Normal Computing 🧠🌡️

I ended up making it - trace taxi is up!

OK super narrow hiring post for a high-prio role (actually, there may not be anyone in the world who fits this) but if you: - have some familiarity with both RL+agents, and have gone deep on at least one - have experience with hardware engineering, device verification in particular - are ready to dive into the insanely deep hole that is AI for hardware - are already based in CPH or NY, or eager to move there asap - can start a new FT role within 30 days - are interested in going deep on application-specific agents - lean extroverted, and are comfortable with public-facing comms - are keen to join a fast-moving + fast-growing startup DM me with “APPLICATION” as the first token, followed by evidence for the above do NOT use that token for any other role inquiries (your DM will be deleted); apply through links, or DM others on the team. same goes for replies. this is far from the only role open, but it’s the one i personally have the highest vested interest in finding a killer fit for it + will be prioritizing it when reviewing DM apps it’s a pretty specific list, and i’m looking for high alignment on ~all of the above, but if your life journey has somehow led you to this specific set of skills, there likely isn't anywhere you can get more use of them signal-boosts appreciated. it’s a very high-leverage position + will be compensated accordingly :)

Claude Sonnet 4.5 runs autonomously for 30+ hours of coding?! The record for GPT-5-Codex was just 7 hours. What’s Anthropic’s secret sauce?

Today we're sharing our first research work exploring diffusion for language models: Autoregressive-to-Diffusion Vision Language Models We develop a state-of-the-art diffusion vision language model, Autoregressive-to-Diffusion (A2D), by adapting an existing autoregressive vision language model for parallel diffusion decoding. Our approach makes it easy to unlock the speed-quality trade-off of diffusion language models without training from scratch, by leveraging existing pre-trained autoregressive models.