Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.
MCP Client2026
A read-only mirror of Anthropic's community plugin marketplace for Claude Cowork and Claude Code, listing vetted third-party plugins in a nightly-synced marketplace.json. Intended for discovering and installing community plugins via the Claude plugin system.
AI Agent2026
Defines 10 design principles and reference implementations for building agent-native, token-efficient CLIs that reduce token and turn costs for AI agents; includes the TOON output format, benchmarks (browser and GitHub), and an AXI catalog of tools.
AI Agent2026
Provides a local-first desktop workspace that runs and coordinates AI agents across your files, browser, and third-party tools with a shared, editable memory. Offers built-in models or bring-your-own-keys, one-click OAuth to 100+ integrations, and browser-driven agent automation.
AI Agent2026
Manages discovery, quality evaluation, sharing and evidence-driven evolution of skills used by AI agents. Local-first deployment with MCP integration, CLI and Python API, skill lineage and task-based quality summaries for auditable agent workflows.
MCP Server2026
Bridges an LLM assistant to your locally running TradingView Desktop via Chrome DevTools Protocol for AI-assisted chart analysis, Pine Script development, and UI automation. Key features: Claude Code MCP integration, a JSON/CLI 'tv' toolset, local-only operation and streamable chart data.
AI Agent2026
Curated collection of resources, patterns, and reference implementations for building reliable AI agent harnesses—covering context delivery, tool/MCP design, memory, permissions, observability, verification, and orchestration for production agent engineering.
AI Agent2026
Turns natural-language instructions into runnable trading research: data loaders, strategy generation, backtests, reports, and optional broker connectors. Focuses on a tool-driven agent model (36+ MCP tools, 77 finance skills) and an Alpha Zoo of 452 pre-built alphas for reproducible research and gated agentic trading.
AI Agent2026
Provides a lightweight Python harness that turns LLMs into working agents with tool-use, skills, persistent memory, permission controls and multi-agent coordination. Ships with a CLI/React TUI, 43+ built-in tools, a plugin/skill system and the ohmo personal-agent for chat gateways. Best for developers prototyping agent workflows and multi-agent experiments.
AI Coding2026
Maps a codebase plus docs, PDFs, media and configs into a local, queryable knowledge graph; parses code with a local tree-sitter AST (no LLM), uses configurable backends for semantic extraction of non-code, and outputs graph.json, graph.html and a brief report.
AI Client2026
Compresses LLM/agent replies into a terse “caveman” style to cut output tokens (~65–75%) while preserving technical accuracy. Offers per-agent skills, intensity modes, memory-compression and middleware to lower token cost and extend usable context.
MCP Client2026
Stores conversation history verbatim and retrieves it via local semantic search with a structured index (wings/rooms/drawers). Pluggable vector backends and a local-first default mean high recall (benchmarked) without cloud or API keys—useful for agent memory and private RAG.