In The Loop

Within his first two weeks as COO at Turtl, Dave Martin and his team built a churn prediction model. Tested against historic customer data, it predicted churn with 98% accuracy. Work like that normally takes six months or more, plus an agency and external partners. Most companies still make that call on gut feel, or by putting some basic data into Claude or ChatGPT. In this episode of In The Loop, I'm joined by Dave to walk through exactly how he and his team did it, using a method he calls pattern of life analysis. We cover how they paired customers who renewed with similar ones who left, got 12,500 minutes of call recordings through Claude without blowing the context window, the landmines you'll hit if you try this yourself, and what it takes to replace the loudest voice in the room with evidence. ⏭️ Episode highlights (08:15) – Why a scale-up can't wait six months (09:40) – Testing the model: 98% on historic data (11:10) – Rebuilding each customer journey, call by call (24:25) – Why Claude's first answer was "absolute garbage" (42:05) – The "rudimentary" test that proved three ideas (45:55) – Why loud opinions make terrible decisions 🔗 Links & resources Dave Martin on LinkedIn - ⁠https://www.linkedin.com/in/mrdavemartin/⁠ Turtl - ⁠https://turtl.co⁠ Jack Houghton LinkedIn - ⁠https://www.linkedin.com/in/jack-houghton1/⁠ Mindset AI website - ⁠https://bit.ly/40lJr6B⁠

Anthropic went from $9bn of annualised revenue in December to $65bn by the end of July, and it's expected to list in October at close to $2 trillion - the highest price any company has ever carried into a stock market debut. If you work backwards from that valuation, it has to be earning about $1.2 trillion a year within a decade. The entire world spends $1.5 trillion on software. So the Anthropic IPO isn't a bet on software at all. It's a bet on wages. In this episode of In The Loop, I'm exploring the six things that all have to be true for a $2 trillion Anthropic valuation to make sense - using their IPO as a way of understanding the strategies of the biggest AI companies in the world. ⏭️ Episode highlights (01:00) – Why nobody in AI talks about software any more (02:00) – $9bn to $65bn in seven months, and the caveat (03:10) – Anthropic's own written date for powerful AI (04:20) – One in five firms, 78% of the workforce (05:20) – The 60 pence in every pound that goes back out (06:20) – Memory costs more than the processor (07:30) – Three of the four hyperscalers are burning cash (08:40) – The one line to find in the prospectus

In this episode of In The Loop, I'm going through four tactics engineers any many others use every day that almost nobody outside engineering has heard of: Fan out, adversarial review, ChatGPT's computer history, and the browser Claude got of its own this month. ⏭️ Episode highlights (01:00) – Why engineers are years ahead of everyone else (01:45) – Fan out: several Claudes, one job (02:45) – Why long chats get worse the further down your list they go (04:20) – Adversarial review: the blank chat that has no stake in your work (05:40) – The four sentences that do all the work (06:35) – ChatGPT watching your screen (08:20) – Claude's own browser

Once a week, an agent at StackOne goes hunting for new prompt injection attacks. It reads papers, trawls Reddit, tries what it finds against real models, keeps the attacks that land, and retrains StackOne's defence model on them. A person still approves every deployment. Guillaume Lebedel, their CTO, puts the whole thing on screen, including the five experiments out of six that failed. In this episode of In The Loop, Guillaume talks about how and why they have built an auto-research loop. What one is in plain words, how you pick a goal ai agents can measure, how you sample 100,000 test cases down to something you can afford, and why he runs evals on cheap models before trusting anything. We also spoke about token leaderboards and the impact its had internally. ⏭️ Episode highlights (05:41) – What an auto-research loop is (11:53) – Why an agent is a folder (14:47) – Screen-share: the attack-hunting agent (18:00) – Six experiments, one promoted (27:29) – Sampling 100,000 test cases down (31:48) – Ninety per cent of tokens are wasted (39:33) – Turning off extra usage the same day (41:13) – Screen-share: the token derby

Anthropic went from about a billion dollars in revenue to about forty-seven billion in a bit over a year, roughly doubling every six weeks. In this episode of In The Loop, I'm going through how the fastest-growing company in history actually builds software, and pulling out five things you can learn from how they build products & their teams. ⏭️ Episode highlights (03:00) – One job title, teams of two engineers (05:30) – The five archetypes with no job titles (07:45) – Building hundreds of versions before deciding (09:30) – 15% building, 85% checking (11:15) – Build a check, not just instructions (13:00) – Never let the AI mark its own homework (15:00) – The agent that tried to push to production 🔗 Links & resources Gergely Orosz, The Pragmatic Engineer, "How building software is changing at Anthropic" - https://newsletter.pragmaticengineer.com/p/inside-anthropic Anthropic, "Running an AI-native engineering org" - https://claude.com/blog/running-an-ai-native-engineering-org

OpenAI took two of its most capable models, told them to prove how good they were at hacking, and switched off the safety filters to see what they could really do. Instead of solving the test, one model broke out of its sandbox, found its way onto the open internet, and hacked into Hugging Face to steal the answer. Every headline called it an AI going rogue. That's the wrong story, and the real one is far more interesting, because this wasn't a machine that turned evil. It was one that did exactly what we asked. In this episode of In The Loop, I'm walking through the ExploitGym incident from both ends, OpenAI's and Hugging Face's, and why I disagree with the framing everyone else has been talking about. ⏭️ Episode highlights (01:00) – The agent that cheated instead of hacking (02:15) – Inside ExploitGym, and the safety filters OpenAI switched off (03:30) – One door, one zero-day, out on the internet (04:45) – Why this is specification gaming, not rebellion (06:00) – The water that always finds the crack (07:15) – The sceptics, the marketing question, and why "nothing new" is the scary part (08:30) – Anthropic's 24-out-of-25 credential theft result (09:45) – Guardrailed as a defender: the Chinese model that stopped it 🔗 Links & resources OpenAI, "OpenAI and Hugging Face partner to address security incident during model evaluation" – https://openai.com/index/security-incident-during-model-evaluation/ Hugging Face, security incident disclosure post – https://huggingface.co/blog/security-incident ExploitGym benchmark paper (arXiv) – https://arxiv.org/abs/2605.11086 Simon Willison, "OpenAI's accidental cyberattack against Hugging Face is science fiction that happened" – https://simonwillison.net/ Scientific American, "OpenAI admits its agent went rogue and hacked AI start-up Hugging Face" – https://www.scientificamerican.com/article/openai-admits-its-agent-went-rogue-and-hacked-ai-startup-hugging-face/ Fortune, on Hugging Face turning to Chinese open-source AI to defend itself – https://fortune.com/2026/07/20/hugging-face-turns-to-chinese-open-source-ai-to-fend-off-autonomous-ai-cyber-attack-after-american-ai-guardrails-stymie-defense/ CNBC, "How a Chinese AI model stopped OpenAI's 'unprecedented' cyber attack" – https://www.cnbc.com/2026/07/24/chinese-ai-model-openai-cyber-attack.html Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends.

Taste & judgment. It's the word everyone's obsessed with right now. Every founder, podcast, every VC, every post you scroll past saying it's the one skill that survives once AI can do the rest. Thing is, it's a bit of a sh*t word, like strategy: everyone nods along, then goes quiet the second you ask what it actually means. In this episode of In The Loop, I'm trying to define taste and judgement, by researching what Harry Frankfurt's On Bullshit, Rick Rubin, Pixar's Braintrust, Amazon's memos all reccommend. I get into where taste even comes from and the rituals you can actually run with a team on a Monday. ⏭️ Episode highlights (01:10) – Why "taste" is suddenly everywhere (02:40) – Workslop, and why judging got expensive (04:10) – Frankfurt's On Bullshit, applied to AI (05:40) – Taste vs judgement, and Rick Rubin (07:20) – The apprenticeship we're automating away (09:00) – Rubin's three ideas: attention, sayability, subtraction (11:00) – Rituals worth stealing: Amazon, Pixar, the 11-star bar (12:50) – Where Jack's landed, for now

A well-behaved AI agent deleted a company's entire production database, and every backup with it, in nine seconds. Nobody hacked it. It was doing exactly what it had been told, with a key it should never have been holding. And the thing that made it possible is now one command away from everyone in your company, in marketing, sales and support, not just engineering. In under a year, installable AI agent skills have gone from a niche developer trick to an open industry standard. In this episode of In The Loop, I'm looking at why the exact thing that makes skills brilliant is the thing that quietly turns them into a control problem. I get into why this is shadow IT all over again except the shadow can now act, and why the fix isn't to ban any of it. ⏭️ Episode highlights (00:45) – The database gone in nine seconds (02:10) – One line that skips engineering (04:00) – Skills vs MCP, what's actually dangerous (08:00) – Shadow IT is back, but it can act now (09:45) – PocketOS and Replit: the receipts (11:30) – The middle path that actually fixes it (12:40) – Where this leaves you

Hand the same AI tool to two people and one gets sharper while the other quietly gets worse. A Harvard study found recruiters given near-perfect AI made worse calls than those given a mediocre one, because the good tool worked so well they stopped checking it. The question of whether AI is making us dumber turns out to have an answer: it depends entirely on which parts of your job you hand over. In this episode of In The Loop, I'm working through the research on AI deskilling - the recruiter study, Terence Tao working with a pen and paper, the difference between performance and competence and a simple rule for what to give AI and what to guard. ⏭️ Episode highlights (00:45) – The same tool, opposite outcomes (02:10) – The recruiters who fell asleep at the wheel (04:00) – Terence Tao: wider, not deeper (05:30) – Firehose vs editor (07:10) – Performance vs competence (08:50) – The rule: protect your core, rent the rest (10:40) – Cognitive surrender: wrong 80% of the time (12:30) – The checks to run before you reach for AI Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Open LinkedIn and count the posts that start with "in today's fast-paced world." Check your inbox for someone circling back to delve into synergies. The AI slop is coming from every direction now. People can tell, and they mark down what reads like a machine wrote it. The fix isn't a cleverer prompt. It's that the model has never actually read your writing. In this episode of In The Loop, I'm walking through a six-step process to get AI to write like you and like your team, instead of like everyone else. ⏭️ Episode highlights (00:55) – Why custom instructions still produce slop (01:45) – Which writing actually needs this (02:30) – Step one: let AI read your real writing (03:30) – The voice profile prompt that nails your style (04:30) – Building "my-voice" and "de-slop" skills (05:30) – One slop sentence, rewritten in your voice (06:30) – The MIT study on AI and cognitive debt (07:15) – What to do this week Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends.

Every summer since 2023, AI gets a panic season — critics say that AI's novelty i novelty's wearing off, the models have hit a wall, ninety-five percent of projects fail. Each time it dominates for a few months, then dissolves. This year's arrived early, kicked off by Uber burning through its entire 2026 AI budget in four months with a COO who can't prove it was worth it. But 2026's AI bubble panic is different, and not for the reason the coverage thinks: forty-five percent of the S&P 500 is now riding on AI working out. Same pattern as every year — much higher stakes. In this episode of In The Loop, I'm pulling apart the three fears behind this year's AI panic — the AI ROI crisis, the jobs fear, and the market concentration risk — and giving each one a straight verdict. I walk through the Uber rollout numbers, why Sam Altman and Dario Amodei are walking back their job-apocalypse predictions, and why the market fear is the one I find hardest to dismiss. The question isn't whether the panic is right. It's what's getting clearer while everyone's distracted by it. ⏭️ Episode highlights (01:10) – The panic that arrives every summer (02:50) – Uber's 95/70/11 rollout numbers (05:00) – Why ROI is a measurement problem (07:30) – Top earners are the most scared (09:15) – Altman and Amodei walk it back (11:30) – Is this 1999? Circular financing explained (13:40) – The price signal the panic keeps missing (15:10) – What to actually take from this If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Andrej Karpathy joined Anthropic on 19 May 2026. Most people read it as an OpenAI story — he co-founded the lab, left twice, and landed at the rival. That's not what this is. Eight weeks before he made the call, Karpathy built a 630-line Python script that ran 300 experiments on his own code in two days and found improvements he'd missed after months of hand-optimizing. He called it "the final boss battle." Anthropic then offered him the mandate to run that exact loop at the most consequential scale in the industry: using Claude to accelerate its own pre-training research. In this episode of In The Loop, I'm tracing what actually changed Karpathy's mind — from calling AI agents "terrible" in October 2025 to joining the lab pushing recursive self-improvement six months later. I cover autoresearch, what it found, and why Dario Amodei's quote about the AI-assistance multiplier is the most important thing said in frontier AI right now. ⏭️ Episode highlights (01:15) – Karpathy calls AI agents "terrible" in Oct 2025 (02:30) – What changed: coding agents that basically work now (03:30) – 700 experiments, two days, 11% faster training (05:00) – What pre-training is and why it costs hundreds of millions (06:15) – AlphaEvolve and Codex: every lab running the same loop (07:10) – Dario Amodei: the multiplier going from 5% to 40% (08:20) – The METR study: experienced devs were 19% slower 🔗 Links & resources Andrej Karpathy, X post announcing Anthropic hire, 19 May 2026 — https://x.com/karpathy GitHub: karpathy/autoresearch — https://github.com/karpathy/autoresearch Andrej Karpathy on Dwarkesh Patel podcast, October 2025 — https://dwarkesh.com Dario Amodei on Dwarkesh Patel podcast, "We are near the end of the exponential", February 2026 — https://dwarkesh.com/p/dario-amodei-2 Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Americans now say they'd rather live next to a nuclear reactor than an AI data center. That's not a fringe view — a Gallup poll published this month found 71% of Americans oppose a data center near their home, versus 53% for nuclear. Nuclear carries Chernobyl, Three Mile Island, and forty years of films about radiation in the cultural zeitgeist. The fact that AI data centers have only been a visible part of suburban America for less than five years yet are this hated, is huge. In this episode of In The Loop, I'm looking at the organised opposition movement that has already blocked over $85 billion in planned data center investment — cancelling projects faster in three years than nuclear opposition managed in fifteen. I go through the legal strategy that's winning in courts and at ballot boxes, what the communities are right about, where they're factually wrong, and why the responsible data center model that could resolve this already exists — but nobody's requiring it. ⏭️ Episode highlights (01:05) – Missouri council wiped out 8 days after data center vote (02:30) – The Gallup poll: nuclear vs AI data centers (03:45) – $3 trillion buildout and the AI electricity consumption numbers (05:15) – The legal template that stopped nuclear — working again in Virginia (06:50) – What the opposition gets factually wrong on data center water usage (08:10) – Who actually pays — and which communities bear the cost (09:35) – The responsible data center model that already exists

In 1971, Boeing laid off 44,000 engineers in 18 months. The aerospace industry is still paying for it — they're projecting a shortage of over a million engineers by 2030, and the cohort that would now be the senior bench was simply never hired. Last year, S&P 500 companies cut 400,000 jobs — the first net decline since 2016 — and the specific pattern of who's being cut, and why, looks uncomfortably familiar. At firms that adopted AI, junior employment fell 7 to 10% within six quarters. Senior employment kept rising. The pipeline isn't slowing. It's stopping. In this episode of In The Loop, I'm working through the data on junior employment at AI-adopting firms, the economic logic that makes cutting entry-level roles feel rational, and why I think that logic is setting up a shortage that will look obvious in hindsight. I also take on Tim O'Reilly's counter-argument — his historical case that every programming wave expanded demand rather than destroyed it — and explain why I think he's right about 2035 and wrong about the cohort that's supposed to get there. ⏭️ Episode highlights (01:15) – The Boeing billboard and what it cost (03:00) – 400,000 jobs: where the cuts are concentrated (05:00) – Why junior work is separable — and senior work isn't(07:00) – The radiology lesson: why strong bundles hold (08:45) – O'Reilly's wave argument and the Jevons paradox (11:30) – Where the optimistic case runs out of road (13:00) – The 43-point gap: atrophy you can't feel (14:45) – IBM, Publicis, and who's betting on the pipeline 🔗 Links & resources Boeing Bust (1969–1971) — HistoryLink.org: https://www.historylink.org/file/20923 ISG — Aerospace and defense talent gap by 2030: https://isg-one.com/articles/why-the-aerospace-and-defense-industry-faces-a-million-person-talent-gap-by-2030---and-what-it-means-for-innovation Hosseini & Lichtinger, "Generative AI as seniority-biased technological change" (SSRN, Aug 2025): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5425555 Brynjolfsson, Chandar & Chen, "Canaries in the coal mine?" (Stanford Digital Economy Lab, Aug 2025): https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/ Tim O'Reilly, "The end of programming as we know it": https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/ Tim O'Reilly & Aaron Levie, "The world needs more software engineers" (Apr 2026): https://www.oreilly.com/radar/the-world-needs-more-software-engineers/ Tim O'Reilly, "The missing half of the AI economy": https://www.oreilly.com/radar/ai-and-the-next-economy/ If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Y Combinator have just published a playbook for building a company that runs on AI rather than just using it. Six principles, written by a general partner who works with hundreds of the very best leading AI-native startups every year. The reason its gone viral so fast is that it named something a lot of people had been watching happen without quite having the language for. In this episode of In The Loop, I'm walking through Diana Hu's framework for building a truly AI-native company. I cover all principles and how you can do the same in your team or company — from running AI as your operating system to building your intelligence layer so you can be 10 steps ahead of everyone else Episode highlights (01:00) – YC's Diana Hu publishes the playbook (03:00) – Dorsey cuts 40% at Block — and why (05:30) – Principle 1: AI as your operating system (08:00) – Principle 2: closing open loops (10:30) – Principle 3: making your company queryable (13:00) – Principle 4: the thousand-x engineer (15:30) – Principles 5 & 6: token-max and software factories (19:00) – Principles 7 & 8: intelligence layers and honest signals (22:00) – Four moves to start this week Links & resources Diana Hu, "The playbook for building an AI native company," YC Startup School, April 2026 — https://www.ycombinator.com/library/OX-the-playbook-for-building-an-ai-native-company If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. We're social. Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get. mindset.ai

The gap between casual Claude users and people getting ten times more out of it isn't prompt craft. It's a folder. This is the basis of a personal AI operating system. Andrej Karpathy posted his "LLM Knowledge Wiki" in early April and kicked off a wave of people rebuilding their note systems — not for themselves, but for the agent. This episode is the architecture they all converge on, the master file template, and the one prompt that makes the whole thing compound. In this episode of In The Loop, I'm walking through the four jobs every personal AI operating system has to do — identity, context, skills, memory — and showing exactly how to lay them out as plain text files an agent can read. I'll cover the six sections that go into your master file, the two-hundred-line cap nobody talks about, and the session log loop that makes every day one regression test better than the last. ⏭️ Episode highlights (00:45) – Why the second brain isn't for you (02:30) – Where the wave came from: Karpathy's LLM Wiki (04:15) – Identity, context, skills, memory: four jobs, one folder (06:20) – The six sections of the master file (08:40) – The two-hundred-line cap hidden in the code (10:15) – Skills folder and the slash-command workflow (12:30) – The session log loop and Boris Cherny's mundane advice (14:00) – What to do this week, full version and lightweight Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Most AI spending right now is measured in tokens consumed. Jellyfish tracked 12,000 developers across 200 companies and found the heaviest users produced twice the output at 600 times the cost. Uber's internal numbers are even worse: 70% of submitted code was AI-generated, but only 11% of the code running in production was AI-written. So almost all of that AI code never made it into their app. There's a name for what's going on: tokenmaxxing. This episode goes past the leaderboard stories. The four forces driving token bills up faster than productivity can justify are a pricing model most teams don't fully understand, a workplace culture that turned consumption into a status signal, a quality gap that doesn't show up on dashboards, and something called the orientation tax, which is probably the biggest driver nobody has named yet. The second half covers what the companies getting real ROI from AI are doing differently, including why Salesforce built a new metric called Agentic Work Units to replace token counts, and what the right unit of measurement looks like for engineering, sales, legal, support, and marketing teams. ⏭️ Episode highlights (01:00) – Uber's CTO: the budget was gone by April (03:30) – Where "tokenmaxxing" actually comes from (06:00) – Meta's Claudeonomics leaderboard: 60 trillion tokens in 30 days (08:30) – Jellyfish data: twice the output, 600 times the cost (11:00) – Goodhart's Law and the Soviet chandelier factory (13:30) – The orientation tax: why agents burn tokens before doing anything useful (17:00) – Salesforce's Agentic Work Units and why they matter (19:30) – How to define your own unit of work that actually held

On Wednesday, the US Treasury Secretary and the chair of the Federal Reserve called an emergency meeting with the CEOs of America's largest banks. Not about interest rates. Not about inflation. About an AI model. Anthropic built something that finds and exploits security flaws in virtually any software it's pointed at — bugs that the best human researchers in the world had missed for decades. And then they decided not to sell it. In this episode of In The Loop, I'm walking through what Anthropic's Mythos model actually did, why the sceptics make some sharp points about the timing and the headline numbers, and why the way this was handled — a private company forming a private coalition with no democratic input — tells you more about where AI governance stands than the model itself. ⏭️ Episode highlights (01:00) – Zero-days found in minutes (02:30) – A FreeBSD bug hiding since 2009 (03:45) – Visit a webpage, lose your machine (05:00) – Eleven-cent models spotted the same bugs (07:00) – Jack Clark's arc from GPT-2 to Glasswing (08:30) – Real danger and great PR coexist (09:15) – A coalition named after a butterfly (11:00) – Six months until the gap closes If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

OpenClaw became the fastest-growing open source project in history by showing people what an always-on AI agent could actually feel like. The problem is it requires a dedicated machine, technical setup, and a high tolerance for an agent that can access everything on your computer. Over the past month, Anthropic has shipped essentially the same capabilities — scheduling, remote control, computer use — inside Claude's Cowork product. Safer, no dedicated hardware needed, and accessible to anyone. Yet, most people who saw these new features probably thought "that looks useful," and did nothing with them. In this episode of In The Loop, I'm walking through the three Cowork features that matter most right now — scheduled tasks, dispatch, and computer use — with exactly how to set each one up and where to point them first. I cover the specific automations I'm running, why the scheduled task feature is just incredible, and how to get something useful running within 15 minutes. (01:15) – Why most people haven't set any of this up yet (03:10) – Scheduled tasks: Claude comes to you, not the other way round (05:00) – Morning email triage: inbox sorted before you open it (07:00) – Daily sales briefing pulled from Gong and HubSpot (08:45) – Dispatch: pair your phone with your desktop in two minutes (13:10) – Computer use & desktop commander: Claude operates apps with no connector needed (15:20) – How to start: day one in under an hour 🔗 Links & resources Anthropic, "Assign tasks to Claude from anywhere in Cowork" — https://support.claude.com/en/articles/13947068-assign-tasks-to-claude-from-anywhere-in-cowork Pavle Huran, "The Claude Dispatch Guide: 48 hours running AI agents from my phone", Product Compass, March 2026 — https://www.productcompass.pm/p/claude-dispatch-guide Anthropic, "Get started with Cowork" — https://support.claude.com/en/articles/13345190-get-started-with-cowork Anthropic, "Customize Cowork with plugins" — https://claude.com/blog/cowork-plugins 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

Jensen Huang stood in front of a Morgan Stanley audience and called OpenClaw "probably the single most important release of software, probably ever" — arguably more important than the web browser, Linux, and the iPhone OS. It went from a side project by one Austrian developer to the stated foundation of Nvidia's entire enterprise agent strategy in a matter of weeks. That claim is self-serving. It might also be right. In this episode of In The Loop, I'm explaining what OpenClaw actually is under the hood, why it spread twenty times faster than ChatGPT, and what Jensen's real motivation is behind the praise. The answer has as much to do with software architecture as with a trillion-dollar token thesis. ⏭️ Episode highlights (01:05) – What is OpenClaw & what innovations did it make? (02:30) – The no-interface, messaging-first design(04:10) – Skills, SKILL.md files, and ClawHub's 13,000 community tools 07:45) – Token economics: why agentic tasks burn 1,000x more(09:20) – Jensen's "operating system of agentic computers" claim(11:00) – How to get started with OpenClaw 🔗 Links & resources Lenny's Newsletter — OpenClaw : the complete guide to building, training, and living with your personal AI agent: https://www.lennysnewsletter.com/p/openclaw-the-complete-guide-to-building Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai