Open Source
5 MIN READTutorial: Use Malloy Publisher to model and analyze data with an agent
A few-minute setup: point your agent at Malloy Publisher and start asking real questions of your own data.
Monty Lennie
Software Engineer @ Credible
From the Credible team
Ideas built on meaning
Practical thinking for teams building AI, analytics, and data products that people can trust.
Open Source
7 MIN READ
The skills and tools that make an agent a data expert
Malloy Publisher now ships five MCP tools, thirty agent skills, and the design principles that decide what belongs in each.
Monty Lennie
Software Engineer @ Credible
More from Credible
Open Source
9 MIN READWe Open-Sourced the Thing Everyone Else Is Selling
The agent skills and MCP tools behind Credible are now open source, in Malloy Publisher. Excellence is no longer a moat — so we gave the layer away and bet on trust, durability, and distribution instead.
Kyle Nesbit
CEO @ Credible
AI & ML
15 MIN READBuilding Atlas: A Data Exploration Platform on Credible and Malloy
Atlas lets anyone explore a catalog of public datasets by asking questions in plain English. Built on Credible and Malloy, it turns natural-language questions into governed, interactive charts — and full data stories that you can publish, all in one place.
Girish Jeswani
Software Engineer @ Credible
AI & ML
5 MIN READCharting is Commoditized. Meaning Is the Product.
Charting is commoditized. Meaning is the product — why the semantic model is the layer analysts, engineers, and AI agents all build on.
Kyle Nesbit
CEO @ Credible
AI & ML
13 MIN READMaking Healthcare Data AI-Ready
AI agents are learning to read documents; the harder problem is using the structured data that runs the business. Here's how Malloy turns a complex healthcare schema (OMOP) into a governed model agents can query reliably — and how Credible serves it to production agents over MCP.
Ofer Mendelevitch
DevRel @ Credible
AI & ML
7 MIN READThe Future of ML Pipelines
Data warehouses can now run embeddings, classification, and LLM inference natively. Anyone can build an ML pipeline — but without evaluation, governed logic, and version control, you don't know what you're getting. Malloy brings structure to warehouse-native ML.
James Swirhun
Head of Product @ Credible
AI & ML
7 MIN READEntity Matching with Embeddings
Entity matching pipelines built on string-matching heuristics are brittle, expensive to maintain, and impossible to scale. Embedding models dramatically outperform them — but only if you can evaluate and iterate on results. Here's how to build production-grade entity matching you can actually trust.
James Swirhun
Head of Product @ Credible
Engineering
6 MIN READRethinking Data Transformation with Malloy
dbt brought software engineering to SQL, but SQL + Jinja + YAML complexity compounds at scale. Malloy unifies transformation, modeling, and materialization in one declarative language — type-safe, composable, and AI-ready.
James Swirhun
Head of Product @ Credible