Category: 3-SometimeTechTalk

Ryan chats with  Karen Ng, EVP of Product at HubSpot, to chat about Model Context Protocol (MCP) and how they implemented it for their server for their CRM product. They chat the emergence of this as the standard for agentic interactions, the challenges of implementing the server and integrating it with their ecosystem, and how agentic AI has affected work at Hubspot. 

Episode notes:

Hubspot is a customer-relationship management (CRM) platform that aims to help businesses grow. 

MCP is an open-source protocol for connecting AI agents to external systems, originally developers at Anthropic. 

Connect with Karen on LinkedIn. 

Virtual hi-five to Rob Truxal for asking What is the purpose of "pip install --user ..."? and garnering themselves a Stellar Question badge.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Topics covered in this episode:

• * Mozilla’s Lifeline is Safe After Judge’s Google Antitrust Ruling*
• * troml - suggests or fills in trove classifiers for your projects*
• * pqrs: Command line tool for inspecting Parquet files*
• * Testing for Python 3.14*
• Extras
• Joke

Watch on YouTube

About the show

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Connect with the hosts

• Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)
• Brian: @brianokken@fosstodon.org / @brianokken.bsky.social
• Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky)

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.

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Michael #1: Mozilla’s Lifeline is Safe After Judge’s Google Antitrust Ruling

• A judge lets Google keep paying Mozilla to make Google the default search engine but only if those deals aren’t exclusive.
• More than 85% of Mozilla’s revenue comes from Google search payments.
• The ruling forbids Google from making exclusive contracts for Search, Chrome, Google Assistant, or Gemini, and forces data sharing and search syndication so rivals get a fighting chance.

Brian #2: troml - suggests or fills in trove classifiers for your projects

• Adam Hill
• This is super cool and so welcome.
• Trove Classifiers are things like <code>Programming Language :: Python :: 3.14</code> that allow for some fun stuff to show up in PyPI, like the versions you support, etc.
• Note that just saying you require 3.9+ doesn’t tell the user that you’ve actually tested stuff on 3.14. I like to keep Trove Classifiers around for this reason.
• Also, License classifier is deprecated, and if you include it, it shows up in two places, in Meta, and in the Classifiers section. Probably good to only have one place. So I’m going to be removing it from classifiers for my projects.
• One problem, classifier text has to be an exact match to something in the classifier list, so we usually recommend copy/pasting from that list.
• But no longer! Just use troml!
• It just fills it in for you (if you run troml suggest --fix). How totally awesome is that!
• I tried it on pytest-check, and it was mostly right. It suggested me adding 3.15, which I haven’t tested yet, so I’m not ready to add that just yet. :)
• BTW, I talked with Brett Cannon about classifiers back in ‘23 if you want some more in depth info on trove classifiers.

Michael #3: pqrs: Command line tool for inspecting Parquet files

• pqrs is a command line tool for inspecting Parquet files
• This is a replacement for the parquet-tools utility written in Rust
• Built using the Rust implementation of Parquet and Arrow
• pqrs roughly means "parquet-tools in rust"
• Why Parquet?
  • Size
    • A 200 MB CSV will usually shrink to somewhere between about 20-100 MB as Parquet depending on the data and compression. Loading a Parquet file is typically several times faster than parsing CSV, often 2x-10x faster for a full-file load and much faster when you only read some columns.
  • Speed
    • Full-file load into pandas: Parquet with pyarrow/fastparquet is usually 2x–10x faster than reading CSV with pandas because CSV parsing is CPU intensive (text tokenizing, dtype inference).
      • Example: if read_csv is 10 seconds, read_parquet might be ~1–5 seconds depending on CPU and codec.
    • Column subset: Parquet is much faster if you only need some columns — often 5x–50x faster because it reads only those column chunks.
    • Predicate pushdown & row groups: When using dataset APIs (pyarrow.dataset) you can push filters to skip row groups, reducing I/O dramatically for selective queries.
    • Memory usage: Parquet avoids temporary string buffers and repeated parsing, so peak memory and temporary allocations are often lower.

Brian #4: Testing for Python 3.14

• Python 3.14 is just around the corner, with a final release scheduled for October.
• What’s new in Python 3.14
• Python 3.14 release schedule
• Adding 3.14 to your CI tests in GitHub Actions
  • Add “3.14” and optionally “3.14t” for freethreaded
  • Add the line allow-prereleases: true
• I got stuck on this, and asked folks on Mastdon and Bluesky
• A couple folks suggested the allow-prereleases: true step. Thank you!
• Ed Rogers also suggested Hugo’s article Free-threaded Python on GitHub Actions, which I had read and forgot about. Thanks Ed! And thanks Hugo!

Extras

Brian:

• dj-toml-settings : Load Django settings from a TOML file. - Another cool project from Adam Hill
• LidAngleSensor for Mac - from Sam Henri Gold, with examples of creaky door and theramin
• Listener Bryan Weber found a Python version via Changelog, pybooklid, from tcsenpai
• Grab PyBay

Michael:

• Ready prek go! by Hugo van Kemenade

Joke: Console Devs Can’t Find a Date

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Apple's impressive new iPhone Pro models 2) Who is the iPhone Air for? 3) Has the phone reached its ultimate form factor 4) Is generative AI threatening to upend the smartphone market 5) Meta's new smartglasses are coming 6) Nepal's Gen Z overthrows the government and picks a new leader on Discord 7) OpenAI growth stats after GPT-5 launch 8) Oracle and OpenAI's new $300 billion deal 9) Flirting with ChatGPT 10) The AI companionship use case is real 11) Does San Francisco have 996 work culture?

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Ryan welcomes Geraint North, AI and developer platforms fellow at Arm, to dive into the impact of GenAI on chip design, Arm’s approach to designing flexible CPU architectures, and the challenges of optimizing large language models at the chip level for edge devices. 

Episode notes: 

Arm is a global compute platform that allows the world’s leading technology companies to innovate and deliver AI experiences.

Arm just announced their Lumex CSS Platform, which provides a complete compute subsystem platform for mobile and desktop providers to enable efficient AI workloads. 

Connect with Geraint on LinkedIn.

Congrats to Lifejacket badge winner I.sh., who won the badge for answering How to take screenshot on failure.

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Dustin Haisler, State and Local Government expert and President of e.Republic joins the show for a wide-ranging conversation on the evolving landscape of state and local government. Together, we dive into the findings from the 2025 Adobe Digital Government Index and explore the tension between rising citizen expectations and governments’ ability to keep pace. From AI adoption and accessibility mandates to cloud modernization, workforce challenges, and shifting federal-to-state responsibilities, we unpack the trends shaping public sector innovation. We also discuss how states are addressing technical debt, why building strong digital foundations is critical for AI success, and how leaders can move from pilots to enterprise rollouts with confidence.

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Simon Ritter has been in the IT industry for 40 years. He went from university to work on Unix in the early days, employed by AT&T and programming in the C language. In 1996, he switched gears to join Sun Microsystems, programming in Java. Years later, after the Oracle transition, he started to dig into what might be next. Outside of tech, he is married with an older son. He is a complete petro-head - meaning, he is really into cars. In fact, in the last few years, he and his son re-built a classic mini from the ground up.

While Simon was at Oracle, he started to crave a different opportunity, but still in the Java space. He stumbled upon a company digging into powering the Java platform, to make it the most secure, efficient and trusted platform on the planet - and he, and the company, found a great fit.

This is Simon's creation story at Azul.

Sponsors

• Full Scale
• Paddle.com
• Sema Software
• PropelAuth
• Postman
• Meilisearch

Links

• https://www.azul.com/
• https://www.linkedin.com/in/siritter/

Support this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donations

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In this two-part episode of Leaders of Code, Peter O’Connor, Director of Platform Engineering, welcomes Ryan J. Salva, Senior Director of Product at Google, Developer Experiences, for a deep dive into the future of software development. They explore how AI-assisted tools are reshaping the developer experience, going far beyond just writing code. From breaking down deployment bottlenecks to streamlining operations and transforming how teams collaborate, this conversation unpacks where developer tooling is headed and how AI is changing the game at every stage of the software lifecycle.

The discussion also:

• Addresses how AI is transforming team structures, enabling engineering teams to operate effectively with just a few people, reducing collaboration overhead and accelerating decision-making.
• Highlights the future of platform engineering and DevOps, where AI will assist with standardization and dynamically create and manage deployment pipelines in real time.

Episode notes:

• Connect with Peter O’Connor and Ryan J Salva.
• Check out Google’s open source repository, Gemini-CLI, on GitHub.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Bill Vass is the Chief Technology Officer of Booz Allen. Vass joins Big Technology Podcast to discuss how governments can harness AI to cut redundancy and deliver better citizen services. Tune in to hear his inside view on LLM deployments from the VA to the International Space Station and the difficulty of modernizing mass bureaucracies. We also cover autonomous driving, humanoid robots, and quantum computing’s first real use-cases. Hit play for fascinating look into public sector AI, along with deep perspective on technology’s state of the art.

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Adam Cohen grew up in Toronto, in North York. He showed early signs of entrepreneurship by putting his lemonade stand on a wagon, and taking it door to door - or hustling his friends to buy souvenirs on a school field trip. His Dad was in VC, and was a big influence on his life, pushing him to succeed. Outside of professional life, he is big into sports, specifically basketball. In the past, he loved playing fantasy sports, which also influence how he built his business ventures.

Adam and his team went through several iterations of AI tooling - summarizing AI, integrating git and JIRA, etc. While they were doing this, they realized that the best way to make a difference, was to first focus on the data itself.

This is the creation story of Weave.

Sponsors

• Paddle.com
• Sema Software
• PropelAuth
• Postman
• Meilisearch
• Mailtrap
• .TECH Domains (https://get.tech/codestory)

Links

• https://workweave.dev/
• https://www.linkedin.com/in/adam-b-cohen/

Support this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donations

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Ryan is joined by our very own Ash Zade, Product Manager, and Alex Warren, Staff Software Engineer, to discuss our newly released stackoverflow.ai, how it’s enhancing user experience by combining human-validated answers with AI, and our future plans for deeper personalization and community integration. 

Episode notes: 

stackoverflow.ai is helping you get the technical answers you need with less friction, all powered by our 16 years of community knowledge.

Connect with Ash on LinkedIn.

Connect with Alex on LinkedIn.

This week we’re shouting out user Ketan Ramani for winning a Populist badge for their answer to How to go about formatting 1200 to 1.2k in Android studio.

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