📌 Spotted on GitHub Trending: tirth8205/code-review-graph — let's break it down.

🔗 https://github.com/tirth8205/code-review-graph
📝 Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
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Code Review Made Smart: The code-review-graph repository revolutionizes code reviews by building a structural map of your codebase and providing your AI assistant with precise context, reducing token waste and increasing efficiency.

Key features include:
- Incremental updates in under 2 seconds
- Broad language coverage, including support for Jupyter notebooks and the ability to add custom languages
- Risk-scored PR reviews in CI through a GitHub Action

To get started, simply run pip install code-review-graph and code-review-graph install to set up everything.

The repository boasts an impressive 82x median per-question token reduction across 6 real repositories, making it a game-changer for developers.

In short, code-review-graph is a must-have for any development team looking to streamline their code review process and make the most out of their AI assistants.
The punchy one-liner takeaway: Stop burning tokens, start reviewing smarter with code-review-graph.

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🧠 Channel: https://t.iss.one/GithubRe

🌟 1jehuang/jcode caught my eye on GitHub Trending today.

🔗 https://github.com/1jehuang/jcode
📝 The most intelligent agent harness for code
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The jcode project is a next-generation coding agent harness designed for multi-session workflows, infinite customizability, and performance. It's built to raise the skill ceiling, and its key features include infinite customizability and high performance. To get started with jcode, you can install it using a simple curl command or by using the provided PowerShell script for Windows.

One of the standout features of jcode is its resource efficiency, with a focus on optimizing every metric to the bone. This is particularly important for scaling multi-session workflows. The project includes detailed comparisons with other tools, showcasing its superiority in terms of RAM usage and boot-up time.

The jcode project is suitable for developers and power users looking to streamline their coding workflow. With its highly customizable and performant nature, it's an attractive option for those seeking to take their coding skills to the next level.

The project's technical highlights include a memory system that allows the agent to automatically recall relevant information, as well as explicit memory tools for active searching and storing of memories.

In a nutshell, jcode is the ultimate coding agent harness for those who want to code faster, better, and more efficiently - and it's available now!

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet diegosouzapw/OmniRoute: a gem from today's GitHub trending list.

🔗 https://github.com/diegosouzapw/OmniRoute
📝 Never stop coding. Free MIT AI gateway: one endpoint, 268+ providers (50+ free), 500+ models — Claude, GPT, Gemini, Kimi K3, GLM, DeepSeek. Works with Claude Code, Codex, Cursor, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, multimodal, Desktop/PWA. Built by 500+ contributors.
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OmniRoute is a free AI gateway that connects every AI tool to 250 providers, with 90+ free options, through one endpoint. It allows you to never stop coding and saves you money by maximizing subscriptions, tracking quota, and using every token before reset. RTK + Caveman compression helps save 15-95% of eligible tokens per request. Key features include one endpoint for every tool, cost-optimized routing, and a 4-tier auto-fallback system. Technical highlights include circuit breakers, TLS stealth, and 21,000+ tests. Audience includes developers who want to save time and money while using AI tools. With OmniRoute, you can focus on coding without worrying about rate limits or expensive APIs. OmniRoute is production-grade, with a 0 to start approach, requiring no card or payment. In a nutshell, OmniRoute helps you code more, pay less.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 rohitg00/ai-engineering-from-scratch landed on trending. Worth a proper look.

🔗 https://github.com/rohitg00/ai-engineering-from-scratch
📝 Learn it. Build it. Ship it for others.
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The AI Engineering from Scratch curriculum is a comprehensive, 20-phase, 503-lesson journey to master AI engineering. It's designed to bridge the gap between using AI tools and understanding how they work. With a focus on hands-on learning, each lesson follows a consistent structure: read the problem, derive the math, write the code, run the test, and keep the artifact.

Key features include:
- A linear progression from math foundations to autonomous systems
- Implementation in four languages: Python, TypeScript, Rust, and Julia
- Reusable artifacts from each lesson, including prompts, skills, agents, and MCP servers

To get started, you can read any lesson on the website, clone and run the repository, or use the /find-your-level skill in a compatible agent to determine your starting point. Prerequisites are minimal: you should be able to write code in any language, and Python knowledge is helpful but not required.

Technical highlights of the curriculum include building algorithms from raw math, understanding backpropagation and attention mechanisms, and deploying autonomous agents. The audience for this curriculum includes anyone looking to deeply understand AI, from beginners to experienced engineers.

In short, AI Engineering from Scratch is a rigorous, hands-on curriculum that empowers you to build, understand, and deploy AI systems from the ground up. Don't just use AI — build it, and build it to last.

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🧠 Channel: https://t.iss.one/GithubRe