The newsletter that ships
working code —
not just takes.
AI for the Stack covers how to actually integrate LLMs into real data pipelines — dbt, BigQuery, Redshift, Airflow, GCP. Every Tuesday: a deep-dive workflow with copy-paste-ready code. Every Thursday: the best tools, workflow setups, and community signal worth your time this week.
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What you get
Two issues.
Two formats. Both useful.
Deep Dive
One real-world use case. One concrete workflow. Every issue ships a working GitHub repo with code you can copy-paste into your stack before end of day — not example snippets, not pseudocode.
Roundup
Three to five items, scannable, opinionated. The tools worth evaluating right now, the workflow setups people are actually shipping, and the community signal you'd otherwise miss. Curated for practitioners, not observers.
What's covered
Real workflows.
Concrete skills.
LLMs in your dbt workflow
Use Claude and GPT-4 to write, review, and document dbt models — with sensible guardrails for production.
AI-assisted SQL debugging
Feed failing queries to an LLM with schema context and get fixes that actually account for your data model.
Automating data documentation
Generate and keep fresh column descriptions, lineage notes, and README files — on every schema change.
Agentic pipelines with n8n
Automate LLM calls, data quality checks, and alert routing without writing a full orchestration layer.
Self-healing pipeline patterns
Classify pipeline failures with an LLM, route them to the right fix path, and reduce on-call pages.
When NOT to use AI
The takes nobody else publishes — where LLMs make your pipeline worse, slower, or harder to debug.
Recent issues
From the archive
LangChain + BigQuery: Building AI-powered data pipelines that actually scale on GCP
How to wire LangChain chains into BigQuery workflows — vector search, function calling, and cost guardrails included.
Read issue →Auto-documenting your dbt models with GPT-4 — a workflow that holds up at 200+ models
A practical system for generating schema.yml descriptions from column names, tests, and upstream lineage.
Read issue →LLM tool use and agents: what data engineers actually need to know (and what to skip)
A ground-up explanation of function calling and agentic loops — with a working Python example you can adapt.
Read issue →Why it exists
Most AI resources are written for two audiences: ML researchers, and non-technical people who want the highlights. There's almost nothing for the engineer in the middle — the one already running dbt and Airflow, who's been asked to "add AI" to a system they're responsible for keeping reliable.
AI for the Stack fills that gap. Every issue is written by a practitioner, for practitioners — with the assumption that you already know your stack and just need to know what actually works when LLMs meet production data pipelines.
No breathless AGI takes. No "Top 10 AI Tools" listicles written by someone who hasn't touched a pipeline. Just concrete patterns, honest verdicts, and working code.
Join the newsletter
Two issues a week.
Both worth your time.
Tuesday: a real-world workflow with working code you can ship the same day.
Thursday: the best tools, setups, and production signal — curated, not aggregated.
For data engineers integrating AI into real stacks. Not for beginners. Not a news digest.
Free. No spam. Unsubscribe anytime.