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OKF for Claude Code: structured, portable memory your agent (and team) can read

The problem: agents forget your project every session If you pair with a coding agent, you have lived this: a new session starts and the context is gone. The agent re-discovers your auth flow, re-guesses why a decision was made, re-reads the same files to rebuild a mental model you already explained yesterday. Project knowledge — the why behind your systems, the runbooks, the "don't touch this, here's the reason" — lives scattered across wikis, code comments, and people's heads. None of it travels with the code, and none of it survives a fresh context window. CLAUDE.md helps, but it's for standing instructions , and it gets loaded wholesale into every prompt. Auto-memory captures what an agent picked up, but it's implicit, per-agent, and not reviewed. A wiki is for humans and needs exporting. There's a gap: curated team knowledge that's structured, versioned with the code, and readable by any agent or person. What OKF is Open Knowledge Format is an open, vendor-neutral format (announced by the Google Cloud Data Cloud team in June 2026, Apache-2.0) that represents knowledge as a directory of markdown files with YAML frontmatter . That's the whole idea. No schema registry, no runtime, no SDK. If you can cat a file you can read it; if you can git clone a repo you can ship it. A bundle looks like this: .okf/ ├── index.md # progressive disclosure (root carries okf_version) ├── log.md # ISO-dated change history, newest first ├── services/auth-api.md # one concept = one file; path is its ID ├── datasets/orders-db.md ├── decisions/use-okf.md ├── runbooks/payment-failures.md └── metrics/checkout-conversion.md Each concept needs exactly one thing to be conformant: YAML frontmatter with a non-empty type . Everything else is optional. --- type : Service title : " Auth API" description : " Issues and verifies short-lived access tokens." resource : https://github.com/acme/auth tags : [ auth , platform ] timestamp : 2026-06-14T10:00:00Z --- # Endpoints | Method | Path | Descriptio

2026-06-28 原文 →
AI 资讯

How to Run Reliable Local LLM Agents on an RTX 3090: A Benchmark (5 Models, Priced in Watts)

I gave GLM-4.5-Air (106B, open weights) 12 coding tasks through opencode on my RTX 3090. It scored 0% — never edited a single file. Same model, same GPU, same tasks, but driven by a ~150-line LangGraph agent instead: 93% . The model was never the problem. The orchestrator was. Here's the benchmark — including the part nobody else measures, the electricity cost per correct task . Setup RTX 3090 (24 GB) + 128 GB RAM , models via ollama , Q4 quants, temp 0.2 5 recent open models × 2 orchestrators (opencode vs custom LangGraph ReAct with ollama-native tool-calling) 17 graded tasks (12 coding in Python/JS/C++ + 5 general-agent) with hidden unit tests Every run priced in GPU watts via my open-source homelab-monitor Results Model tok/s opencode adh. LangGraph adh. LangGraph coding LangGraph general Qwen3-Coder 30B-A3B 130 92% 100% 100% 100% GLM-4.5-Air 106B 5.7 0% 100% 89% 100% Devstral Small 24B 49 8% 53% 8% 40% Seed-OSS 36B 9.5 0% 7% 0% 20% DeepSeek-R1-Distill 32B 6.7 0% 0% 0% 0% Tool-adherence = % of tasks where the model actually called a tool instead of just printing code in chat. It was the master variable. (GLM's headline "93%" is its blended score across all 17 tasks: 89% coding + 100% general.) Three takeaways The framework can matter more than the model. opencode sends a frontier-shaped system prompt + 12 tools over its OpenAI-compat path; most local models fall back to chatting. Native tool-calling through a lean agent fixes that — GLM went 0% → 93%. (Qwen3-Coder is the exception: it's tuned for agentic tool use and aces opencode out of the box.) Acting ≠ solving. LangGraph made Devstral act (8% → 53% adherence) but not solve (coding stayed 8%). The framework decides whether a model acts; the model decides whether it's right. The wattmeter ranks honestly. Qwen solved tasks at ~0.0005 BGN each; the models that scored zero still burned 10–30× more energy for nothing. On a home rig, the cheapest model is the fast, correct one — and MoE (Qwen activates ~3B of 30B pe

2026-06-28 原文 →
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