🌟 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

📌 Spotted on GitHub Trending: msitarzewski/agency-agents — let's break it down.

🔗 https://github.com/msitarzewski/agency-agents
📝 A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
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The Agency is a collection of AI agent personalities, each with deep expertise, unique voice, and deliverable-focused approach. The agents are specialized, personality-driven, and production-ready, making them a valuable addition to any team.

The agency-agents repository provides various installation options, including a native desktop app for macOS, Linux, and Windows, as well as script-based installations for different tools like Claude Code, Cursor, and Gemini CLI.

To get started, you can download the app or use the command line to install the agents. For example:

./scripts/install.sh --tool claude-code

The Agency roster includes a wide range of agents, from Frontend Developer to AI Engineer, each with their own specialty and use case.

Whether you're looking to build a modern web app or optimize your database, there's an agent to help.

In short, The Agency is your dream team of AI specialists, and with this repository, you can assemble them in just a few clicks - your new team of AI agents is just a download away!

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: kvcache-ai/ktransformers — let's break it down.

🔗 https://github.com/kvcache-ai/ktransformers
📝 A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations
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The ktransformers project is a flexible framework for experiencing cutting-edge large language model (LLM) inference and fine-tuning optimizations. It focuses on efficient inference and fine-tuning through CPU-GPU heterogeneous computing. Key features include high-performance kernel operations, efficient mixture-of-experts (MoE) inference, quantization support, and easy integration with other frameworks.

The project has two main capabilities: Inference and SFT (Fine-Tuning). Inference provides CPU-optimized kernel operations for heterogeneous LLM inference, while SFT offers fine-tuning with LLaMA-Factory integration for ultra-large MoE models.

The framework supports various models, including DeepSeek-R1, Qwen3-Next, and Kimi-K2, and provides tutorials and documentation for each. It also offers technical highlights such as AMX/AVX acceleration, MoE optimization, and quantization support.

KTransformers is suitable for researchers, developers, and anyone interested in efficient LLM inference and fine-tuning. The project is developed and maintained by MADSys Lab, Approaching.AI, and community contributors, and welcomes contributions and feedback.

Get started with KTransformers today and unlock the full potential of your LLMs: efficient inference and fine-tuning made easy!

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🧠 Channel: https://t.iss.one/GithubRe