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REA is a GitHub tool that uses AI agents to reverse engineer applications and native binaries by analyzing runtime behavior. It operates as a single Model Context Protocol (MCP) integration, allowing engineers to investigate how specific features work at a low level. The setup is initiated via npx, and it supports multi-language documentation for a global user base.

• Uses MCP to standardize agent interaction with reverse engineering tasks.
• Covers full stack analysis from high-level app behavior down to native binaries.
• Enables feature investigation by asking agents to deconstruct specific functionalities.
• Quick start available via npx rea-agents setup for immediate testing.

Context Mode addresses context window bloat in AI coding agents by sandboxing tool outputs to reduce token consumption by 98%. It persists session memory and enforces routing across 17 platforms using MCP and hooks. The tool aims to prevent agents from forgetting active tasks and wasting tokens on filler text during long sessions.

• Sandboxing tool output reduces context bloat by 98%, saving significant tokens.
• Persists session memory to prevent agents from losing track of editing state.
• Supports routing across 17 platforms via MCP integration and hooks.
• Targets filler text and verbose explanations to preserve context capacity.

This repository provides a single CLAUDE.md file designed to tune Claude Code's behavior based on Andrej Karpathy's analysis of LLM coding errors. It addresses issues where models make unchecked assumptions, fail to seek clarification, or overcomplicate code structures. The file acts as a system prompt override to enforce better engineering practices during automated coding tasks.

• Apply this CLAUDE.md to enforce stricter reasoning in Claude Code sessions.
• Mitigates LLM habits like making wrong assumptions or overcomplicating APIs.
• Use as a reusable skill within the Multica agent management platform.
• Helps models push back on ambiguous requirements instead of guessing.

GitHub has rewritten its underlying storage infrastructure, retiring the legacy Spokes system to address reliability issues. The new backend delivers a reported 35x improvement in write performance, designed specifically to handle the surge in automated activity from AI agents. This architectural shift aims to stabilize repository operations under heavy synthetic load.

• GitHub retired the Spokes storage layer to fix persistent reliability bottlenecks in repository writes.
• New engine reports 35x write throughput gains, critical for scaling agentic automation workloads.
• Infrastructure update aligns GitHub's backend capacity with the expected rise in AI-driven API traffic.
• Practitioners should monitor GitHub status pages for migration impacts on high-frequency write operations.