Companion resources for Chip Huyen's AI Engineering book: chapter summaries, study notes, prompt examples, case studies, and a few analysis scripts. Focuses on engineering practices for adapting foundation models to production rather than step-by-step code tutorials.
AI Agent2025
Twelve engineering principles for building production-grade LLM agents, modeled on the 12-Factor App. Argues the best agents are mostly deterministic software with a few well-placed LLM calls, not a prompt-and-tools loop.
AI Agent2025
Spec-driven agentic dev platform that turns a prompt into requirements, a design doc, and sequenced tasks before any code is written, then implements from the spec. Runs across IDE, CLI, web, and mobile; validates output with property-based tests.
AI Agent2025
Packages Every's 'compound engineering' workflow into 26 slash commands for AI coding agents like Claude Code — brainstorm, plan, work, review, debug. Skews effort toward planning and review, and saves each run's lessons so the next task is easier.
AI Agent2026
Provides a set of versioned "skills" that codify UI design standards and automated checks for design engineers and AI agents. Includes a CLI to discover, install, and run skills like baseline UI rules, accessibility fixes, motion-performance tuning, and metadata corrections.
AI Agent2026
Automates decompiling APK/AAR/JAR and extracting HTTP APIs — Retrofit endpoints, OkHttp calls, hardcoded URLs, and auth patterns — so you can document and reproduce an app's network surface without source code. Integrates jadx/Vineflower/Fernflower and scripts for call-flow tracing.
AI Agent2026
Provides modular “skills” that help designers and engineers audit, find, and improve UI animations and interaction decisions — optimized for use by AI agents or human-in-the-loop workflows. Distills domain-expert rules into actionable SKILL.md modules (review, improve, find opportunities, vocabulary).
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 Dataset2026
Curated 100K subset of geometrically diverse CAD construction sequences sampled from a 1M agentically synthesized corpus — each item includes executable CadQuery scripts, 8 rendered views, STL/STEP exports, and precomputed DINOv3 embeddings for retrieval and benchmarking.
AI Dataset2026
Multilingual benchmark for evaluating LLMs' industrial domain knowledge via 2,049 expert-curated QA pairs spanning 10 product verticals and four languages, with each item grounded to industry or national standards and an LLM-as-judge evaluation pipeline.
Autonomously proposes, modifies, executes, and evaluates ML experiments to study recursive self-improvement in machine learning engineering. Implements an open stack (OpenMLE-Gym, -RL, -Evo) and post-trains Frontis-MA1 (35B) around four evolution operators (Draft, Improve, Debug, Crossover); releases model weights and the full codebase.
AI Dataset2026
Provides 1,021.64 hours across 597 CAD/BIM workflows with synchronized screen recordings and interaction logs; each workflow includes video, timestamped input events, task specs, source files, final outputs, and evaluation rubrics for training or evaluating desktop CAD agents.