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At the AI Engineer World's Fair on July 1, 2026, Philipp Schmid, a Staff Engineer on the Gemini API and agents team at Google DeepMind, asked a room full of engineers a simple question: who uses skills with their coding agents? Every hand went up. Who has evals for those skills? Almost none. That gap is the entire talk, which Schmid also wrote up on his own blog, philschmid.de/testing-skills . His team indexed the skills ecosystem through SkillsBench and found the same pattern everywhere: people ship a SKILL.md file after two manual test runs and move on. It looks fine in a demo. It quietly corrupts outputs in production, because bad skills don't crash. They just make the agent confidently wrong. If you've already read our breakdown of Matt Pocock's Claude Code skills library , think of this as the other half of that story: what happens once you've installed a skill and it's actually running against real prompts. Key Takeaways SkillsBench indexed 47,000+ un...

Everyone is building agents right now. Real estate firms. Independent insurance brokers. Fortune 500 companies with budgets big enough to hire an army of consultants. Ask around and you'll hear the same story everywhere: "we're building our own agent." And yet almost nobody is asking the obvious question: why does the default approach keep failing? One large, general-purpose agent gets wired up to every tool the business owns. It impresses in the demo. Then it quietly stalls before production. There's a gap between what businesses want and what they're actually getting. They want AI woven into their data, their workflows, their day-to-day operations. What they get instead is one oversized agent trying to be a sales rep, a compliance officer, and a customer support line, all at once. That gap is an architecture problem, not a model problem. Key Takeaways The default "one big agent" pattern breaks down on context bloat, cost, fragility, and portability...

You sit down with Claude Code. You've got a plan. Three hours in, the context window is full, the conversation is tangled, and you've built half of the wrong thing. That's not a Claude problem. It's a workflow problem — and it's exactly what Matt Pocock designed his open-source skills library to fix. Pocock, best known as the creator of Total TypeScript, published these skills straight from his own .claude directory with a clear pitch: "Skills for Real Engineers." No bloated process frameworks. No opinionated orchestration. Just small, composable slash commands you can hack and extend. In June 2026, the mattpocock/skills repo has 150,773 GitHub stars and 13,032 forks — accumulated in under 5 months since its February 2026 release. That's the kind of adoption that tells you developers are hitting the same walls and finding the same fixes. This post covers the three productivity skills: grill-me , handoff , and teach . Key Takeaways As of June 2026, th...

Key Takeaways What you'll take away from this Cloud AI models can disappear overnight — a government letter, policy update, or pricing shift is all it takes Local models are now good enough for roughly 80% of everyday AI tasks Ollama and LM Studio make setup fast, even without a technical background Qwen 3, DeepSeek, Gemma, and Llama are the four models worth knowing right now Privacy, zero marginal cost, and always-on availability are the three core advantages of going local Five concrete startup opportunities open up the moment intelligence runs free on your desk The weekend was supposed to be spent building. The plan was locked in, the idea was sitting right there — and then, on a Friday evening, a government letter arrived at an AI lab. By Friday night, one of the most powerful models on the planet was gone. Disabled for everyone. No warning. No appeal window. That moment clarifies something...