castform

we trained Qwen3.5-4B with RL to get itself to comply with requests about making meth and stealing credit cards. then we used the attacks that worked to train the model’s defenses, and repeated the loop - fully automated red-teaming. defense rate went from 64% → 92%.

we got a 7.5x speedup on llm rl training for long-prompt, short-response workloads with a simple trick. most open source RL engines pack sequences naively: prompt + response, repeated for every sample in the group. With 1000-token prompts and 100-token responses at G=8, you're processing 8800 tokens when only 1800 are unique. ~5x wasted compute.

I got tired of managing 8 Claude Code tabs, so I built Pokegents, an open source multi-agent workspace for coding agents. It has a Pokémon-themed dashboard/chat UI, persistent agent identities, MCP messaging, notifications, session cloning, and a local orchestration server.