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Pytest for Agent-Generated Code: Concrete Testing Strategies to Put Into Practice

January 26, 2026

Pytest for Agent-Generated Code: Concrete Testing Strategies to Put Into Practice

When agents write tests, intent matters as much as correctness. By defining clear testing levels, preferred patterns, and explicit anti-patterns, we give agents the structure they need to produce fast, reliable Pytest suites that scale with automation.

Dennis Hume

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January 21, 2026

Dagster + Snowflake: Building Production AI Pipelines with Cortex

Snowflake handles AI compute while Dagster handles orchestration, observability, and the operational patterns that turn AI experiments into reliable production pipelines.

Alex Noonan

January 15, 2026

Your GTM Data, Finally Untangled

Compass now connects directly to your go-to-market tools, letting you ask questions about pipeline, ad spend, and sales conversations in Slack without exporting CSVs or waiting on the data team.

Alex Noonan

Dignified Python: 10 Rules to Improve your LLM Agents

January 9, 2026

Dignified Python: 10 Rules to Improve your LLM Agents

Modern LLMs generate patterns, not principles. Dignified Python gives agents the intent they lack, ensuring code is explicit, consistent, and engineered with care. Here are ten rules from our Claude prompt.

Colton Padden

January 7, 2026

Evaluating Model Behavior Through Chess

Benchmarks measure outcomes, not behavior. By letting AI models play chess in repeatable tournaments, we can observe how they handle risk, repetition, and long-term objectives, revealing patterns that static evals hide.

Dennis Hume

January 6, 2026

How to Enforce Data Quality at Every Stage: A Practical Guide to Catching Issues Before They Cost You

This post gives you a framework for enforcing data quality at every stage so you catch issues early, maintain trust, and build platforms that actually work in production.

Alex Noonan

January 5, 2026

When Sync Isn’t Enough

This post introduces a custom async executor for Dagster that enables high-concurrency fan-out, async-native libraries, and incremental adoption, without changing how runs are launched or monitored.

Steven Ayers

December 22, 2025

How to Build a Data Platform That Actually Scales

Most teams build data platforms reactively when you should be architecting one that scales with your business, not against it.

Alex Noonan

Orchestrating Nanochat: Deploying the Model

December 16, 2025

Orchestrating Nanochat: Deploying the Model

Once the model is trained, the final step is getting it into users’ hands. This guide walks through turning your model into a fast, reliable RunPod endpoint—complete with orchestration and automated updates from Dagster.

Dennis Hume

December 15, 2025

Data Ingestion Patterns: When to Use Push, Pull, and Poll (With Real Examples)

A practical guide to choosing between push, pull, and poll data ingestion patterns. With real Dagster code examples to help you build reliable, maintainable pipelines.

Alex Noonan

December 11, 2025

Dagster + Atlan: Real-Time Asset Observability in Your Data Catalog

Automatically sync asset materialization events and lineage from Dagster Cloud to Atlan

Alex Noonan

Orchestrating Nanochat: Training the Models

December 9, 2025

Orchestrating Nanochat: Training the Models

Training an LLM isn’t one job—it’s a sequence of carefully managed stages. This part shows how Dagster coordinates your training steps on RunPod so every experiment is reproducible, scalable, and GPU-efficient.

Dennis Hume

Orchestrating Nanochat: Building the Tokenizer

December 3, 2025

Orchestrating Nanochat: Building the Tokenizer

Every great model starts with great data. This first part walks through how to structure ingestion with Dagster, prepare your text corpus, and build a tokenizer that shapes how your model understands the world.

Dennis Hume

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