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Samuel Colvin

@sa.muelcolvin.com

Building Pydantic Logfire - uncomplicated observability for Python. Sequoia Scout.

70 Posts1788 Follower114 Folgt
Pydantic@pydantic.dev · 5mo ago

A few weeks ago our founder announced Monty, a minimal, secure Python interpreter written in Rust, for running code written by AI agents. People got excited. @sa.muelcolvin.com just wrote up the full story on why he's excited about it and you should be too. pydantic.dev/articles/pyd...

Pydantic Monty: A Minimal Python Sandbox for AI Agents | Pydantic

Meet Monty — a secure, minimal Python interpreter written in Rust for running AI-generated code. Learn where it sits between tool calling and full computer use, and why CodeMode matters for AI agents.

pydantic.dev

filtra.io@filtra.bsky.social · 6mo ago

Rust continues its march to power all of the Python ecosystem... Samuel Colvin of @pydantic.dev just announced Monty, "a minimal, secure Python interpreter written in Rust for use by AI" Learn more about the 🐍 + 🦀 magic happening at Pydantic with our podcast linked below:

Software Engineering Daily@softwaredaily.bsky.social · 8mo ago

Samuel Colvin is the creator of @pydantic.dev and Pydantic AI. He joins Gregor Vand to talk about Pydantic origin, type safe AI applications, Pydantic AI’s evolution, LogFire observability, and the future of sustainable open-source AI tooling. @sa.muelcolvin.com bit.ly/3Mj4SkE

Pydantic AI with Samuel Colvin - Software Engineering Daily

Python’s popularity in data science and backend engineering has made it the default language for building AI infrastructure. However, with the rapid growth of AI applications, developers are increasin...

softwareengineeringdaily.com

Qian Li@qianli.dev · 9mo ago

I wrote a tutorial on integrating DBOS with Logfire. It's super easy to set up (kudos to the @pydantic.dev team!) -> just a few lines to configure the exporter and your Logfire write token. Once it's running, you'll get a unified view of your app with logs + traces in one place.

A screenshot of using Logfire + DBOS + Pydantic AI agent.
Adam Azzam@aaazzam.bsky.social · 10mo ago

I'm teaming up with @sa.muelcolvin.com to launch a new meetup series, Py AI. Our first one is November 11 in San Francisco with talks from @pydantic.dev, @modal-labs.bsky.social, ReductoAI and @fastmcp.bsky.social. Hosted by our friends at pebblebed.com RSVP here: luma.com/gjtfecc7

Py AI Meetup · Luma

Join us for an evening of cutting-edge AI discussions featuring lightning talks from leading practitioners: Samuel Colvin (CEO of Pydantic) Reducto + Modal…

luma.com

Qian Li@qianli.dev · 10mo ago

Several people (thanks to @mitsuhiko.at and @sa.muelcolvin.com) noted early on that DBOS had too many dependencies. In the latest Python release, we trimmed it down to just 6 direct deps while still implementing durable workflows and queues in one library. It's lightweight (for real).

uv tree for dbos v2.0.0
Samuel Colvin@sa.muelcolvin.com · last yr.

I'm chatting to Hugo Bowne-Anderson tomorrow about Human Seeded Evals and @logfire.pydantic.dev on Youtube at 4pm UK. I'm pretty confident something like the think I'm describing here is where we'll end up with evals. Please join, I'd love to hear what you think. lu.ma/voeyx7n1

Human-seeded Evals: Scaling Judgement with LLMs (with Samuel Colvin) · Luma

Everyone agrees evals are essential—but almost no one implements them rigorously. Why? They're hard to write, time-consuming, and brittle. Samuel Colvin,…

lu.ma

Samuel Colvin@sa.muelcolvin.com · last yr.

@hannes.muehleisen.org I want this about 3 times a day and I can't be the only one. We discussed it at data council and you seemed keen to implement something. Please can it happen 🙏. github.com/duckdb/duckd...

Feature Request: `mode` parquet, or access to the preview query as a view via `_` · duckdb duckdb · Discussion #17122

I'd really like a way to output a query to a parquet file (in order to plot the results). I think the simplest way to do this would be to support .mode parquet This wouldn't affect the mode of prin...

github.com

Samuel Colvin@sa.muelcolvin.com · last yr.

Are you building agents, or building with AI more generally? Double win: * Help make @logfire.pydantic.dev better * Get $100 Amazon voucher Sign up for a short interview with the amazing Greg Kogan: docs.google.com/forms/d/e/1F...

Python devs building AI (agents): Can we chat about your experience?

My name is Greg Kogan and I'm conducting user research with experienced Python developers who build AI applications, particularly agent-based systems. If selected, we'll do a 45-minute Zoom interview ...

docs.google.com

Samuel Colvin@sa.muelcolvin.com · last yr.

THE EVAL HAS LANDED. 🦅 pydantic-evals has been a major piece of work from David Montague over the last month. I'm pretty excited to see it move the dial on how easily Python developers can benchmark and improve AI code.

Pydantic@pydantic.dev · last yr.

Pydantic Evals is a powerful evaluation framework designed to help you systematically test and evaluate the performance and accuracy of the systems you build, especially when working with LLMs. More info here: ai.pydantic.dev/evals/

Samuel Colvin@sa.muelcolvin.com · last yr.

Logfire's MCP server could fundamentally change how people complete the software development feedback loop: development -> runtime errors -> bug fix. Unlike other observability platforms, @logfire.pydantic.dev let's the model (or you!) write full SQL to find the relevant data.

Pydantic@pydantic.dev · last yr.

Pydantic Logfire MCP Server is here! Easily hook your AI agents into observability data and supercharge debugging in real time. For Cursor integration, just update .cursor/mcp.json to use Logfire and you’re set: www.youtube.com/watch?v=lCda... Full details: pydantic.dev/articles/mcp...