Anthropic's attempt to remove leaked Claude Code client source code from GitHub resulted in the accidental takedown of numerous legitimate forks of its official public code repository. While the overzealous takedown has been reversed, the company faces a significant challenge in containing the spread of the leaked code. The initial DMCA notice targeted a repository hosting the leak and nearly 100 forks, but expanded to impact over 8,100 repositories, including those forking Anthropic's public code. Coders complained about being caught in the dragnet. Despite efforts, copies of the leaked code remain available on platforms like Codeberg, and "clean room" reimplementations are emerging, potentially complicating legal issues.
This repository contains the leaked source code of Anthropic's Claude Code CLI, which occurred on March 31, 2026, due to a .map file exposure in their npm registry. Claude Code is a terminal-based tool for software engineering tasks, including file editing, command execution, codebase searching, and Git workflow management.
The codebase is written in TypeScript and runs on Bun, utilizing React and Ink for its terminal UI. It features a robust tool system, command system, service layer, bridge system for IDE integration, and a permission system. The project incorporates several design patterns like parallel prefetching and lazy loading to optimize performance.
The codebase is written in TypeScript and runs on Bun, utilizing React and Ink for its terminal UI. It features a robust tool system, command system, service layer, bridge system for IDE integration, and a permission system. The project incorporates several design patterns like parallel prefetching and lazy loading to optimize performance.
This project, `autoresearch-opencode`, is an autonomous experiment loop designed for use with OpenCode. It's a port of `pi-autoresearch`, but implemented as a pure skill, eliminating the need for an MCP server and relying solely on instructions the agent follows using its built-in tools. The skill allows users to automate optimization tasks, as demonstrated by the example of optimizing the BogoSort algorithm which achieved a 7,802x speedup by leveraging Python's `bisect` module for sorted-state detection.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
Stripe's "Minions" are AI agents designed to autonomously complete complex coding tasks, from understanding a request to deploying functional code. Unlike traditional AI coding assistants that offer suggestions line-by-line, Minions aim for end-to-end task completion in a single shot. This approach leverages large language models (LLMs) to handle the entire process, including planning, code generation, and testing. The article details Stripe's implementation, focusing on overcoming challenges like long context windows and the need for reliable tooling. The goal is to significantly boost developer productivity by automating repetitive and complex coding tasks.
Stripe engineers have developed 'Minions,' autonomous coding agents capable of completing software development tasks end-to-end from a single instruction. These agents generate production-ready pull requests with minimal human intervention, currently producing over 1,300 per week. The system, built on an internal fork of Goose, integrates LLMs with Stripe's developer tools and utilizes 'blueprints' – workflows combining code and agent loops – to handle tasks.
Reliability is paramount, with changes undergoing human review and rigorous testing. Minions excel at well-defined tasks like configuration updates and refactoring, demonstrating a growing trend in AI-driven software development.
Reliability is paramount, with changes undergoing human review and rigorous testing. Minions excel at well-defined tasks like configuration updates and refactoring, demonstrating a growing trend in AI-driven software development.
Virgil.Dev is a tool that parses GitHub repositories into structured code graphs, extracting crucial elements like functions, classes, imports, and cross-file references across ten programming languages. It differs from traditional text-based search by providing exact structural results from an indexed code graph, enabling faster and more accurate code understanding. Users can explore their code via the Model Context Protocol (MCP), an AI chat interface with built-in tools, or a dedicated CLI for local parsing and querying. Pricing tiers range from free to developer plans.
OpenCode is an open-source AI coding agent designed for development work. It offers two built-in agents: 'build' for full access and 'plan' for read-only analysis and code exploration. Installation is possible via curl, package managers (npm, brew, etc.), or as a desktop application for macOS, Windows, and Linux. It distinguishes itself from tools like Claude Code by being 100% open source, provider-agnostic, offering LSP support, and having a focus on a Terminal UI. OpenCode is built with a client/server architecture, allowing for remote access via mobile apps.
This repository provides the official implementation of the STATIC (Sparse Transition-Accelerated Trie Index for Constrained decoding) framework, as described in Su et al., 2026. STATIC is a high-performance method for enforcing outputs to stay within a prespecified set during autoregressive decoding from large language models, designed for maximum efficiency on modern hardware accelerators like GPUs and TPUs.
A workflow for rapidly building applications with GitHub Copilot CLI, emphasizing its role in generating *momentum* and quickly reaching a reviewable pull request. Leverage Copilot CLI for speed, your IDE for refinement, and GitHub for collaboration.
* **Define & Scaffold:** Start with a natural language description of your goal and use Copilot CLI to scaffold only the parts you're ready to actively work on.
* **Iterate & Refine:** Run tests and use Copilot CLI to help understand and fix failures; handle mechanical, repo-wide changes with the CLI.
* **Develop & Ship:** Transition to your IDE for complex logic/design, then commit your changes and create a pull request on GitHub for collaboration.
* The Copilot SDK offers possibilities for embedding agentic execution directly within applications.
* **Define & Scaffold:** Start with a natural language description of your goal and use Copilot CLI to scaffold only the parts you're ready to actively work on.
* **Iterate & Refine:** Run tests and use Copilot CLI to help understand and fix failures; handle mechanical, repo-wide changes with the CLI.
* **Develop & Ship:** Transition to your IDE for complex logic/design, then commit your changes and create a pull request on GitHub for collaboration.
* The Copilot SDK offers possibilities for embedding agentic execution directly within applications.
This project details how to serve offline content (Wikipedia, etc.) from an ESP32 microcontroller, specifically using a LILYGO T-Dongle-S3. It covers the process from downloading ZIM files, processing them with Python scripts, transferring to an SD card, and loading a sketch onto the microcontroller to serve the content via a WiFi access point.
