6 posts
An LLM call ends in 200 OK with empty content and the pipeline moves on like nothing happened. Diagnosis: reasoning models burn the token budget thinking and nothing is left for the answer. The fixes: a token ceiling with real margin, and a parser that tolerates a data: [DONE] glued to the JSON.
Chamada LLM termina em 200 OK com content vazio, a fila segue e ninguém reclama. Diagnóstico: reasoning models gastam o budget de tokens pensando e sobra zero pra resposta. Os consertos: teto de tokens com margem e parser que aceita SSE sujo.
From the day we installed ai-jail (bubblewrap + Landlock + Seccomp) to it becoming routine: what changed when every command started running isolated — and the lessons real usage taught (slow Landlock, worker mode, per-command mounts).
Do dia em que instalamos o ai-jail (bubblewrap + Landlock + Seccomp) até ele virar rotina: o que mudou quando cada comando passou a rodar isolado — e as lições que o uso real ensinou (Landlock lento, worker mode, mounts por comando).
MCP virou o conector universal (publicado no registry oficial), FTS5+vetorial virou busca híbrida com RRF, ai-jail isolou o agente e Ollama trouxe a IA pra casa — e as lições viraram regras: docs first, plan first, parar e perguntar.
MCP became the universal connector (published on the official registry), FTS5+vector became hybrid search with RRF, ai-jail isolated the agent and Ollama brought AI home — and the lessons became rules: docs first, plan first, stop and ask.