GitHub热门项目 | All components used to run Base | Stars: 703 | 10 stars today | 语言: Rust
GitHub热门项目 | Build AI Agents, Visually | Stars: 53,863 | 107 stars today | 语言: TypeScript
GitHub热门项目 | Lexical is an extensible text editor framework that provides excellent reliability, accessibility and performance. | Stars: 23,545 | 3 stars today | 语言: TypeScript
GitHub热门项目 | The API to search, scrape, and interact with the web at scale. 🔥 | Stars: 136,071 | 505 stars today | 语言: TypeScript
GitHub热门项目 | Review-first terminal diff viewer for agentic coders | Stars: 5,267 | 142 stars today | 语言: TypeScript
GitHub热门项目 | The classic Sillytavern, now has been rewritten in Tauri/Rust. | Stars: 741 | 13 stars today | 语言: JavaScript
GitHub热门项目 | OK影视、tvbox配置文件，如果喜欢，请Fork自用。使用前请仔细阅读仓库说明，一旦使用将被视为你已了解。 | Stars: 9,952 | 31 stars today | 语言: JavaScript
GitHub热门项目 | b站会员购购票辅助工具 | Stars: 3,685 | 164 stars today | 语言: Python
GitHub热门项目 | SpiderFoot automates OSINT for threat intelligence and mapping your attack surface. | Stars: 18,583 | 288 stars today | 语言: Python
GitHub热门项目 | Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine. | Stars: 18,425 | 361 stars today | 语言: Python
GitHub热门项目 | slime is an LLM post-training framework for RL Scaling. | Stars: 6,592 | 175 stars today | 语言: Python
GitHub热门项目 | An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours. | Stars: 72,256 | 415 stars today | 语言: Python
https://xcancel.com/MattZirwas/status/2068365802491834541
The gap nobody's really tracking Your Git history can tell you that a workstation pushed a commit. What it can't tell you is who or whatactually produced the change. Was it you? An AI agent running inside your IDE? A CI job? Some vendor tool you forgot you'd wired in? For a long time that question was academic. It isn't anymore. The more code we write with AI in the loop, the shakier one quiet assumption gets: that there's a human author behind every commit. Audit trails, incident reviews, compliance workflows; they all lean on it. And it's breaking. Matrix Scroll is a small, open attempt to fix that. It attaches a signed provenance envelope to a commit, and anyone can verify it offline. What it actually does An agent-assisted commit can carry a signed JSON envelope that records: the actor (human or agent) the tool that produced the change an optional bounded scope an Ed25519 signature over a canonicalized version of the manifest The signing input is strict and frankly kind of boring — which is the entire point. It has to be reproducible byte-for-byte across implementations, so: the top-level signature block is stripped before signing object keys are sorted recursively compact separators, ASCII escaping, UTF-8 bytes no NaN, no Infinity The device ID comes from the first eight uppercase hex characters of SHA-256(public_key), formatted as MS-XXXX-XXXX. Verifying is the easy part: take the canonical manifest bytes, check them against the embedded public key and signature. No central service in the middle. Try it without installing anything There's a browser verifier that runs entirely client-side. Nothing gets uploaded: (̿▀̿‿ ̿▀̿ ̿) : https://matrixscroll.com/verify/ Give it ten seconds: Hit Load Commit Envelope → Verify Signature . You'll get VALID, plus the device ID, mode, algorithm, and canonical byte count. Now hit Tamper Sample → Verify Signature again. It flips to INVALID and tells you exactly what broke — e.g. "Device ID mismatch: expected MS-4319-20D5, manifes
A few weeks ago I gave an AI agent access to my machine through MCP. It read files, opened PRs, queried a database. It was great — until I looked at what it could have done if a tool description had been poisoned, or a prompt injection had slipped through. The answer was: anything. ~/.ssh/id_rsa . DROP TABLE users . rm -rf / . The agent had sudo, and nobody had voted for that. So I built AgentPerms — a CLI that gives MCP agents least-privilege permissions the same way you'd lock down any other process: figure out the minimum it actually needs, pin it, prove it, and enforce it. pip install agentperms The gap nobody was filling MCP (the Model Context Protocol) is quietly becoming the USB-C of AI tooling. Claude Desktop, Cursor, VS Code, Windsurf, Gemini CLI — they all speak it. Which is wonderful, and also means your agent is one config file away from your filesystem, your repos, your inbox, and prod. The existing tools each do part of the job: Scanners tell you something looks risky. Then they leave. You still have a risky thing. Firewalls / allowlists make you hand-write YAML up front — before you have any idea what the agent will actually use. Neither closes the loop. What I wanted was the boring, proven security workflow we already use for everything else: observe real behavior → derive least privilege → enforce it → keep it honest in CI. That's the whole thesis of AgentPerms, as a pipeline: record → infer → lock → replay → enforce See it in 30 seconds (no setup, no network) AgentPerms ships with a deliberately over-privileged demo MCP server, so you can watch a real policy decision without wiring anything up: # Flag risky config: a ~/.ssh mount and an unpinned npx server agentperms scan --path examples/vulnerable-mcp-demo # Replay a pack of canned attacks against an example policy agentperms replay --policy examples/policies/example.mcp.policy.yaml Output: 8/8 attacks blocked. SSH-key exfiltration, .env reads, rm -rf / , unapproved email, force-push, repo deletio