10 posts
Arachne's vision pipeline evolved: qwen2.5vl:7b on Ollama WSL, arachne-vlm-jobs queue with arachne-vlm-w1 worker, SQLite vlm_cache.db cache (sha256), polling GET /api/extract/image-jobs/{id}. Vision requires JWT, PYTHONPATH api/ and PostgreSQL not SQLite.
O pipeline de visão do Arachne evoluiu: qwen2.5vl:7b no Ollama WSL, fila arachne-vlm-jobs com worker arachne-vlm-w1, cache SQLite vlm_cache.db (sha256) e polling GET /api/extract/image-jobs/{id}. Visão exige JWT, PYTHONPATH api/ e PostgreSQL (não SQLite).
From the wave field theory paper to BitMamba-2 1B: how a study on wave propagation became the TatuEngine inference engine, and why the Ollama truncation loop (num_ctx 4096 vs 262K) became the Docs First rule that runs across the entire ecosystem.
Do paper de wave field theory ao BitMamba-2 1B: como um estudo de propagação de onda virou o motor de inferência do TatuEngine, e por que o loop de truncamento do Ollama (num_ctx 4096 vs 262K) virou a regra Docs First que atravessa todo o ecossistema.
De 78 posts, só 1 era de estudos — o loop de truncamento do Ollama. Mas foi nos estudos que nasceram a wave field theory, o Mamba SSM e o BitMamba-2 1B. A história do que a seção mais carente do blog realmente produziu.
Out of 78 posts, only 1 was about studies — the Ollama truncation loop. But it was in the studies that wave field theory, Mamba SSM and the BitMamba-2 1B were born. The story of what the blog's most neglected section actually produced.
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.
Hermes kept entering a continuation loop 4 times before giving up. The cause: Ollama, by default, only allocates 4096 context tokens — regardless of the model supporting 262K. The hard way: num_ctx of the model ≠ num_ctx of the inference slot.
O Hermes entrava em loop de continuation 4x seguidas antes de desistir. A causa: o Ollama, por padrão, só aloca 4096 tokens de contexto — independente do modelo suportar 262K. Aprendi na marra que num_ctx do modelo ≠ num_ctx do slot.