⚡ rtk-ai/rtk is making waves. Here's the full picture.
🔗 https://github.com/rtk-ai/rtk
📝 CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
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Introducing rtk-ai/rtk, a high-performance CLI proxy that reduces LLM token consumption by 60-90%. This single Rust binary supports over 100 commands and has an overhead of less than 10ms. Key features include smart filtering, grouping, truncation, and deduplication, which are applied per command type to minimize token usage.
To
Technical highlights of rtk include its ability to filter and compress command outputs before they reach the LLM context. This is achieved through a hook-based system that rewrites Bash commands to their rtk equivalents before execution. The result is a significant reduction in token consumption, making it an ideal solution for developers, data scientists, and anyone working with large language models.
Audience for rtk includes anyone looking to optimize their LLM workflow and reduce token consumption. Whether you're working with Claude Code, Copilot, or other AI tools, rtk can help you achieve your goals.
In summary, rtk-ai/rtk is a powerful tool for reducing LLM token consumption, and its ease of use, flexibility, and high-performance capabilities make it a must-have for anyone working with large language models:
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/rtk-ai/rtk
📝 CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
──────────────────────────────
Introducing rtk-ai/rtk, a high-performance CLI proxy that reduces LLM token consumption by 60-90%. This single Rust binary supports over 100 commands and has an overhead of less than 10ms. Key features include smart filtering, grouping, truncation, and deduplication, which are applied per command type to minimize token usage.
To
get started, users can install rtk using Homebrew, a quick install script, Cargo, or pre-built binaries. The installation process is straightforward, and the rtk --version command can be used to verify the installation. Technical highlights of rtk include its ability to filter and compress command outputs before they reach the LLM context. This is achieved through a hook-based system that rewrites Bash commands to their rtk equivalents before execution. The result is a significant reduction in token consumption, making it an ideal solution for developers, data scientists, and anyone working with large language models.
Audience for rtk includes anyone looking to optimize their LLM workflow and reduce token consumption. Whether you're working with Claude Code, Copilot, or other AI tools, rtk can help you achieve your goals.
In summary, rtk-ai/rtk is a powerful tool for reducing LLM token consumption, and its ease of use, flexibility, and high-performance capabilities make it a must-have for anyone working with large language models:
rtk your way to token savings today!──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔥 msitarzewski/agency-agents is trending — and it deserves your attention.
🔗 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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Introducing The Agency: a collection of AI agent personalities, each with unique expertise and deliverables. From engineering to design and sales, these agents can transform your workflow.
Key features include:
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-
-
-
Usage options:
- Integrate with
- Use as a
- Browse the
Technical highlights include:
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-
-
This repository is perfect for developers, designers, and sales teams looking to augment their workflows with AI-powered agents.
In a nutshell: The Agency is your ultimate AI team, ready to deliver - never sleep, never complain, always deliver!
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🧠 Channel: https://t.me/GithubRe
🔗 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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Introducing The Agency: a collection of AI agent personalities, each with unique expertise and deliverables. From engineering to design and sales, these agents can transform your workflow.
Key features include:
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Specialized deep expertise-
Personality-Driven communication styles-
Deliverable-Focused outcomes-
Production-Ready workflowsUsage options:
- Integrate with
Claude Code or other tools like GitHub Copilot or Antigravity- Use as a
reference for best practices- Browse the
agent roster and copy/adapt what you needTechnical highlights include:
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Multi-tool integrations-
Production-ready workflows-
Success metrics for evaluationThis repository is perfect for developers, designers, and sales teams looking to augment their workflows with AI-powered agents.
In a nutshell: The Agency is your ultimate AI team, ready to deliver - never sleep, never complain, always deliver!
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🧠 Channel: https://t.me/GithubRe
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💡 colbymchenry/codegraph just hit the trending charts — here's why it matters.
🔗 https://github.com/colbymchenry/codegraph
📝 Pre-indexed code knowledge graph for Claude Code, Codex, Cursor, and OpenCode — fewer tokens, fewer tool calls, 100% local
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CodeGraph is a game-changer for developers, supercharging tools like Claude Code, Cursor, Codex, and OpenCode with semantic code intelligence. This powerful tool provides a pre-indexed knowledge graph, enabling agents to query instantly instead of scanning files. With CodeGraph, you can enjoy
To get started, simply run
Some key features of CodeGraph include smart context building, full-text search, impact analysis, and framework-aware routes. It supports
CodeGraph is perfect for developers looking to boost their productivity and streamline their workflow. So why wait? Try CodeGraph today and experience the power of semantic code intelligence - your code, supercharged!
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/colbymchenry/codegraph
📝 Pre-indexed code knowledge graph for Claude Code, Codex, Cursor, and OpenCode — fewer tokens, fewer tool calls, 100% local
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CodeGraph is a game-changer for developers, supercharging tools like Claude Code, Cursor, Codex, and OpenCode with semantic code intelligence. This powerful tool provides a pre-indexed knowledge graph, enabling agents to query instantly instead of scanning files. With CodeGraph, you can enjoy
94% fewer tool calls and 77% faster exploration. To get started, simply run
npx @colbymchenry/codegraph and follow the interactive installer. Initialize your projects with codegraph init -i, and you're ready to roll. Some key features of CodeGraph include smart context building, full-text search, impact analysis, and framework-aware routes. It supports
19+ languages and is 100% local, ensuring your data never leaves your machine.CodeGraph is perfect for developers looking to boost their productivity and streamline their workflow. So why wait? Try CodeGraph today and experience the power of semantic code intelligence - your code, supercharged!
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🧠 Channel: https://t.me/GithubRe
⚡ multica-ai/andrej-karpathy-skills is making waves. Here's the full picture.
🔗 https://github.com/multica-ai/andrej-karpathy-skills
📝 A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
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The multica-ai/andrej-karpathy-skills GitHub repository provides a set of guidelines to improve the behavior of Claude Code, a coding agent. Inspired by Andrej Karpathy's observations on the pitfalls of Large Language Models (LLMs) in coding, these guidelines aim to address issues such as wrong assumptions, overcomplication, and lack of clarity. The guidelines are based on four principles: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution.
The guidelines can be installed as a Claude Code plugin or added to a project's
For example, to install the guidelines as a Claude Code plugin, you can use the following commands:
These guidelines are working if you see fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clean, minimal PRs. Overall, the multica-ai/andrej-karpathy-skills repository provides a valuable resource for improving the behavior of coding agents and reducing costly mistakes. Give your LLMs success criteria and watch them go - it's that simple!
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/multica-ai/andrej-karpathy-skills
📝 A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
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The multica-ai/andrej-karpathy-skills GitHub repository provides a set of guidelines to improve the behavior of Claude Code, a coding agent. Inspired by Andrej Karpathy's observations on the pitfalls of Large Language Models (LLMs) in coding, these guidelines aim to address issues such as wrong assumptions, overcomplication, and lack of clarity. The guidelines are based on four principles: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution.
The guidelines can be installed as a Claude Code plugin or added to a project's
CLAUDE.md file. They provide a framework for LLMs to loop until they meet specific goals, reducing the need for constant clarification. The guidelines are designed to be merged with project-specific instructions and can be customized to fit the needs of a particular project.For example, to install the guidelines as a Claude Code plugin, you can use the following commands:
/plugin marketplace add forrestchang/andrej-karpathy-skills
/plugin install andrej-karpathy-skills@karpathy-skills
These guidelines are working if you see fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clean, minimal PRs. Overall, the multica-ai/andrej-karpathy-skills repository provides a valuable resource for improving the behavior of coding agents and reducing costly mistakes. Give your LLMs success criteria and watch them go - it's that simple!
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🧠 Channel: https://t.me/GithubRe
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🔥 humanlayer/12-factor-agents is trending — and it deserves your attention.
🔗 https://github.com/humanlayer/12-factor-agents
📝 What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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The 12-factor-agents GitHub repository provides a set of principles for building reliable and scalable LLM-powered applications. The project, inspired by the 12 Factor Apps methodology, aims to help developers create more maintainable and efficient software.
Purpose: The main goal of 12-factor-agents is to establish a set of guidelines for developing LLM-powered applications that are production-ready and can be easily maintained.
Some key features of the project include:
- A set of 12 factors that provide guidance on building reliable LLM-powered software
- A brief history of software development and how it led to the creation of the 12-factor-agents project
- Examples and illustrations to help explain complex concepts
Usage of the project involves following the 12 factors, which cover topics such as:
-
-
-
- And many more
From a technical standpoint, the project highlights the importance of:
- Using a stateless reducer to manage the application's state
- Implementing a loop that consists of LLM determination, deterministic code execution, and context window updates
- Utilizing a DAG (Directed Acyclic Graph) to represent the application's workflow
The target audience for the project includes:
- Developers interested in building LLM-powered applications
- Software engineers looking to create more maintainable and efficient software
- Anyone interested in learning about the 12-factor-agents principles and how to apply them
In conclusion, the 12-factor-agents project provides a comprehensive set of guidelines for building reliable and scalable LLM-powered applications. By following the 12 factors and understanding the technical concepts behind the project, developers can create more maintainable and efficient software. The ultimate takeaway is: build software that is designed to thrive in a world where LLMs are getting exponentially more powerful.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/humanlayer/12-factor-agents
📝 What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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The 12-factor-agents GitHub repository provides a set of principles for building reliable and scalable LLM-powered applications. The project, inspired by the 12 Factor Apps methodology, aims to help developers create more maintainable and efficient software.
Purpose: The main goal of 12-factor-agents is to establish a set of guidelines for developing LLM-powered applications that are production-ready and can be easily maintained.
Some key features of the project include:
- A set of 12 factors that provide guidance on building reliable LLM-powered software
- A brief history of software development and how it led to the creation of the 12-factor-agents project
- Examples and illustrations to help explain complex concepts
Usage of the project involves following the 12 factors, which cover topics such as:
-
Factor 1: Natural Language to Tool Calls-
Factor 2: Own your prompts-
Factor 3: Own your context window- And many more
From a technical standpoint, the project highlights the importance of:
- Using a stateless reducer to manage the application's state
- Implementing a loop that consists of LLM determination, deterministic code execution, and context window updates
- Utilizing a DAG (Directed Acyclic Graph) to represent the application's workflow
The target audience for the project includes:
- Developers interested in building LLM-powered applications
- Software engineers looking to create more maintainable and efficient software
- Anyone interested in learning about the 12-factor-agents principles and how to apply them
In conclusion, the 12-factor-agents project provides a comprehensive set of guidelines for building reliable and scalable LLM-powered applications. By following the 12 factors and understanding the technical concepts behind the project, developers can create more maintainable and efficient software. The ultimate takeaway is: build software that is designed to thrive in a world where LLMs are getting exponentially more powerful.
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🧠 Channel: https://t.me/GithubRe
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💡 Diolinux/PhotoGIMP just hit the trending charts — here's why it matters.
🔗 https://github.com/Diolinux/PhotoGIMP
📝 A Patch for GIMP 3+ for Photoshop Users
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PhotoGIMP transforms GIMP into a layout familiar to Adobe Photoshop users. It offers a
The patch is suitable for users of GIMP 3.0 and newer, and it does not delete custom brushes, fonts, or plug-ins. You can customize shortcuts after installation via
Installation steps vary for Linux, Windows, and macOS, but generally involve downloading the release, extracting it, and overwriting config files.
The patch replaces or adds several files in GIMP's configuration directory, including
Troubleshooting tips are available for common issues, such as PhotoGIMP not changing GIMP's layout or errors when opening GIMP.
In short, PhotoGIMP is a must-have for Photoshop users switching to GIMP — it makes the transition seamless, and you'll be editing like a pro in no time!
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/Diolinux/PhotoGIMP
📝 A Patch for GIMP 3+ for Photoshop Users
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PhotoGIMP transforms GIMP into a layout familiar to Adobe Photoshop users. It offers a
Photoshop-like tool layout, custom splash screen, maximized canvas space, Photoshop keyboard shortcuts, and a custom icon & name. To use it, simply download and install the latest release, overwriting GIMP's configuration files. The patch is suitable for users of GIMP 3.0 and newer, and it does not delete custom brushes, fonts, or plug-ins. You can customize shortcuts after installation via
Edit → Keyboard Shortcuts. Installation steps vary for Linux, Windows, and macOS, but generally involve downloading the release, extracting it, and overwriting config files.
The patch replaces or adds several files in GIMP's configuration directory, including
shortcutsrc, toolrc, and sessionrc. Troubleshooting tips are available for common issues, such as PhotoGIMP not changing GIMP's layout or errors when opening GIMP.
In short, PhotoGIMP is a must-have for Photoshop users switching to GIMP — it makes the transition seamless, and you'll be editing like a pro in no time!
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🧠 Channel: https://t.me/GithubRe