The public support repository where Postman users file feature requests, integration ideas and bug reports.
It gives Postman users one public place to report bugs and ask for features, and to find known solutions before opening a new issue. A GitHub issue tracker used as the community support channel, with reporting guidelines and pointers to the forum, docs and support page.
Octomind is an open-source coding agent harness that gives an LLM real access to a repository through MCP tools.
It is built for agent work that keeps going after the human leaves the screen, enforcing policy as code instead of approval popups. One Rust engine with one TOML config serves an interactive CLI, stdin, a background daemon, WebSocket and an ACP agent.
Agent Autoloop lets a command-line agent pick up work in the background and follow it through, asking for a decision only when something is uncertain.
It addresses command-line agents that only work while someone watches the terminal, so the job stops once the window is closed or the person walks away. A kernel of about 1650 lines of code with zero runtime dependencies coordinates the work.
A local video-production workspace driven by a Claude Code skill that turns a product walkthrough into a polished demo video.
It turns real product interactions into demo videos, from browser capture and title cards to a mastered and inspected file. The skill coordinates existing tools rather than replacing them: Playwright for capture, HyperFrames for composition and FFmpeg for mastering.
SOML is a configuration format for humans that keeps JSON's data model and strictness while dropping braces, quotes and commas.
It gives the comfort of YAML with the clarity of JSON, so a value's type comes from its syntax and is never guessed from its content. A single-pass grammar with no lookahead, plus one canonical form, so every implementation and formatter reads equal values as equal bytes.
claude-mem preserves context across coding-agent sessions by capturing tool usage and generating semantic summaries.
It stops an agent from forgetting a project between sessions by capturing tool observations, summarizing them and recalling them later. It runs as a local worker service with a web viewer and search endpoints, managed by Bun.
Scrapling is a Python web scraping framework that combines fast HTTP requests, stealth browsers, parsing and concurrent crawling in one library.
It covers the full scraping workflow, from extracting a few fields to collecting a whole website as clean Markdown, from Python, a terminal or an AI agent. It combines fast HTTP requests, stealth browsers, JavaScript rendering and Cloudflare challenge handling behind one interface.
agent-desktop gives AI agents computer use on macOS, Windows and Linux by reading the OS accessibility tree instead of screenshots.
It lets an agent operate desktop applications through element references rather than pixel matching, while the control loop stays in the agent. A CLI and a C library share one core that reaches the operating system only through a platform adapter.
An agent skill that generates architecture diagrams with official logos from a one-sentence description of a tech stack.
It solves the problem of manually creating technical architecture diagrams by allowing developers to describe their stack in a sentence and automatically generating a visual diagram with official logos for documentation or sharing. It operates as an agent skill for Claude Code, Codex, and Cursor, fetching official logos from sources like svgl and devicon to render self-contained HTML, PNG, or React components.
Craft Anything is an open-source, pure Rust engine that studies software to understand its mechanics and uses AI agents to build new versions.
It allows users to understand how any website, app, or game works and then have an AI coding agent build a new, clean-room version of it. It is a pure-Rust reverse-engineering engine with 320 tools that powers a desktop app, CLI, and MCP server, with parts running in the browser as WebAssembly.
A repository containing Machine Learning Engineer interview preparation materials and from-scratch algorithm implementations.
The repository serves as a collection of interview preparation materials and from-scratch algorithm implementations for Machine Learning Engineers. The repository provides from-scratch algorithm implementations with mathematical formulations and verification tests, using pure Python or NumPy/PyTorch depending on the notebook.