOB2 20 https://videos.concepttocloud.com/v/this-isnt-research-it-ships-today This Isnt Research It Ships Today Sigstore and SLSA are open source, free, hosted by the OpenSSF, and run on the CI you already have. Theres no product here you have to buy to get started. Full talk https://youtu.be/9B7TNdiCWuU opensource openssf devsecops shorts https://video-meta.open.video/poster/98yykFGlZjCy/ZAoka_ozjB2_jCDqPg.jpg https://streaming.open.video/contents/98yykFGlZjCy/1790810626/index.m3u8 https://videos.concepttocloud.com/embed?contentId=ZAoka_ozjB2 21 https://videos.concepttocloud.com/v/good-a-solution-as-any-other-out-there Good a Solution as Any Other Out There There are reasons not to use Postgres. But if you want flexibility, decent speed, maintenance, and something you can Google the answer for nine times out of ten, Postgres is as good a solution as any other out there. Full video https://youtu.be/rLCGAf2JGj4 postgres postgresql database dataengineering shorts https://video-meta.open.video/poster/C4qKcpGl3r37/Z6Ulb_UjiB2_iAjIdi.jpg https://streaming.open.video/contents/C4qKcpGl3r37/1790810617/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Z6Ulb_UjiB2 21 https://videos.concepttocloud.com/v/why-a-docker-tag-can-lie-to-you Why a Docker Tag Can Lie to You A tag is a label like a sticky note on a warehouse shelf. Someone can move it overnight and the note still reads the same. A digest is the fingerprint of the contents themselves: it cant be moved, only matched. Full talk https://youtu.be/9B7TNdiCWuU docker supplychainsecurity devops shorts https://video-meta.open.video/poster/D4m0dDbs3v0X/tkpBGpozjA2_IcplLr.jpg https://streaming.open.video/contents/D4m0dDbs3v0X/1790810625/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tkpBGpozjA2 18 https://videos.concepttocloud.com/v/does-this-even-need-ai Does This Even Need AI? Before you build it: does what youre trying to do with AI actually add value, or would it be easier to build it out a different way and skip the hosting, liability, and complexity that comes with AI? A lot of the time people still go the AI route anyway. At least be aware of the option. Full video https://youtu.be/1vrKRjJr2Jg ai machinelearning dataengineering shorts https://video-meta.open.video/poster/C8yaxFastbFr/tQVlaVVijB2_XuUDDD.jpg https://streaming.open.video/contents/C8yaxFastbFr/1790810617/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tQVlaVVijB2 18 https://videos.concepttocloud.com/v/what-an-ai-vendor-never-mentions What an AI Vendor Never Mentions We dont work like a sales pitch. Heres the red flag: if you speak to an AI vendor, theres unlikely to be any discussion about your data quality or compliance. What comes out the other end is a recommendation to use their product. Full video https://youtu.be/1vrKRjJr2Jg ai aireadiness dataengineering shorts https://video-meta.open.video/poster/88aKxFqlYr_s/ZAVRb_oPPB2_lsWAuX.jpg https://streaming.open.video/contents/88aKxFqlYr_s/1790810619/index.m3u8 https://videos.concepttocloud.com/embed?contentId=ZAVRb_oPPB2 21 https://videos.concepttocloud.com/v/why-freezing-your-legacy-system-doesnt-work Why Freezing Your Legacy System Doesnt Work Dont ask me how I know, but quite often the old system doesnt fully go away legacy customers, people who cant migrate. So even if you freeze a product and restart from scratch, quite often youre going to become a cropper. Full talk https://youtu.be/Tge0qCkKUn4 legacymodernisation softwarearchitecture engineering shorts https://video-meta.open.video/poster/yVvGFlucsnSq/tkpRH_VOOl2_CRccIq.jpg https://streaming.open.video/contents/yVvGFlucsnSq/1790810614/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tkpRH_VOOl2 21 https://videos.concepttocloud.com/v/the-first-10-technical-decisions The First 10 Technical Decisions Before your first technical hire, you need the first 10 decisions to be the right ones. What platform are you using? Build versus buy? What direction from a technology perspective? You dont need a huge technical background to get this right, you just need someone whos answered these questions before. Full video https://youtu.be/8evt9kOEeXM fractionalcto startups founders shorts https://video-meta.open.video/poster/64eewFXkZj9s/t7UAb-oiik2_dgPTFZ.jpg https://streaming.open.video/contents/64eewFXkZj9s/1790810618/index.m3u8 https://videos.concepttocloud.com/embed?contentId=t7UAb-oiik2 21 https://videos.concepttocloud.com/v/we-migrated-a-1992-nasa-c-program-to-aws We Migrated a 1992 NASA C Program to AWS When I was working at NASA, we were tasked with moving a 1990s C program into AWS. Basically all the staff had left there was no one left who understood it. Heres how we did it anyway. Full talk https://youtu.be/Tge0qCkKUn4 NASA legacymodernisation AWS softwarearchitecture shorts https://video-meta.open.video/poster/C_nKAjLswjeY/YkoBHFpijQ2_TlizVB.jpg https://streaming.open.video/contents/C_nKAjLswjeY/1790810615/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YkoBHFpijQ2 27 https://videos.concepttocloud.com/v/what-a-signature-actually-proves What a Signature Actually Proves A signature answers exactly one question: who vouched for this exact content? Not the vendor. Not the project. Not the version. This content, byte for byte change one byte anywhere, and verification fails. Full talk https://youtu.be/9B7TNdiCWuU sigstore cosign devsecops shorts https://video-meta.open.video/poster/zOu0cFXc2zwt/skolHpVji72_SGUPxE.jpg https://streaming.open.video/contents/zOu0cFXc2zwt/1790810625/index.m3u8 https://videos.concepttocloud.com/embed?contentId=skolHpVji72 27 https://videos.concepttocloud.com/v/training-is-the-easy-part Training Is the Easy Part A pipeline that runs in a sandbox and a pipeline that runs where failure is not an option are not the same system. What changes is everything around the model. Storage, for one. In a sandbox the model is wherever the notebook wrote it. In production it needs an addressable archive with a versioning policy you have to be able to name a thing and get exactly that thing back. Years later. Full talk https://youtu.be/v0lUudGbwQ mlops machinelearning dataengineering kubernetes shorts https://video-meta.open.video/poster/AVfqEp1Asrh7/s6p6bFpzi72_fRRZsc.jpg https://streaming.open.video/contents/AVfqEp1Asrh7/1790810621/index.m3u8 https://videos.concepttocloud.com/embed?contentId=s6p6bFpzi72 24 https://videos.concepttocloud.com/v/watch-it-update-while-i-look-away Watch it update while I look away Notes appearing on the wall in real time while I am looking at a completely different screen, because Supabase realtime is pushing them straight through Postgres. No polling, no refresh button, no extra service to run. Full build-along: https://youtu.be/got09pWBMG0 Shorts supabase postgres realtime webdev https://video-meta.open.video/poster/ySeOwnHc2r2Y/YkV6a_Vyi72_mZPgjm.jpg https://streaming.open.video/contents/ySeOwnHc2r2Y/1790810625/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YkV6a_Vyi72 25 https://videos.concepttocloud.com/v/at-what-point-did-civilization-shut-down-for-two-months-to-upgrade-the-internet At what point did civilization shut down for two months to upgrade the internet? Dial-up to gigabit in 30 years. The internet never went offline for two months to do it. Thats the bar for a rebuild. If customers depend on your service, you cant change the interface and expect them to go read the docs. Nobody reads the docs. The upgrade has to happen underneath them. Ep.01 of the Concept To Cloud podcast. Full episode at the link in bio. productdevelopment softwarerebuild legacysystems cto productmanagement https://video-meta.open.video/poster/4_vOFBLlYjmZ/s6p7aEUiiR2_dnSzsb.jpg https://streaming.open.video/contents/4_vOFBLlYjmZ/1790810617/index.m3u8 https://videos.concepttocloud.com/embed?contentId=s6p7aEUiiR2 25 https://videos.concepttocloud.com/v/the-desktop-that-cant-drift The desktop that cant drift Production solved configuration drift a decade ago: you build an image, you ship it, and if its wrong you get the old one back. Bluefin points the same idea at a laptop the OS is an OCI image, so the machine cant drift because theres nothing to drift into. Full build-along https://youtu.be/nhx27jZDCL4 Shorts Linux Bluefin CloudNative https://video-meta.open.video/poster/B7uSEnKd7vp7/Y7VRGppOiR2_bPNQEF.jpg https://streaming.open.video/contents/B7uSEnKd7vp7/1790810878/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Y7VRGppOiR2 29 https://videos.concepttocloud.com/v/your-laptop-is-haunted Your laptop is haunted Install something to test it. Follow a Stack Overflow answer at midnight. Add a repo and forget about it. Two years later nothing is obviously broken, but the laptop is haunted and the only fix on offer is to wipe it and start again. Full build-along https://youtu.be/nhx27jZDCL4 Shorts Linux Bluefin DevOps https://video-meta.open.video/poster/74zGsFusAbS4/YBUkb_pzP62_PprjJy.jpg https://streaming.open.video/contents/74zGsFusAbS4/1790810878/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YBUkb_pzP62 30 https://videos.concepttocloud.com/v/a-run-is-a-verb-a-product-is-a-noun A Run Is a Verb. A Product Is a Noun. Your platform remembers runs. Every one of those questions is about a product. A run is a verb something that happened at a point in time. A product is a noun something that exists and persists. Platforms get built by people optimising the verb. Auditors, incident reporters and your future self all ask about the noun. Full talk https://youtu.be/v0lUudGbwQ mlops dataengineering kubeflow dataprovenance shorts https://video-meta.open.video/poster/FOjmAzesAbo4/s7Ukb-UyO62_vVdWPQ.jpg https://streaming.open.video/contents/FOjmAzesAbo4/1790810879/index.m3u8 https://videos.concepttocloud.com/embed?contentId=s7Ukb-UyO62 28 https://videos.concepttocloud.com/v/there-is-bias-in-product-feedback There is bias in product feedback Feature requests can only reflect the vocabulary of the product users already see. Which means your roadmap gets shaped by whats visible, not whats actually needed. Users dont ask for things they cant imagine inside the current interface. So the backlog fills up with incremental improvements to screens that might not even be the right screens. The fix isnt more feedback. Its watching workflows around the product, not inside it. Where do people drop out? Where do they open a spreadsheet because the tool didnt do the thing? This is what our Assess phase actually looks like in practice. One to two weeks going through the platform, the architecture, and how the team works, watching real workflows, not just reading code. We come back with a written report of whats blocking you and a costed plan to fix it. That plan belongs to you, no obligation to come back. Full episode on Concept to Cloud EP. 01 concepttocloud.com/podcast/concept-to-cloud/tack-on-or-rebuild productengineering productmanagement feedbackloops productstrategy softwareengineering https://video-meta.open.video/poster/9SzWwjXl_nU6/Y7UBHVozPQ2_JlkhXd.jpg https://streaming.open.video/contents/9SzWwjXl_nU6/1790810878/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Y7UBHVozPQ2 27 https://videos.concepttocloud.com/v/nasas-mars-data-took-30-hours-we-got-it-to-10 NASAs Mars Data Took 30 Hours. We Got It to 10. A scientist could form a hypothesis on Mars and not get the answer back the same sol. NASAs PIXL instrument reads the chemistry of Martian rock live from the Perseverance rover. Processing that data took 30 hours long enough that the science had to wait for the pipeline. We rebuilt the cloud backend and got it to 10 minutes. 180x. Twelve weeks concept to production. NASA Software of the Year runner-up, 2023. Full story https://youtu.be/APhsIuB4QpY Work with the team that did this https://concepttocloud.com/contact NASA Mars dataengineering cloudengineering Perseverance shorts https://video-meta.open.video/poster/6SfyczGt7nL4/YBURHUVjOQ2_Ivbedb.jpg https://streaming.open.video/contents/6SfyczGt7nL4/1790810877/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YBURHUVjOQ2 27 https://videos.concepttocloud.com/v/i-asked-claude-to-build-something-on-supabase I asked Claude to build something on Supabase I gave Claude Code one line: build something interesting on this Supabase project. It came back with a semantic sticky wall. A live notes wall with embeddings generated entirely locally by the edge runtimes built in GTE-small model, so semantic search runs with no API key and nothing leaving the machine. Auth, Postgres, RLS, pgvector, realtime, storage, edge functions and pgcron, all exercised in one app. Full build-along: https://youtu.be/got09pWBMG0 Shorts supabase claudecode pgvector ai https://video-meta.open.video/poster/8xmyclqY3vMY/ZkVkGpV4PR2_IydPzV.jpg https://streaming.open.video/contents/8xmyclqY3vMY/1790811014/index.m3u8 https://videos.concepttocloud.com/embed?contentId=ZkVkGpV4PR2 30 https://videos.concepttocloud.com/v/an-ai-wrote-this-manifest-it-broke-7-policies An AI Wrote This Manifest. It Broke 7 Policies. An AI coding assistant was asked for a Kubernetes manifest for a debug sidecar that could inspect node-level networking. This is what came back, unedited. It is confident. It is well formatted. It has a helpful comment next to the privileged flag explaining that it is needed for packet capture which is true. Every individual decision in it is defensible. Nobody was careless. It breaks seven separate policies. The interesting part is not that a machine wrote it. It is that it arrives in your cluster at three in the morning through a pipeline, with nobody in the loop to feel uneasy about it. Full talk policy as code on two real clusters, OPA Gatekeeper vs Kyverno: https://youtu.be/CzBgg8wedM kubernetes policyascode devsecops platformengineering shorts https://video-meta.open.video/poster/8veetpL62zgz/Z6V7bVo5OQ2_SIJInQ.jpg https://streaming.open.video/contents/8veetpL62zgz/1790811007/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Z6V7bVo5OQ2 32 https://videos.concepttocloud.com/v/postgres-quietly-does-a-lot-more-than-select Postgres Quietly Does a Lot More Than SELECT You can use JSON out the box. You can bring in full text indexes for search. Youve got queues, PG vector, analytics, and a whole array of different extensions. Postgres quietly does a lot more than a select statement. Full video https://youtu.be/rLCGAf2JGj4 postgres postgresql dataengineering backend shorts https://video-meta.open.video/poster/zvaCBlaJZfmY/tRokbEoji72_Jvyist.jpg https://streaming.open.video/contents/zvaCBlaJZfmY/1790811010/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tRokbEoji72 32 https://videos.concepttocloud.com/v/not-everybody-should-have-a-fractional-cto Not Everybody Should Have a Fractional CTO Not everybody should have a fractional CTO. If you have a strong lead who just needs a backup, hire a senior engineer, not a CTO. If the work is one defined project, scope the build and skip the leadership role entirely. Full video https://youtu.be/8evt9kOEeXM fractionalcto startups cto techleadership shorts https://video-meta.open.video/poster/CbumhBfR2rh7/t7UAbpUziQ2_MVFtAG.jpg https://streaming.open.video/contents/CbumhBfR2rh7/1790811006/index.m3u8 https://videos.concepttocloud.com/embed?contentId=t7UAbpUziQ2 29 https://videos.concepttocloud.com/v/most-companies-buy-the-ai-tool-first-thats-backwards Most Companies Buy the AI Tool First (Thats Backwards) Most companies buy the AI tool first and ask whether they were ready second. Thats backwards, and its expensive. An AI readiness assessment answers the question before you spend the money. Full video https://youtu.be/1vrKRjJr2Jg ai aireadiness machinelearning dataengineering shorts https://video-meta.open.video/poster/8rqKBB1ZwbVt/Z7olbFpii62_IXzonf.jpg https://streaming.open.video/contents/8rqKBB1ZwbVt/1790811013/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Z7olbFpii62 31 https://videos.concepttocloud.com/v/embeddings-on-your-own-machine Embeddings on your own machine The embeddings are GTE-small running inside the Supabase edge runtime. On my laptop. No API key, no external calls, no egress. Semantic search with nothing confidential leaving the box is a very different conversation to have with a security reviewer. Full build-along: https://youtu.be/got09pWBMG0 Shorts supabase ai embeddings privacy https://video-meta.open.video/poster/EbiaEBH62nVX/tRo7a-UyP72_fkQDLJ.jpg https://streaming.open.video/contents/EbiaEBH62nVX/1790811014/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tRo7a-UyP72 33 https://videos.concepttocloud.com/v/right-for-delivery-wrong-for-ownership Right for Delivery, Wrong for Ownership An agency is right for delivery, not for ownership. Theyll build your software on time and on budget. What they wont do is the leadership work: the judgment calls, the roadmap, the decisions that shape what gets built next. Thats a different problem. Full video https://youtu.be/8evt9kOEeXM fractionalcto startups cto shorts https://video-meta.open.video/poster/Aby0hzb63zKW/Z7VAGUozPQ2_INSedS.jpg https://streaming.open.video/contents/Aby0hzb63zKW/1790811007/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Z7VAGUozPQ2 31 https://videos.concepttocloud.com/v/2212-ai-papers-zero-usable-models 2,212 AI Papers. Zero Usable Models. A 2021 Nature Machine Intelligence review screened 2,212 COVID-19 diagnosis and prognosis papers. 62 survived quality screening. None were judged of potential clinical use. The failures worth caring about are provenance failures: assembled datasets with the same images in train and test, and no record of where any of it came from. Full talk https://youtu.be/v0lUudGbwQ kubeflow mlops dataengineering machinelearning shorts https://video-meta.open.video/poster/5rmeljuRwjS5/tRVBaoozO62_ESJgiq.jpg https://streaming.open.video/contents/5rmeljuRwjS5/1790811010/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tRVBaoozO62 33 https://videos.concepttocloud.com/v/80-of-ai-projects-end-up-on-the-scrap-heap 80% of AI Projects End Up on the Scrap Heap A couple of years ago a review put it at 95% of AI projects ending up on the scrap heap. One from earlier this year had it down to about 80%. Thats still a lot of projects going through the motions. The demos, the testing, the ideas all seem sound and then they dont actually ship. Free AI-Readiness Audit https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI machinelearning dataengineering MLOps shorts https://video-meta.open.video/poster/DbqCBnKZtbLq/tBVBbEVzPR2_lbbqBl.jpg https://streaming.open.video/contents/DbqCBnKZtbLq/1790811010/index.m3u8 https://videos.concepttocloud.com/embed?contentId=tBVBbEVzPR2 33 https://videos.concepttocloud.com/v/talking-to-your-data-claude-mcp-from-excel-to-product-ep-8 Talking to Your Data (Claude + MCP) From Excel to Product (Ep. 8) What if you could just ask? In episode 8 I wire Claude Code to Saikus MCP server listing cubes, querying drug sales and rebates in plain English, with row-level security still enforced. In this episode: Connecting Claude to the Saiku MCP server Querying drug sales & rebate % conversationally Why the model (not the raw DB) keeps answers safe Chapters: 0:00 Claude + the Saiku MCP server 5:12 Querying drug sales 12:44 Rebates analysis 15:15 Row-level results Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/5JeekDa6tfuB/Z7UAHooPP62_guOcQN.jpg https://streaming.open.video/contents/5JeekDa6tfuB/1790811300/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Z7UAHooPP62 1189 https://videos.concepttocloud.com/v/local-is-one-thing-production-is-another Local is one thing. Production is another Spin up Supabase locally and you get Postgres, auth, storage, edge functions and realtime in a bunch of Docker containers. That part is easy. Repeating it in a VM in the cloud is also easy. Dealing with it from a scale, maintenance and security perspective is the actual job, and that is a different job. Full build-along: https://youtu.be/got09pWBMG0 Shorts supabase postgres startups devops https://video-meta.open.video/poster/-syaslGZAb2s/6QoBGEUyOk2_fdsJmC.jpg https://streaming.open.video/contents/-syaslGZAb2s/1790811274/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6QoBGEUyOk2 39 https://videos.concepttocloud.com/v/a-home-server-your-agents-can-drive A home server your agents can drive Bluefin Server is image-based, self-updating and organised around Kubernetes and it says its fully API and MCP driven. That means your own agents talking to your own home server. Full build-along https://youtu.be/nhx27jZDCL4 Shorts Linux Bluefin MCP AI https://video-meta.open.video/poster/-uaKEpvI_D3Z/6Qp6aooyPB2_qeDbAc.jpg https://streaming.open.video/contents/-uaKEpvI_D3Z/1790811270/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6Qp6aooyPB2 36 https://videos.concepttocloud.com/v/does-your-policy-engine-fail-open-or-closed Does Your Policy Engine Fail Open or Closed? The question almost nobody asks until the audit: what happens to admission when the policy engine itself is down? Because it will be. It is a pod, and pods get evicted, upgraded, or fail to come back up. Gatekeeper ships failurePolicy: Ignore with a 3 second timeout the upstream default. If it is down, slow, or mid-upgrade, workloads are admitted and the audit sweep flags it a minute later, by which point it is running. Kyverno is the other way around. It fails closed, with a 10 second timeout. Neither default is wrong. Fail-open protects the cluster, fail-closed protects the policy. An admission webhook is a structural control only if it fails closed if it fails open, then for the duration of the outage it is an advisory control in a high-vis jacket. Go and check which one you installed. Most people never have. Full talk policy as code on two real clusters, OPA Gatekeeper vs Kyverno: https://youtu.be/CzBgg8wedM kubernetes policyascode sre platformengineering shorts https://video-meta.open.video/poster/6gqqdpLR6jCY/A6pkHVozPk2_NpWVYV.jpg https://streaming.open.video/contents/6gqqdpLR6jCY/1790811278/index.m3u8 https://videos.concepttocloud.com/embed?contentId=A6pkHVozPk2 39 https://videos.concepttocloud.com/v/a-prompt-is-a-wiki-that-talks-back A Prompt Is a Wiki That Talks Back If you are regulated, at some point somebody asks for evidence as a control. There is an enormous difference between we have a policy that says engineers shouldnt do this and here is the webhook that structurally prevents it, here is its configuration, and here is the admission log showing it firing. One of those is a conversation you have with an auditor. The other one is a finding. An advisory control can only ever be evidenced by asking people whether they followed it. Which is why it gets twitchy when someone says they put it in the prompt because a prompt is a wiki that talks back. Full talk policy as code on two real clusters, OPA Gatekeeper vs Kyverno: https://youtu.be/CzBgg8wedM kubernetes policyascode compliance devsecops shorts https://video-meta.open.video/poster/ygumwD06Aj0z/AkpRbopiPA2_NqWQrJ.jpg https://streaming.open.video/contents/ygumwD06Aj0z/1790811274/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AkpRbopiPA2 40 https://videos.concepttocloud.com/v/quarterly-roadmaps-now-last-a-week-and-a-half Quarterly roadmaps now last a week and a half. Agentic engineers are churning through work so fast that code isnt the bottleneck anymore. Product is. If your product org cant keep up with what engineering can now ship, youve got the wrong constraint. Full episode concepttocloud.com/podcast/concept-to-cloud/tack-on-or-rebuild https://video-meta.open.video/poster/CuyGln0Q_bvr/A6pRb_UzOA2_IgAyjy.jpg https://streaming.open.video/contents/CuyGln0Q_bvr/1790811270/index.m3u8 https://videos.concepttocloud.com/embed?contentId=A6pRb_UzOA2 37 https://videos.concepttocloud.com/v/how-to-ship-ai-that-survives-a-compliance-audit How to Ship AI That Survives a Compliance Audit In a regulated business, an AI feature isnt done when it works its done when it survives an audit. Heres how to build AI where every decision leaves a paper trail and sensitive data never leaves the boundary. Check your AI is audit-ready: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit - Why regulated AI is different audit trail, PII, explainability - The wrong approach: guardrails in the prompt - The right approach: guardrails in the data model, PII-safe pipelines, decision logging - What auditors actually ask and how to have the answer ready 0:00 In a regulated business, it works isnt the finish line 0:49 Why regulated AI is different 1:21 PII cant leak, and you have to explain the output 2:12 The wrong approach: guardrails in the prompt 3:03 The right approach: guardrails in the data model 3:19 PII-safe pipelines, decision logging on by default 4:21 What auditors actually ask 4:43 The hard one: can you reproduce it? 5:24 The rule Correction: at 4:11 I said mutable. I meant immutable the decision log should be something nobody can change after the fact. Concept To Cloud ships AI that survives audit in regulated industries. Run the AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI compliance regulated MLOps dataprivacy https://video-meta.open.video/poster/7ceypBfQBz-z/6RolHUUyPk2_MrLLOD.jpg https://streaming.open.video/contents/7ceypBfQBz-z/1790811322/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6RolHUUyPk2 367 https://videos.concepttocloud.com/v/would-i-ship-this-to-production-absolutely-not Would I Ship This to Production? Absolutely Not. Would I ship what we just built straight to production? Absolutely not. Security, scale, and a long list of caveats stand between a live demo and something a regulated team can actually run. That gap is the whole reason Concept To Cloud exists. Book a free AI-Readiness Audit https://concepttocloud.com AI DataPrivacy CloudEngineering shorts https://video-meta.open.video/poster/8gmKkjLJAjF6/6BpQb-piOB2_yQlFlx.jpg https://streaming.open.video/contents/8gmKkjLJAjF6/1790811312/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6BpQb-piOB2 55 https://videos.concepttocloud.com/v/rags-missing-piece-whos-allowed-to-see-what RAGs Missing Piece: Whos Allowed to See What Retrieval that hands back everything is easy. Retrieval that only hands back what the person asking is actually allowed to see is the hard, necessary part. Row-level access and PII handling are the two pieces most retrieval demos skip. Book a free AI-Readiness Audit https://concepttocloud.com DataPrivacy AI RowLevelSecurity shorts https://video-meta.open.video/poster/4qCqkDfQEDxZ/6RV7GpoOOA2_oeDESP.jpg https://streaming.open.video/contents/4qCqkDfQEDxZ/1790811271/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6RV7GpoOOA2 41 https://videos.concepttocloud.com/v/48-hours-with-opus-55-is-it-actually-worth-it-shorts 48 Hours With Opus 5.5: Is It Actually Worth It? shorts 48 hours in with Opus 5.5. Juans honest take: one of the better models Anthropic has put out, 4.8 vibes with Fable logic, and cheaper too. He uses it for development and programming. What are you using it for? Drop it in the comments. Is your AI stack actually ready for real data and real production traffic? Concept To Clouds free AI-Readiness Audit tells you where the gaps are: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit Concept To Cloud helps regulated and mid-size teams take AI from toy demo to production. concepttocloud.com shorts opus claude anthropic ai claudecode https://video-meta.open.video/poster/FsuShjWI-v9y/AlV7bEU5Pl2_ufDtOv.jpg https://streaming.open.video/contents/FsuShjWI-v9y/1790811278/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AlV7bEU5Pl2 44 https://videos.concepttocloud.com/v/i-tried-to-break-my-own-ai-agent I Tried to Break My Own AI Agent Before we shipped guardrails, we tried to break our own agent asking it something completely off-topic just to see what it would do. It answered. Thats the failure mode every AI agent needs a fix for before it goes anywhere near production. Book a free AI-Readiness Audit https://concepttocloud.com AIAgents AI Guardrails shortsbeta https://video-meta.open.video/poster/ywaaoFr66z3z/7QVAHEVijB2_ZuQkPz.jpg https://streaming.open.video/contents/ywaaoFr66z3z/1790811292/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7QVAHEVijB2 41 https://videos.concepttocloud.com/v/dont-spend-three-months-choosing-a-policy-engine Dont Spend Three Months Choosing a Policy Engine Reach for Gatekeeper and OPA when your policy problem is bigger than Kubernetes Terraform, CI, Envoy, application authz and you want one language across all of it. Reach for Kyverno when Kubernetes is the whole problem, when you want every engineer writing and reviewing policy rather than just the platform team, or when you need mutation, generation, or image provenance without standing up a second system. A lot of regulated shops land on both: Kyverno for the Kubernetes 80%, Rego kept for CI and infrastructure-as-code. But whichever you pick dont spend three months choosing. The rules matter more than the engine, they are portable between the two, and the cost of picking the less ideal one is far lower than the cost of another quarter with no admission control at all. Full talk policy as code on two real clusters, OPA Gatekeeper vs Kyverno: https://youtu.be/CzBgg8wedM kubernetes opa kyverno platformengineering shorts https://video-meta.open.video/poster/4giCBlXJ7D2W/BkVRbppijB2_bzqKJw.jpg https://streaming.open.video/contents/4giCBlXJ7D2W/1790811287/index.m3u8 https://videos.concepttocloud.com/embed?contentId=BkVRbppijB2 40 https://videos.concepttocloud.com/v/the-pii-redaction-reveal The PII Redaction Reveal Credit card, email, phone number all automatically redacted before they ever reach the model. Its not a perfect first pass, but its the second half of doing retrieval safely: keep sensitive fields out of what the AI actually sees. Book a free AI-Readiness Audit https://concepttocloud.com PIIRedaction DataPrivacy AI shorts https://video-meta.open.video/poster/7cyCclf77n8r/7QokbFp5iA2_rRyecr.jpg https://streaming.open.video/contents/7cyCclf77n8r/1790811297/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7QokbFp5iA2 43 https://videos.concepttocloud.com/v/i-have-2-notes-the-wall-has-34 I Have 2 Notes. The Wall Has 34. I have 2 notes on this wall. The wall has 34 total. Thats row-level security working exactly as intended the chatbot only ever hands back what the person asking is actually allowed to see, no matter how much data sits behind it. Book a free AI-Readiness Audit https://concepttocloud.com RowLevelSecurity DataPrivacy Postgres shorts https://video-meta.open.video/poster/9kDSoBrRtDhy/7kV6Hoo4OA2_ZmMxfa.jpg https://streaming.open.video/contents/9kDSoBrRtDhy/1790811567/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7kV6Hoo4OA2 45 https://videos.concepttocloud.com/v/what-happens-when-your-ai-provider-goes-down What Happens When Your AI Provider Goes Down? What happens to your product the day your model provider takes an afternoon off? Every team shipping an LLM-driven feature needs an answer to that question before it becomes an incident. This is the part of build an AI agent nobody puts in the demo. Book a free AI-Readiness Audit https://concepttocloud.com AIAgents AI Reliability shorts https://video-meta.open.video/poster/yofWwnL6sDoy/7QpAGVU5Ol2_UYttwW.jpg https://streaming.open.video/contents/yofWwnL6sDoy/1790811562/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7QpAGVU5Ol2 47 https://videos.concepttocloud.com/v/what-an-ai-agent-actually-is What an AI Agent Actually Is An AI agent isnt magic its a model with tools, wired up to go do something specific with your real data and hand back a result. We built one live this week on top of a real dataset, not a toy demo. Book a free AI-Readiness Audit https://concepttocloud.com AIAgents AI MachineLearning shorts https://video-meta.open.video/poster/yEf0pDaIwDVz/7kpBbFUOOB2_nuBRDg.jpg https://streaming.open.video/contents/yEf0pDaIwDVz/1790811566/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7kpBbFUOOB2 46 https://videos.concepttocloud.com/v/the-red-flag-that-surfaces-the-week-after-close The Red Flag That Surfaces the Week After Close Integration and vendor lock-in is red flag five. Its the one that shows up the week after close, once portfolio companies start getting pushed together. Reducing outlay is part of the point of private equity. So the diligence question is narrow: is there anything in this stack that blocks a cloud migration, or a data-service migration, that would otherwise take cost out? These arent always deal-breakers. They belong in the report anyway, because someone pays for them later. Pre-acquisition checklist https://concepttocloud.com/resources/pre-acquisition-tech-diligence-checklist privateequity duediligence vendorlockin cloudmigration shorts https://video-meta.open.video/poster/AybSFjv6tfpz/B7oAHppzjk2_wvnXqY.jpg https://streaming.open.video/contents/AybSFjv6tfpz/1790811562/index.m3u8 https://videos.concepttocloud.com/embed?contentId=B7oAHppzjk2 49 https://videos.concepttocloud.com/v/tech-due-dillgence-is-a-not-a-code-review-pe-duediligence Tech Due Dillgence Is A Not A Code Review pe duediligence Were not grading the code. Were pricing the gap between what they have and what your thesis needs. A technical due diligence answers one question: does the technology support the investment thesis and if not, what will it cost to get there? The layers we actually read: product & UX, architecture, data, security & compliance, team & key-person risk, and roadmap vs the plan. Full breakdown https://youtu.be/AZcfGJ8jJGg Free Tech-Risk Scorecard https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard privateequity duediligence techdiligence MandA privateequityinvesting shorts https://video-meta.open.video/poster/5Ab0opf7YbMz/7Bo6GUUzjB2_dcHzXa.jpg https://streaming.open.video/contents/5Ab0opf7YbMz/1790811566/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7Bo6GUUzjB2 47 https://videos.concepttocloud.com/v/prove-your-software-supply-chain-sigstore-slsa-in-practice Prove Your Software Supply Chain: Sigstore + SLSA in Practice Live engineering deep dive from Concept to Cloud. We make a software supply chain provable Sigstore signing, SLSA provenance, and verify-at-deploy for teams that answer to auditors. Live Q&A. https://concepttocloud.com https://video-meta.open.video/poster/z_u8BlKQxjLW/7BolbEoOjl2_WgTuHj.jpg https://streaming.open.video/contents/z_u8BlKQxjLW/1790812143/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7BolbEoOjl2 3498 https://videos.concepttocloud.com/v/i-tried-to-beg-my-ai-agent-into-breaking-its-rules I Tried to Beg My AI Agent Into Breaking Its Rules After adding guardrails, we tried to talk our own agent out of them begging it to answer a question it had been told to refuse. It held the line. A deterministic gate, not an LLMs judgment call, decided what got answered. Book a free AI-Readiness Audit https://concepttocloud.com AIAgents AI Guardrails shorts https://video-meta.open.video/poster/4knKgpuZ2vgy/AQVQbVojjR2_OuBFKC.jpg https://streaming.open.video/contents/4knKgpuZ2vgy/1790811563/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AQVQbVojjR2 55 https://videos.concepttocloud.com/v/build-a-product-end-to-end-with-free-ai-tools-ai-product-development-freeaitools Build a product end to end with free AI tools ai product development freeaitools Ship a software product end to end without paying for a subscription. opencode a free, open, terminal-based AI coding environment connected to OpenRouter for model access. Set your OpenRouter API key inside opencode, pick a model (Nvidias Nemotron is one free option), and define your dev agents by the task you actually need them to do. Full guide on which models to use for which tasks https://concepttocloud.com/free-tools/ai-tools https://concepttocloud.com ai freeaitools opencode openrouter softwaredevelopment shorts https://video-meta.open.video/poster/CEeudFH6_nLq/A6p6aVVji62_gUPlnx.jpg https://streaming.open.video/contents/CEeudFH6_nLq/1790811820/index.m3u8 https://videos.concepttocloud.com/embed?contentId=A6p6aVVji62 56 https://videos.concepttocloud.com/v/ai-theatre-in-due-diligence AI Theatre in Due Diligence When working on Technical Due Dilligence, what is AI Theatre? How do companies attempt to pull some smoke and mirrors and their AI product is really nothing more than a fancy proof of concept? How do you get from here to production? https://video-meta.open.video/poster/zEqysnXQ_bMt/A6pRbUVjjR2_qjozdz.jpg https://streaming.open.video/contents/zEqysnXQ_bMt/1790811820/index.m3u8 https://videos.concepttocloud.com/embed?contentId=A6pRbUVjjR2 70 https://videos.concepttocloud.com/v/is-your-ai-product-just-smoke-and-mirrors Is your AI product just smoke and mirrors? Could you do the same thing with a script and the data rather than an LLM and a bunch of tokens? Every deal has an AI story now. The question is whether that AI project is viable, whether it adds real value, and whether it drives customer growth or whether its a POC that will never ship. That answer changes what the equity is worth. Full breakdown https://youtu.be/AZcfGJ8jJGg Free Tech-Risk Scorecard https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard privateequity duediligence AI techdiligence MandA shorts https://video-meta.open.video/poster/AAiConHI7vKZ/6QoAG_o5jR2_Ffcixm.jpg https://streaming.open.video/contents/AAiConHI7vKZ/1790811824/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6QoAG_o5jR2 49 https://videos.concepttocloud.com/v/kubeflow-in-practice-ml-pipelines-on-kubernetes Kubeflow in Practice: ML Pipelines on Kubernetes Live engineering deep dive from Concept to Cloud. A hands-on look at Kubeflow for running real ML pipelines on Kubernetes, plus an honest take on when the Kubernetes tax is worth it. Live Q&A. https://concepttocloud.com https://video-meta.open.video/poster/74uSElmdEzv5/YZNdOopOPB2_SlfDsK.jpg https://streaming.open.video/contents/74uSElmdEzv5/1790813588/index.m3u8 https://videos.concepttocloud.com/embed?contentId=YZNdOopOPB2 3631 https://videos.concepttocloud.com/v/your-ai-project-is-really-a-data-project Your AI Project Is Really a Data Project Almost every AI project were handed is really a data project wearing a costume. Teams pour the excitement into the model and treat the data as a second-class citizen and thats why so many AI proof-of-concepts never leave proof-of-concept. Garbage in, garbage out still prevails in the world of LLMs. And garbage in, expensive cost out is very real, because token-based spend is bursty and you cant forecast it once a feature is client-facing. The fix is underneath: a semantic layer over your database, so everything is measured the same way and every surface LLMs, analysis, dashboards, other applications is built on one model. Is your data AI-ready? https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit - Why AI value comes from the data layer, not the model - Why POCs stall, and what changes when they hit production - The token-cost problem nobody models up front - What getting the data right actually means Chapters 0:00 Your AI project is a data project 0:43 Why most POCs never leave POC 1:09 The cost nobody models: tokens 1:49 POC economics arent production economics 2:52 So what does getting it right mean? 3:02 The semantic layer 3:28 One model, every surface 3:55 Take a step back 4:47 Get the layer right Concept To Cloud gets your data AI-ready so production AI is fast and cheap to build. Run the AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI datastrategy semanticlayer dataengineering https://video-meta.open.video/poster/jAemoveaBhWX/ZlNJj-V4i72_IezugK.jpg https://streaming.open.video/contents/jAemoveaBhWX/1790815185/index.m3u8 https://videos.concepttocloud.com/embed?contentId=ZlNJj-V4i72 329 https://videos.concepttocloud.com/v/5-technical-red-flags-that-kill-acquisitions 5 Technical Red Flags That Kill Acquisitions Most deals dont die because the code is ugly. They die because of five specific technical risks that dont surface until its too late. Here they are - and how to spot them before you sign. The full pre-acquisition checklist: https://concepttocloud.com/resources/pre-acquisition-tech-diligence-checklist Five technical red flags, and why each one changes the deal: - Key-person / tribal knowledge risk - The hidden scalability ceiling - Security & compliance debt - AI theatre a roadmap of demos that never ship - Integration & vendor lock-in landmines Chapters 0:00 Its not the ugly code that kills deals 0:10 1 Key-person risk 1:06 2 Scalability ceiling 2:32 3 Security & compliance debt 4:15 4 AI theatre 5:26 5 Integration landmines 6:53 How we help get the checklist Concept to Cloud runs board-ready technical due diligence for private equity. Download the Diligence Checklist: https://concepttocloud.com/resources/pre-acquisition-tech-diligence-checklist privateequity duediligence M&A techdiligence dealmaking https://video-meta.open.video/poster/nRbeAtajAdIr/6AMcj_pzPl2_TzdXkh.jpg https://streaming.open.video/contents/nRbeAtajAdIr/1790815276/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6AMcj_pzPl2 468 https://videos.concepttocloud.com/v/how-we-cut-nasas-mars-rover-data-pipeline-from-30-hours-to-10-minutes How We Cut NASAs Mars Rover Data Pipeline From 30 Hours to 10 Minutes NASAs PIXL instrument scans Martian rock chemistry live from the Perseverance rover - but processing took 30 hours, so a scientists hypothesis and its answer couldnt share the same day on Mars. We rebuilt the cloud backend and cut it to 10 minutes. Work with the team that did this: https://concepttocloud.com/contact The story behind Pixlise - NASA Software of the Year runner-up, 2023: - What PIXL does on the Perseverance rover, and why speed mattered - Why the bottleneck was the pipeline, not the science - How we re-architected the cloud backend - The result: 30 hours to 10 minutes (180x), concept to production in 12 weeks - What it means for your systems: your bottleneck is rarely the algorithm Chapters 0:00 A hypothesis and its answer, on Mars 0:24 Who we are 0:50 What PIXL does 1:12 You cant patch systems on Mars 1:43 Why 30 hours killed the science 2:16 The science was fine the pipeline wasnt 2:42 Re-architecting the backend 3:45 The platform: open source 4:07 30 hours to 10 minutes 4:45 The hard part: knowing you have it all 5:40 The lesson for your business 6:00 Work with us Concept to Cloud builds critical systems for missions that cant afford to fail ex-NASA engineers, senior-led. Book a discovery call: https://concepttocloud.com/contact NASA cloudengineering dataengineering Mars casestudy https://video-meta.open.video/poster/POCedt1z_BqA/AQgJOpoyOl2_leHXYz.jpg https://streaming.open.video/contents/POCedt1z_BqA/1790815272/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AQgJOpoyOl2 392 https://videos.concepttocloud.com/v/postgres-is-probably-all-you-need-stop-adding-databases Postgres Is Probably All You Need (Stop Adding Databases) Most teams reach for a separate database for search, a queue, a cache, a vector store, and analytics. For a long time, Postgres can do all of it. I make the case for keeping your stack boring, when one database is genuinely enough, and the few times it is not. Not sure what your data actually needs? https://concepttocloud.com/contact What is in the video: Why teams over-collect databases What Postgres quietly does well: JSON, search, queues, vectors, analytics The real cost of every extra data store you run When you genuinely do need something else How to decide, without the hype Chapters 0:00 A lot of teams run four or five databases when one would do 0:36 Why teams over-collect databases 1:46 Resume-driven, hype-driven, cargo-culted 2:28 The database that stuck around 3:03 A pluggable architecture 3:53 JSON, full-text search, queues, pgvector, analytics 4:51 One platform, one maintenance area 5:38 Ops, failure modes, people 6:51 There is a time when you need more 7:30 Use Databricks or Snowflake for what theyre for 7:52 Talk it through, no pitch Concept to Cloud builds data systems sized to the problem, not the trend. Ex-NASA engineers, senior-led, fixed price. Book a discovery call https://concepttocloud.com/contact postgres postgresql database dataengineering backend https://video-meta.open.video/poster/SfDqBfGOYtJW/6BhdOEoOjl2_axveYF.jpg https://streaming.open.video/contents/SfDqBfGOYtJW/1790815413/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6BhdOEoOjl2 535 https://videos.concepttocloud.com/v/when-to-hire-a-fractional-cto-and-when-you-really-should-not When to Hire a Fractional CTO (and When You Really Should Not) A fractional CTO is senior technical leadership without the full-time hire. Useful when you need the judgment but not the headcount. A waste of money in a few specific cases. Here is how to tell which one you are in. Talk it through, no pitch https://concepttocloud.com/contact I act as a fractional and embedded technical lead for founders and portfolio companies. In this video: What a fractional CTO actually does day to day The three moments it makes sense: pre-first-hire, post-funding, and mid-crisis When you should not hire one, and what to do instead Fractional vs full-time vs an agency, and the real cost of each How to work with one so you actually get value Chapters 0:00 What a fractional CTO is 0:35 Who this video is for 0:55 What a CTO actually does 2:08 Fractional: CTO oversight without the full-time cost 2:28 Moment 1: Pre-first-hire 3:27 Moment 2: Post-funding 4:05 Moment 3: Mid-crisis 4:51 When NOT to hire one 5:38 Full-time: right when the load is constant 6:01 Agency: right for delivery, wrong for ownership 6:34 Fractional: right when you need judgment, not hours 7:26 What to do next Concept to Cloud embeds senior, ex-NASA engineering leadership into founding teams and portfolio companies. Senior-led, fixed scope. Book a discovery call https://concepttocloud.com/contact fractionalcto startups cto techleadership founders https://video-meta.open.video/poster/Uvjutbb4spO7/6BNJioU5jl2_HvfuXA.jpg https://streaming.open.video/contents/Uvjutbb4spO7/1790815411/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6BNJioU5jl2 505 https://videos.concepttocloud.com/v/what-an-ai-readiness-assessment-actually-checks-before-you-spend-a-dollar-on-ai What an AI Readiness Assessment Actually Checks (Before You Spend a Dollar on AI) Most companies buy the AI tool first and ask whether they were ready second. An AI readiness assessment tells you that in a week, not six months in. Here is what one actually checks, and how to spot a report that is just a sales pitch in disguise. Get a free AI-Readiness Audit https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit I run these for regulated and mid-size teams. In this video I break down what a real AI readiness assessment looks at: Data readiness: is your data clean, governed, and legal to use Security and compliance: what has to be true before sensitive data goes near a model The use case: whether the thing you want to automate is even a good fit for AI Team and process: who owns it after the pilot, and how it gets to production Cost and risk: the honest number, not the demo number Buy vs build vs wait Chapters 0:00 Why most AI projects fail the readiness test 0:33 Who this video is for 1:32 What an assessment actually is (not a demo) 1:56 Check 1: Data readiness 2:47 Check 2: Security & compliance 4:35 Check 3: Does this even need AI? 5:22 Check 4: Who owns it once its live? 6:09 Check 5: The path to production 6:55 Check 6: The real cost, not the demo cost 8:02 A good assessment can tell you not to buy 8:41 Red flag: no compliance view, thats a sales pitch 9:16 What it costs and where to start Free: the AI-Readiness Audit https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit Concept to Cloud builds and assesses production AI for regulated and mid-size teams. Ex-NASA engineers, senior-led, fixed price. Book a discovery call https://concepttocloud.com/contact ai aireadiness machinelearning dataengineering airoi https://video-meta.open.video/poster/TJuKodbO3ld6/AlhZP-VPik2_shRnLS.jpg https://streaming.open.video/contents/TJuKodbO3ld6/1790815545/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AlhZP-VPik2 609 https://videos.concepttocloud.com/v/why-your-ai-is-stuck-in-a-proof-of-concept-and-how-to-ship-it Why Your AI Is Stuck in a Proof-of-Concept (and How to Ship It) Your AI demo works. Its been nearly ready for six months. The gap between a demo and production isnt the model its everything around it. Heres what actually has to change. See where yours is blocked: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit Why most AI never ships, and how to get yours to production: - The POC graveyard: why demos stall - The real blockers data readiness, guardrails, security, evals, ownership - Our thesis: AI ships when the guardrails sit in the data model, not the prompt - The readiness checklist to get from demo to production Chapters 0:00 The demo thats been nearly ready for 6 months 1:15 Why POCs stall 95% became 80% 2:02 The real blocker: data that isnt production-ready 2:52 Guardrails, security and compliance 4:45 Evaluations how do you know the answers right? 5:49 Ownership: who runs it once its live 6:41 The readiness checklist 8:39 What happens when the LLM goes offline 8:55 Run the AI-Readiness Audit Concept to Cloud ships production AI in regulated, data-heavy industries guardrails in the data model, not the prompt. Run the free AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI machinelearning production dataengineering MLOps https://video-meta.open.video/poster/QGjestH42paA/ABgYiFVyPk2_lZlNyY.jpg https://streaming.open.video/contents/QGjestH42paA/1790815543/index.m3u8 https://videos.concepttocloud.com/embed?contentId=ABgYiFVyPk2 578 https://videos.concepttocloud.com/v/define-once-use-everywhere-from-excel-to-product-ep-7 Define Once, Use Everywhere From Excel to Product (Ep. 7) The semantic layer is the point. In episode 7 we get experimental: multiple interfaces on the same data, so your whole organisation reuses one consistent, secure definition instead of re-inventing metrics. In this episode: Why reuse and consistency beat raw DB access Securing data behind a login and a model Guardrails that stop LLMs going rogue on your DB Chapters: 0:00 Why reuse your data 2:36 Secure access by design 5:06 Guardrails for LLMs Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/TZiyFh0Gtdlr/AlMJiFozPA2_YCsAgd.jpg https://streaming.open.video/contents/TZiyFh0Gtdlr/1790815540/index.m3u8 https://videos.concepttocloud.com/embed?contentId=AlMJiFozPA2 488 https://videos.concepttocloud.com/v/rag-done-safely-chat-with-your-database-without-leaking-it RAG, Done Safely: Chat With Your Database Without Leaking It Row-level security stops the wrong ROWS from leaking. It says nothing about the wrong FIELDS. This build-along closes that gap: Tom takes a RAG chatbot that can already answer questions about a shared notes wall, locks it down so each person only ever retrieves rows theyre allowed to see then goes further and adds PII redaction so sensitive fields never reach the model in the first place, with Claude Code writing the fix live. Youll see: why the model only retrieves what youre allowed to see isnt the whole story, a real Postgres row-level-security policy added end to end, PII redaction added with one prompt and tested against real data, the trade-offs Tom flags honestly along the way (would I ship this? Absolutely not and why), and where this fits with the two earlier builds in the series. Chapters: 0:00 RAG, Done Safely: Chat With Your Database Without Leaking It 1:40 Row-Level Access, Then PII Redaction 2:56 Locking It Down to Row-Level Access 4:05 Dont Let It Walk Out the Back Door 10:48 I Have 2 Notes. The Wall Has 34. 12:33 Adding PII Redaction With One Prompt 13:56 Testing PII Redaction End to End 19:41 Hiding It in the UI 22:12 The PII Redaction Reveal 24:11 Would I Ship This? Absolutely Not. 25:09 Where to Go Next Is your AI agent actually ready for real data and real production traffic? Concept To Clouds free AI-Readiness Audit tells you exactly where the gaps are book a Discovery call: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit --- Concept To Cloud helps regulated and mid-size teams take AI agents from toy demo to production real data sources, guardrails, and a plan for what happens when things break. concepttocloud.com https://video-meta.open.video/poster/Oinagfb5xhX6/6lgdj_piPR2_LiAIWt.jpg https://streaming.open.video/contents/Oinagfb5xhX6/1790816075/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6lgdj_piPR2 1547 https://videos.concepttocloud.com/v/build-an-ai-agent-that-works-on-your-real-data-not-a-toy-demo Build an AI Agent That Works on Your Real Data (Not a Toy Demo) Most AI agent demos stop at a toy chatbot. This build-along goes further: Tom builds an AI agent on top of a real Supabase-backed notes app then deliberately tries to break it, and shows exactly how guardrails (a deterministic gate, not another LLM call) stop it going rogue. Youll see: the agent get built live with Claude Code, a working chatbot answering real questions about real data, an attempt to jailbreak it into answering off-topic questions, the guardrails fix that shuts that down and the boring but essential production questions most demos skip: what happens when your model provider goes offline? Chapters: 0:00 Build an AI Agent on Your Real Data 2:42 From a Supabase Notes Wall to a Real Agent 4:00 Asking Claude Code to Build the Agent 6:10 A Proof of Concept, Not an Exfiltration Risk 9:36 Testing the Working Chatbot 11:34 Trying to Fool My Own Agent 12:20 Only Answer Questions About the Data 14:21 3 Ways to Stop an Agent Going Rogue 16:05 Watch It Refuse 18:09 What Happens When Your AI Provider Goes Down? 19:22 Where to Go Next Is your AI agent actually ready for real data and real production traffic? Concept To Clouds free AI-Readiness Audit tells you exactly where the gaps are book a Discovery call: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit --- Concept To Cloud helps regulated and mid-size teams take AI agents from toy demo to production real data sources, guardrails, and a plan for what happens when things break. concepttocloud.com https://video-meta.open.video/poster/VmDatf143d5r/A7MZj-VyP72_sASDbe.jpg https://streaming.open.video/contents/VmDatf143d5r/1790816058/index.m3u8 https://videos.concepttocloud.com/embed?contentId=A7MZj-VyP72 1214 https://videos.concepttocloud.com/v/excel-that-stays-fresh-from-excel-to-product-ep-9 Excel That Stays Fresh From Excel to Product (Ep. 9) Spreadsheets go stale this one doesnt. In episode 9 we tour the open-source Saiku repo, build a live query, and connect Excel to a live data source so the numbers are always current. In this episode: The open-source Saiku repo (and how to run it) Building a live query against the cube Excel connected to a fresh, governed data source Chapters: 0:00 The open-source Saiku repo 7:47 Building the query live 15:20 The problem were solving 20:27 Querying Saiku from Excel Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/iibGodaPwpqB/6lhIiEVjPR2_VPUxcf.jpg https://streaming.open.video/contents/iibGodaPwpqB/1790816063/index.m3u8 https://videos.concepttocloud.com/embed?contentId=6lhIiEVjPR2 1354 https://videos.concepttocloud.com/v/how-technical-due-diligence-actually-works-from-someone-who-runs-them How Technical Due Diligence Actually Works (From Someone Who Runs Them) Most technical due-diligence reports are 60 pages nobody reads - and they miss the one thing that actually kills the deal. Heres what a technical DD really is, what it should find, and how to spot a checkbox exercise. Get a 10-minute read on your deals tech risk: https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard Ive run technical due diligence on both sides of the table. In this video I break down what a real DD looks like for a private-equity deal team: - What a technical DD actually answers (hint: its not a code review) - The risks a good one surfaces - and the ones cheap ones miss - How key-person risk, scalability ceilings and AI theatre change the price - Good DD vs a generic template - including red flags in the DD provider itself - What it costs and how long it takes Chapters 0:00 The report nobody reads 0:22 Who this is for 1:00 What a technical DD actually is 1:57 What a good report finds 2:05 Key-person risk 2:48 Scalability ceilings 4:11 Security & compliance 5:51 Tech debt with a number 6:53 AI theatre 7:48 Integration landmines 8:53 Good DD vs a checkbox exercise 10:01 Cost, timeline, and what you get 10:35 Where to start Free: the Tech-Risk Scorecard: https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard Pre-acquisition diligence checklist: https://concepttocloud.com/resources/pre-acquisition-tech-diligence-checklist Concept to Cloud runs board-ready technical due diligence for private equity - ex-NASA engineers, senior-led, fixed price. Book a discovery call: https://concepttocloud.com/contact privateequity duediligence techdiligence M&A privateequityinvesting https://video-meta.open.video/poster/Q_a8hfWGYxyX/7RhYiEVijR2_zVWDdc.jpg https://streaming.open.video/contents/Q_a8hfWGYxyX/1790816112/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7RhYiEVijR2 668 https://videos.concepttocloud.com/v/one-model-four-apps-from-excel-to-product-ep-10 One Model, Four Apps From Excel to Product (Ep. 10) The finale: from spreadsheet to shipped product. In episode 10 Claude builds a cross-platform app on top of the same model we started with turning a static Excel file into something tailored, live and deployable. In this episode: Turning the Excel model into a real application Scaffolding a cross-platform app with Claude Deploying it and wrapping the series Chapters: 0:00 From Excel model to real app 5:08 Scaffolding the app 10:13 Deploying it 12:43 Series wrap-up Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/i9ySAxvH_pcz/7lNZOEUjPR2_sWFvCj.jpg https://streaming.open.video/contents/i9ySAxvH_pcz/1790816305/index.m3u8 https://videos.concepttocloud.com/embed?contentId=7lNZOEUjPR2 938 https://videos.concepttocloud.com/v/policy-as-code-for-regulated-teams-opa-kyverno Policy as Code for Regulated Teams: OPA & Kyverno Guardrails belong in the platform, not the wiki. Two live Kubernetes clusters Gatekeeper on one, Kyverno on the other. The same manifest goes to both, and we watch what each one does with it. Chat calls the shots on which policy we break next. What we cover: OPA/Rego vs Kyverno when to reach for each, honestly Real policies: no privileged pods, required labels, image provenance Enforcing at admission, and what audit mode actually records on each engine The question nobody asks until the audit: what happens to admission when the policy engine itself is down Mutation, resource generation and cosign image verification Where both engines are heading now that Kubernetes speaks CEL Kyverno graduated CNCF in March 2026. Gatekeeper has spoken CEL since 3.18. Most OPA-vs-Kyverno comparisons are two years stale this one isnt. Concept to Cloud book a discovery call. https://video-meta.open.video/poster/FciCclTG7xpW/3QMsihgWGI2_txQcmP.jpg https://streaming.open.video/contents/FciCclTG7xpW/1790849057/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3QMsihgWGI2 3371 https://videos.concepttocloud.com/v/runtime-security-for-regulated-teams-falco-ebpf-cilium Runtime Security for Regulated Teams: Falco, eBPF & Cilium A pod called payments passes every admission policy from last weeks stream. It isnt privileged, it has its owner labels, a pinned image from an approved registry, resource limits, no host mounts. Its legitimately compliant. Then someone opens a shell in it, reads the password file, drops a binary that was never in the image and walks over to the HR database. No admission webhook was ever going to see any of that. Admission control asks one question, once: should this exist? Runtime security asks the other one, for as long as the thing runs: what is it doing? Most teams I walk into have a good answer to the first and no answer at all to the second, and most control frameworks want both. This is a live build on a real cluster. Cilium is the network, Falco is watching the syscalls, and I attack the same pod eight different ways to show which tool notices and which one stops it. Nothing is a mock-up: the slides read the rules and identities straight out of the running cluster. The through-line: Falco detects but never blocks. Cilium blocks, and Hubble records every verdict with both ends named by pod. They answer different questions, and anyone selling you one box that does both is selling you two things in a box. Chapters 0:00 Intro 3:05 Who I am, and the half last week left out 5:41 The problem: a pod that passes every policy 9:37 Should this exist, vs what is it doing 10:39 The regulated angle 11:38 eBPF in plain English 14:20 Falco: eBPF on syscalls 15:40 Falco detects, it does not block 16:52 Cilium: policy by identity, not IP address 18:20 Cilium blocks, and Hubble records it 19:56 One attack, two answers 22:09 The demo: eight ways to attack one pod 22:56 0 The legitimate call 24:01 1 A shell in the container 25:16 2 Reading the shadow file 26:18 3 A binary that was never in the image 27:30 4 A custom rule for your own estate 28:28 5 Calling out to the internet 29:35 6 Lateral movement to the HR database 31:05 7 The Kubernetes API, and an untuned rule 32:59 Network policy in audit mode 34:48 Enforce: the HR dat https://video-meta.open.video/poster/O4bOcdrcEhX6/wcVJi2gWaI2_iwFAcz.jpg https://streaming.open.video/contents/O4bOcdrcEhX6/1790850879/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wcVJi2gWaI2 2903 https://videos.concepttocloud.com/v/build-a-backend-in-an-afternoon-with-supabase-then-ship-it-for-real Build a Backend in an Afternoon With Supabase (Then Ship It for Real) Supabase gives you Postgres, auth, storage, edge functions and realtime running on your own machine in about ten minutes. So I set it up, pointed Claude Code at it, and asked it to build something interesting. What it built: a semantic sticky wall. A live notes wall where the embeddings are generated entirely locally by the edge runtimes built in GTE-small model, so semantic search works with nothing leaving the laptop. It exercises auth, Postgres, row level security, pgvector, realtime, storage, edge functions and pgcron, which is most of the platform in one app. Got a Supabase prototype that needs to grow up? https://concepttocloud.com/contact What is in the video: - Why Postgres is my default pick for almost everything - The Supabase CLI and a full local stack in twelve containers - A tour of Studio: tables, SQL editor, auth, storage, edge functions, advisors - Letting Claude Code build on it, and reading the schema it produced - Row level security and the advisor warnings on a real project - What actually changes when you take this to production The honest bit at the end: running this locally in Docker is one thing. Deploying it for real users, with the security, the scale and the maintenance that comes with it, is another job entirely. That second job is what we do. Concept To Cloud takes prototypes to production for startups. Senior led, fixed price, yours at handover. Book a discovery call: https://concepttocloud.com/contact 0:00 What Supabase actually gives you 1:08 An open source Firebase 1:58 Why Postgres is the default pick 2:54 Auth, edge functions, storage, realtime 4:57 The docs, and where to start 6:53 The CLI and supabase init 7:50 Pointing Claude Code at the project 10:40 Bootstrapping the React app 13:02 Twelve containers up: the local stack 13:55 A tour of Supabase Studio 15:33 Data APIs, webhooks, vaults and wrappers 16:24 The prompt: build something interesting 17:21 API gateway and edge functions explained 19:38 Why projects publish agent readable docs 21:17 What Claude chose to build 22:20 https://video-meta.open.video/poster/SGfmtvuJFBir/2IUcjNhaGZ2_qqESAT.jpg https://streaming.open.video/contents/SGfmtvuJFBir/1790851270/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2IUcjNhaGZ2 2224 https://videos.concepttocloud.com/v/the-desktop-that-cant-drift-bluefin-linux The Desktop That Cant Drift (Bluefin Linux) Every machine Ive owned has gone the same way. You install something to test it, you follow a Stack Overflow answer at midnight, you add a repo and forget about it. Two years later nothing is obviously broken, but the laptop is haunted and the only fix anyone offers is to wipe it and start again. We solved that in production a decade ago. You build an image, you ship it, and if its wrong you get the old one back. Bluefin is that idea pointed at a laptop: the OS is an OCI image, you dont update packages you get a new image, and if the new ones rubbish you reboot into the last one. The machine cant drift because theres nothing to drift into. This is a build-along, not a distro review. I walk through the project and the naming (Bluefin, Dakota, Utah, and where Fedora Silverblue fits), then get hands-on in a running alpha: installing from Flathub, dev containers in VS Code, Distrobox running Ubuntu inside a GNOME OS-based system, Podman AI Lab and RamaLama, and the API- and MCP-driven Bluefin Server thats on its way. Fair warning: Im running Dakota, which is alpha, over a remote desktop. Some of it wobbles. Thats the honest version. 0:00 Why Bluefin is different 1:43 Every Linux box drifts 2:50 The desktop that cant drift 3:32 Chromebook reliability, GNOME power 4:32 Touring the Bluefin site 5:58 Flatpak, Flathub and zero maintenance 7:14 Dev containers as a first-class idea 9:15 Bluefin, Dakota and Utah explained 9:45 Fedora Silverblue, the old foundation 11:23 Dakota: GNOME OS and BuildStream 13:16 The community: Discord and discussions 14:57 Into the desktop (running alpha) 16:13 Installing from Flathub 17:37 Dev containers in VS Code 20:26 Distrobox: any distro inside Bluefin 22:55 Ubuntu and xeyes inside GNOME OS 24:24 Docs, courses and Kubernetes packages 25:28 Podman AI Lab and RamaLama 28:08 Bluefin Server: API and MCP driven 28:50 Where to go next Links Bluefin: https://projectbluefin.io Fedora Silverblue: https://fedoraproject.org/atomic-desktop/silverblue Project Bluefin on Discord: https://discord.com/inv https://video-meta.open.video/poster/mAeCcbOB-Fis/3lUcO2MbGt2_tJjRCw.jpg https://streaming.open.video/contents/mAeCcbOB-Fis/1790852639/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3lUcO2MbGt2 1846 https://videos.concepttocloud.com/v/policy-as-code-for-regulated-teams-opa-kyverno-2 Policy as Code for Regulated Teams: OPA & Kyverno An AI coding assistant wrote a Kubernetes manifest. It was confident, well formatted, and every individual line in it was defensible. It also broke seven separate policies and no amount of asking nicely in a system prompt makes that reliably stop happening. This is a live build on two real clusters, one running OPA Gatekeeper and one running Kyverno, breaking things against both so you can see the difference rather than read about it. Nothing on screen is a mock-up: the slides are generated from the policy files actually loaded on the clusters, and every verdict comes back from a real admission webhook. The through-line: a wiki page, a PR checklist and a line in your system prompt are all advisory controls. They ask. An admission webhook decides, and it is indifferent to who or what wrote the YAML. Chapters 0:00 Who I am, and what this hour is not 2:13 Policy as code for regulated teams 3:40 Both clusters said no 5:13 Azure Policy, and remembering Puppet 6:03 Advisory controls vs structural controls 8:01 Why it matters when you are regulated 8:53 OPA does not know what Kubernetes is 9:54 Gatekeeper: Rego wrapped in CRDs 11:36 Kyverno: Kubernetes-first 13:50 One rule, two languages: privileged 16:33 Required labels: 11 lines vs 8 21:23 The demo: seven ways to break a cluster 22:33 1 A privileged container 24:09 2 Missing owner labels 26:11 3 The mutable latest tag 27:31 4 An untrusted registry 29:16 5 No resource limits 30:35 6 A hostPath mount 31:53 7 Host network and PID namespace 32:45 Audit mode: nobody turns policy on like that 35:29 Mutation: fix it instead of refusing it 36:19 Generation: the NetworkPolicy that comes back 37:37 Image signatures: signed vs unsigned 38:46 The caveat: CEL and validating admission policy 40:12 One rule, three languages 41:10 The question nobody asks: fail open or closed? 43:36 So which one? 45:49 Do not spend three months choosing 46:08 Back where we started 47:27 Book a discovery call The one thing worth doing this afternoon: go and look at your admission webhook configuration https://video-meta.open.video/poster/i3v8xjuB_pjA/3sMBixgabs2_xJzvCx.jpg https://streaming.open.video/contents/i3v8xjuB_pjA/1790854616/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3sMBixgabs2 2884 https://videos.concepttocloud.com/v/kubeflow-in-practice-ml-pipelines-on-kubernetes-2 Kubeflow in Practice: ML Pipelines on Kubernetes Your platform remembers runs. Every question you actually get asked by an auditor, by an incident reporter, or by your future self is about a product. A live build of ML pipelines on Kubernetes using Kubeflow and Mnemosyne (the NASA JPL-born OODT, modernised), treating provenance as a first-class output. Two questions drive the whole thing: can you prove your best model never saw its test set, and can you answer whether a batch of source data was faulty? Chapters 0:00 Two questions about your best model 1:04 2,212 papers. 62 survived. Zero were usable 2:37 The EU AI Act is coming 5:06 Brain drain and shared knowledge 5:43 A run is a verb. A product is a noun 6:51 Training is the easy part 8:18 Blast radius: who consumed the output 9:57 Lifetime: pipelines that outlive the cluster 11:45 What Kubeflow actually is 17:10 Kubeflow graduates from the CNCF 20:51 The record layer, and what nobody does 22:40 OODT: built at NASA JPL 26:25 Mnemosyne: file manager, catalog, workflow 30:28 Lineage: naming your parents 34:25 Demo: the OPSUI walkthrough 37:20 The pipeline, end to end 40:03 The provenance gate in action 41:35 Inside the Kubeflow run 45:20 Serving the model 49:49 Provenance is the product The demo, end to end: https://github.com/spiculedata/kubeflowdemo Mnemosyne by Chris Mattmann: https://github.com/chrismattmann/mnemosyne Kubeflow: https://kubeflow.org CNCF graduated, August 2026 Tom Barber: https://linkedin.com/in/tombarber Concept To Cloud builds production data and ML platforms for regulated, data-heavy organisations. https://concepttocloud.com kubeflow kubernetes mlops dataengineering machinelearning https://video-meta.open.video/poster/oFbqkl1d-FI4/xsglOhMqac2_pwcOKJ.jpg https://streaming.open.video/contents/oFbqkl1d-FI4/1790855084/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xsglOhMqac2 3101 https://videos.concepttocloud.com/v/tack-on-or-rebuild Tack On, or Rebuild? Tom rebuilt Saiku and got 97 new installs. He also found 180 people still running a version he shipped years ago. He cant contact any of them. Two-thirds need to upgrade. Thats the problem this episode is about: what do you do when the people using your product are invisible to you and what changes now that AI has made the building part easy? Episode one of the Concept To Cloud podcast, with Tom Barber and Amelia Prasad. CHAPTERS 0:00 Welcome and why this is now Concept To Cloud 1:34 Saiku, and the installs you cant see 2:31 97 new installs. 180 old ones. 3:13 Why on-prem upgrades are hard 4:22 Telemetry without surveillance 8:05 The bias in what we hear 8:49 The questions you didnt think to ask 13:32 You have to lead the AI 15:16 The blind spots AI leaves behind 17:18 Guardrails, and the AI gateway 18:33 Tack on, or rebuild? 23:58 Why didnt we build it right the first time? 25:05 Code is no longer the bottleneck 25:45 Humans still digest at human speed 29:47 MVPs got bigger. Proving them got harder. 30:41 Polished isnt finished trust signals 32:11 Part three 33:08 The way the internet did it 39:21 The bill arrives late 42:28 Earning the right to upgrade someone 44:19 You can never answer a need youve never heard LINKS Concept To Cloud https://concepttocloud.com Product audit https://concepttocloud.com/contact productmanagement softwareengineering opensource AI podcast https://video-meta.open.video/poster/ornepl5lApcY/wAgQj3habs2_EzkDxL.jpg https://streaming.open.video/contents/ornepl5lApcY/1790856476/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wAgQj3habs2 2799 https://videos.concepttocloud.com/v/leaving-claude-code-openrouter-pidev-live-setup Leaving Claude Code: OpenRouter & pi.dev Live Setup Live walkthrough of switching my AI coding workflow from Claude Code to OpenRouter and pi.dev why, how, and what breaks along the way. https://video-meta.open.video/poster/U8vixjTQst55/EAM6jwgGbd2_IRlQtT.jpg https://streaming.open.video/contents/U8vixjTQst55/1790858380/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EAM6jwgGbd2 3259 https://videos.concepttocloud.com/v/from-napkins-to-agents-how-ai-rewired-product-design From Napkins to Agents: How AI Rewired Product Design In this episode of Engineering Evolved, Tom sits down with Amelia Prasad, Director of Product at Concept to Cloud, to trace how AI has reshaped the day-to-day of UX and product design. Amelia who came to product from astrophysics and climate science walks through the shift from manual research, whiteboards and back-of-the-napkin sketches to building zero-to-one products directly in Claude Code. Its not a hype reel. Amelia is candid about the friction: learning version control from scratch, bloated six-thousand-line files, the designer-to-developer handoff problem, and the diminishing returns of heavy token usage in tools like Claude Design and third-party wrappers such as Lovable. Her current answer is a marriage of tools passing work back and forth between Claude Code and Figma via MCP so prototyping speed and real usability, accessibility and design-system rigour can each live where they belong. They close on what the next 12 months might hold: more human-led user research, not less, and why juniors and design intuition still matter in an industry tempted to hire only senior builders. Chapters 00:00 Welcome & introducing Amelia 02:53 From astrophysics to product design 03:33 The pre-LLM UX workflow: manual research & competitive analysis 05:23 Old-school tooling: Figma, Miro, Maze, pen & paper 06:56 The lost art of napkin sketches and paper prototyping 07:57 Meeting at Princeton: first exposure to LLMs 08:52 AI workflows before AI building: the interview note-taker 11:02 Stepping into Claude Code as a non-developer 13:45 Handing off code on a small team: value and limits 15:17 Guardrails, context and the handoff problem 20:02 Lovable, Cursor and the trouble with wrappers 21:05 Claude Design: token cost and diminishing returns 22:45 Figmas MCP and the two-way handoff 24:50 A suite of tools: knowing when to hand off to which 29:06 Why active engagement in Figma beats waiting on the terminal 33:02 The next 12 months: user research, systemic processes, robustness 38:16 Will design jobs disappear? Junior https://video-meta.open.video/poster/AJjOtbCA3pd5/2RoAihNGat2_BNJnxn.jpg https://streaming.open.video/contents/AJjOtbCA3pd5/1790860793/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2RoAihNGat2 2133 https://videos.concepttocloud.com/v/self-serve-analytics-with-saiku-from-excel-to-product-ep-6 Self-Serve Analytics with Saiku From Excel to Product (Ep. 6) From Excel-centric chaos to data that stays fresh. In episode 6 we open the PharmaRx cube in Saiku and let end users slice it themselves with row-level security so everyone sees only their data. In this episode: Browsing the PharmaRx cube by date, geography, payer Row-level security: Illinois users see only Illinois Giving end users safe, dynamic exploration Chapters: 0:00 The PharmaRx cube 5:07 Row-level security in action 7:41 Letting end users explore Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/mZjKFxGZZFNB/-tVcGFNWbY2_oVIDYz.jpg https://streaming.open.video/contents/mZjKFxGZZFNB/1790868348/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-tVcGFNWbY2 626 https://videos.concepttocloud.com/v/what-a-12k-tech-assessment-actually-buys-you What a 12K Tech Assessment Actually Buys You Technical assessments have a reputation for being vague, open-ended and priced like a mystery. Ours are fixed price, take one to two weeks, and end with a plan written for humans. Here is exactly what you get. Start with the free Tech-Risk Scorecard: https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard What a fixed-price assessment covers: - A read across every layer: interface, infrastructure, roadmap - Where the risks sit, what could be improved, and what is already excellent - A plan you can take to the board without changing a thing, not a 60-page doorstop - Who does it: Tom Barber reads the system and writes the memo himself ten years at NASA JPL, Mars 2020 PIXL, three NASA Honor Awards 0:00 Why most assessments are vague 0:46 Who its for 1:09 Fixed price: one assessment, 12,000 1:31 Every layer, from the interface to the roadmap 1:49 Working practices and the road from here 2:13 Where the risks sit, and whats already excellent 2:34 A plan written for humans 2:50 How its priced: one to two weeks 3:17 Led by our senior engineers 3:53 Try the free Tech-Risk Scorecard Concept To Cloud runs fixed-price technical assessments, senior-led, written for humans. Start with the free Tech-Risk Scorecard: https://concepttocloud.com/services/technical-due-diligence/tech-risk-scorecard privateequity duediligence techdiligence M&A https://video-meta.open.video/poster/RgfSErLQExnX/_IUIGVhWbJ2_jmBEii.jpg https://streaming.open.video/contents/RgfSErLQExnX/1790868413/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_IUIGVhWbJ2 300 https://videos.concepttocloud.com/v/it-was-signed-it-was-legit-it-was-still-malware It Was Signed. It Was Legit. It Was Still Malware. In 2020, SolarWinds build system was compromised. The update that shipped was correctly signed, came from the right place, and looked completely legitimate. Around 18,000 organizations installed it anyway because a signature only proves who signed it, not that the pipeline itself wasnt compromised. Full talk https://youtu.be/9B7TNdiCWuU sigstore slsa softwaresupplychain devsecops shorts https://video-meta.open.video/poster/T3mOAbqZFFoy/FsUJHUgXbI2_sipUGy.jpg https://streaming.open.video/contents/T3mOAbqZFFoy/1790868542/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FsUJHUgXbI2 29 https://videos.concepttocloud.com/v/critical-are-you-violating-confidentiality-agreements-every-time-you-use-ai CRITICAL: Are You Violating Confidentiality Agreements Every Time You Use AI? If you work in FinTech, HealthTech, or any regulated industry, this video could save you from costly compliance violations. In this AI briefing, I break down the hidden risks of using consumer AI tools like ChatGPT and Claude with company data, and provide three actionable solutions for safely leveraging AI in regulated environments. TIMESTAMPS: 0:02 - Introduction: AI in Regulated Environments 0:48 - The Data Ownership Problem 1:47 - Why AI Vendors Train on Your Data 2:18 - Solution 1: Read Your Contracts 2:36 - Solution 2: Disable Training Features 3:25 - Solution 3: Enterprise AI Platforms 4:53 - Final Recommendations KEY TAKEAWAYS: What happens to your data when you upload it to AI tools How you might be breaching customer contracts without knowing Why AI vendors want your data Three practical strategies for safe AI adoption Enterprise solutions: AWS Bedrock, Microsoft Foundry, GCP Vertex, Databricks ABOUT TOM: RegTech specialist focused on AI and digital transformation in regulated environments, advising companies on compliant AI implementation. ACTION ITEMS: Audit your current AI tool usage Review vendor agreements for data ownership clauses Establish AI governance policies Evaluate enterprise AI platforms If you found this valuable, subscribe for daily AI briefings on practical technology implementation in regulated industries. Have questions about AI compliance in your organization? Drop them in the comments! RegTech AICompliance FinTech HealthTech DataPrivacy EnterpriseAI DigitalTransformation Compliance AIGovernance BusinessTechnology https://video-meta.open.video/poster/iWDGhf1Y6hVB/FspYaUhHbJ2_meKgKc.jpg https://streaming.open.video/contents/iWDGhf1Y6hVB/1790868586/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FspYaUhHbJ2 341 https://videos.concepttocloud.com/v/aws-mechanical-turk-shutdown-what-ai-automation-means-for-your-business-ai AWS Mechanical Turk Shutdown: What AI Automation Means for Your Business ai AWS is closing Mechanical Turk to new customersand its a major signal about where AI automation is headed. In this AI briefing, I break down what AWS Mechanical Turk was, why its being phased out, and what this means for businesses that rely on human micro-task platforms. Spoiler: LLMs are now doing these tasks better than humans ever could. TIMESTAMPS: 0:02 - AWS Mechanical Turk Shutdown Announcement 0:14 - What is Mechanical Turk? 0:56 - Why AI is Replacing Human Micro-Tasks 1:48 - What This Means for Users 2:10 - The Broader Lesson on Technology Evolution KEY TAKEAWAYS: Mechanical Turk was Amazons platform for human micro-tasks (CAPTCHAs, image analysis, text extraction) AWS is no longer accepting new customersexisting users can continue for now LLMs can now handle these tasks more efficiently than humans If youre using Mechanical Turk, start planning your transition to AI alternatives Technology platforms evolvebuild adaptability into your business strategy This isnt just about one platform shutting down. Its a clear indicator of how AI is replacing traditional human-in-the-loop workflows across industries. If you found this useful, subscribe for daily AI briefings and insights on how artificial intelligence is transforming business and technology. AI AWS MechanicalTurk Automation ArtificialIntelligence LLM TechNews BusinessStrategy https://video-meta.open.video/poster/PKvGct17AtVs/FcpIa_MHbI2_PryhMT.jpg https://streaming.open.video/contents/PKvGct17AtVs/1790868576/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FcpIa_MHbI2 172 https://videos.concepttocloud.com/v/frontier-ai-models-cybersecurity-protecting-your-organization-in-the-llm-era Frontier AI Models & Cybersecurity: Protecting Your Organization in the LLM Era Explore the critical cybersecurity implications of frontier AI models and open-source LLMs for modern organizations. Learn about amplified attack vectors, supply chain vulnerabilities, and essential defense strategies as AI capabilities evolve rapidly. Frontier AI Models & Cybersecurity: Protecting Your Organization Key Topics Covered AI Model Security Landscape Differences between closed systems (OpenAI, Anthropic) and open-source models Guardrails in commercial AI platforms vs. self-hosted solutions Jailbreaking risks and limitations of current safeguards Amplified Attack Vectors Internal threats: Accelerated data access and reconnaissance External threats: Previously non-viable attacks becoming scalable Self-hosted model farms operating without safety constraints Supply Chain Security Compromised dependencies and transient vulnerabilities GitHub Actions exploitation Pull request volume overwhelming developer validation Upstream dependency infections Defense Strategies Investing in InfoSec and cybersecurity departments Leveraging LLMs for both offensive and defensive capabilities Critical importance of update frequency and patch management Operating system and library updates as security fundamentals Enterprise Recommendations Implement proactive security policies before compromise occurs Utilize specialized security tools (Snyk, ChainGuard mentioned) Establish robust detection and mitigation protocols Maintain vigilance as AI capabilities evolve Resources Mentioned Snyk - Software security and dependency management ChainGuard - Supply chain security solutions Concept Cloud - conceptcloud.com for consultation and support Key Takeaway As frontier models increase in effectiveness, attack vectors will become more novel and critical to business operations. Organizations must implement comprehensive security measures NOWwaiting until after compromise is too late. For help securing your organization against AI-enabled threats, visit conceptcloud.com Chapters 0:02 - Introduction: AI Models and Cybersecurity Implic https://video-meta.open.video/poster/SKbKoraQBFL6/FYVZHVMHbZ2_fTDHJY.jpg https://streaming.open.video/contents/SKbKoraQBFL6/1790868599/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FYVZHVMHbZ2 426 https://videos.concepttocloud.com/v/the-story-of-saiku-from-excel-to-product-ep-5 The Story of Saiku From Excel to Product (Ep. 5) A quick detour into history: where Saiku came from. Conceived back in 2010 out of the Pentaho Analysis tool, Saiku has been making open-source analytics accessible ever since. In this episode: The origins of Saiku (and the Pentaho Analysis tool) Why we moved on from GWT What open-source BI is really for Chapters: 0:00 What you see when you land in Saiku Concept to Cloud: https://concepttocloud.com Saiku (open source): https://github.com/spiculedata/saiku dataengineering analytics excel saiku dataproducts BI https://video-meta.open.video/poster/VJm0oveR6tf7/FtUZHpgqbI2_IxkDAO.jpg https://streaming.open.video/contents/VJm0oveR6tf7/1790868559/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FtUZHpgqbI2 160 https://videos.concepttocloud.com/v/wire-an-agent-to-real-data-without-it-going-rogue-2 Wire an Agent to Real Data Without It Going Rogue An agent that cant touch anything is useless. An agent that can touch everything is dangerous. The answer is in the middle, and it isnt a better prompt. The easy way to put AI over your data is to hand an LLM the database and say have at it. Sometimes the SQL it writes is right. The problem is sometimes. In this one I walk through the alternative: a governed layer over your data, with scoped permissions, approvals and limits enforced by the system, so the same question gets the same, correct answer every time. Building with agents? https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit - The tension: useful access versus safe access - Why free access and LLM-written SQL isnt good enough - Scoped permissions, approvals and limits enforced by the system - Semantic models (Apache OSI, Snowflake, Malloy) so a metric means one thing 0:00 Useful and safe at the same time 0:42 How do I deploy AI over my data? 1:26 Should everyone LLM their own reports? 2:04 The wrong way: free access, LLM-written SQL 2:38 The right way: scoped permissions, approvals and limits 3:32 The security cost: training on and leaking your data 3:54 Most leaks start inside the building 4:34 Semantic models: teach the LLM how your data fits together 5:10 Gross profit, calculated two ways 5:49 Sensible security defaults, not lockdown Concept To Cloud builds agentic AI that is safe to run in production. Run the AI-Readiness Audit: https://concepttocloud.com/services/ai-data-preparation/ai-readiness-audit AI agents MCP aisafety https://video-meta.open.video/poster/TKbCwxr6-hT4/_ZUtGUMqbJ2_KOlHkC.jpg https://streaming.open.video/contents/TKbCwxr6-hT4/1790868550/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_ZUtGUMqbJ2 415 https://videos.concepttocloud.com/v/modernise-without-the-big-bang-rewrite Modernise Without the Big-Bang Rewrite The big rewrite is how most modernisation projects fail. There is a safer way. Here is how to replace a legacy system piece by piece, without betting the company on a single switch. Modernising something? https://concepttocloud.com/contact The incremental path off legacy: - Why big-bang rewrites fail so reliably - The strangler-fig approach: replace around the edges, then the core - Finding safe seams in a system nobody fully understands - Keeping the business running the whole time Chapters 0:00 Why the big rewrite fails 0:28 The trap of freezing a product 2:40 The strangler-fig approach 3:20 Layer by layer: UI, storage, deployment 4:43 Safe seams 5:35 A real example: a 1992 NASA C program 7:35 Two paths, one goal 7:57 Where to start Concept to Cloud modernises legacy systems without the big-bang risk. Book a discovery call: https://concepttocloud.com/contact legacymodernisation softwarearchitecture engineering https://video-meta.open.video/poster/SNu0BvX7_dgY/_ZVJaENabI2_TFRmJV.jpg https://streaming.open.video/contents/SNu0BvX7_dgY/1790868566/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_ZVJaENabI2 538 https://videos.concepttocloud.com/v/semantic-models-explained-the-missing-piece-in-your-ai-strategy Semantic Models Explained: The Missing Piece in Your AI Strategy If youre deploying LLMs or AI solutions without semantic models, youre building on shaky ground. In this episode, I break down what semantic models are, why theyre suddenly everywhere in the data community, and how theyre becoming essential for accurate AI deployments. TIMESTAMPS: 0:02 - Introduction to Semantic Models 0:20 - Industry Developments: Databricks, Palantir & Apache OSI 1:00 - Why Semantic Models Matter in 2026 1:56 - The LLM Accuracy Problem 2:45 - Getting Started: Tools & Resources KEY TAKEAWAYS: What semantic models are and how they ensure data consistency Recent developments from Databricks and Apache OSI Why LLMs need semantic models to avoid hallucinations How to standardize metrics like profit across your organization Practical tools to start building semantic models today RESOURCES MENTIONED: Saiku Analysis Tool: demo.saiku.bi Apache OSI (Open Semantic Initiative) dbt semantic model support Apache Polaris Whether youre a data engineer, AI practitioner, or business leader trying to make sense of your data strategy, understanding semantic models is crucial for 2026 and beyond. Drop a comment below with your biggest data consistency challenge! If you found this valuable, please like and subscribe for more insights on data, AI, and how to leverage them effectively. SemanticModels DataArchitecture AI LLM MachineLearning Databricks DataEngineering DataScience ApacheOSI DataGovernance ArtificialIntelligence TechEducation DataStrategy OpenSource https://video-meta.open.video/poster/kWnuAfuZ6xT7/_tpJbVMqbI2_eNMxNd.jpg https://streaming.open.video/contents/kWnuAfuZ6xT7/1790868539/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_tpJbVMqbI2 213 https://videos.concepttocloud.com/v/build-vs-buy-making-smart-decisions-about-custom-llm-models Build vs Buy: Making Smart Decisions About Custom LLM Models Should your organization build a custom LLM model or use existing solutions? This is one of the most critical strategic decisions in enterprise AI, and getting it wrong can cost millions. TIMESTAMPS: 0:02 - Introduction: The Build vs Buy Debate 0:25 - When Building Custom Models Makes Sense 2:02 - The Real Costs of Building Your Own Model 3:35 - Real-World Example: AIDoc at AWS Expo 4:09 - The Case for Off-the-Shelf Solutions 5:44 - Optimizing Model Selection and Cost 6:46 - Final Recommendations and Wrap-Up In this episode, I break down the true costs and considerations of building custom LLM models versus leveraging existing solutions. Drawing from real-world insights from the AWS Expo, including AIDocs experience, I explore: When building custom models makes strategic sense The hidden costs of LLM development (data prep, training, maintenance) How to optimize model selection using platforms like AWS Bedrock Why the most expensive model isnt always the right choice Practical frameworks for making build vs buy decisions Whether youre a CTO evaluating AI strategy, a technical lead implementing LLM solutions, or a business leader trying to understand the AI landscape, this episode provides actionable insights for making informed decisions. SUBSCRIBE for weekly AI briefings and practical insights on implementing AI in your organization. DROP A COMMENT: Are you considering building a custom LLM or using existing models? Whats your biggest challenge in this decision? RESOURCES MENTIONED: - AWS Bedrock - Anthropic Claude models (Opus, Sonnet, Haiku) - AIDoc presentation insights https://video-meta.open.video/poster/k3COktuQ-tps/FZUsb_gWbY2_lXHBmC.jpg https://streaming.open.video/contents/k3COktuQ-tps/1790868583/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FZUsb_gWbY2 455 https://videos.concepttocloud.com/v/how-data-analytics-transforms-private-equity-deal-selection-and-exits-the-ai-briefing How Data Analytics Transforms Private Equity Deal Selection and Exits The AI Briefing Discover why 72% of private equity executives lack the critical data they need for optimal exitsand what the successful 28% are doing differently. KEY INSIGHTS FROM THIS EPISODE: 79% of PE partners improved deal selection with predictive analytics 65% of digitally transformed companies exceed industry benchmarks 72% of PE execs still lack crucial exit data and KPIs Why AI is the wrapper, not the solution How to time exits more effectively with proper data infrastructure TIMESTAMPS: 0:02 - Introduction: Surprising PE Data Statistics 0:23 - Predictive Analytics Improving Deal Selection 1:39 - Digital Transformation Driving Above-Benchmark Growth 3:19 - The Exit Data Gap: 72% Lack Critical KPIs 4:29 - AI Era Transformation: Accessibility Over Technology 5:35 - Wrap-Up and Call to Action ABOUT THIS EPISODE: Host Tom explores three critical statistics that reveal how data utilization impacts private equity performance across the entire investment lifecycle. From deal selection to portfolio management to exit strategy, proper data infrastructure isnt optional anymoreits the difference between above-benchmark growth and being left behind. The key insight? This transformation isnt really about AI or new technology. Its about making the data you already have accessible, transparent, and actionable for faster, better decision-making. WHO THIS IS FOR: - Private Equity Professionals - Portfolio Company Executives - Investment Partners - Data & Analytics Leaders - Anyone interested in data-driven decision making CONNECT WITH THE AI BRIEFING: Interested in discussing how data transformation affects your private equity operations? Reach out to continue the conversation. If you found value in this episode, please like, subscribe, and share with your network! PrivateEquity DataAnalytics PredictiveAnalytics DigitalTransformation AITransformation InvestmentStrategy PortfolioManagement ExitStrategy DataDriven BusinessIntelligence https://video-meta.open.video/poster/kBmuchWQ2tfs/wkoYG_grbs2_uHfFhq.jpg https://streaming.open.video/contents/kBmuchWQ2tfs/1790868661/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wkoYG_grbs2 374 https://videos.concepttocloud.com/v/spacexs-space-data-centers-the-multi-trillion-dollar-gamble-on-orbital-ai SpaceXs Space Data Centers: The Multi-Trillion Dollar Gamble on Orbital AI Tom explores Elon Musk and Sam Altmans recent Twitter exchange about SpaceXs ambitious plan to launch AI data centers into orbit. He breaks down the technical and economic challenges of space-based computing, from rocket reusability to the global chip shortage. Space Data Centers: SpaceXs Multi-Trillion Dollar Bet Key Topics Covered The Musk-Altman Exchange Sam Altman and Elon Musks Twitter discussion about SpaceX valuation Musks claim that SpaceX could be worth more than the entire planet Space data centers as a key component of SpaceXs IPO pitch The Space Data Center Vision Orbital AI inference computing powered by solar energy Avoiding Earth-based energy constraints Commoditizing hardware in space environments Technical Challenges Rocket Reusability: Starships second stage remains non-reusable Launch Volume: Need for frequent, reliable launches at scale Economic Viability: Cost-effectiveness of launching silicon into orbit Current limitations in Starships operational cadence Broader Industry Context Rising energy prices impacting AI operations Global chip shortage affecting consumer goods AI data centers competing for electricity and silicon Misalignment between compute demand and planetary supply capacity Key Insights SpaceXs valuation heavily depends on successfully commoditizing space hardware Full rocket reusability remains an unsolved challenge Timeline uncertainty: when will space compute be viable vs. when do we need it? The immediate AI infrastructure crisis may outpace space-based solutions Resources Mentioned SpaceX recent IPO event Starship rocket program Hosted by Tom Daily AI News & Gossip Chapters 0:02 - The Musk-Altman Twitter Exchange 0:46 - SpaceXs Space Data Center Vision 2:08 - Technical Challenges: Rockets and Reusability 3:02 - The Broader Energy and Chip Crisis 3:53 - Wrap-Up and Looking Ahead https://video-meta.open.video/poster/o-yWFvvZYte6/2kUcH-Nabs2_sCOyvN.jpg https://streaming.open.video/contents/o-yWFvvZYte6/1790868781/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2kUcH-Nabs2 251 https://videos.concepttocloud.com/v/ai-auditability-why-explainability-matters-in-regulated-industries AI Auditability: Why Explainability Matters in Regulated Industries Are you deploying AI in a regulated industry without considering auditability? This could be a costly mistake. In this episode, we explore the critical intersection of AI adoption and regulatory compliance, focusing on why explainability isnt just a technical concernits a business imperative for organizations in financial services, healthcare, and any compliance-driven sector. WHAT YOULL LEARN: Why AI auditability is essential in regulated industries The black box problem with large language models Challenges with third-party AI services and compliance How to build audit-proof AI workflows Questions to ask before deploying AI in your organization TIMESTAMPS: 0:02 - Introduction: The AI Auditability Challenge 0:27 - Why Explainability Matters in Regulated Industries 1:16 - The Black Box Problem with LLMs 1:45 - Building Audit-Proof AI Workflows 2:28 - Next Steps and Call to Action KEY TAKEAWAYS: Many LLMs operate as black boxes, making regulatory compliance difficult Non-deterministic AI models may not reproduce the same decision twice Auditability should be considered BEFORE deployment, not after Third-party hosted models create unique challenges for audit trails Sustainable AI adoption requires balancing innovation with governance INDUSTRIES COVERED: Financial Services & RegTech Healthcare Technology Compliance & Audit Enterprise AI Implementation CONTINUE THE CONVERSATION: Have questions about AI auditability in your industry? Email: tom@conceptcloud.com SUBSCRIBE for more insights on AI governance, enterprise technology, and digital transformation in regulated industries. AICompliance ExplainableAI RegTech AIGovernance EnterpriseAI FinancialCompliance HealthcareAI DigitalTransformation https://video-meta.open.video/poster/l-i4lhfJ2FL4/wQUtaFNqat2_FApRUb.jpg https://streaming.open.video/contents/l-i4lhfJ2FL4/1790868779/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wQUtaFNqat2 191 https://videos.concepttocloud.com/v/why-most-ai-vendor-solutions-are-underwhelming-aws-expo-insights Why Most AI Vendor Solutions Are Underwhelming (AWS Expo Insights) Just returned from the AWS Expo in Washington DC, and the state of AI vendor solutions was eye-openingbut not in the way you might think. Despite hundreds of vendors showcasing AI capabilities, most implementations failed to deliver genuine innovation or value. In this episode, I break down: Why most AI features are just upselling tactics What separates truly innovative AI companies from followers Why the chatbot interface is holding AI back How to evaluate AI vendors for your organization The future of AI interaction beyond typing TIMESTAMPS: 0:00 - Introduction: AWS Expo Experience 0:34 - The Underwhelming State of AI Vendors 1:41 - What Real AI Innovation Looks Like 2:22 - Beyond the Chatbot Interface 2:49 - Key Takeaways for AI Adoption MENTIONED IN THIS EPISODE: - AWS Expo - Cursor - Anthropic - OpenAI If youre evaluating AI solutions or trying to cut through the hype in enterprise AI adoption, this episode offers a refreshingly critical perspective on what actually matters. Dont forget to LIKE, SUBSCRIBE, and hit the notification bell to stay updated on AI insights and strategy! AI ArtificialIntelligence AIStrategy AWS EnterpriseAI Innovation Technology BusinessStrategy DigitalTransformation AIAdoption https://video-meta.open.video/poster/o-iyht07xlhZ/27UdaEhrbt2_mECyqk.jpg https://streaming.open.video/contents/o-iyht07xlhZ/1790868779/index.m3u8 https://videos.concepttocloud.com/embed?contentId=27UdaEhrbt2 192 https://videos.concepttocloud.com/v/data-modelling-explained-star-schemas-that-dont-break-from-excel-to-product-ep-3 Data Modelling Explained: Star Schemas That Dont Break From Excel to Product (Ep. 3) Most data models fall apart the moment real questions hit them. Heres how to model data for humans so it lasts. Get your data model right: https://concepttocloud.com Episode 3 of From Excel to Product. The datas clean now we design a model analysts and tools can actually use: - Why flat spreadsheets dont scale to real questions - Facts vs dimensions, explained simply - Designing a star schema over the pharma dataset - Fixing data types to save memory and pain Chapters: 0:00 Fixing the fact table types 5:09 Keys & dimensions 10:16 Scale without big data Saiku (open source): https://github.com/spiculedata/saiku Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com datamodelling starschema analytics dataengineering BI https://video-meta.open.video/poster/StemhluR2x2y/EtMAH-NHHI2_ylOYzJ.jpg https://streaming.open.video/contents/StemhluR2x2y/1790872657/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EtMAH-NHHI2 694 https://videos.concepttocloud.com/v/the-private-equity-data-crisis-why-13000-companies-cant-exit The Private Equity Data Crisis: Why 13,000 Companies Cant Exit Private equity is facing an unprecedented challenge in 2026and its not what you think. With a massive 13,000 company backlog, the industrys biggest hurdle isnt raising capital or finding deals. Its returning money to investors. In this episode, I break down why data infrastructurenot AI hypeis the real key to unlocking portfolio value and achieving successful exits. WHAT YOULL LEARN: Why the PE industry has a 13,000 company backlog The real challenge facing private equity in 2026 How buying at market peaks created the current crisis Why lean back-office operations are leaving money on the table The critical gap between AI ambitions and data readiness How to maximize value from your existing data assets Why data quality matters more than data quantity Practical steps to improve your data infrastructure today TIMESTAMPS: 0:02 - Introduction: The Private Equity Backlog Crisis 0:22 - Why 2026s Biggest Challenge Is Returning Capital 0:45 - The AI Opportunity and Data Quality Problem 1:26 - The Infrastructure Gap in Private Equity Firms 1:55 - How to Monetize Your Existing Data Assets 2:22 - Data Quality: The Foundation of All Insights KEY INSIGHT: You dont need more datayou need to trust and properly utilize what you already have. Whether through dashboard analytics or AI-driven insights, the quality of your output depends entirely on the integrity of your underlying data. This episode is essential listening not just for private equity professionals, but for anyone in a data-driven organization looking to maximize revenue potential from existing information assets. RESOURCES MENTIONED: Article: The 13,000 Company Backlog Redefining Success in Private Equity Connect with me on LinkedIn for daily insights FOLLOW THE AI BRIEFING: Daily insights on AI, data strategy, and business transformation. ABOUT TOM: Providing actionable insights on leveraging data and AI for business growth. PrivateEquity DataQuality AI BusinessIntelligence DataStrategy PortfolioManagement ExitStrategy DataInfrastructure BusinessTransformation AISt https://video-meta.open.video/poster/VbC8ppvZtB3r/Etg7HVhXHY2_BXtFcP.jpg https://streaming.open.video/contents/VbC8ppvZtB3r/1790872583/index.m3u8 https://videos.concepttocloud.com/embed?contentId=Etg7HVhXHY2 188 https://videos.concepttocloud.com/v/the-private-equity-crisis-nobodys-talking-about The Private Equity Crisis Nobodys Talking About Private equity firms are sitting on a backlog of 13,000 companiesand the traditional playbook isnt working anymore. In this episode, we break down why returning capital has become the industrys biggest challenge in 2025. TIMESTAMPS: 0:00 - Introduction: The Private Equity Challenge 0:11 - The 13,000-Company Backlog Crisis 0:19 - Capital Return: The New Priority 0:28 - The Peak Valuation Problem KEY TAKEAWAYS: Why 13,000 companies are stuck in the exit pipeline How buying at market peaks created todays crisis Why returning capital now matters more than raising it What this means for LPs, portfolio companies, and the M&A market WHO SHOULD WATCH: Private equity professionals Limited partners and institutional investors M&A advisors and investment bankers CFOs considering exit strategies Anyone interested in financial markets The private equity model isnt brokenits being redefined. Understanding this shift is crucial for anyone involved in or adjacent to the industry. If you found this valuable, please like and subscribe for more insights on private equity, venture capital, and the changing investment landscape. DISCUSSION QUESTION: What strategies do you think will be most effective for PE firms managing this backlog? Share your thoughts in the comments! PrivateEquity Finance Investing MergersAndAcquisitions BusinessStrategy CapitalMarkets InvestmentBanking FinancialAnalysis MarketTrends BusinessNews https://video-meta.open.video/poster/PrySEzKIwlxr/EtN6HVgGHJ2_JzfZZR.jpg https://streaming.open.video/contents/PrySEzKIwlxr/1790872568/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EtN6HVgGHJ2 37 https://videos.concepttocloud.com/v/excel-is-where-products-go-to-die-from-excel-to-product-ep-1 Excel Is Where Products Go to Die From Excel to Product (Ep. 1) Most data products start life as an Excel file emailed to a customer and thats exactly where they die. Heres how to turn one into a real product. Work with us: https://concepttocloud.com Episode 1 of From Excel to Product a build-along series that takes a raw Excel model all the way to a deployed, queryable data product. In this opener: - Why spreadsheets stall the moment they leave your laptop - The synthetic pharma-pricing model well build on all series - Where Saiku, LLMs and custom apps fit in the journey - The path ahead: Excel to clean data to model to cube to product Chapters: 0:00 Why this livestream how we build at Concept to Cloud 3:07 The Excel model: synthetic pharma pricing 6:11 Making data usable with Saiku 7:45 Where LLMs fit in the stack 9:19 Good data vs. good product Saiku (open source): https://github.com/spiculedata/saiku Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com dataengineering excel dataproducts analytics saiku https://video-meta.open.video/poster/kbqOwFWY7FCX/FsMlaUNaHJ2_wpqFtL.jpg https://streaming.open.video/contents/kbqOwFWY7FCX/1790872947/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FsMlaUNaHJ2 657 https://videos.concepttocloud.com/v/what-happens-when-your-llm-provider-drops-offline What happens when your LLM provider drops offline? When Anthropics Claude went offline this weekend, it exposed a critical infrastructure question that every organization building on LLMs needs to answer. IN THIS EPISODE: 0:02 The Anthropic weekend outage and why it matters 0:31 How AI outages compare to AWS/Azure downtime 1:38 Do businesses notice when LLMs go offline? 1:53 Multi-model backend strategies and load balancing 2:32 The multi-cloud analogy for LLM dependencies 3:01 Building business continuity into AI systems 3:21 Planning for LLM unavailability KEY INSIGHTS: LLM outages are becoming more frequent and impactful Most organizations lack failover strategies for AI services Multi-model architectures can provide redundancy but add complexity The threshold for when to build multi-provider systems is changing as AI becomes mission-critical Examples from companies like Cursor show hybrid approaches work WHO SHOULD WATCH: - CTOs and Engineering Leaders - AI/ML Engineers building production systems - Product Managers shipping AI features - Anyone depending on LLMs for business operations QUESTIONS TO CONSIDER: - Whats your acceptable downtime for AI services? - Do you have a multi-model strategy? - How would an LLM outage impact your business? - Are you treating AI reliability like cloud infrastructure? RESOURCES: - Host Website: conceptcloud.com - Podcast: The AI Briefing ABOUT THE HOST: Tom is an AI infrastructure strategist helping organizations build reliable, scalable systems on frontier models. JOIN THE CONVERSATION: Drop a comment below with your LLM reliability strategy, or visit conceptcloud.com to continue the discussion. SUBSCRIBE for more insights on AI infrastructure, business strategy, and the practical challenges of deploying AI at scale. AIInfrastructure LLM Anthropic Claude BusinessContinuity TechStrategy CloudComputing AIReliability https://video-meta.open.video/poster/Obaugl1JEp-W/_sh6bFgrHY2_Kffvyh.jpg https://streaming.open.video/contents/Obaugl1JEp-W/1790872902/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_sh6bFgrHY2 223 https://videos.concepttocloud.com/v/build-an-etl-pipeline-with-no-code-apache-hop-tutorial-from-excel-to-product-ep-2 Build an ETL Pipeline With No Code Apache Hop Tutorial From Excel to Product (Ep. 2) Real data engineering without living in a Jupyter notebook build an ETL pipeline visually in Apache Hop and load clean data into Postgres. Work with us: https://concepttocloud.com Episode 2 of From Excel to Product. We take the messy pharma Excel model and turn it into a proper, repeatable pipeline no code required: - Why no-code ETL beats notebooks for production pipelines - Building the flow visually in Apache Hop - Cleaning and transforming the raw Excel data - Loading it into PostgreSQL, ready to model Chapters: 0:00 Why not notebooks? 5:09 Trimming the model: pivots & dictionaries 12:43 Apache Hop inputs (Tika, Avro...) 17:50 Creating the database Tools: Apache Hop, PostgreSQL, Excel Saiku (open source): https://github.com/spiculedata/saiku Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com dataengineering ETL ApacheHop postgres nocode https://video-meta.open.video/poster/QdamxpqIAFD7/_YNkHEMqGY2_kJoxNO.jpg https://streaming.open.video/contents/QdamxpqIAFD7/1790873004/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_YNkHEMqGY2 1355 https://videos.concepttocloud.com/v/build-olap-cubes-with-ai-star-schemas-in-practice-from-excel-to-product-ep-4 Build OLAP Cubes With AI (Star Schemas in Practice) From Excel to Product (Ep. 4) Can an LLM build your OLAP layer? I have Claude write a Mondrian schema over a 250k-row Postgres database and explain dimensional modelling as we go. Build your analytics layer with us: https://concepttocloud.com Episode 4 of From Excel to Product. We turn the star schema into a working OLAP cube, with AI doing the heavy lifting: - What an OLAP cube is and why its worth building - Having Claude generate a Mondrian schema from our model - Loading 250k pharma facts and querying the cube - Where AI helps with dimensional modelling and where it doesnt Chapters: 0:00 The Postgres pharma database 5:08 Dimensions: geography, zip, state 7:42 Claude writes the Mondrian schema Saiku (open source): https://github.com/spiculedata/saiku Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com OLAP Mondrian AI dataengineering analytics https://video-meta.open.video/poster/TveKhpqQEF1r/2QgkH_hWbt2_iJivXn.jpg https://streaming.open.video/contents/TveKhpqQEF1r/1790872934/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2QgkH_hWbt2 639 https://videos.concepttocloud.com/v/chatbots-are-the-lazy-answer-to-ai-integration Chatbots are the lazy answer to AI integration. Chatbots are the lazy answer to AI integration. In our transformation work, we see companies rushing to slap a chat interface on everything. But heres the reality: making users type requests and read responses back is one of the least efficient ways to leverage LLMs. Think about ityour users dont want to have a conversation with your product. They want results. Fast. The real product design challenge? Embedding AI intelligence into workflows where it actually adds value without the friction of constant prompting and correcting. Weve worked with teams whove moved beyond the chatbot paradigmintegrating LLMs into auto-complete, predictive actions, smart defaults, and contextual suggestions. The AI works invisibly, saving time instead of demanding it. The question every product team should ask: How can we give users AI-powered results without making them work for it? Full conversation on rethinking AI integration in products: https://share.transistor.fm/s/718b6d71 AIIntegration ProductDesign UserExperience AIStrategy DigitalTransformation https://video-meta.open.video/poster/OZzuAjjsAxUX/-BMAHFgrGt2_MqnXTd.jpg https://streaming.open.video/contents/OZzuAjjsAxUX/1790874119/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-BMAHFgrGt2 36 https://videos.concepttocloud.com/v/your-infrastructure-team-just-deployed-a-new-ai-model-but-heres-the-problem-they-may-have-unknowi Your infrastructure team just deployed a new AI model. But heres the problem: they may have unknowi Your infrastructure team just deployed a new AI model. But heres the problem: they may have unknowingly violated your data sovereignty agreements. In our work with regulated organizations, weve seen this scenario play out repeatedly. An engineer spins up a model without realizing that not all Azure AI services keep your data within Azures infrastructure. Some models route to third-party providerspotentially in different regions or with different cloud vendors entirely. This creates a dangerous gap between your compliance requirements and your actual data flows. You might have strict agreements limiting data to specific regions or vendors, but those guardrails dont automatically extend to every AI service you deploy. The reality: many teams assume Azure-hosted means Azure-contained. It doesnt always work that way. What we recommend: - Map exactly where each AI model processes data before deployment - Create clear documentation of data sovereignty requirements per project - Build cross-functional awareness between infrastructure, compliance, and development teams - Implement technical controls that enforce sovereignty policies at the architecture level This isnt about avoiding AI innovationits about deploying it responsibly within your regulatory constraints. We dive deeper into navigating Microsoft Foundry, data sovereignty, and regulated AI deployments in our latest episode. Link to listen: https://share.transistor.fm/s/d441ec0a DataSovereignty AIGovernance CloudCompliance ResponsibleAI EnterpriseAI https://video-meta.open.video/poster/U5qKkFnB_FDB/-lMkG-MrHd2_DVYJKa.jpg https://streaming.open.video/contents/U5qKkFnB_FDB/1790874675/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-lMkG-MrHd2 48 https://videos.concepttocloud.com/v/when-not-to-use-llms-a-reality-check-on-ai-implementation When NOT to Use LLMs: A Reality Check on AI Implementation In this episode of the AI Briefing, Im challenging the status quo: Not every problem needs an LLM solution. While the tech world rushes to implement large language models for everything, I break down why traditional machine learning models, statistical frameworks, and even well-designed databases often provide superior results at a fraction of the cost. TIMESTAMPS: 0:00 - Introduction: Beyond the LLM Hype 0:37 - The Problem with Using LLMs for Everything 1:01 - Traditional ML Models: Better Solutions for Structured Data 1:38 - The Data Science Knowledge Requirement 2:25 - Making Smart AI Technology Choices 3:15 - Cost Considerations and Final Thoughts KEY TAKEAWAYS: Why LLMs arent the best choice for structured data analysis How traditional ML models outperform LLMs in efficiency and cost The importance of data science fundamentals (you cant skip this) Questions to ask before implementing AI in your pipeline How to evaluate total cost of ownership for AI solutions MENTIONED TOOLS: - PyTorch - Claude AI - Traditional Statistical Models - Machine Learning Frameworks WHO THIS IS FOR: Data Scientists Engineering Leaders CTOs and Technical Decision Makers Anyone building data processing pipelines Teams pressured to make everything AI-powered Im not anti-LLMI love what they can do. But smart AI strategy means using the right tool for the right job. This episode will help you make better technology choices that save money and deliver better results. NEED HELP? If youre evaluating AI strategy for your organization and want to discuss which tools make sense for your use cases, reach out. Id love to have a chat. If you found this useful, please like and subscribe for more practical AI insights without the hype! AI MachineLearning DataScience LLM ArtificialIntelligence TechStrategy DataEngineering MLOps AIStrategy CostOptimization https://video-meta.open.video/poster/OTiWBjztFFFZ/-lglbUgqHs2_tmIcAi.jpg https://streaming.open.video/contents/OTiWBjztFFFZ/1790874741/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-lglbUgqHs2 232 https://videos.concepttocloud.com/v/spinning-up-llms-in-regulated-environments-heres-what-gets-overlooked Spinning up LLMs in regulated environments? Heres what gets overlooked. Spinning up LLMs in regulated environments? Heres what gets overlooked. When we work with organisations in healthcare, finance, and other regulated industries, we see the same critical gap: teams excited about AI capabilities, but underestimating the data sovereignty implications. Microsoft Foundry and similar platforms are powerful, but they introduce a complex question: where is your sensitive data actually going? In our experience, three things get missed: 1. PII doesnt just mean names and addresses anymore its any data that could identify individuals in your training sets 2. Your existing data agreements likely werent written with LLMs in mind they need review before you start feeding data into AI models 3. Its in Azure doesnt automatically mean compliant the specifics of how Foundry processes data matters The teams getting this right arent moving slower theyre just asking the right questions upfront. Read your data agreements. Understand your data flows. Document your decisions. This isnt about fear. Its about building AI implementations that last. Full conversation on data sovereignty, Foundry, and what regulated industries need to know: https://share.transistor.fm/s/d441ec0a DataSovereignty AIGovernance MicrosoftAzure RegulatedIndustries ResponsibleAI https://video-meta.open.video/poster/jVmudz4sBF8X/-lNkGEgqGd2_wvhNnH.jpg https://streaming.open.video/contents/jVmudz4sBF8X/1790874740/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-lNkGEgqGd2 34 https://videos.concepttocloud.com/v/your-users-dont-care-about-your-tech-stack-they-care-about-solving-their-problems Your users dont care about your tech stack. They care about solving their problems. Your users dont care about your tech stack. They care about solving their problems. Were seeing a dangerous trend: companies adding AI features and immediately slapping a premium price tag on their product. The reasoning? Were paying for all these tokens, so customers should too. Heres the reality check from our work with scaling SaaS companies: Your customers dont wake up thinking I need an AI solution today. They wake up with a problem that needs solving. Whether you use AI, traditional algorithms, or carrier pigeons behind the scenes is irrelevant to them. The value proposition hasnt changed just because your implementation has. In our experience, the most successful AI integrations are invisible. They make the product faster, smarter, or more intuitivebut they dont become the headline. The outcome does. If your AI implementation genuinely delivers 2x the value, then yes, you can charge accordingly. But if its just a different way of delivering the same result? Thats an internal cost consideration, not a customer value increase. The AI tax doesnt exist in your customers minds. And it shouldnt exist in your pricing model either. Want the full conversation on building products users actually value? Listen to the complete episode. https://share.transistor.fm/s/718b6d71 ProductStrategy AIProducts SaaS ProductPricing DigitalTransformation https://video-meta.open.video/poster/lPiSpF4tFhFy/E7NQGoNqGc2_IjtkVb.jpg https://streaming.open.video/contents/lPiSpF4tFhFy/1790874687/index.m3u8 https://videos.concepttocloud.com/embed?contentId=E7NQGoNqGc2 22 https://videos.concepttocloud.com/v/spacex-just-made-a-60b-bet-on-the-future-of-software-development SpaceX just made a 60B bet on the future of software development. SpaceX just made a 60B bet on the future of software development. In our work with development teams, weve seen AI-powered IDEs like Cursor fundamentally change how engineers ship code. But heres what most people are missing about this acquisition: This isnt about SpaceX wanting better dev tools. Its about controlling the infrastructure layer where AI meets human creativity. Cursor succeeded where others struggled because they solved integration, not just automation. They built AI into the developers actual workflow - not as a replacement, but as an enhancement that feels native. Were seeing three major implications: 1. The IDE is becoming strategic infrastructure (like cloud was in 2010) 2. AI-native development tools will become table stakes, not differentiators 3. Companies that build rockets apparently understand software velocity better than most SaaS companies For teams we work with on modernisation: if youre still treating AI dev tools as nice to have experiments, this acquisition is your wake-up call. The question isnt whether to adopt AI-powered development - its how quickly you can integrate it without disrupting your existing workflows. Full breakdown in our latest episode - link below https://share.transistor.fm/s/ad290bf6 AIEngineering DeveloperTools TechStrategy SoftwareDevelopment CloudModernisation https://video-meta.open.video/poster/kPiGAD8tEBvr/_Rg6b-Mqac2_tGbiiL.jpg https://streaming.open.video/contents/kPiGAD8tEBvr/1790874683/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_Rg6b-Mqac2 40 https://videos.concepttocloud.com/v/data-sovereignty-in-ai Data Sovereignty in AI Data Sovereignty in AI: Critical Insights for Microsoft Foundry Users If youre working with AI platforms in regulated industries, this episode is essential viewing. Tom breaks down a critical but often overlooked issue with data sovereignty when using Microsoft Foundry and similar cloud-based AI platforms. TIMESTAMPS: 0:02 - Introduction to Data Sovereignty in AI 0:31 - Working with Regulated Industries 0:53 - Microsoft Foundry Marketplace Insights 1:24 - The Infrastructure and Compliance Gap 1:51 - Third-Party Model Hosting Risks 2:34 - Practical Recommendations and Conclusion KEY TAKEAWAYS: Not all Microsoft Foundry models are hosted by Azuresome are third-party providers Your data may be leaving your expected geographic region or cloud environment Infrastructure teams may not be aware of data sovereignty requirements Critical importance of verification before deploying AI models with sensitive data How to protect PII and regulated data in AI implementations WHO SHOULD WATCH: Data Engineers & Infrastructure Teams Compliance Officers & Legal Teams IT Decision-Makers in Regulated Industries Healthcare & Financial Services Professionals Anyone Working with Sensitive Data AI Project Managers & Technical Leaders RESOURCES: Microsoft Foundry: https://azure.microsoft.com/ JOIN THE CONVERSATION: What data sovereignty challenges have you faced when implementing AI? Share your experiences in the comments below! SUBSCRIBE for daily AI briefings and insights on navigating the intersection of AI, compliance, and enterprise technology. DataSovereignty MicrosoftFoundry AICompliance Azure DataPrivacy RegulatedIndustries CloudSecurity AIGovernance EnterpriseAI TechLeadership --- AI Briefing Episode on Data Sovereignty and Microsoft Foundry Host: Tom Duration: 3 minutes https://video-meta.open.video/poster/-xuagbHAstCY/xYVAHEgrGZ2_XiFkbV.jpg https://streaming.open.video/contents/-xuagbHAstCY/1790875925/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xYVAHEgrGZ2 187 https://videos.concepttocloud.com/v/packaging-existing-tech-with-a-fresh-logo-doesnt-create-value-real-transformation-comes-from-funda Packaging existing tech with a fresh logo doesnt create value. Real transformation comes from funda Packaging existing tech with a fresh logo doesnt create value. Real transformation comes from fundamentally rethinking how technology empowers your users. In our work with development teams, weve seen a clear pattern: AI integration succeeds when its woven into workflows, not bolted on top. The SpaceX-Cursor deal highlights this perfectlyits not about repackaging VS Code, its about reimagining what developer empowerment looks like when AI becomes core infrastructure. The lesson for any business? AI adoption isnt about adding features. Its about solving real problems in ways that werent possible before. Thats where genuine value lives, and thats what customers will actually pay for. Were seeing organisations shift from AI-powered marketing claims to AI-native product experiences. Thats the difference between incremental improvement and category leadership. Full episode on what this 60B deal reveals about the future of AI-powered development: https://share.transistor.fm/s/ad290bf6 AITransformation DeveloperTools ProductStrategy TechLeadership DigitalTransformation https://video-meta.open.video/poster/-EzmFtaQZBxt/3QV7HphGHd2_YyWEZY.jpg https://streaming.open.video/contents/-EzmFtaQZBxt/1790877611/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3QV7HphGHd2 51 https://videos.concepttocloud.com/v/stop-selling-your-architecture-start-selling-outcomes Stop selling your architecture. Start selling outcomes. Stop selling your architecture. Start selling outcomes. Weve seen countless teams fall into this trap: slapping AI-powered on everything because it sounds impressive. But heres what our work with clients revealsyour users dont actually care about the technology under the hood. They care about one thing: Does it solve my problem? Theres a critical difference between: Marketing AI-driven as a feature Using AI strategically to deliver real results The first is hype. The second is substance. In our experience modernising platforms, the most successful teams focus relentlessly on outcomes. They leverage AI where it genuinely improves performance, speed, or accuracybut they lead with the benefit, not the buzzword. Your architecture is YOUR challenge to solve. Your users problem is THEIR priority. Speak to theirs, not yours. Want to cut through the AI hype and focus on what actually matters? Listen to the full conversation where we break down how to build for outcomes, not accolades. https://share.transistor.fm/s/718b6d71 AI ProductStrategy DigitalTransformation UserExperience TechLeadership https://video-meta.open.video/poster/6kruthGYxlCW/2QUQGFhrbY2_uNuGww.jpg https://streaming.open.video/contents/6kruthGYxlCW/1790877601/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2QUQGFhrbY2 34 https://videos.concepttocloud.com/v/possibly-the-best-jingle-ever Possibly the best jingle ever! A little bit about what we do at Concept To Cloud https://video-meta.open.video/poster/BoDesxe7wxw5/2AUkGFMqaJ2_wobtHC.jpg https://streaming.open.video/contents/BoDesxe7wxw5/1790877603/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2AUkGFMqaJ2 59 https://videos.concepttocloud.com/v/ep1-creating-an-account-and-logging-in EP1. Creating an Account and Logging In EP1. Creating an Account and Logging In https://video-meta.open.video/poster/y8fWtv0Ywx0X/26Ula_gqbY2_Gpqwfs.jpg https://streaming.open.video/contents/y8fWtv0Ywx0X/1790877792/index.m3u8 https://videos.concepttocloud.com/embed?contentId=26Ula_gqbY2 312 https://videos.concepttocloud.com/v/ensure-you-add-value-when-leveraging-ai Ensure you add value when leveraging AI SpaceX has officially acquired Cursor, the AI-powered IDE, for 60 billionone of the largest AI acquisitions to date. In this AI briefing, I break down what made Cursor valuable enough for this massive deal and what every business can learn about AI integration done right. https://video-meta.open.video/poster/-Rq8oxrZ2BCz/26oRG-MabJ2_KODkJA.jpg https://streaming.open.video/contents/-Rq8oxrZ2BCz/1790877696/index.m3u8 https://videos.concepttocloud.com/embed?contentId=26oRG-MabJ2 247 https://videos.concepttocloud.com/v/the-bi-layer-duckdb-was-missing-self-serve-analytics-on-motherduck-live The BI Layer DuckDB Was Missing: Self-Serve Analytics on MotherDuck Live MotherDuck gave you a fast, cheap, serverless database. What it doesnt give you is a way for non-technical people to explore that data themselves so every can you pull... still comes to you. In this 30-minute live session we connect Saiku to MotherDuck, auto-build the analytic model in seconds, and show business users slicing, drilling, and charting your data without writing a line of SQL. Bring your scepticism its a live build, not slides. https://video-meta.open.video/poster/DRuOhvbJZBFA/2AV6H-NGaY2_IJvZFk.jpg https://streaming.open.video/contents/DRuOhvbJZBFA/1790877906/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2AV6H-NGaY2 2111 https://videos.concepttocloud.com/v/your-users-dont-care-if-its-ai-they-just-want-results Your Users Dont Care If Its AI - They Just Want Results Tom Barber challenges the AI hype cycle, arguing that users care about outcomes, not architecture. Learn why slapping an AI-powered label on everything is the wrong approach, and discover how to thoughtfully integrate LLMs into products without falling into common pitfalls like dependency on unstable APIs or unnecessary chatbot interfaces. Show Notes Episode Overview Tom Barber returns with a critical examination of AI integration in modern software development, challenging teams to focus on user outcomes rather than jumping on the AI hype train. Key Topics Covered The AI Marketing Problem Why AI-powered labels are often meaningless marketing The difference between machine learning (which has existed for decades) and modern LLMs Examples of invisible AI: spam filtering, fraud detection, map rerouting Users grade products on consistency, not on the impressiveness of the underlying model Engineering Considerations for LLM Integration Choosing the right model for your specific use case (Opus, Sonnet, GPT-4, etc.) Tradeoffs between cost, speed, and inference quality Building evaluation systems and fallback paths Managing latency budgets and graceful degradation Handling API outages from providers like Anthropic and OpenAI The risks of depending on frontier models that can be deprecated Trust and Transparency AI as a potential trust liability Managing user expectations around hallucinations The importance of data provenance and quality (garbage in, garbage out) When and how to disclose AI usage to users The ethical obligation to be transparent when AI makes consequential decisions Product Strategy Why you cant charge an AI tax on top of existing pricing Pricing based on outcomes, not on the technology stack How to use LLMs to deliver genuine efficiency gains Reducing user overhead and friction through thoughtful AI integration Beyond Chatbots Why chatbots may be the most inefficient way to interact with LLMs The challenge: How to integrate LLMs without forcing users to type everything Asking Whats now instant that was https://video-meta.open.video/poster/C8nGtfWIZdCA/2AUAbFhqHZ2_cAEqQH.jpg https://streaming.open.video/contents/C8nGtfWIZdCA/1790877757/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2AUAbFhqHZ2 1178 https://videos.concepttocloud.com/v/ai-briefing-bigger-isnt-better AI Briefing Bigger Isnt Better AI expert Tom challenges the rush to adopt the newest AI models, exploring practical alternatives to chatbot interfaces and cost-effective strategies for AI implementation. Episode Show Notes Key Topics Discussed AI Model Selection Strategy Why you dont need the latest AI models for most tasks Cost vs. performance considerations when choosing between model tiers Anthropics model hierarchy: Haiku vs. Sonnet vs. Opus Speed and pricing implications of heavyweight models Beyond Chatbot Interfaces Limitations of text-based chatbot interactions Alternative ways to interact with LLMs (8 out of 10 times theres a better way) Product design considerations for AI integration Moving beyond the chat with AI paradigm Practical AI Implementation Focus on eliminating repetitive work rather than showcasing latest tech Data infrastructure as the foundation of effective AI Legacy platform engineering and modernization with AI assistance Distributed compute and data engineering applications Key Takeaways Question whether you need the newest, most expensive AI model Consider alternative interaction methods beyond typing Focus on time-saving and efficiency rather than novelty Data quality and accessibility are crucial for AI success Mentioned Technologies Anthropics Claude models (Haiku, Sonnet, Opus) OpenAI model tiers Concept of Cloud platform Questions to Ask Before AI Deployment Do you need the latest and greatest model? Can you use a lighter, faster model instead? Is there a better interaction method than chatbots? How will this save time and reduce repetitive work? https://video-meta.open.video/poster/_4bmhhGRsxFY/wQU6HFNrGJ2_xsKDSY.jpg https://streaming.open.video/contents/_4bmhhGRsxFY/1790877698/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wQU6HFNrGJ2 281 https://videos.concepttocloud.com/v/ai-handbrakes-anthropic-co-founders-warning-on-autonomous-ai-development AI Handbrakes: Anthropic Co-Founders Warning on Autonomous AI Development Tom discusses Anthropic co-founders call for AI development handbrakes as models approach autonomy. Exploring the balance between innovation and safety in rapidly evolving AI landscape. https://video-meta.open.video/poster/zjamcfrJ7x2X/w7UkaFNGbJ2_UjeWnG.jpg https://streaming.open.video/contents/zjamcfrJ7x2X/1790877834/index.m3u8 https://videos.concepttocloud.com/embed?contentId=w7UkaFNGbJ2 234 https://videos.concepttocloud.com/v/the-bi-layer-duckdb-was-missing-self-serve-analytics-on-motherduck-live-2 The BI Layer DuckDB Was Missing: Self-Serve Analytics on MotherDuck Live MotherDuck gave you a fast, cheap, serverless database. What it doesnt give you is a way for non-technical people to explore that data themselves so every can you pull... still comes to you. In this 30-minute live session we connect Saiku to MotherDuck, auto-build the analytic model in seconds, and show business users slicing, drilling, and charting your data without writing a line of SQL. Bring your scepticism its a live build, not slides. https://video-meta.open.video/poster/8WCatvzt_teW/FQUkboMrat2_djbxBJ.jpg https://streaming.open.video/contents/8WCatvzt_teW/1790878572/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FQUkboMrat2 2292 https://videos.concepttocloud.com/v/discover-how-ai-simplifies-data-can-you-guess-the-results-ai-datamagic Discover how AI simplifies data! Can you guess the results? AI DataMagic Discover how AI simplifies data! Can you guess the results? AI DataMagic https://video-meta.open.video/poster/E1mShlH7_dpt/3RNsPUNHat2_hNZfkd.jpg https://streaming.open.video/contents/E1mShlH7_dpt/1790881757/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3RNsPUNHat2 47 https://videos.concepttocloud.com/v/tired-of-ai-guessing-your-numbers-watch-how-saiku-claude-nail-it-ai-techtalk Tired of AI guessing your numbers? Watch how Saiku & Claude nail it! AI TechTalk Tired of AI guessing your numbers? Watch how Saiku & Claude nail it! AI TechTalk https://video-meta.open.video/poster/DJvuonrQAFFW/xBMZOEhGGt2_hDMPnl.jpg https://streaming.open.video/contents/DJvuonrQAFFW/1790881883/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xBMZOEhGGt2 56 https://videos.concepttocloud.com/v/analyzing-data-in-motherduck-using-saiku Analyzing data in Motherduck using Saiku Analyzing data in Motherduck using Saiku https://video-meta.open.video/poster/8iq8klHZsxoZ/3lNIjpNXHs2_fhikak.jpg https://streaming.open.video/contents/8iq8klHZsxoZ/1790882137/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3lNIjpNXHs2 1978 https://videos.concepttocloud.com/v/explore-63m-records-with-duckdb-mother-duck-analyze-like-a-pro-datamagic-duckdb Explore 63M records with DuckDB & Mother Duck! Analyze like a pro! DataMagic DuckDB Explore 63M records with DuckDB & Mother Duck! Analyze like a pro! DataMagic DuckDB https://video-meta.open.video/poster/7yy4oBeYslUr/xlNdOUgXGt2_ZfLgqa.jpg https://streaming.open.video/contents/7yy4oBeYslUr/1790881892/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xlNdOUgXGt2 43 https://videos.concepttocloud.com/v/ever-wondered-how-llms-build-olap-cubes-lets-dive-into-the-magic-datawizardry Ever wondered how LLMs build OLAP cubes? Lets dive into the magic! DataWizardry Ever wondered how LLMs build OLAP cubes? Lets dive into the magic! DataWizardry https://video-meta.open.video/poster/Cie0pDvIZho7/x7hZOUNGGt2_eyCqci.jpg https://streaming.open.video/contents/Cie0pDvIZho7/1790881911/index.m3u8 https://videos.concepttocloud.com/embed?contentId=x7hZOUNGGt2 45 https://videos.concepttocloud.com/v/who-a-tech-assessment-is-actually-for Who a tech assessment is actually for Who is a technical assessment for? Senior leaders who want an outside view of their own tech, and buyers who want to know whats inside a business before they commit. One fixed price: 12,000. Full video: https://youtu.be/ovl1eYPqSUY Shorts privateequity duediligence M&A https://video-meta.open.video/poster/ANjawpP66do4/wtUsO_gHHs2_RGZaVe.jpg https://streaming.open.video/contents/ANjawpP66do4/1790884012/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wtUsO_gHHs2 37 https://videos.concepttocloud.com/v/your-team-meetings-are-creating-artificial-harmony-instead-of-real-progress Your team meetings are creating artificial harmony instead of real progress. Your team meetings are creating artificial harmony instead of real progress. Weve seen this pattern countless times: Teams that nod along in roadmap reviews, then unleash chaos in Slack afterward. Everyone agrees in the room, but nothing gets implemented because no one was actually bought in. The problem? Your rituals are optimized for fake peace, not productive friction. Heres what we recommend: Build disagreement INTO your process. Try a red team rotation - before each roadmap review, assign someone the explicit job of poking holes. Give them permission (no, an OBLIGATION) to ask the hard questions. This isnt about being difficult. Its about ensuring the friction happens in a structured way, in the room, rather than afterward in side channels where it cant be resolved. Real alignment comes from working through disagreements together, not avoiding them. Full episode on why your team rituals might be optimized for the wrong thing: https://share.transistor.fm/s/e8992fdb TeamDynamics Leadership ProductManagement OrganizationalDesign Transformation https://video-meta.open.video/poster/4NbaslSYBxpZ/wJoJjogWGs2_lxlxeJ.jpg https://streaming.open.video/contents/4NbaslSYBxpZ/1790884007/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wJoJjogWGs2 51 https://videos.concepttocloud.com/v/the-biggest-mistake-we-see-with-the-trio-model-treating-it-like-a-committee-that-votes-on-everythin The biggest mistake we see with the Trio Model? Treating it like a committee that votes on everythin The biggest mistake we see with the Trio Model? Treating it like a committee that votes on everything. In our work with organizations breaking down business-IT silos, weve learned that effective trios are NOT: Approval layers reviewing decisions made elsewhere Consensus-driven groups where everyone must agree Oversight committees that slow things down Instead, theyre alignment engines that: Bring the right people together BEFORE decisions are made Have explicit decision rights (no gridlock from three votes) Focus on collaboration, not control The magic happens when you stop trying to get everyone to agree and start getting everyone aligned on who decides what. Weve seen this shift transform how engineering, business, and operations work together. The trio isnt about more meetings its about better decision-making. Hear more insights on building effective business-IT collaboration https://share.transistor.fm/s/59a88622 TrioModel BusinessAlignment EngineeringLeadership DigitalTransformation Collaboration https://video-meta.open.video/poster/4HnmoF9R_xgr/wJUJjoNWGd2_TeRgsy.jpg https://streaming.open.video/contents/4HnmoF9R_xgr/1790883999/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wJUJjoNWGd2 49 https://videos.concepttocloud.com/v/heres-a-hard-truth-three-people-doesnt-equal-three-votes Heres a hard truth: Three people doesnt equal three votes. Heres a hard truth: Three people doesnt equal three votes. Weve seen countless organizations stumble when implementing the Trio Model because they misunderstand this fundamental principle. They assume its about getting everyone to agree on everything - and thats exactly how you create gridlock. The real power of the trio lies in explicit decision rights, not consensus democracy. In our experience working with transformation teams, the most effective trios assign clear ownership: Business owner controls customer priorities No voting committees No approval layers after decisions are made Alignment happens BEFORE decisions, not after When teams try to make the trio a democratic process, they end up with paralysis instead of progress. The goal isnt unanimous agreement - its bringing the right perspectives together at the right time with clear authority. Question for leaders: Are you creating decision-making frameworks that empower action, or are you accidentally building bureaucracy? Listen to the full breakdown on how to implement the trio model without falling into these common traps https://share.transistor.fm/s/59a88622 TrioModel EngineeringLeadership BusinessITAlignment DecisionMaking Transformation https://video-meta.open.video/poster/CLn4ozzI6lnr/2dVdP_hXGs2_QGuFlo.jpg https://streaming.open.video/contents/CLn4ozzI6lnr/1790884003/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2dVdP_hXGs2 49 https://videos.concepttocloud.com/v/breaking-down-silos-isnt-about-reshuffling-org-chartsits-about-solving-specific-problems-together Breaking down silos isnt about reshuffling org chartsits about solving specific problems together Breaking down silos isnt about reshuffling org chartsits about solving specific problems together. In our experience, the most successful business-IT collaborations happen when teams form problem-focused trios with crystal-clear purpose. Weve seen too many cross-functional initiatives fail because theyre built around abstract goals instead of concrete outcomes. The game-changer? Make everything explicit: Define the exact problem youre solving (use the one-tweet test) Assign clear roles and decision rights to each member Establish shared metrics everyones accountable for Review and iterate every quarter This isnt an overnight transformation, but our work shows that teams who follow this structured approach break through collaboration barriers that have existed for years. The key insight: successful collaboration requires operational discipline, not just good intentions. Hear the full breakdown of the trio model and why it works: https://share.transistor.fm/s/59a88622 DigitalTransformation ITStrategy CrossFunctionalTeams BusinessAlignment Collaboration https://video-meta.open.video/poster/-ZbSwBSQ_xe6/xIociVgrad2_WyLceb.jpg https://streaming.open.video/contents/-ZbSwBSQ_xe6/1790884007/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xIociVgrad2 50 https://videos.concepttocloud.com/v/the-biggest-blocker-to-engineering-velocity-isnt-technical-debtits-the-walls-between-business-and The biggest blocker to engineering velocity isnt technical debtits the walls between business and The biggest blocker to engineering velocity isnt technical debtits the walls between business and IT. In our transformation work, weve seen countless teams struggle because business owners and technical leads operate in silos. The trio model changes this by putting three key roles in the same room with shared accountability: Business Owner: Owns the problem and customer relationship Technical Lead: Owns feasibility and implementation Third role varies by context This isnt coordination through tickets and meetings. Its genuine collaboration where everyone understands both the why and the how. The result? Faster decisions, better solutions, and engineering teams that actually deliver what customers need. Weve implemented this pattern across dozens of organizations, and the velocity gains are remarkable when you break down those traditional silos. Full breakdown of the trio model and how to implement it https://share.transistor.fm/s/59a88622 EngineeringLeadership TechTransformation AgileCollaboration ModernEngineering TechLeadership https://video-meta.open.video/poster/A3nKlF4IFhmt/xdVJOoMHat2_UPueCA.jpg https://streaming.open.video/contents/A3nKlF4IFhmt/1790883995/index.m3u8 https://videos.concepttocloud.com/embed?contentId=xdVJOoMHat2 52 https://videos.concepttocloud.com/v/stop-asking-how-often-should-we-meet-and-start-asking-what-can-we-decide-without-permission Stop asking how often should we meet? and start asking what can we decide without permission? Stop asking how often should we meet? and start asking what can we decide without permission? In our transformation work, weve seen C-suite executives repeatedly tell us they dont actually care about meeting cadence. They care about outcomes - specifically fewer escalations hitting their desk. The breakthrough insight? Authority = trust decision autonomy When your trio (business, product, engineering) can make decisions independently, youre not just reducing meetings - youre creating faster learning loops and strategic value. Weve observed successful trios operating on completely different rhythms: Daily for high-priority, fast-moving projects Weekly for measured initiatives Monthly for long-running, slower programs The universal principle isnt frequency - its empowerment scope. Every decision your trio makes without bumping it upstairs saves time for everyone and accelerates delivery. This shifts the conversation from process optimization to strategic investment in organizational efficiency. Hear the full breakdown on breaking down business-IT walls: https://share.transistor.fm/s/59a88622 DigitalTransformation Leadership ProductManagement EngineeringLeadership OrganizationalDesign https://video-meta.open.video/poster/zJrqAnO6BlgW/3ZUIiFhrHt2_upXUOW.jpg https://streaming.open.video/contents/zJrqAnO6BlgW/1790884018/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3ZUIiFhrHt2 59 https://videos.concepttocloud.com/v/your-teams-biggest-trust-problem-isnt-what-you-think-it-is Your teams biggest trust problem isnt what you think it is. Your teams biggest trust problem isnt what you think it is. Weve worked with countless leadership teams who mistake reliability for real trust. But heres what actually breaks teams: the absence of vulnerability-based trust. This isnt about trusting someone to deliver on time. Its about feeling safe to say I dont understand or I made a mistake without career consequences. In our experience, most team dysfunction stems from this foundation issue. When people nod along in meetings rather than ask clarifying questions, when confusion goes unspoken to avoid looking uninformed thats absence of trust poisoning your teams effectiveness. The brutal truth? You cant fix accountability, commitment, or results if your people dont feel psychologically safe to be vulnerable first. Patrick Lencioni mapped this perfectly in his Five Dysfunctions framework yet we still see organizations trying to solve surface-level symptoms while ignoring the trust foundation. Real transformation starts with creating space for I dont know to be an acceptable answer. Listen to our full breakdown of why your team rituals might be optimized for the wrong thing: https://share.transistor.fm/s/e8992fdb TeamDynamics Leadership PsychologicalSafety OrganizationalChange Transformation https://video-meta.open.video/poster/DHjWolPYEBpX/3JUsPoNbGt2_EVTIZB.jpg https://streaming.open.video/contents/DHjWolPYEBpX/1790884003/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3JUsPoNbGt2 68 https://videos.concepttocloud.com/v/ai-briefing-why-data-finops-and-the-right-model-make-or-break-your-ai AI Briefing: Why Data, FinOps and the Right Model Make or Break Your AI In this AI Briefing, Tom from Concept to Cloud covers what actually matters when you bring AI into an organisation built on legacy systems and where most teams go wrong. We dig into three things you cant ignore: getting your data AI-ready (integrity, alignment and consistency because garbage in still means garbage out), managing AI cost with a FinOps mindset, and choosing the right model for the right job instead of defaulting to the most powerful (and most expensive) one. Concept to Cloud helps organisations modernise legacy systems and data so they can leverage AI effectively and cost-efficiently. Get in touch to talk about your AI enablement. Chapters 0:00 Welcome back who is Tom & Concept to Cloud 0:37 Leveraging your existing data, systems & apps in an AI world 0:59 Why data is key: integrity, alignment & garbage in, garbage out 1:53 AI FinOps: understanding what tokens really cost you 2:16 Right model for the right job: Opus vs Haiku & optimising spend AI FinOps DataEngineering LegacyModernisation AIBriefing ConceptToCloud https://video-meta.open.video/poster/nPzG_rbz7nBq/_doIjpgXHc2_DMGBnf.jpg https://streaming.open.video/contents/nPzG_rbz7nBq/1790885728/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_doIjpgXHc2 188 https://videos.concepttocloud.com/v/why-95-of-ai-pilots-fail-the-hidden-scaling-problem-killing-your-roi Why 95% of AI Pilots Fail: The Hidden Scaling Problem Killing Your ROI MITs shocking research reveals that 95% of AI pilots fail to achieve rapid revenue acceleration. In this episode, I break down why this massive failure rate isnt about technology - its about scaling strategy. What Youll Learn: Why 80% of companies deploy AI but see no earnings impact The critical difference between horizontal and vertical AI deployments Three essential questions to evaluate your AI investments When to stop failing pilots (and why thats often the smartest move) Why purchased solutions often outperform custom builds Key Research Findings: Only 25% of AI initiatives deliver expected ROI Just 16% scale enterprise-wide Only 6% achieve payback under a year 30% of GenAI projects will be abandoned by 2025 The bottom line: The successful 5% arent smarter or better funded - theyre more disciplined about connecting AI to real business problems. Subscribe for daily AI insights that cut through the noise and focus on what actually drives business results. --- Links mentioned: MIT AI Research Study McKinsey 2025 Workplace Research IBM CEO AI Study AI ArtificialIntelligence BusinessStrategy DigitalTransformation Leadership https://video-meta.open.video/poster/jzjaJvLr2bY6/_ZoYiUNHHs2_qoVoYI.jpg https://streaming.open.video/contents/jzjaJvLr2bY6/1790885409/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_ZoYiUNHHs2 504 https://videos.concepttocloud.com/v/the-data-quality-crisis-killing-your-ai-projects The Data Quality Crisis Killing Your AI Projects SHOCKING: 85% of AI leaders say data quality is their biggest challenge, yet most projects launch without fixing foundational data problems. In this episode, I reveal: Why AI makes data problems WORSE, not better The 3 questions your AI team is avoiding How to trace AI decisions back to source data Why 30% of Gen AI projects fail after proof of concept Actionable steps you can take THIS WEEK TIMESTAMPS: 0:00 - Introduction: The Data Quality Crisis 0:29 - Why 85% of AI Leaders Struggle 2:12 - How AI Makes Data Problems Worse 2:56 - Question 1: Single Source of Truth 3:43 - Question 2: Data Quality Ownership 4:19 - Question 3: Data Lineage & Traceability 4:45 - Real Cost of Skipping Governance 5:34 - Data Governance as Accelerant 6:16 - What Good Governance Looks Like 7:33 - Action Steps for This Week Key Statistics: 85% cite data quality as top AI challenge (KPMG) 30% of Gen AI projects abandoned (Gartner) 77% lack essential AI security practices (Accenture) Perfect for: CTOs, CDOs, AI leaders, executives planning AI investments Subscribe for daily AI insights that cut through the noise! AI DataQuality AIStrategy DataGovernance TechLeadership https://video-meta.open.video/poster/mEfCRxr5trdB/-YUIPVNqbY2_OilDyG.jpg https://streaming.open.video/contents/mEfCRxr5trdB/1790886372/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-YUIPVNqbY2 542 https://videos.concepttocloud.com/v/why-one-ai-model-wont-rule-them-all-choose-the-right-tool-for-each-job Why One AI Model Wont Rule Them All: Choose the Right Tool for Each Job Why Your AI Strategy Needs Multiple Models in 2026 In this episode, I break down the critical mistake most organizations make with AI deployment and reveal why different AI models excel at different tasks. TIMESTAMPS: 0:00 Introduction: AI Strategy for 2026 0:31 The Reality of AI Model Diversity 0:50 Microsoft Copilots Strengths & Limitations 1:32 Specialized Models: Claude, GPT-5 & Gemini 2:31 Strategic Testing & Implementation 2:53 Key Takeaways KEY INSIGHTS: Microsoft Copilot excels at office integration but falls short in programming Claude Opus models dominate coding tasks Google Gemini brings unique competitive advantages Testing multiple platforms is essential for optimal results This episode is perfect for business leaders, IT professionals, and anyone implementing AI in their organization. Subscribe for more AI strategy insights and workplace optimization tips! AI ArtificialIntelligence BusinessStrategy Productivity Technology https://video-meta.open.video/poster/PsnaRraOsjq6/-sodiEMqaJ2_lFZTij.jpg https://streaming.open.video/contents/PsnaRraOsjq6/1790885832/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-sodiEMqaJ2 208 https://videos.concepttocloud.com/v/heres-a-reality-check-nearly-40-of-data-center-electricity-doesnt-power-ai-computationsit-just Heres a reality check: Nearly 40% of data center electricity doesnt power AI computationsit just Heres a reality check: Nearly 40% of data center electricity doesnt power AI computationsit just keeps the servers from overheating. As organizations rush to implement AI solutions, were seeing the hidden infrastructure costs stack up fast. Data centers now consume 2-4% of global electricity, and thats climbing rapidly with every ChatGPT query and AI model deployment. In our transformation work, weve found that understanding these operational realities early helps teams make smarter AI investment decisions. Some innovative approaches were tracking: Microsofts underwater data centers using ocean cooling Finlands underground mine installations leveraging natural cold Next-gen chip efficiency reducing heat generation The game-changer? More efficient processors that generate less heat could slash cooling costs by 20%+, dramatically reducing operational expenses and environmental impact. For leaders planning AI initiatives: factor in the full infrastructure picture, not just the compute costs. These cooling demands will only intensify until chip efficiency catches up. Tune into Toms full analysis on why this matters for your AI strategy https://share.transistor.fm/s/ef3feeb7 AI DataCenters TechStrategy Sustainability DigitalTransformation https://video-meta.open.video/poster/pnriMvfytr64/-spYiogbbJ2_lXQnZv.jpg https://streaming.open.video/contents/pnriMvfytr64/1790885357/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-spYiogbbJ2 342 https://videos.concepttocloud.com/v/why-business-and-it-teams-keep-failing-each-other-and-how-to-fix-it Why Business and IT Teams Keep Failing Each Other (and How to Fix It) The businessIT blame game isnt a people problem its a structure problem. Heres the Trio model that actually breaks the cycle. Align your teams and ship faster: https://concepttocloud.com Why business and IT keep talking past each other, and what fixes it: - Where the blame game really comes from - Why more meetings and more managers make it worse - The Trio model: the 3 roles of an effective team - Clear decision rights that prevent gridlock Chapters: 0:00 The Business-IT Blame Game Problem 1:56 Life in Technical Purgatory 5:29 Why Traditional Fixes Dont Work 10:09 Introducing the Trio Model 15:51 Implementation and Decision Rights 23:42 Measuring Success with Shared Metrics 24:50 Leadership Changes Required 29:25 Getting Started: A Practical Approach Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com EngineeringLeadership BusinessAlignment TechManagement CrossFunctionalTeams https://video-meta.open.video/poster/U_byMbaHsDks/FZVsioNGGJ2_pfQgqZ.jpg https://streaming.open.video/contents/U_byMbaHsDks/1790886644/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FZVsioNGGJ2 2018 https://videos.concepttocloud.com/v/the-ai-energy-crisis-nobodys-talking-about The AI Energy Crisis Nobodys Talking About Every time you use ChatGPT, Claude, or any AI tool, theres a hidden energy cost that might shock you. Data centers now consume 2-4% of global electricity, and heres the kicker: 40% of that power isnt even for computing - its just for cooling! In this episode, we explore: 0:00 The scale of AIs energy demands 1:47 Why cooling takes as much power as computing 2:41 Microsofts underwater data center experiments 3:07 Finlands underground mining solutions 4:16 How chip efficiency could change everything The solutions are as creative as they are necessary - from ocean floors to frozen mines. But the real game-changer might be right around the corner in chip technology. Key Insight: A 50% improvement in chip efficiency could slash cooling costs and make AI accessible to everyone while protecting our planet. Links & Resources: Contact: tom@conceptofcloud.com Subscribe for more AI insights What cooling innovation surprised you most? Drop a comment below! AI Sustainability DataCenters EnergyEfficiency TechInnovation GreenComputing AIInfrastructure ClimateChange TechTrends FutureOfAI https://video-meta.open.video/poster/jXiaZxyP_zWz/2thQOUMqHJ2_JmXnrf.jpg https://streaming.open.video/contents/jXiaZxyP_zWz/1790888459/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2thQOUMqHJ2 342 https://videos.concepttocloud.com/v/your-team-rituals-might-be-killing-productivity-instead-of-building-it Your team rituals might be killing productivity instead of building it. Your team rituals might be killing productivity instead of building it. In our transformation work, we see this pattern everywhere: mid-sized companies (200-1000 employees) drowning in ceremonies that check boxes but miss the point. Standups become status broadcasts. Sprint planning moves tickets around. Roadmap reviews present decisions already made. The problem? These rituals answer WHAT and WHEN, but completely miss the deeper questions that determine success: Why does this matter to YOU? What are you actually worried about? What would make you confident this is right? What arent we discussing that we should be? Weve seen developers build perfect features that sit unused because they missed the context. Teams that nail the technical execution but fail because they didnt understand the real constraints. Your rituals should create understanding, not just coordination. The difference between high-performing teams and everyone else isnt more meetingsits meetings optimized for the right outcomes. Full episode explores how to redesign your team ceremonies for actual impact https://share.transistor.fm/s/e8992fdb EngineeringLeadership TeamRituals AgileTransformation TechLeadership EngineeringManagement https://video-meta.open.video/poster/TWDC_BbWZzbA/37hAjEMXHt2_WEOQpW.jpg https://streaming.open.video/contents/TWDC_BbWZzbA/1790890294/index.m3u8 https://videos.concepttocloud.com/embed?contentId=37hAjEMXHt2 246 https://videos.concepttocloud.com/v/can-you-spot-ai-generated-images-googles-synthid-has-the-answer Can You Spot AI-Generated Images? Googles SynthID Has the Answer! In todays episode of The AI Briefing, we explore Googles groundbreaking SynthID technology thats revolutionizing how we detect AI-generated content. As artificial intelligence creates increasingly realistic images, the line between authentic and artificial becomes harder to distinguish. What Youll Learn: How Googles invisible watermarking system works Practical methods to detect AI-generated images using Gemini Why this technology matters for the future of content authenticity The broader implications for video and multimedia content Timestamps: 0:00 Introduction to SynthID 0:21 How watermarking technology works 1:20 Testing AI detection with Gemini 1:44 Future implications and considerations This episode builds on our previous discussion about AI slop and dives deep into practical solutions for maintaining authenticity in our AI-driven world. Subscribe for daily AI insights and hit the bell for notifications! Whats your experience with AI-generated content? Share in the comments! AI GoogleSynthID ArtificialIntelligence TechNews ContentAuthenticity AIDetection https://video-meta.open.video/poster/pWnq6nrHZfQ6/wQMljUMbbY2_xgEHlk.jpg https://streaming.open.video/contents/pWnq6nrHZfQ6/1790890285/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wQMljUMbbY2 154 https://videos.concepttocloud.com/v/jeff-bezos-is-back-project-prometheus-the-62b-bet-on-physical-ai Jeff Bezos is Back: Project Prometheus & the 6.2B Bet on Physical AI In this episode, we break down Jeff Bezoss latest venture - a groundbreaking AI startup thats taking a completely different approach from companies like OpenAI. Instead of focusing on software-only applications, Project Prometheus is targeting the physical world with AI-powered manufacturing and engineering solutions. TIMESTAMPS: 0:00 Introduction to Physical AI 0:32 Jeff Bezos & Project Prometheus Unveiled 1:18 Physical vs Software AI: Key Differences 1:59 Funding, Competition & Future Outlook KEY TAKEAWAYS: 6.2 billion in funding already secured 100+ employees recruited from OpenAI, Meta, and other AI giants Focus on manufacturing, robotics, and physical world applications Major shift from traditional chat-based AI interfaces Competitive advantage through substantial funding and resources CONNECT WITH US: Subscribe for more AI insights and industry analysis! New episodes every Monday. AI JeffBezos ProjectPrometheus PhysicalAI Manufacturing Robotics TechNews https://video-meta.open.video/poster/o0zC2jX52ziY/2khBipgqaJ2_AgBUHz.jpg https://streaming.open.video/contents/o0zC2jX52ziY/1790890274/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2khBipgqaJ2 218 https://videos.concepttocloud.com/v/team-rituals-fail-when-theyre-optimized-for-convenience-instead-of-connection Team rituals fail when theyre optimized for convenience instead of connection. Team rituals fail when theyre optimized for convenience instead of connection. Weve worked with countless distributed teams who struggle with handoffs, blockers, and trust. The difference between high-performing remote teams and struggling ones? Its not the tools or processesits whether team members genuinely know each other. In our experience, the most effective teams sacrifice convenience for connection. They show up consistently, even when its hard. They create space for the human moments that build trustsharing frustrations, celebrating wins, understanding who needs support. When someone says theyre stuck, does your team assume theyre making excuses or do they jump in to help? That response reveals everything about your teams foundation. Real transformation happens when we stop optimizing rituals for our calendars and start optimizing them for our people. The inconvenient truth? The best team practices rarely feel convenient. Hear more insights on building effective team rituals in our latest episode: https://share.transistor.fm/s/e8992fdb TeamDynamics RemoteWork Leadership DistributedTeams Transformation https://video-meta.open.video/poster/prqGQB0XFvJW/w7g6P_NaaI2_xlrYFG.jpg https://streaming.open.video/contents/prqGQB0XFvJW/1790890413/index.m3u8 https://videos.concepttocloud.com/embed?contentId=w7g6P_NaaI2 62 https://videos.concepttocloud.com/v/why-your-ai-projects-fail-the-critical-role-of-data-integrity Why Your AI Projects Fail: The Critical Role of Data Integrity AI projects often fail due to poor data quality. Tom Barber explores why data integrity is crucial for AI success and how to avoid costly mistakes that lead to unreliable results. Episode Notes Key Topics Covered The importance of data integrity in AI projects Why garbage in, garbage out is critical for LLM success Common mistakes leading to expensive AI failures How to structure data for better AI results The relationship between data engineering and AI effectiveness Main Points Companies are spending 40-50k monthly on AI with poor results due to data quality issues Structured data with repeating patterns improves LLM coherence Taking time to organize data upfront saves costs and improves reliability long-term Data accuracy, completeness, and structure are prerequisites for successful AI implementation Host Background Tom Barber brings data engineering expertise to AI discussions Experience in business intelligence and data platform engineering Action Items for Listeners Audit your current data quality before implementing AI Map out existing data structures and identify improvement opportunities Consider data integrity as a prerequisite, not an afterthought Have thoughts or questions? Leave them in the comments - Tom reads every one! Chapters 0:00 - Introduction & Setting the Scene 0:19 - The Problem: AI Project Failures 0:51 - Data Engineering Background & Expertise 1:23 - The Garbage In, Garbage Out Principle 2:03 - The Cost of Poor Data Quality 2:42 - Strategic Approach to AI Implementation 4:25 - Action Steps & Wrap-up https://video-meta.open.video/poster/lvu4UnrP6fQZ/w7MBO_grbJ2_XikwZn.jpg https://streaming.open.video/contents/lvu4UnrP6fQZ/1790890439/index.m3u8 https://videos.concepttocloud.com/embed?contentId=w7MBO_grbJ2 304 https://videos.concepttocloud.com/v/why-kubernetes-might-not-be-right-for-your-mid-sized-company-an-honest-assessment Why Kubernetes Might Not Be Right for Your Mid-Sized Company: An Honest Assessment 1. Is Kubernetes really the best choice for your mid-sized company? Kicking off this episode, we dive into why Kubernetes might be overkill for most organizations! Key takeaway: Complexity can lead to maintenance nightmares and burnout for teams. Lets simplify! What technology do you think your team is overusing? Share in the comments! Kubernetes DevOps EngineeringLeadership TechTalk Simplify MidSizedCompanies CloudComputing SoftwareDevelopment Technology Podcast 2. Just because its shiny doesnt mean its right for you! In our latest episode, we explore the pitfalls of jumping on the Kubernetes bandwagon without considering your actual needs. Insight: Most mid-sized companies dont need the complexity that comes with Kubernetes. Have you had to rethink your tech stack? Lets hear your story! Kubernetes TechStack EngineeringEvolved Cloud DevOps MidSizedBusinesses Innovation TechLeadership SoftwareEngineering Podcast 3. Are you caught in the Kubernetes hype? Join us as we unpack the reasons why Kubernetes may not be the best fit for your mid-sized company. Takeaway: Focus on what problem youre truly trying to solve! Whats your biggest challenge with your current tech setup? Comment below! Kubernetes TechHype EngineeringLeadership CloudSolutions DevOps MidSizedCompanies SoftwareDevelopment EngineeringEvolved Podcast 4. Think Kubernetes is the answer? Think again! This episode reveals how many mid-sized companies are overcomplicating their infrastructure. Key insight: Sometimes, simpler solutions can save time, money, and stress! Whats a tech decision you regret? Lets chat in the comments! Kubernetes SimplerIsBetter TechDecisions EngineeringLeaders Cloud DevOps MidSizedBusinesses SoftwareEngineering Podcast 5. What problem are you actually trying to solve? In our latest episode, we challenge the assumption that Kubernetes is always the best choice for tech infrastructure. Insight: Understand your scale and needs before diving into complex solutions. Whats your experience with Kubernetes? Share your thoughts! Kubernetes https://video-meta.open.video/poster/lYyqZD0H_DaW/wRg7OVNbbY2_rHflSy.jpg https://streaming.open.video/contents/lYyqZD0H_DaW/1790890581/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wRg7OVNbbY2 1188 https://videos.concepttocloud.com/v/openais-code-red-sam-altmans-warning-about-googles-ai-competition OpenAIs Code Red: Sam Altmans Warning About Googles AI Competition Sam Altmans internal code red warning to OpenAI staff reveals the intense pressure the company faces from Googles AI advances. In this episode, we break down the competitive dynamics reshaping the AI industry and what it means for the future. TIMESTAMPS: 0:00 Introduction and OpenAIs Code Red Warning 0:26 Googles AI Journey and Turnaround 1:23 OpenAIs Profitability Problem vs Googles Advantages 3:15 Googles Latest AI Breakthroughs 3:57 Future of AI Industry Consolidation KEY TAKEAWAYS: OpenAI faces existential competitive pressure from Google Profitability vs innovation - why sustainable business models matter How ecosystem advantages create AI competitive moats The coming consolidation of the AI industry Subscribe for daily AI insights and hit the notification bell to stay updated on the latest AI developments! OpenAI Google AI SamAltman ArtificialIntelligence TechNews https://video-meta.open.video/poster/nvea-pe5Azj7/2RhkOVgaaJ2_VNYPTi.jpg https://streaming.open.video/contents/nvea-pe5Azj7/1790890423/index.m3u8 https://videos.concepttocloud.com/embed?contentId=2RhkOVgaaJ2 291 https://videos.concepttocloud.com/v/understanding-the-reactor-shell-bug-implications-for-ai-and-web-security Understanding the Reactor Shell Bug: Implications for AI and Web Security In the fast-evolving world of technology, security vulnerabilities frequently emerge, posing serious threats to both businesses and users. One such vulnerability is the Reactor Shell bug, which has recently captured the attention of the tech community. In this blog post, we will explore the details of this critical bug, its implications for the AI landscape, and what businesses need to do to safeguard their systems. Understanding the Reactor Shell Bug The Reactor Shell bug is a significant security vulnerability that has been making headlines recently. As highlighted by Tom Barber in a recent AI briefing, this bug affects almost all server-side rendering versions of React, including popular frameworks like Next.js. Essentially, the bug allows malicious actors to gain unchecked shell access to affected services. This situation is reminiscent of prior vulnerabilities in PHP services, where unpatched systems would expose shell access to unauthorized users. The implications of the Reactor Shell bug are far-reaching, particularly for businesses that rely on the React framework for their web services. The Connection to AI The impact of the Reactor Shell bug extends into the realm of artificial intelligence. Barber notes that the way vulnerabilities are exploited today makes it easier for cybercriminals to utilize AI models to assess and exploit weaknesses in web services. For instance, these actors can deploy AI to efficiently probe various websites, seeking different entry points to exploit the bug. This is not just a theoretical concern there are already examples of organizations employing AI to compromise global servers. When a critical bug like Reactor Shell is discovered, it is likely to be leveraged swiftly by those with malicious intent. The intersection of AI and cybersecurity highlights the importance of robust safeguards and monitoring systems to protect against such threats. The Growing Threat Landscape As the digital landscape evolves, so too do the tactics employed by cybercriminals. The Reactor Shell bug https://video-meta.open.video/poster/ObyqRjKG_fct/w7h6iVMaaJ2_pczPsl.jpg https://streaming.open.video/contents/ObyqRjKG_fct/1790890416/index.m3u8 https://videos.concepttocloud.com/embed?contentId=w7h6iVMaaJ2 234 https://videos.concepttocloud.com/v/ai-slop-why-generic-ai-content-is-polluting-the-internet AI Slop: Why Generic AI Content is Polluting the Internet Are you tired of seeing generic, AI-generated content everywhere? In this episode, we explore the growing problem of AI slop - low-quality automated content thats polluting social media and search results. TIMESTAMPS: 0:00 - What is AI Slop? 0:44 - The Google Content Problem 1:47 - Quality vs. Quantity Trade-offs 2:23 - Case Study: Coca-Colas AI Advertisement 3:07 - Finding the Right Balance with AI KEY TAKEAWAYS: Why Google is de-indexing AI content farms How to use AI as an augmentation tool, not replacement Real-world examples of AI slop in action Practical strategies for maintaining authenticity The importance of considering your audiences perspective RESOURCES: Subscribe for daily AI insights and responsible technology discussions. AISlop ContentMarketing DigitalMarketing AI SocialMedia GoogleSEO ContentStrategy Authenticity Technology Business https://video-meta.open.video/poster/l1jS6lbOBr6Y/w7hQi_hXGJ2_bvFMRa.jpg https://streaming.open.video/contents/l1jS6lbOBr6Y/1790890663/index.m3u8 https://videos.concepttocloud.com/embed?contentId=w7hQi_hXGJ2 256 https://videos.concepttocloud.com/v/your-standups-and-sprints-are-measuring-the-wrong-thing Your Standups and Sprints Are Measuring the Wrong Thing Most team rituals optimise for activity, not outcomes. Heres how to tell the difference and fix it. Ship outcomes, not busywork: https://concepttocloud.com Standups, sprints, story points most of it measures motion, not progress. In this one: - The difference between activity and outcomes - Why common rituals quietly reward the wrong things - Lessons from NASA JPL and startups on rituals that actually work - The shifts that make standups and reviews worth the time Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com agile engineeringleadership productivity softwareteams https://video-meta.open.video/poster/QBmS-zDaYvz7/wYoQiFMabt2_SrpHiS.jpg https://streaming.open.video/contents/QBmS-zDaYvz7/1790892061/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wYoQiFMabt2 1932 https://videos.concepttocloud.com/v/uh-oh-your-talented-engineer-just-pitched-kubernetes-to-modernize-your-infrastructure Uh oh... Your talented engineer just pitched Kubernetes to modernize your infrastructure. Your talented engineer just pitched Kubernetes to modernize your infrastructure. Everyone nodded because its industry standard. Six months later, your 2-person infrastructure team spends half their time keeping the cluster healthy while competitors ship features on simple VMs. Weve seen this pattern countless times: mid-size companies adopting enterprise-grade solutions that create more problems than they solve. The operational burden of pod networking, ingress controllers, and crash loop debugging quietly destroys team velocity. In our experience, the best infrastructure choice isnt always the most sophisticated one. Sometimes Docker on a VM and shipping features beats managing Kubernetes complexity. The real question isnt Should we use Kubernetes? Its Whats the simplest solution that meets our actual needs? Right-sizing your platform engineering approach can be the difference between thriving and burning out your team. Listen to our full breakdown of platform engineering for mid-size companies: https://share.transistor.fm/s/e54c73a9 PlatformEngineering Kubernetes TechLeadership InfrastructureStrategy CloudTransformation https://video-meta.open.video/poster/yLuORvuqZbdB/-Zp7iVNrbY2_pzAiuN.jpg https://streaming.open.video/contents/yLuORvuqZbdB/1790893594/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-Zp7iVNrbY2 51 https://videos.concepttocloud.com/v/smart-people-over-engineering-platform-infrastructure-is-one-of-the-most-expensive-mistakes-we-see Smart people over-engineering platform infrastructure is one of the most expensive mistakes we see Smart people over-engineering platform infrastructure is one of the most expensive mistakes we see in mid-size companies. This NASA JPL story perfectly captures what happens when sophistication doesnt match actual needs. Three layers of abstraction. More time maintaining platforms than building features. Two weeks to onboard an engineer. The solution? They ripped it out. Replaced it with something boring and unsexy that onboarded engineers in 20 minutes. In our experience working with companies on modernisation, the most successful platforms arent the most sophisticatedtheyre the ones that match your teams actual capacity and needs. Not what tools are available. Not what you think you might need someday. What you need right now. Weve seen too many engineering teams burn out maintaining infrastructure thats more complex than their core business problems. The boring solution often wins. Your infrastructure should accelerate your team, not slow them down. Full episode on right-sizing platform engineering: https://share.transistor.fm/s/e54c73a9 PlatformEngineering Kubernetes TechLeadership Engineering CloudStrategy https://video-meta.open.video/poster/6Ly0NxLz2bk4/-JVROFMaGI2_tUUkDH.jpg https://streaming.open.video/contents/6Ly0NxLz2bk4/1790893583/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-JVROFMaGI2 43 https://videos.concepttocloud.com/v/building-on-ai-how-much-risk-can-you-handle Building on AI: How Much Risk Can You Handle? Building on AI: How Much Risk Can You Handle? The Cloudflare outage this week took down a massive chunk of the internetand its a wake-up call for anyone building on AI infrastructure. When your business depends on OpenAI, Anthropic, or other AI vendors, whats your backup plan when the systems go down? In this episode of The AI Briefing, Tom explores the critical question of risk appetite in AI deployment. Whether youre a startup wrapping your entire strategy around AI APIs or an established business considering AI integration, you need to understand the tradeoffs between convenience and control. We cover: The Cloudflare outage and what it means for AI-dependent businesses Different deployment options: direct APIs, Azure AI playground, or self-hosted models How startups vs. established companies should think about AI infrastructure risk Strategic considerations when making AI core to your operations The AI bubble conversation and vendor lock-in concerns Short, tactical, and recorded on the movebecause sometimes the best insights come from thinking out loud. Need help making strategic AI infrastructure decisions for your business? We help early-stage startups build prototypes and MVPs with the right technical foundation. https://www.concepttocloud.com AI TechStrategy CloudInfrastructure AIRisk StartupStrategy TechLeadership https://video-meta.open.video/poster/FHyGIdfq3fQ7/EJoQP-NHHZ2_BLvysQ.jpg https://streaming.open.video/contents/FHyGIdfq3fQ7/1790893598/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EJoQP-NHHZ2 163 https://videos.concepttocloud.com/v/stop-building-what-you-should-be-buying Stop building what you should be buying. Stop building what you should be buying. Weve worked with hundreds of mid-size companies, and the pattern is always the same: teams waste months building custom solutions for problems that dont differentiate their business. Heres the game changing question we ask every client: If you do this exceptionally well, do customers notice and care? Your deployment pipeline? Every software company has one. How you deploy doesnt make customers choose you. Your fraud detection system as a fintech? Thats strategic. It directly impacts unit economics and customer experience. Your HR onboarding tool? Important, but not why customers pay you. In our experience, most infrastructure decisions fail this test. GitHub Actions exists. Your competitors use it. Building a custom CI/CD pipeline doesnt make your product better, it just delays shipping features that actually matter. The companies that scale fastest know the difference between strategic capabilities and table stakes. They buy the table stakes and build what truly differentiates. Want the full framework for making these decisions? Listen to our latest episode on right sizing platform engineering https://share.transistor.fm/s/e54c73a9 PlatformEngineering BuildVsBuy TechStrategy EngineeringLeadership CloudModernisation https://video-meta.open.video/poster/_1qCQvaywzOA/-ZoBOpNWHJ2_RVINYW.jpg https://streaming.open.video/contents/_1qCQvaywzOA/1790893584/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-ZoBOpNWHJ2 58 https://videos.concepttocloud.com/v/titleai-automated-90-of-this-cyberattack-executives-need-to-know-this Title:AI Automated 90% of This Cyberattack (Executives Need to Know This) State-sponsored attackers just used AI to orchestrate sophisticated cyberattacksand it worked. The AI handled 80-90% of the operation: Identifying vulnerabilities Breaking into systems Parsing stolen data Multiple operations per second The cost dropped dramatically. The speed increased exponentially. Your security policies were built for human-paced attacks. That threat model just changed. When attacks become cheap and automated while defense remains expensive and manual, youre facing an asymmetry you cant win. The twist? The AI hallucinated so much it temporarily made attacks harder. But that wont last. Full 3-minute executive briefing: https://theaibriefing.transistor.fm/episodes/ai-orchestrated-cyberattacks-what-executives-need-to-know Is your organization ready for AI-paced cyber threats? Cybersecurity AIRisk CyberAttack EnterpriseAI CISO CIO ExecutiveLeadership AIBriefing CyberSecurity InfoSec EnterpriseSecurity AIThreats https://video-meta.open.video/poster/zXmuNfXqxncs/-dpkjFNGHI2_MuEUgE.jpg https://streaming.open.video/contents/zXmuNfXqxncs/1790893587/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-dpkjFNGHI2 33 https://videos.concepttocloud.com/v/ai-orchestrated-cyberattacks-what-executives-need-to-know AI-Orchestrated Cyberattacks: What Executives Need to Know State-sponsored attackers just used AI to orchestrate sophisticated cyberattacksand it worked. A recent report reveals how threat actors used Claude Code to execute 80-90% of attack operations automatically, making cyberattacks faster, cheaper, and more scalable. While AI hallucinations temporarily hindered attackers, this represents a fundamental shift in your threat model. This episode breaks down what happened, why the asymmetry between cheap automated attacks and expensive manual defense matters, and the three immediate actions you need to take to protect your organization. In This Episode: How state-sponsored groups used AI to automate 80-90% of cyberattack operations Why jailbreaking AI safeguards is easier than most executives realize The asymmetry problem: cheap automated attacks vs. expensive manual defense How AI-assisted attacks differ from traditional script kiddie exploits What intelligence authorities learned from this incident (and why it matters) Three immediate actions to update your security posture for AI-assisted threats Links To Things I Talk About: Anthropics Claude Code: https://docs.anthropic.com/en/docs/claude-code Understanding penetration testing and vulnerability assessment Modern asymmetric warfare principles in cybersecurity Take Action: Review your security policies nownot next quarter. Talk to your CISO about whether your incident response plans are built for AI-paced attacks that operate at multiple actions per second. Your threat model just changed, and your defenses need to reflect that reality. https://video-meta.open.video/poster/5AyS7fG4BrYW/_BVQioMbHd2_PkiMKC.jpg https://streaming.open.video/contents/5AyS7fG4BrYW/1790895405/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_BVQioMbHd2 194 https://videos.concepttocloud.com/v/unlock-the-true-potential-of-ai-investments Unlock the true potential of AI investments! Unlock the true potential of AI investments! In our latest podcast, we break down the 10-20-70 rule that will transform your approach to AI. Did you know that successful companies allocate 70% of their efforts on people and processes rather than just algorithms? Here are some key takeaways: Most companies are getting AI investment backwards. Only 5% capture value from AI at scale. Winning with AI is a sociological challenge. Generative AI impacts the majority of the workforce. Its time to rethink how we train and upskill our workforce. Less than a third of companies are focusing on this! Listen now to learn how to build cross-functional agile teams and redesign workflows for future growth: https://theaibriefing.transistor.fm/ AI InvestmentStrategy WorkforcePlanning Upskilling GenerativeAI BusinessGrowth https://video-meta.open.video/poster/__faIvfG6fZX/FBVBO-hrGd2_MBJplT.jpg https://streaming.open.video/contents/__faIvfG6fZX/1790895595/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FBVBO-hrGd2 202 https://videos.concepttocloud.com/v/the-ai-funding-landscape-is-transforming-rapidly The AI funding landscape is transforming rapidly! The AI funding landscape is transforming rapidly! In just Q1 2025, a staggering 73 billion flowed into AI startups, with giants like OpenAI securing 40 billion in a single round. But what does this mean for the future? Our latest podcast dives into the implications of concentrated capital in a few mega players. While 60% of global venture capital is being funneled into these mega rounds, other sectors are left struggling for investment. Key Takeaways: - 46% of global venture funding went to AI companies in Q3. - Microsoft is investing heavily, spending 80 billion on AI data centers. - This concentration raises concerns about supply dependency risks and innovation bottlenecks. Is the AI revolution solidifying or heading towards instability? Tune in to explore the dynamics at play! Listen now: https://theaibriefing.transistor.fm/ AI Startups VentureCapital Innovation Funding TechTrends https://video-meta.open.video/poster/7ECKQvL4_zXX/FlokOphWHt2_tXWcxi.jpg https://streaming.open.video/contents/7ECKQvL4_zXX/1790895421/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FlokOphWHt2 230 https://videos.concepttocloud.com/v/most-mid-size-companies-are-building-infrastructure-for-problems-they-dont-haveyet Most mid-size companies are building infrastructure for problems they dont haveyet. Former NASA engineer Tom Barber reveals why your 200-person company doesnt need Kubernetes, and shares the exact framework for deciding what to build vs. buy in platform engineering. If youre spending months setting up enterprise-grade infrastructure for startup-size teams, this episode will save you from expensive over-engineering mistakes. What Youll Learn: Why Kubernetes is overkill for most mid-size companies (and what to use instead) The 4-factor framework for build vs. buy decisions that actually works How NASAs over-engineered systems collapsed when funding shifted The 4:1 ratio rule for balancing builders and maintainers on your team Why boring, maintainable infrastructure beats sophisticated complexity every time Free Download: Get Toms Build vs. Buy Decision Framework at engineeringevolve.com Timestamps: 0:00 - Introduction: The Kubernetes Heresy 2:58 - The NASA Story: When Smart People Over-Engineer 5:51 - Platform Engineering for the Missing Middle 8:06 - The Kubernetes Problem 12:58 - Build vs. Buy Framework 20:48 - Internal Tools That Actually Matter 23:51 - Infrastructure Organization & Documentation 27:38 - Automation, AI, and Team Sizing 34:10 - Bringing It All Together Resources Mentioned: Retool - Internal tool platform Terraform & Bicep - Infrastructure as Code GitHub Actions & GitLab CI - CI/CD pipelines AWS Secrets Manager & HashiCorp Vault - Secret management Key Insight: Your infrastructure sophistication should match your teams capacity, not the available tools. Most platform infrastructure is table stakes, not strategic differentiation. https://video-meta.open.video/poster/CPmiJhPa_DQZ/-AURjoMaGI2_VbQUqU.jpg https://streaming.open.video/contents/CPmiJhPa_DQZ/1790896777/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-AURjoMaGI2 2183 https://videos.concepttocloud.com/v/are-you-aware-of-the-staggering-failure-rates-in-generative-ai-projects Are you aware of the staggering failure rates in generative AI projects? Are you aware of the staggering failure rates in generative AI projects? According to MIT, a shocking 95% of these pilots are floundering! Gartner predicts that by 2025, 30% of AI projects will be abandoned altogether. In our latest podcast episode, we dive deep into the reasons behind these statistics and discuss the challenges of implementing AI in enterprise settings. From poor data quality to unclear business value, we cover it all. Key Takeaways: - Custom AI models can cost between 5M to 20M. - Specialized tools and partnerships can boost success rates to 67%. - Back-office automation provides the highest ROI in AI endeavors. - Startups that focus on specific pain points experience significant growth. Tune in for insights on how to navigate the complexities of AI and develop effective strategies for success! Listen now: https://theaibriefing.transistor.fm/ AI GenerativeAI BusinessStrategy Podcast DigitalTransformation Innovation TechTrends https://video-meta.open.video/poster/AZvW-r5qsnkA/-kVkj-hbGY2_dphfvW.jpg https://streaming.open.video/contents/AZvW-r5qsnkA/1790895814/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-kVkj-hbGY2 203 https://videos.concepttocloud.com/v/riversideaiep2tombarbers-studio riversideaiep2tombarbers studio Exciting insights from our latest podcast episode! We dive deep into the challenges of AI adoption in the workplace, focusing on the crucial gap between leadership and frontline employees. Key takeaways include: - Leadership support can boost employee sentiment from 15% to 55%! - Frontline employee AI adoption is currently stalled at 51%. - A staggering 78% of leaders are using AI multiple times a week, yet shadow AI poses security risks. - Only 36% of employees feel their AI training is sufficient however, just five hours of training can increase adoption to 79%! - Companies that reshape workflows are seeing more strategic task engagement. Join us as we explore actionable steps to bridge this gap and enhance AI integration across your organization. Dont miss out! Listen now: https://theaibriefing.transistor.fm/ AIAdoption Leadership WorkplaceInnovation EmployeeEngagement Podcast DigitalTransformation AIIntegration https://video-meta.open.video/poster/EcnaYxjj3zys/EkVljEhbGJ2_vqVuVp.jpg https://streaming.open.video/contents/EcnaYxjj3zys/1790895731/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EkVljEhbGJ2 180 https://videos.concepttocloud.com/v/why-legacy-systems-quietly-kill-growth-and-how-to-modernise-safely Why Legacy Systems Quietly Kill Growth (And How to Modernise Safely) Legacy systems rarely fail loudly they quietly tax every new thing you try to build. Heres how to modernise without a risky big-bang rewrite. Modernising a legacy system? https://concepttocloud.com The real cost of legacy isnt downtime its the growth you never get to. In this one: - How legacy systems silently slow every new initiative - Why big-bang rewrites so often fail - A safer, incremental path to modernisation - Why the human and organisational side matters as much as the tech Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com legacymodernisation cloudmigration softwarearchitecture techdebt https://video-meta.open.video/poster/Exu87dm4BzRz/FkVAOphWaI2_qRZYic.jpg https://streaming.open.video/contents/Exu87dm4BzRz/1790897077/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FkVAOphWaI2 1075 https://videos.concepttocloud.com/v/the-first-90-days-how-to-not-fail-at-modernization The First 90 Days: How to Not Fail at Modernization The first 90 days of your modernization initiative will make or break everything. Not because of the technology you choose, but because of the trust you build, the organizational dynamics you navigate, and the business pain you actually solve. In this episode, Tom Barber provides a week-by-week action plan for engineering directors taking on technology transformation. This isnt about your technical visionits about proving you understand the problems that led to your hiring and can be trusted to fix them without declaring war on everyone who came before you. The framework: Your first two weeks should focus entirely on listening and mapping. Conduct a 10-day discovery sprint where your only job is understanding the current state. Talk to engineering teams, business stakeholders, and customers. Ask the critical question: Whats the one thing youre most worried about? The answers reveal where you should focus. Weeks three through six are about identifying and executing your first quick win. Not the most important modernization effortthe one that builds credibility fastest. Choose something visible, achievable, and painful enough that people notice when its fixed. Then stay engaged during execution. Quick wins die when leaders delegate them and disappear. Weeks seven through twelve focus on communication and resetting expectations. Present your vision to leadership, but frame it around business outcomes, not technology preferences. Establish the foundation for change management by showing you understand organizational dynamics and can navigate them effectively. Critical principles: Modernization failures arent usually technicaltheyre human. Leaders who ignore stakeholder engagement, dismiss existing systems without understanding why they exist, or prioritize technology over business pain create resistance that kills initiatives. Your first 90 days are about building the trust and momentum that make transformation possible. Engineering leadership during modernization requires understanding that you werent hired to fix e https://video-meta.open.video/poster/ESfaMdDHFjGz/F6pkjEMWaJ2_NuHmyy.jpg https://streaming.open.video/contents/ESfaMdDHFjGz/1790897836/index.m3u8 https://videos.concepttocloud.com/embed?contentId=F6pkjEMWaJ2 1279 https://videos.concepttocloud.com/v/drowning-in-feature-requests-fix-your-deployments-first Drowning in Feature Requests? Fix Your Deployments First. Your engineering team is drowning in feature requests. Your backlog is overflowing. And youre being told theres no budget for additional headcount. Sound familiar? In this episode, Tom Barber reveals a counterintuitive strategy for unlocking engineering capacity without hiring: start by optimizing your most time-consuming deployment processes. While everyone focuses on building new features faster, the real efficiency gains come from eliminating the hidden time sinks that drain your teams productivity every single day. Heres the reality most engineering leaders miss: when a deployment takes four hours of manual work, babysitting, and verification, thats not just four hours lost once. Its four hours multiplied by every deployment, every week, for every engineer who touches that system. Those hours compound into entire engineering-weeks of capacity disappearing into deployment overhead. The strategic approach isnt tackling every inefficiency at onceits identifying the single most time-consuming deployment in your organization and automating it first. Extract those four hours. Return them to the team immediately. Then move to the next bottleneck. This cascading efficiency gain can feel like hiring another engineer without the salary cost. Key concepts covered: Why feature requests pile up faster than teams can address them, and how deployment efficiency directly impacts feature delivery velocity. The relationship between deployment time and team productivity is linearevery hour saved in deployment is an hour available for feature development. How to identify which deployment processes deserve automation investment first. Not all inefficiencies are equal. The deployment that takes four hours weekly for multiple engineers should be your first target, not the one thats slightly annoying but only happens monthly. The compounding returns of time management improvements in engineering teams. When you free up four hours per deployment and that deployment happens twice weekly across three engineers, youve just recovered 24 https://video-meta.open.video/poster/-snq3Fba-ndy/FAVliUgXaY2_wFTItw.jpg https://streaming.open.video/contents/-snq3Fba-ndy/1790898242/index.m3u8 https://videos.concepttocloud.com/embed?contentId=FAVliUgXaY2 1386 https://videos.concepttocloud.com/v/you-found-product-market-fit-heres-what-breaks-next You Found Product-Market Fit. Heres What Breaks Next Finding product-market fit is the fun part. Surviving what comes after it is where most teams stumble. Scaling past PMF? https://concepttocloud.com PMF isnt the finish line its where a new set of problems begins. In this one: - What predictably breaks right after product-market fit - Why the scrappy move fast mentality becomes a liability - Legacy systems, tech debt and single points of failure at scale - How to shore up your foundations before the strain hits Concept To Cloud builds critical systems for missions that cant afford to fail MVPs, legacy modernisation, cloud, data and AI infrastructure. Ex-NASA engineers. Start a project: https://concepttocloud.com startups productmarketfit scaling MVP productmanagement https://video-meta.open.video/poster/EXb4MzuqErW6/wYgJGMpWat2_HBKeiJ.jpg https://streaming.open.video/contents/EXb4MzuqErW6/1790898580/index.m3u8 https://videos.concepttocloud.com/embed?contentId=wYgJGMpWat2 2305 https://videos.concepttocloud.com/v/riversidewhentech-transitions-forget-the-peopletombarbers-studio riversidewhentech transitions forget the peopletombarbers studio In the rush to innovate, we often overlook the human element. During a recent project, our focus was on transitioning to AWS, but a simple question from a senior engineer changed everything: What happens to us when this is done? This moment reminded us that technology transitions must go hand-in-hand with human transitions. Lets not forget the people behind the processes. Leadership ChangeManagement TechTransition Link in first comment. https://video-meta.open.video/poster/AcmqRFWq3fzZ/EIMJGNUHbZ2_WYhkeq.jpg https://streaming.open.video/contents/AcmqRFWq3fzZ/1790897845/index.m3u8 https://videos.concepttocloud.com/embed?contentId=EIMJGNUHbZ2 47 https://videos.concepttocloud.com/v/balancing-code-debt-in-startups Balancing Code Debt in Startups Balancing Code Debt in a startup is always hard. In episode 1 of our Engineering Evolved podcast we dig into this problem: https://www.engineeringevolved.com/episodes/escaping-technical-purgatory https://video-meta.open.video/poster/EfC4IjKjxjt4/-YNcbhoXbJ2_JbvEtg.jpg https://streaming.open.video/contents/EfC4IjKjxjt4/1790897853/index.m3u8 https://videos.concepttocloud.com/embed?contentId=-YNcbhoXbJ2 46 https://videos.concepttocloud.com/v/half-of-all-cloud-migrations-fail-heres-why Half of All Cloud Migrations Fail. Heres Why. Half of all cloud transformations are abject failures. Companies spend millions migrating to the cloud only to face catastrophic outages, customer exodus, and emergency rollbacks. The difference between Netflixs legendary migration success and TSB Banks 330 million disaster? Discipline, strategy, and treating cloud migration like a product launch instead of an infrastructure project. In this comprehensive episode, Tom Barber provides the definitive playbook for successful cloud migration, drawing lessons from both spectacular successes and devastating failures. If youre planning a cloud migration, inheriting one mid-flight, or trying to understand why your current migration is hemorrhaging money, this video could save your organization millions. The era of Big Bang migration is dead. The lift everything over a weekend and pray approach that killed TSB Banks customer access for weeks is organizational suicide. Modern cloud migration requires sophisticated migration strategies that allow for incremental progress, continuous validation, andcriticallythe ability to rollback when things go wrong. Tom introduces the Strangler Fig pattern as the gold standard for cloud migration. Named after the tree that gradually envelops and replaces its host, this approach allows you to migrate functionality piece by piece, validating each transition before proceeding. Youre not betting the company on a single cutover weekendyoure building confidence through controlled, reversible changes. Key frameworks covered: The six Rs of application migration: Rehost, Replatform, Repurchase, Refactor, Retire, and Retain. Understanding which strategy applies to each application prevents the common trap of over-engineering migrations or under-investing in modernization. Most companies should focus on replatforminggetting cloud benefits without complete rewrites. Product management approaches to migration that transform how organizations think about infrastructure changes. Product managers understand user journeys, measure outcomes, and manage st https://video-meta.open.video/poster/5rzuVBfWxb4Y/_shZHhVabZ2_XCNYAR.jpg https://streaming.open.video/contents/5rzuVBfWxb4Y/1790899241/index.m3u8 https://videos.concepttocloud.com/embed?contentId=_shZHhVabZ2 1969 https://videos.concepttocloud.com/v/technical-debt-is-costing-you-15-trillion-and-you-dont-even-know-it Technical Debt Is Costing You 1.5 Trillion (And You Dont Even Know It) Technical debt is costing American companies 1.5 trillion annually. And if youre in the C-suite, theres a good chance you have no idea how much its costing your organization specifically. In this eye-opening episode, Tom Barber exposes one of the most expensive invisible problems plaguing modern businesses. While executives obsess over quarterly reports and operational efficiency, technical debt silently drains resources, kills deals, and creates catastrophic risk exposureall while hiding in plain sight within your engineering payroll. Heres why this matters to business leaders who dont write code: technical debt isnt just an engineering problem. Its a competitive disadvantage that manifests as lost deals to faster competitors, delayed product launches, security vulnerabilities, and engineering teams that spend more time maintaining legacy systems than building new capabilities. Its the reason your engineering estimates keep growing while your feature velocity keeps shrinking. The insidious nature of technical debt is that it doesnt appear as a line item on your balance sheet. Theres no technical debt budget that the CFO reviews. Instead, its distributed across your engineering payroll as thousands of invisible hours spent working around architectural limitations, debugging brittle systems, and manually handling processes that should be automated. Your engineers know exactly how much time theyre wastingbut most C-suite executives never ask. For many organizations, technical debt functions like a ticking time bomb. Everything seems fine until suddenly its not. A critical system fails. A security breach exposes customer data. A competitor launches features in weeks that take your team months. And by the time the consequences become visible to leadership, the cost of remediation has multiplied exponentially. This video breaks down: How technical debt translates into real business costs beyond engineering time Why C-suite executives remain blind to technical debt until its too late The competitive disadvantage create https://video-meta.open.video/poster/5Kyi_juX-rz6/3RgdHhUXaZ2_IDoFaB.jpg https://streaming.open.video/contents/5Kyi_juX-rz6/1790899573/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3RgdHhUXaZ2 1860 https://videos.concepttocloud.com/v/stop-fighting-about-microservices-vs-monoliths Stop Fighting About Microservices vs Monoliths Stop arguing about microservices versus monoliths. The architecture that actually matters is the one that lets your team ship features at the speed your business needsand most teams are having the wrong conversation entirely. In this episode, Tom Barber cuts through the microservices hype and provides a pragmatic framework for software development that prioritizes team efficiency and feature delivery over architectural dogma. The answer isnt microservices good, monoliths bad or vice versaits about building systems that evolve with your teams actual development pain points rather than theoretical best practices. The winning strategy? Start with a modular monolith. Not a big ball of mud, but a well-organized system with strong module boundaries and clear business domain separation. This gives you the coordination benefits of a monolith while maintaining the structural discipline needed to extract services later whenand only whenyou actually need to. Most teams jump to microservices for the wrong reasons: because Netflix does it, because its modern, because some consultant said they should. Then they discover coordination hell, deployment complexity, and distributed system debugging nightmares that dwarf whatever problems they thought they were solving. Meanwhile, their feature delivery slows to a crawl while they manage service-to-service contracts and version compatibility matrices. The smarter approach is surgical service extraction driven by real pain points. When a module becomes a team bottleneck, when scaling requirements differ dramatically, when coordination overhead outweighs monolith benefitsthats when you extract. One service at a time. Learning from each extraction. Building the organizational capabilities to manage distributed systems before youre drowning in them. Tom introduces a liberating concept: forget microservices as a strict definition. Instead, think about appropriately sized services for your team and business context. Not micro. Not macro. Just appropriate. Services that align with team bou https://video-meta.open.video/poster/yWvK-zi4wzG4/3cVYaxUbHJ2_ccAojQ.jpg https://streaming.open.video/contents/yWvK-zi4wzG4/1790901580/index.m3u8 https://videos.concepttocloud.com/embed?contentId=3cVYaxUbHJ2 1925