Talk Python To Me
Talk Python To Me

#549: Great Docs

25 May 2026 1:07:00 Michael Kennedy

Listen to episode

About this episode

Your documentation has two audiences now - humans reading the rendered HTML, and AI agents trying to make sense of your library. Rich Iannone and Michael Chow from Posit are back on Talk Python with a brand new Python documentation tool called Great Docs that takes both seriously. Rich is the creator of Great Tables, and before that the R package GT, the man has a serious eye for design, and he's pointed that energy at the Python docs ecosystem. We'll talk about how Great Docs spins up a polished site in three commands, why every page ships as Markdown for your favorite LLM, how it leans on Quarto for executable code blocks and tabbed install sections, and where it lands against Sphinx, MkDocs, and Zensical. Plus, you'll meet Tablin. Here we go.

Episode sponsors

Sentry Error Monitoring, Code talkpython26
Temporal
Talk Python Courses

Links from the show

Guests
Michael Chow: github.com
Rich lannone: github.com

Python Web Security with OWASP Top 10 and Agentic AI Course: talkpython.fm

GT: posit-dev.github.io
Episode: talkpython.fm
Sphinx: www.sphinx-doc.org
mkdocs: www.mkdocs.org
Zensical: zensical.org
Hugo: gohugo.io
Ghost: ghost.org
Rs pkgdown: pkgdown.r-lib.org
Quarto: quarto.org
quickstart: posit-dev.github.io
llms.txt file: llmstxt.org
llms.txt: talkpython.fm
mcp: talkpython.fm
cli: talkpython.fm

Watch this episode on YouTube: youtube.com
Episode #549 deep-dive: talkpython.fm/549
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---
YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm
Mastodon: @[email protected]
X.com: @talkpython

Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @[email protected]
Michael on X.com: @mkennedy

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Talk Python To Me. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.