AI Client2025
Turns web reading into an in-context language-learning experience by injecting context-aware translations, explanations, subtitle translation, and TTS directly into the browser. Supports selection translation, batch requests and configurable AI providers to balance cost and quality.
MCP Client2025
A curated index of community resources for Claude Code — skills, hooks, slash commands, agent orchestrators, and plugins. Entries live in a source-of-truth CSV that generates the README; submissions are bot-checked, then manually vetted by the maintainer.
AI Audio2025
Open-source TTS that clones a voice from a short reference clip across 23+ languages, with adjustable emotional intensity via exaggeration/cfg controls and a built-in Perth neural watermark on every output.
AI Others2025
Archive of extracted and leaked system prompts behind major AI chatbots — Claude, ChatGPT, Gemini, Grok, Copilot, Perplexity and more — sorted by vendor and version with update dates, so you can read the hidden instructions and track how they change.
AI Infra2025
Converts PDFs into AI-ready structured outputs (Markdown, JSON with bounding boxes, HTML) for RAG and accessibility workflows; offers deterministic local parsing plus a hybrid AI mode for complex tables, OCR, formulas, and auto-tagging previews.
MCP Server2025
Exposes Azure DevOps resources (projects, repos, pipelines, work items, wikis) to AI agents via an MCP server; remote-first (hosted HTTP endpoint) with an optional local stdio Node.js server for VS Code and domain-filtered toolsets.
AI Agent2025
Builds production AI agents around a model-driven loop with provider abstraction, tools, guardrails, streaming, MCP, tracing, and multi-agent patterns across Python and TypeScript SDKs.
AI Client2025
Drops Claude Code into GitHub Actions so it responds to @claude mentions in PRs and issues — answering questions, reviewing diffs, and committing fixes or features on a branch. Runs on your runners via the Anthropic API, Bedrock, or Vertex AI.
AI Agent2025
Packages an AI agent's memory — data, embeddings, search indexes, and metadata — into one portable .mv2 file, replacing multi-service RAG stacks. Combines BM25 and HNSW search with temporal queries and sub-millisecond local reads, fully offline.