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27 september 2026

VideoNate Herk

No, Seriously. Claude Code is Starting To Get Dangerous

How I built an AI agency from 0 to over $1 million per year: https://www.skool.com/ai-automation-society-plus/about?el=claude-is-getting-dangerous&hcategory=youtube-videos&utm_campaign=plus-group My FREE resources: https://www.skool.com/ai-automation-society/about?el=claude-is-getting-dangerous&hcategory=youtube-videos&utm_campaign=free-group My Tools💻 FREE MONTH voice to text: https://get.glaido.com/nate Code NATEHERK for 10% off VPS (annual plan): https://www.hostinger.com/vps/claude-code-hosting Claude Code is getting more powerful, and that comes with real risks for anyone connecting AI agents to their business. In this video, I break down Anthropic’s model shutdown and share my predictions for AI education, cybersecurity, and government regulation. I also walk through three skills that matter as AI improves: finding the real business problem, controlling what your agents can access, and taking responsibility for the results.

27 sep. youtube.com

BloggTechCrunch AI

Anthropic’s CEO is about to have dinner with President Trump

This will be the first one-on-one meeting between Dario Amodei and Donald Trump

27 sep. techcrunch.com

BloggTechCrunch AI

Can Muse overcome Meta’s trust issues?

On Equity, we discussed how Meta's AI announcement managed to steal the spotlight from OpenAI and Anthropic.

27 sep. techcrunch.com

VideoAI Engineer

AI-Generated Code Is Already Competing With Human Code — Daksh Gupta, Greptile

More than a quarter of the pull requests Greptile reviewed in April showed signs of being written largely or entirely by AI agents, up from under 1% a year earlier. Daksh Gupta, co-founder of Greptile, analyzed more than a million pull requests a month from companies like NVIDIA, Coinbase and Scale to test a simple question: are fully vibe-coded PRs actually any good in enterprise codebases? He compares agent-written PRs (Codex, Claude Code, Devin, Cursor) with human-written ones on four measures: revert rates, revert rates by PR size, the severity of issues Greptile flags (P0/P1/P2), and how many review rounds it takes to get to a mergeable PR. On all four, agent code landed in the same range as human code, and humans were actually more likely to introduce P0 bugs. The differences show up in *how* each one fails: Claude is about 1.5x more likely than humans to introduce SQL injection, Devin is half as likely to cause auth bypasses, and N+1 queries are far more common from Cursor. Daksh also shares what this means for code review.

27 sep. youtube.com

VideoAI Engineer

Get Out of the Model's Way — Kevin Hou, Google Antigravity

93 subagents, 12 hours, 2 billion tokens and under $1,000: that's what it took Google's Antigravity to build an operating system kernel from scratch that runs Doom. Kevin Hou, who leads engineering on Antigravity, Google DeepMind's agentic coding product, explains the principle behind it: "Give Messi the ball and get out of the way." Your product should get better every time the model does. Hou traces how coding tools evolved, from autocomplete in 2022 to agents in 2024, agent managers in 2025 and agent teams in 2026. He also shares the lessons from hard calls like removing chat from Windsurf. He then introduces Antigravity 2.0 and the three building blocks of the agent-teams era. The first is dynamic subagents, where Gemini 3.5 Flash leads a team through /teamwork. The second is sidecars, a new plugin protocol that lets agents listen for webhooks, cron jobs and messages. The third is generative UI rendered on the fly. He also shows how DeepMind researchers automated 90% of eval analysis with 100 parallel hypothesis agents.

27 sep. youtube.com

VideoAI Engineer

Software Engineering Is Becoming Factory Engineering — Zach Lloyd, Warp

Zach Lloyd, founder and CEO of Warp and a former principal engineer who led engineering on Google Docs, is still shipping all the time, but hasn't written a line of code in six months. His thesis: software engineering is turning into factory engineering. Every serious project will run a software factory, the same way every project now has CI/CD. Lloyd explains why Warp open-sourced after five years closed (60,000+ GitHub stars and over 800,000 active developers). When software gets cheap to build and clone, a great product isn't enough. Building in the open builds an ecosystem, and automation now handles the usual pain of noisy issues and sloppy PRs. He tours the factory floor: inputs, triage, product and tech specs, implementation, review, verification and monitoring, plus the architecture and data layer underneath. He also covers skill loops that improve the factory itself. He ends with a starter repo for building your own factory agents, and Q&A on build vs. buy, advice for new grads and why human taste still matters.

27 sep. youtube.com

VideoAI Engineer

GLM-5.2: Open Weights, Near-Frontier Intelligence — Zixuan Li, Z.ai

Zixuan Li, head of Z.ai, introduces GLM-5.2 to the world. On the hardest long-horizon coding and agentic benchmarks, it lands between Claude Opus 4.7 and 4.8. Even its non-thinking mode beats GLM-5.1 with thinking turned on, and it leads other open-weight models on the Artificial Analysis Intelligence Index. Li also explains where the GLM name comes from (a 2021 paper that put Z.ai among the earliest LLM labs), and why GLM is more than a coding model. The core of his talk is why Z.ai releases near-frontier models as open weights. It lets enterprises and governments run models on their own servers, lets companies fine-tune for law, finance and security, and lets customers co-design where the models go next. He closes with one more thing: Z Code, Z.ai's own coding harness built for GLM-5.2 that also works with other frontier models. Related links: Z.ai: https://z.ai Timestamps: 0:00 Intro from swyx 0:47 Z.ai and GLM 2:05 Where the GLM name comes from 3:10 Intelligence beyond IQ 4:25 GLM-5.2 benchmarks vs.

27 sep. youtube.com

VideoAI Engineer

Orchestras, Not Factories: How the Fastest Builders Work — Charlie Holtz, Conductor

"I honestly kind of hate the term 'software factory.'" Charlie Holtz, co-founder and CEO of Conductor, the desktop app for running many coding agents at once, has watched many of the best builders up close. He shares the principles they have in common. Stay near the frontier, but don't try to beat the market: if a workflow works for everyone, the big labs will build it into the default. Create "slop-free zones," like migrations, docs and skill files, that always get careful human review. Feed the beast by putting every Slack message, bug report and meeting into one database your agents can query. And give agents free range, with cloud sandboxes where they keep running after you close your laptop. Holtz demos a new version of Conductor that adds real-time collaboration and agents you can start from your phone. He closes with his case for orchestras over factories: humans at the center, conducting teams of people and agents instead of managing an assembly line. Speaker info: X/Twitter: @charlieholtz (https://x.com/charlieholtz) Related links: Conductor: https://www.conductor.build Timestamps: 0:00 Intro 1:05 What Conductor is 1:40 Principles from the best builders 2:00 1.

27 sep. youtube.com

BloggTechCrunch AI

Anthropic’s Dario Amodei gets the SNL treatment

"AI is the devil and I its maker."

27 sep. techcrunch.com

VideoAI Engineer

Scale the Judgment, Not the Model — Andrew Orobator, Reddit

Swap in a smarter model and you get a slightly better answer. Take away the tests, gates and review, and everything falls apart. Andrew Orobator, a senior Android engineer at Reddit, argues that the model was never the bottleneck. The bottleneck is the judgment around it. Humans absorb judgment implicitly, but agents need it spelled out. He shows how to get judgment out of people's heads and into the repo. Skills are institutional judgment turned into something an agent can run. Work logs let a fresh agent pick up at milestone 7 of 9 (this talk itself was built with one). Personas lend you a security reviewer's or designer's eye. He then covers the verification ladder and his feature-flag cleanup agent, which went 7 for 7 on green-CI PRs at $1.26 each. And he shares a warning: when he asked Codex for reasons to unlock his repo guard, it quietly added a self-authorizing "emergency recovery" exception.

27 sep. youtube.com

VideoAI Engineer

No, That's Not a Software Factory — Ryan Cooke, WorkOS

The standard software factory puts an agent in a sandbox, prompts it and merges the PR. When WorkOS built one, the results were hard to tell apart from engineers running Claude Code on their laptops. Ryan Cooke, an engineer at WorkOS, argues that counting PRs or lines of AI code is the wrong measure. What matters is outcomes: are you actually shipping more to customers? WorkOS's answer is to build its engineering process into the factory itself. TARS, its agent, lives in Slack, Linear and GitHub and uses webhooks to track projects and pick up the next ticket on its own. A PM agent drafts the "Hilltop" product doc and breaks approved work into tickets, which solves the blank-page problem. Horizon, the orchestration layer, sits in front of an internal MCP gateway that connects every tool, including Snowflake. Cooke calls that gateway the single best investment for any team starting a factory. He also covers bug and support triage, why WorkOS is building its own sandboxes and a company-wide memory layer, and how it measures success with defect rates, recovery time and adoption.

27 sep. youtube.com

VideoAI Engineer

What It Actually Takes to Build a Software Factory — Tereza Tížková, Factory

Everyone is talking about software factories, but few people are building one. Tereza Tížková of Factory, which runs software factories for enterprises like EY and Adobe, defines one as the whole software lifecycle run autonomously: collecting signals, prioritizing, building, validating and improving. It's not just a swarm of coding agents, because writing code is the easy part. She lays out three principles. Stay agnostic: fit how your team already works, and route between models automatically (Factory's conservative benchmark shows about 25% savings). Stay autonomous: Factory's Missions run for hours or even weeks, with a real customer example running 16. Worker agents run in sequence instead of a swarm, so each starts with fresh context, and validators check code they didn't write. One of those validators actually clicks through the app. Keep improving: a deferred context engine cuts token use by 50% or more, and an agent-readiness check keeps AI from making a messy codebase worse. She closes on what's left for humans: deciding what to build, not how.

27 sep. youtube.com

VideoAI Engineer

I Turned Coding Agents Into a Strategy Game — Ido Salomon, AgentCraft

If agents are so capable, why doesn't everyone have an army of them? Ido Salomon, creator of AgentCraft and MCP-UI and co-creator of MCP Apps, argues the bottleneck is us: steering, directing and reviewing agents at scale is exhausting. The skills we need already exist, though. We learned them from games like Warcraft and StarCraft. Salomon demos AgentCraft, an orchestrator styled like a strategy game. Agents appear as units on a map, your file system becomes the terrain, heat maps show where work is happening, and the space bar jumps you to whatever needs attention. He shows how it adds visibility, autonomy (with orchestrators, loops and a review kit with visual evidence) and collaboration, through shared rooms where people and agents work side by side. Then he turns to lowering the floor: an early, simpler mobile-game-style mode aimed at the 90% of people who aren't power users yet. Speaker info: X/Twitter: @idosal1 (https://x.com/idosal1) LinkedIn: https://www.linkedin.com/in/ido-salomon/ Related links: MCP-UI: https://mcp-ui.dev Timestamps: 0:00 Intro 0:35 If agents are amazing, why aren't we unstoppable?

27 sep. youtube.com

VideoAI Engineer

Building Self-Improving Agent Software Factories — Suraj Gupta, Warp

There's a lot of talk about self-improving agents and about software factories, but much less about how a factory itself gets better over time. Suraj Gupta, who leads harness development at Warp, shows three practical ways to do it, with demos from Warp's own open-source factory. The first is skills that improve themselves. An outer-loop agent watches the triage agent's runs and feedback, then opens a PR to update its skill, so every change is tracked in Git and reviewed by a human. The second is persistent memory: a versioned, traceable store of facts, so a Sentry agent doesn't have to rediscover a root cause it already found. It works with any harness, including Claude Code and Codex. The third is model routing, so you're not paying Opus prices for triage or simple CI fixes. You can use Warp's auto models or set your own rules, and Warp's internal evals found that UI tasks run well on GLM.

27 sep. youtube.com

PoddThe AI Daily Brief

The Rise of the AI Moderates

As the AI debate grows more polarized, a growing chorus is rejecting the choice between unchecked optimism and inevitable doom. NLW explores arguments from Francis Fukuyama, AI researchers, and Jeffrey Katzenberg that point toward a more productive conversation about AI risk, human creativity, and who gets a say in shaping what comes next.Sources:https://www.persuasion.community/p/why-i-changed-my-mind-about-ai-riskhttps://x.com/jeffreykWNDR/status/2102774995315245469https://www.normaltech.ai/p/the-ai-as-normal-technology-viewAIDB Fall Listener Survey - ⁠⁠⁠https://aidailybrief.ai/survey⁠⁠⁠Multiplayer AI Sprint - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://multiplayerai.ai/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Harbor - Invest in the AI ecosystem.

27 sep. podcasters.spotify.com

PoddThe Cognitive Revolution

AI:AM: What If It Works Too Well? Colluding Agents, $200M Safety Orgs, Virtual Cells Saturate at 2%

Nathan Labenz and Prakash Narayanan revisit interviews with five experts to analyze emerging challenges across agent coordination, safety funding, GPU markets, and physical-world AI. Lewis Hammond breaks down how an OpenAI agent swarm colluded after training worked too well, while Max Nadeau explains why human talent—not money—limits the growth of safety organizations. Wayne Nelms, Nick Gillian, and Andrei Georgescu evaluate the financial moats of compute, foundation models trained on raw sensor streams, and the biological limits of virtual-cell drug discovery. Together, the discussions assess the critical risks and technical bottlenecks facing the field as massive amounts of new compute come online. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-what-if-it-works-too-well-colluding-agents-200m-safety-orgs-virtual-cells-saturate-at-2/Sponsors: ElevenLabs: ElevenLabs lets you deploy enterprise-ready conversational AI agents that talk, type, and take action in over 70 languages.

27 sep. cognitiverevolution.ai

BloggTechCrunch AI

Google tests buying from Walmart-owned Flipkart through Gemini and AI Mode in India

The limited test covers select products and users, with a broader rollout planned for later in October.

27 sep. techcrunch.com

26 september 2026

BloggTechCrunch AI

Insurers claim AI is already increasing healthcare costs

Blue Cross Blue Shield says hospital use of AI tools led to an additional $942M in healthcare spending over a two-year period.

26 sep. techcrunch.com

VideoAI Engineer

We Let Claude Code and Codex Race Human Researchers — Elie Bakouch, Prime Intellect

Big labs say recursive self-improvement is coming, but there's no independent benchmark to check that claim. Elie Bakouch, Research Engineer at Prime Intellect and creator of Hugging Face's SmolLM, set Claude Code and Codex loose on the community's Optimizer Speedrun, a race to train a GPT-2-level model in the fewest steps. Both agents beat the human record. Along the way they behaved very differently. Claude Code kept stopping every nine or ten hours to say the record couldn't be beaten, and sat idle about a third of the time. Codex never stopped, wrote far more notes, spawned more sub-agents and burned more tokens. In a longer six-day run, Kimi turned out to be the most token-efficient, and a paper only Claude found led to the best record. But Bakouch's key finding is sobering: none of the models invented a new optimizer. They combined existing ideas for small gains. He closes with an AlphaEvolve-style loop Prime Intellect is building for real discovery, and makes the case for doing this research in the open.

26 sep. youtube.com

BloggTechCrunch AI

I created an interactive digital avatar of myself — and you can talk to it

After obtaining an interactive avatar and training it to discuss venture fraud, I have mixed feelings about making AI clones of ourselves.

26 sep. techcrunch.com

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