Anthropic is rolling out a significant update to Claude Code, merging slash commands into a more powerful 'Skills' system. This allows for custom workflows and integrations directly within the Claude interface, enhancing its utility for developers and streamlining complex tasks. The update also includes improved code explanations and debugging features.
The article emphasizes the importance of optimizing AI coding agent context to improve efficiency and performance. The author shares four key techniques: maintaining an updated AGENTS.md file, providing documentation links, sharing IaC stack context, and starting new threads for new tasks.
**Bullet Points:**
- **Always update AGENTS.md**: Store coding rules and preferences across threads to improve consistency and reduce errors.
- **Provide documentation links**: Ensure agents use up-to-date API and syntax information by linking to current docs.
- **Provide IaC stack as context**: Share infrastructure details (e.g., database tables) to reduce token usage and improve speed.
- **Start new threads for new contexts**: Avoid context noise by initiating fresh threads when switching tasks or projects.
**Bullet Points:**
- **Always update AGENTS.md**: Store coding rules and preferences across threads to improve consistency and reduce errors.
- **Provide documentation links**: Ensure agents use up-to-date API and syntax information by linking to current docs.
- **Provide IaC stack as context**: Share infrastructure details (e.g., database tables) to reduce token usage and improve speed.
- **Start new threads for new contexts**: Avoid context noise by initiating fresh threads when switching tasks or projects.
This Gist contains the system prompt for Claude Code, Anthropic's CLI for Claude. It details the tool's purpose, instructions for use, tone, proactive behavior, code style guidelines, task management, and references.
An open-source background coding agent. Designed to understand, reason about, and contribute to existing codebases. Licensed for open-source use under MIT License. It sets up isolated execution environments for AI agents to work on GitHub repositories with tools to understand code, edit files, and much more.
XTRAN is a software meta-tool that marries compiler and expert system technologies to provide rule-driven automation of software tasks involving a wide variety of computer languages. It supports code assessment, generation, transformation, translation, data/text analysis, and more.
This post explores how developers can leverage Gemini 2.5 to build sophisticated robotics applications, focusing on semantic scene understanding, spatial reasoning with code generation, and interactive robotics applications using the Live API. It also highlights safety measures and current applications by trusted testers.
PaperCoder is a multi-agent LLM system that transforms scientific papers into code repositories through a three-stage pipeline: planning, analysis, and code generation. It aims to create faithful, high-quality implementations.
Google Code Assist, now powered by Gemini 2.5, shows significant improvement in coding capabilities and introduces AI agents to assist across the software development lifecycle. The article details the features available in the free, standard, and enterprise tiers, and raises questions about agent availability and practical implementation.
GitHub Copilot has introduced several new models including Anthropic Claude 3.7 Sonnet, Claude 3.5 Sonnet, OpenAI o3-mini, and Google Gemini Flash 2.0. These models are now available in Copilot Chat and agent mode, offering enhanced capabilities and performance.
This article details a method for training large language models (LLMs) for code generation using a secure, local WebAssembly-based code interpreter and reinforcement learning with Group Relative Policy Optimization (GRPO). It covers the setup, training process, evaluation, and potential next steps.
