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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 →
AI 资讯

Absolute Revolution in Assistants, ChuroAI.

I've been working on Churo, an open-source voice assistant built entirely in Python. It features high-quality speech-to-text and text-to-speech, web search, image understanding, and agentic capabilities. It runs with Ollama models and is designed to be easy to modify and extend. The goal is to provide a capable, local-first voice assistant that developers can actually inspect, customize, and build on. Repository: https://github.com/MathObsession/Churo-assistant or run it with(You need Ollama): pip install churovoice churovoice --voice male Feedback, issues, and contributions are welcome.

2026-06-28 原文 →
AI 资讯

I Built 3 MCP Servers for AI Agents — Here's How They Work

What are MCP Servers? The Model Context Protocol (MCP) is an open standard that lets AI agents use external tools through a unified interface. Think of it as USB-C for AI — one protocol connects any AI client (Claude Desktop, Cursor, VS Code with Cline) to any tool or data source. I built three production-ready MCP servers and published them to PyPI and GitHub. Here's what they do and how to use them. 1. Web Search MCP Server uvx crewai-web-search-mcp Two tools: web_search(query) — Searches Google/SerpAPI and returns ranked results with snippets extract_content(url) — Fetches and extracts readable content from any web page Use cases: Ask your AI about current events, research competitors, pull documentation, verify facts in real time. { "mcpServers" : { "web-search" : { "command" : "uvx" , "args" : [ "crewai-web-search-mcp" ] } } } 2. Code Review Automation MCP uvx code-review-automation Three tools: review_code(diff) — Analyzes code changes for bugs, security issues, anti-patterns, style violations check_quality(path) — Runs static analysis and returns a quality report analyze_pr(diff) — Produces a structured review: what changed, what's risky, suggestions Use cases: Paste a PR diff and get an instant review. Catch issues before they reach production. 3. Document Intelligence Server uvx document-intelligence-server Three tools: extract_document(path) — OCR and text extraction from PDFs, scanned docs, images classify_document(path) — Identifies document type (invoice, report, contract, article) summarize_document(path) — Generates a structured summary from extracted content Use cases: Process uploaded PDFs, extract data from scanned forms, summarize long reports. Pricing All three servers use a shared credit system: Tier Price Credits Free $0 50 calls/day Starter $20 2,000 calls Pro $100 12,000 calls Buy credits once, use them across any server. Credits never expire. How it works: Install with uvx crewai-web-search-mcp Use 50 free calls per day — no key needed For u

2026-06-28 原文 →
AI 资讯

Why I Built Aegis Pulse - Part 1

Why did I build Aegis Pulse? As always, it started with a simple thought that keeps getting me time and time again: "I should automate this." So, I announced Aegis Stack publicly on Reddit on December 3rd. From that very moment, I became great friends with the Unique Clones / Total Clones & Unique Visitors / Total Views charts in GitHub's analytics page. Due to the nature of aegis-stack, every stack that is spun up will clone the actual repo itself (outside of caching situations, which may vary from user to user). I didn't realize it at the time, but those clone numbers, especially the Unique Clones, would become the most important metric for me to track usage. There's this funny thing that happens when you release an OSS tool. You expect people to say something, maybe tell others, ask questions... just... something... Instead, the person looks at the tool, sees if it makes their life easier, and puts it in their bag of other tools. I know this, because this is me! I never thought about it until I'm on the other side. I had to mentally go through all the tools I had used over the years, and realized I never cared about anything other than the tool itself. And if it didn't work, I would try to make it work, and if not, just move on. Time is money, and all of that. All of that is to say, clones are something I have been tracking since day one. Now... GitHub has a 14-day rolling window period in which they have daily values, and the 14-day rolling totals. And when I say 14 days, I mean it. That's all you get, and it's on you to keep track of everything outside of that. Fair enough. Thus began the daily ritual of going and grabbing the latest numbers from the previous day, and pasting the data into 3 separate AI chats: ChatGPT, Claude Opus, and Google Gemini. I figured that since I was already storing all of this data, I might as well see what type of insights I could get from these chats (which were preloaded with enough context to know what's going on). It was a great

2026-06-28 原文 →
AI 资讯

I Deployed 6 AI Systems Live — Here's What Actually Broke

I Deployed 6 AI Systems Live — Here's What Actually Broke A few weeks ago I wrote about the 5 bugs that cost me 60+ hours building 49 AI systems. Every one of those bugs lived inside the code itself wrong array layout, a renamed model class, a serialization mismatch. This article is the second half of that story, and it taught me something more uncomfortable: code that runs perfectly on your machine can fail completely the moment it leaves your machine for reasons that have nothing to do with your code. I took 6 of my pinned GitHub projects and deployed every one of them live on Streamlit Cloud. Locally, all 6 worked without a single error. Deploying them surfaced 5 failures I had never seen before, none of which were bugs in my logic. Here they are, in the order I hit them. Failure 1 — A Module That Existed Yesterday, Gone Today My RAG chatbot used this import, unchanged for weeks: from langchain.chains import ConversationalRetrievalChain Locally: works. Deployed: instant crash. ModuleNotFoundError: No module named 'langchain.chains' The cause had nothing to do with my code. My local environment had an old, cached version of LangChain installed months ago. The deploy environment did a clean install and pulled whatever the latest version was at that moment and recent LangChain releases moved legacy chain classes like this one out of the core package entirely. The fix that actually worked pin the exact version that still contains the class, rather than chasing the newest API pattern under deployment pressure: langchain = =0.3.7 langchain-community = =0.3.7 The lesson: "it works on my machine" is frequently true specifically because your machine never reinstalled anything recently. A clean deploy environment has no such luxury it gets whatever is newest the moment it builds. Pin your versions before you ever need to debug this at 1 AM. Failure 2 — A File That Exists, Until It Doesn't My construction RAG project loads a prebuilt FAISS vector index from disk: vectorstor

2026-06-28 原文 →
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