ai-that-works/ai-that-works resource
🦄 ai that works - every tuesday 10 AM PST observed · 2026-08-28
Health v2 · maintenance only
64/100
- Activity 99
- Release rhythm 35
- Longevity 37
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 518
- days_rel: n/a
- days_push: 9
- n_releases_24m: 0
Adoption not part of the score
1912 stars · 141 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A weekly live-streamed series (Tuesdays 10 AM PST) by the BAML and HumanLayer creators covering production AI engineering through live coding, Q&A, and episode demo code. The repository hosts code examples and harnesses from episodes on topics like model deprecation testing, agent scaling, and AI coding benchmarks.
Use cases
- learn production AI engineering patterns
- how to handle LLM model deprecation safely
- evaluate new LLM models against regression budgets
- scale AI agents in parallel with A/B testing
- learn context engineering for coding agents
- find live coding sessions on building AI apps
- examples of eval harnesses for LLM apps
When to choose
- you want to learn production AI engineering from experienced practitioners
- you need practical patterns for model swaps, evals, and agent infrastructure
- you enjoy live coding and community Q&A formats
- you use or are curious about BAML and agentic coding tools
When to avoid
- you need a production-ready library rather than educational content
- you want a stable, documented API with a license
- you are not interested in AI/LLM engineering topics
- you cannot attend live sessions and prefer self-paced structured courses
Facets
learning-resource · maturity active
developer-tools prompt-engineering agent-framework llm-inference testing large-language-models developer-tools tutorials education cross-platform python live-coding webinar-series context-engineering ai-engineering podcast baml production-ai evals ai-agents typescript
8 sources
- readme: https://github.com/ai-that-works/ai-that-works · fetched 2026-08-28 · 41685b1a9b58
- homepage: https://www.boundaryml.com/podcast · fetched 2026-08-29 · b4d57b2cbeca
- site_page: https://boundaryml.com/podcast/2026-08-25-software-factory-design-patterns · fetched 2026-08-29 · dec4b986a330
- site_page: https://boundaryml.com/podcast/2026-08-18-syncs-and-ab-testing-200-agents · fetched 2026-08-29 · ccb73386c291
- site_page: https://boundaryml.com/podcast/2026-08-11-unconference-recap · fetched 2026-08-29 · 63e149d1406f
- site_page: https://boundaryml.com/podcast/2026-08-04-slop-code-bench · fetched 2026-08-29 · 475c061005aa
- site_page: https://boundaryml.com/podcast/2026-07-28-your-model-is-already-obsolete · fetched 2026-08-29 · 8988d11e6233
- site_page: https://boundaryml.com/podcast/2026-07-21-no-vibes-july · fetched 2026-08-29 · 60a212f97dd2
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ai-that-works/ai-that-works | main | 64 |
For agents
markdown · JSON · MCP: product_card(name="ai-that-works/ai-that-works")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem