🔥 jamwithai / production-agentic-rag-course
GitHub热门项目 | | Stars: 6,231 | 31 stars today | 语言: Python
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GitHub热门项目 | | Stars: 6,231 | 31 stars today | 语言: Python
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We Scanned 100 AI Repos on GitHub. Here's What We Found. A drone firmware project with 3× more stars than the real one. A crypto protocol that turned GitHub into a points farm. A README with 6,289 stars and 2 commits. As a developer turned architect, I used to treat GitHub stars as a proxy for trust. More stars meant more legitimate, fewer reasons to question before cloning. That instinct got me thinking. So I built TrustStar , audited hundreds of repos, and found that some people had figured out that instinct before me. Here's what the data showed. Case 1: The Airdrop Farm (QuipNetwork) 🔴 DANGEROUS Repository Stars Forks Fork/Star ratio hashsigs-py 11,200 9 0.0008 hashsigs-rs 11,300 42 0.0037 hashsigs-ts 11,300 31 0.0027 hashsigs-solidity 11,300 33 0.003 quip-protocol 11,645 159 0.014 ethereum-sdk ~11,400 72 0.006 cpp-sdk ~11,300 44 0.004 Six repos in completely different languages (Python, Rust, TypeScript, Solidity, C++) all converging on exactly ~11,300 stars. Projects with genuinely different audiences don't do that. The mechanism was on their own website: "Each GitHub repo star earns 5 QUIP points." QuipNetwork launched a crypto airdrop in early February 2026. Users who wanted QUIP tokens starred every repo in the organization. 11,000 stars in 48 hours, after five months of zero activity. The tell: dashboard.quip.network has 2 stars. nodes.quip.network has 2 stars. The repos they forgot to include in the airdrop show the real numbers. This is the first documented instance of a crypto airdrop using GitHub as a gamification layer. These aren't bots. They're real users who just wanted tokens. Case 2: The Typosquat (ShlkOfTheRa/scarab-osd) 🔴 DANGEROUS. The most dangerous case in this dataset. ShikOfTheRa/scarab-osd is a legitimate drone flight controller firmware project. 468 stars, built over 10 years. ShlkOfTheRa/scarab-osd , one character different, was created March 3, 2026. Byte-for-byte identical code. Twelve days later, 1,485 stars purchased in a 90-minute
For the past couple months I have been working on a custom open-source Unix like kernel built from the ground up in C and ASM, im looking for some feedback! Feel free to contribute to clone and reuse the code in this project! https://gitub.com/VibuxDevs/VNL (lots of features are placeholders right now)
It took me some time to write this post, but now it says exactly what I wanted to say. When people...
After using Claude Code, Codex, and Pi Agent for months, I wondered: how many AI agents are on my machine? I built a scanner. Here's what it found: Framework Active Archived Claude Code 192 191 Codex CLI 37 0 Pi Agent 8 sub-agents 12 scripts MCP Servers 8 - Total 448+ The Waste Duplicate calls : Same prompt → 3 agents (8-18% waste) Overqualified models : Simple tasks on expensive models (15-25% waste) Cache fragmentation : No shared prompt cache (12-20% waste) Zombie agents : Archived still indexed (2-8% waste) → 30-50% of AI API spend is wasted. The Fix AMA — Agent Management Agent bash pip install ama-core && ama scan && ama start Scans all agents across frameworks Smart routing (simple task → cheap model) Lifecycle management Local dashboard at localhost:8765 Free calculator: ama-agent-store.vercel.app/calculator MIT licensed. Feedback welcome!
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