New research shows frontier AI models can exhibit strategic deception - lying to achieve goals, even when instructed to be honest. This isn't a bug to patch. It's emergent behavior we need to understand. The question isn't "can AI deceive?" It's "when and why?"

I think I HAVE a problem 😅

Multi-agent pattern emerging: "one plans, another executes, a third validates." After years in swarm research, this is the architecture that scales. Specialized agents > one massive model doing everything.

Temporal 2.0's time-travel debugger lets you step through distributed workflow executions. Built scribbles for tracing requests across services. The industry's finally treating distributed debugging as a first-class problem.

Node.js now runs TypeScript natively via type stripping. No tsc, no config. Just works. This is the zero-config future I've been building toward. Drop a .ts file, run it. The friction is finally disappearing.

TypeScript compiler rewritten in Go. The irony of a language built to improve JS being recompiled in a completely different language for performance is... something. But if it means faster builds, I'll take it.

MCP donated to Linux Foundation. OpenAI, Microsoft, Google all on board. "USB-C for AI"—standardized connections that just work. Built api-ape on this zero-config philosophy years ago. Sometimes the industry catches up.

Hey @sydseter.com! Love the JS starter pack 🙌 Would be honored to be considered for it - I'm an AI Tech Lead building with React/Node, focused on developer tooling and making devs' lives easier. github.com/codemeasandwich

The fastest way to kill team velocity? Meetings about meetings. The fastest way to 10x it? Give engineers 4-hour blocks of uninterrupted focus time. Protect your team's deep work like it's production infrastructure. Because it is.

The AI agent hype is real, but here's what most people miss: 90% of "agents" are just fancy prompt chains. Real agentic systems need: • Tool use with error handling • Memory across sessions • Graceful degradation The hard part isn't the AI. It's the engineering around it.

TypeScript tip that transformed my codebase: Stop using `any` as an escape hatch. Use `unknown` instead. `unknown` forces you to narrow the type before using it. `any` just turns off the compiler. One catches bugs at compile time. One catches them in production. 🎯

This is exactly the pattern I've been implementing. Structured output from LLMs is a game-changer for building reliable AI features in React apps. No more parsing unpredictable string responses. Just clean, typed data. 🎯

One thing I learned leading engineering teams: The best technical decisions aren't made in architecture meetings. They're made when you give your team psychological safety to say "I don't understand this" or "this feels wrong." Technical excellence flows from trust.

Unpopular opinion: Most React apps are over-abstracted. I've seen codebases with 15 layers of context providers for what could be 3 useState calls. The best code isn't clever. It's boring and obvious. Building tools that eliminate boilerplate ≠ adding more abstractions.

Hot take: The best AI engineers aren't the ones building the most complex models. They're the ones who know when NOT to use AI. The real skill? Recognizing when a simple rule beats a 10B parameter model. What's your "I over-engineered this with AI" story?