I used all four in real projects. One wrecked a monorepo at 3am. Another saved my ass in production. Here's the unfiltered truth about every major package manager in 2025.
Circuit simulation, real signal capture, and automatic verification with a chained LLM. What happens when reality doesn't match the simulation — and the agent has to decide what to do about it.
A federal ruling just established that AI chats carry no attorney-client privilege. I'd asked Claude whether a contract clause could bite me. Now I'm reading that ruling and processing what it means for anyone using these tools for things that actually matter.
I've burned myself with Rust and spent posts deep in TypeScript patterns. I'm the worst possible person to be objective here. I read every line anyway — and found three design decisions I think are wrong, and one that's genuinely brilliant.
Glasswing hit a nerve I've had for a while. We deploy with AI, generate code with AI, and the attack surface grew in ways we haven't fully mapped yet. Here's what's concrete: what I changed in my pipeline and what you should change in yours.
I saw the title and figured it was clickbait. I sat down, read the paper, and had to get up and walk around. MegaTrain proposes training 100B+ parameter models on a single GPU in full precision. I won't use it tomorrow. But it shifts who can do what — and that matters to me.
Google open-sourced Scion, a testbed for orchestrating AI agents. I dug into it alongside Freestyle (sandboxes) and finally understood what each one actually does — and I have a take on where this is all going that I haven't seen anywhere else.
When I started using coding agents on real projects, my biggest fear wasn't that they'd write bad code — it was that they'd execute things on my machine without me understanding what. Freestyle hit HN with 188 points touching exactly that nerve.
32 years in the dev trenches. Here I write what I learned, what I broke, and what nobody tells you in the tutorials.
No spam. Unsubscribe anytime.