A free, open textbook on engineering ML systems — building efficient, reliable AI from a single GPU up to warehouse-scale clusters. Goes beyond model design and MLOps tooling to the underlying science: scheduling, quantization, data pipelines, serving.
AI Infra2023
Turns any website into structured data or an API without code: record clicks once to capture lists and tables, or describe fields in plain language for AI extraction. Also crawls full sites, scrapes pages to Markdown, and runs filtered searches.
AI Agent2024
Claude-Mem is a persistent memory compression system built for Claude Code. It automatically captures tool usage observations during coding sessions, generates semantic summaries using Claude's agent-sdk, and injects relevant context into future sessions to maintain continuity of project knowledge.
AI Agent2024
Orchestrates teams of role-based autonomous agents that collaborate on multi-step tasks, plus event-driven Flows for deterministic control. Built from scratch with no LangChain dependency; runs 450M+ agentic workflows monthly.
AI Agent2024
Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.
AI Infra2024
Runs AI-generated code in isolated, elastic sandboxes with SDK, API, and CLI access for agent workflows that need stateful execution and environment control.
AI Agent2024
Controls customer-facing LLM agents turn-by-turn against deterministic guidelines instead of one big system prompt, surfacing only the rules and tools that apply each turn. Adds journeys, pre-approved canned responses, and traces for auditable behavior.
AI Client2024
Memory engine that lets AI apps remember users across conversations: it extracts facts, tracks updates, resolves contradictions, and auto-forgets stale info, returning context in ~50ms. Tops the LongMemEval, LoCoMo and ConvoMem memory benchmarks.
AI Train2024
End-to-end framework for running and reproducing foundation-model research workflows — from data curation and tokenization to training and evaluation. Emphasizes reproducibility by recording every step (including failed runs) and expressing experiments as dependency-ordered steps.
AI Agent2024
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
AI Client2024
Orchestrates and coordinates multiple coding agents in a shared, cross-device workspace — preserving context, files, and durable artifacts so teams can run parallel agent workflows without losing intent or state.