Podcasts about Data science

Ryan Sawchuk is the VP of Product at Crexi, where he leads cross-functional teams to build advanced, data-rich tools aimed at transforming the commercial real estate workflow. With more than 15 years in product leadership, Ryan specializes in crafting AI-enabled, scalable solutions that boost transaction velocity, improve user experience, and integrate seamlessly into CRE operations. Prior to Crexi, Ryan held senior product roles at Indeed, Procore, and LinkedIn, shaping core features and driving growth in high-scale tech environments. He earned his education from Princeton University, which laid the foundation for his data-driven, user-first product philosophy. At Crexi, Ryan's vision is to bridge the gap between real estate professionals and cutting-edge technology, making complex CRE data more accessible, actionable, and efficient. This is episode was recorded live at Blueprint Vegas 2025.

Today we're turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don't have to be enemies. If you want faster insights with fewer yak-shaves, this one's packed with takeaways you can apply before lunch. Let's get into it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Khuyen Tran (LinkedIn): linkedin.com Khuyen Tran (GitHub): github.com CodeCut: codecut.ai Production-ready Data Science Book (discount code TalkPython): codecut.ai Why UV Might Be All You Need: codecut.ai How to Structure a Data Science Project for Readability and Transparency: codecut.ai Stop Hard-coding: Use Configuration Files Instead: codecut.ai Simplify Your Python Logging with Loguru: codecut.ai Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai Text Similarity & Fuzzy Matching Guide: codecut.ai Loguru (Python logging made simple): github.com Hydra: hydra.cc Marimo: marimo.io Quarto: quarto.org Show Your Work! Book: austinkleon.com Watch this episode on YouTube: youtube.com Episode #522 deep-dive: talkpython.fm/522 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Jim Tananbaum of Foresite Capital joins Nick to discuss From Data Science to Drug Design: How AI Shifts Discovery, Target Validation, and Portfolio Construction. In this episode we cover: Data Science and Investment Approach Investment Practices and Market Conditions China's Role in Biotech and Regulatory Considerations Impact of AI on Biotech and Healthcare Healthcare Adoption of AI and Preventive Measures Payers and Insurance Companies' Role Lessons from Successful Investments Generating Liquidity in a Sluggish Market Future of GLP-1 Agonists Guest Links: Jim's LinkedIn Jim's X Foresite's LinkedIn Foresite's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.  

Kathryn sits down with Jill Kellett, VP of Product & Marketing and Annie Yang, Senior Director of Data Science of Root Inc. to unpack how a smaller player competes with giants. The dive into ditching the idea of campaigns, leveraging deep telematics, and precision targeting.Guest Quotes: "Don't try to go head to head. If you know that you're in a saturated market and that everybody else is running Facebook ads, you're probably not gonna win, or you might win and it might be really cost prohibitive to win." - Jill "We're not going to win on brand spend. We're not trying to target every customer or show an impression to every person in the U.S. We're much more about precision and discipline in our experimentation approach." - AnnieEpisode Breakdown:[04:20] Alchemy UnveiledTelematics and predictive data: Root leverages information on driver safety from their app and from third parties, to predict LTV of customers and adjust their pricing models. [23:06] From Nuggets to Campaign GoldScaling back can glean insights: If you have to turn off channels or partnerships, view it as an opportunity to expose which partners truly drove impact.[31:14] Gold Rush!Don't play where you can't win: if you're a challenger, be strategic where and how you invest. You need to think differently. Links & ResourcesConnect with Kathryn: LinkedInConnect with Jill: LinkedInConnect with Annie: LinkedInLearn more about Root: joinroot.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most data scientists follow the same predictable process: gather requirements, collect data, build models, and only at the very end create visualisations to communicate results. This traditional approach seems logical, but what if it's actually working against us? In this episode, David Cohen joins Dr. Genevieve Hayes to reveal how flipping the script on data visualisation - moving it to the beginning of projects rather than the end - can dramatically improve stakeholder buy-in and project success rates.This episode reveals:Why the traditional bottom-up data communication approach often misses the mark [02:36]How moving visual storytelling to the start of a project can transform stakeholder engagement [06:40]The gamified workshop framework that turns requirement gathering into collaborative problem-solving [08:50]The counterintuitive first step that immediately improves data project outcomes [20:28]Guest BioDavid Cohen is a data and AI strategy consultant, with a background in supporting the F500 clients of both Big 4 and boutique consulting firms. He is the founder of Superposition, a consulting firm that builds collaborative workshops focused on data & AI-related use cases.LinksConnect with David on LinkedInSuperposition websiteSuperposition YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

This week, we chat with Maya Bakhai! Maya Bakhai is the founder of Spice Capital, a first-yes fund investing at pre-seed and seed. Maya's Fund II is a $25M Fund that will invest in ~40 companies with check sizes ranging from $250k-$750k. Spice Capital has over 50 companies in the portfolio including Beehiiv, MUBI, Prosper Health, Hype, Skej, and more.In the last 8 years, Maya has backed 100+ founders.. Prior to starting Spice Capital in 2021, she ran venture investments for Kevin Durant at Thirty Five Ventures where she invested in multiple now-unicorns including: Hugging Face, Robinhood, Skydio, Mercury, Whoop, Rubrik, and Underdog Fantasy. She has a B.S. in Finance and Data Science from NYU. In 2023, she was recognized for her investing in Forbes 30 under 30's Venture Capital list.Maya is based in NYC. Fun fact: In a past life, she produced a sold out off-broadway musical- “Aliens Coming”, a narrative podcast -“Illuminati Interns” and an award-winning web series called “Freelance”.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Maya Bakhai: @MayaBakhaiSpice Capital: @spice_cap@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

In this episode of The Dairy Podcast Show, Dr. Enrico Casella, from Penn State University, discusses how artificial intelligence drives innovation in dairy farming. From using computer vision to estimate body weight to implementing sustainable water management and monitoring heat stress, Dr. Casella explains high-impact solutions advancing cattle health and boosting efficiency on the farm. Tune in on all major platforms!"Depth cameras use height data instead of color, enabling precise bodyweight estimation in dairy systems."Meet the guest: Dr. Enrico Casella, Assistant Professor of Data Science for Animal Systems at Penn State University's Animal Science Department and Co-Hire of the Institute for Computational and Data Sciences, specializes in leveraging Artificial Intelligence (AI) for animal health and development. With a PhD in Computer Science from the University of Kentucky and postdoctoral experience at UW-Madison, his interdisciplinary research integrates AI, computer vision, and optimization techniques.Liked this one? Don't stop now — Here's what we think you'll love!What you'll learn:(00:00) Highlight(01:15) Introduction(01:67) Dr. Casella's career(06:58) AI & bodyweight(15:22) Sustainable water use(19:10) Group data analysis(27:46) Future opportunities(31:49) Final three questionsThe Dairy Podcast Show is trusted and supported by innovative companies like:* Adisseo* Priority IAC* Evonik- dsm-firmenich- SmaXtec- Natural Biologics- Berg + Schmidt- ICC- Protekta- AHV

In this episode, we explore how Explainable AI (XAI) is revolutionizing drug development, rare disease research, and precision medicine.Our guest, Frédéric Parmentier, Vice President of Data Science at Ariana Pharma, shares how their Explainable Artificial Intelligence (XAI) platform, KEM (Knowledge Extraction and Management), helps uncover critical biomarker signatures in small cohort clinical trials. Unlike traditional black box AI models, XAI delivers transparent, interpretable results that regulators and clinicians can trust—making it a game-changer for early-phase drug development, rare disease trials, and personalized medicine.We dive into:Why Explainable AI in clinical trials is essential for regulatory acceptance and scientific validation.How XAI can identify biomarkers of best responders in cohorts as small as 20–100 patients.Real-world examples where AI revealed insights that traditional statistics missed.The future of AI in drug development, precision medicine, and rare disease research.The difference between black box AI vs. Explainable AI in healthcare and why transparency matters.If you work in biopharma, clinical research, AI-driven healthcare, or drug discovery, this episode will give you powerful insights into how XAI can accelerate development, reduce trial failures, and enable personalized treatment strategies.About the PodcastAI for Pharma Growth is a podcast focused on exploring how artificial intelligence can revolutionise healthcare by addressing disparities and creating equitable systems. Join us as we unpack groundbreaking technologies, real-world applications, and expert insights to inspire a healthier, more equitable future.This show brings together leading experts and changemakers to demystify AI and show how it's being used to transform healthcare. Whether you're in the medical field, technology sector, or just curious about AI's role in social good, this podcast offers valuable insights.AI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr. Andree Bates created to help organisations understand how the use of AI based technologies can easily save them time and grow their brands and business. This show blends deep experience in the sector with demystifying AI for all pharma people, from start up biotech right through to Big Pharma. In this podcast Dr Andree will teach you the tried and true secrets to building a pharma company using AI that anyone can use, at any budget. As the author of many peer-reviewed journals and having addressed over 500 industry conferences across the globe, Dr Andree Bates uses her obsession with all things AI and futuretech to help you to navigate through the, sometimes confusing but, magical world of AI powered tools to grow pharma businesses. This podcast features many experts who have developed powerful AI powered tools that are the secret behind some time saving and supercharged revenue generating business results. Those who share their stories and expertise show how AI can be applied to sales, marketing, production, social media, psychology, customer insights and so much more. Dr. Andree Bates LinkedIn | Facebook | Twitter

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: * PostgreSQL 18 Released* * Testing is better than DSA (Data Structures and Algorithms)* * Pyrefly in Cursor/PyCharm/VSCode/etc* * Playwright & pytest techniques that bring me joy* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: PostgreSQL 18 Released PostgreSQL 18 is out (Sep 25, 2025) with a focus on faster text handling, async I/O, and easier upgrades. New async I/O subsystem speeds sequential scans, bitmap heap scans, and vacuum by issuing concurrent reads instead of blocking on each request. Major-version upgrades are smoother: pg_upgrade retains planner stats, adds parallel checks via -jobs, and supports faster cutovers with -swap. Smarter query performance lands with skip scans on multicolumn B-tree indexes, better OR optimization, incremental-sort merge joins, and parallel GIN index builds. Dev quality-of-life: virtual generated columns enabled by default, a uuidv7() generator for time-ordered IDs, and RETURNING can expose both OLD and NEW. Security gets an upgrade with native OAuth 2.0 authentication; MD5 password auth is deprecated and TLS controls expand. Text operations get a boost via the new PG_UNICODE_FAST collation, faster upper/lower, a casefold() helper, and clearer collation behavior for LIKE/FTS. Brian #2: Testing is better than DSA (Data Structures and Algorithms) Ned Batchelder If you need to grind through DSA problems to get your first job, then of course, do that, but if you want to prepare yourself for a career, and also stand out in job interviews, learn how to write tests. Testing is a skill you'll use constantly, will make you stand out in job interviews, and isn't taught well in school (usually). Testing code well is not obvious. It's a puzzle and a problem to solve. It gives you confidence and helps you write better code. Applies everywhere, at all levels. Notes from Brian Most devs suck at testing, so being good at it helps you stand out very quickly. Thinking about a system and how to test it often very quickly shines a spotlight on problem areas, parts with not enough specification, and fuzzy requirements. This is a good thing, and bringing up these topics helps you to become a super valuable team member. High level tests need to be understood by key engineers on a project. Even if tons of the code is AI generated. Even if many of the tests are, the people understanding the requirements and the high level tests are quite valuable. Michael #3: Pyrefly in Cursor/PyCharm/VSCode/etc Install the VSCode/Cursor extension or PyCharm plugin, see https://pyrefly.org/en/docs/IDE/ Brian spoke about Pyrefly in #433: Dev in the Arena I've subsequently had the team on Talk Python: #523: Pyrefly: Fast, IDE-friendly typing for Python (podcast version coming in a few weeks, see video for now.) My experience has been Pyrefly changes the feel of the editor, give it a try. But disable the regular language server extension. Brian #4: Playwright & pytest techniques that bring me joy Tim Shilling “I've been working with playwright more often to do end to end tests. As a project grows to do more with HTMX and Alpine in the markup, there's less unit and integration test coverage and a greater need for end to end tests.” Tim covers some cool E2E techniques Open new pages / tabs to be tested Using a pytest marker to identify playwright tests Using a pytest marker in place of fixtures Using page.pause() and Playwright's debugging tool Using assert_axe_violations to prevent accessibility regressions Using page.expect_response() to confirm a background request occurred From Brian Again, with more and more lower level code being generated, and many unit tests being generated (shakes head in sadness), there's an increased need for high level tests. Don't forget API tests, obviously, but if there's a web interface, it's gotta be tested. Especially if the primary user experience is the web interface, building your Playwright testing chops helps you stand out and let's you test a whole lot of your system with not very many tests. Extras Brian: Big O - By Sam Who Yes, take Ned's advice and don't focus so much on DSA, focus also on learning to test. However, one topic you should be comfortable with in algortithm-land is Big O, at least enough to have a gut feel for it. And this article is really good enough for most people. Great graphics, demos, visuals. As usual, great content from Sam Who, and a must read for all serious devs. Python 3.14.0rc3 has been available since Sept 18. Python 3.14.0 final scheduled for Oct 7 Django 6.0 alpha 1 released Django 6.0 final scheduled for Dec 3 Python Test Static hosting update Some interesting discussions around setting up my own server, but this seems like it might be yak shaving procrastination research when I really should be writing or coding. So I'm holding off until I get some writing projects and a couple SaaS projects further along. Joke: Always be backing up

This episode is a little different because Alun turns the microphone toward me. After 456 episodes, it feels both strange and exciting to be the “guest” on my own show. Together, we reflect on the journey so far and then dive into a topic close to both our hearts: the human skills that make statisticians and quantitative scientists truly effective. We talk about leadership as helping others accomplish something, how to influence people across functions (not just departments), why being known inside your organization matters, and how presentation skills can make or break your impact. We wrap up with three actions you can start applying right away.

Colton Pace is a founder and currently the CEO of Ownwell, a Proptech company dedicated to democratizing access to real estate expertise and reducing the hidden costs of homeownership. Under his leadership, Ownwell helps homeowners and property owners identify and appeal overvalued property taxes, reduce insurance and utility costs, and manage other home‑related expenses through data, automation, and local expert teams. Before founding Ownwell, Colton served as an investor, asset manager, and venture capitalist, helping manage billions of dollars across various asset classes. He was part of funds that made early investments in companies such as Uber, Spotify, Redfin, Snowflake, UiPath, Zuora, and Grab. (01:05) - VC to PropTech Founder(03:10) - $797B Property Tax Problem(04:48) - Ownwell Traction: 700K+ Homes and SMB/CRE(06:18) - AI Plus 80 Consultants: How Appeals Get Done(08:50) - Success Rates and Savings: Residential vs Commercial(11:27) - Portfolio Case Study: 124 SFR Properties in Texas(13:45) - Valuation Methods and Local Differences(16:13) - Market Size: $50 to 60B Opportunity(17:29) - Feature: CREtech - Join CREtech New York 2025 on Oct 21-22 for the largest Real Estate Meetings program. Qualified Real Estate pros get free full event pass plus up to $800 in travel and hotel costs. (19:02) - Beyond Taxes: Insurance, Loans, Utilities, Concierge (21:30) - Building Trust with Homeowners and CRE Owners (24:03) - Advice for PropTech Founders Selling into Real Estate (26:49) - Collaboration Superpower: Matthew McConaughey

At PyData Berlin, community members and industry voices highlighted how AI and data tooling are evolving across knowledge graphs, MLOps, small-model fine-tuning, explainability, and developer advocacy.- Igor Kvachenok (Leuphana University / ProKube) combined knowledge graphs with LLMs for structured data extraction in the polymer industry, and noted how MLOps is shifting toward LLM-focused workflows.- Selim Nowicki (Distill Labs) introduced a platform that uses knowledge distillation to fine-tune smaller models efficiently, making model specialization faster and more accessible.- Gülsah Durmaz (Architect & Developer) shared her transition from architecture to coding, creating Python tools for design automation and volunteering with PyData through PyLadies.- Yashasvi Misra (Pure Storage) spoke on explainable AI, stressing accountability and compliance, and shared her perspective as both a data engineer and active Python community organizer.- Mehdi Ouazza (MotherDuck) reflected on developer advocacy through video, workshops, and branding, showing how creative communication boosts adoption of open-source tools like DuckDB.Igor KvachenokMaster's student in Data Science at Leuphana University of Lüneburg, writing a thesis on LLM-enhanced data extraction for the polymer industry. Builds RDF knowledge graphs from semi-structured documents and works at ProKube on MLOps platforms powered by Kubeflow and Kubernetes.Connect: https://www.linkedin.com/in/igor-kvachenok/Selim NowickiFounder of Distill Labs, a startup making small-model fine-tuning simple and fast with knowledge distillation. Previously led data teams at Berlin startups like Delivery Hero, Trade Republic, and Tier Mobility. Sees parallels between today's ML tooling and dbt's impact on analytics.Connect: https://www.linkedin.com/in/selim-nowicki/Gülsah DurmazArchitect turned developer, creating Python-based tools for architectural design automation with Rhino and Grasshopper. Active in PyLadies and a volunteer at PyData Berlin, she values the community for networking and learning, and aims to bring ML into architecture workflows.Connect: https://www.linkedin.com/in/gulsah-durmaz/Yashasvi (Yashi) MisraData Engineer at Pure Storage, community organizer with PyLadies India, PyCon India, and Women Techmakers. Advocates for inclusive spaces in tech and speaks on explainable AI, bridging her day-to-day in data engineering with her passion for ethical ML.Connect: https://www.linkedin.com/in/misrayashasvi/Mehdi OuazzaDeveloper Advocate at MotherDuck, formerly a data engineer, now focused on building community and education around DuckDB. Runs popular YouTube channels ("mehdio DataTV" and "MotherDuck") and delivered a hands-on workshop at PyData Berlin. Blends technical clarity with creative storytelling.Connect: https://www.linkedin.com/in/mehd-io/

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! Kutatás HBR1 HBR2

In this week's episode of the show, I sit down with Justin Evans, author of The Little Book of Data, to talk about what it means to truly think like a data person. Justin shares insights from his 20-year career in data and advertising, reflecting on why so many professionals struggle to embrace data and how his book helps break down those barriers. We discuss the “four layers of data denial,” the qualities that make someone a data person, and the importance of storytelling in making data engaging and useful. Justin also offers stories from Nielsen, Samsung, and beyond to illustrate how data literacy and visualization can create clarity, solve problems, and unlock value. This conversation is both inspiring and practical for anyone working with—or intimidated by—data.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthCheck out Justin's book, The Little Book of Data.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

Join Laborastories host Dr. Paul J. Jannetto and Dr. Patrick Mathias, associate medical director of the informatics division at the University of Washington School of Medicine in Seattle, for an engaging discussion on Dr. Mathias's vision for the ADLM Data Science Certificate Program as the faculty content lead, his thoughts on the benefits of data science for improving the lab's role in patient care, and how lab professionals can start their own learning journey. With special guest: Dr. Patrick Mathias Hosted by: Dr. Paul J. Jannetto

Data science training programs often jump straight into technical methods without teaching one of the most critical skills for project success - problem framing. Without proper framing, data science projects are doomed to fail, right from the start, as data scientists find themselves solving the wrong problems or building models that don't address real business decisions.In this Value Boost episode, Professor Jeff Camm joins Dr. Genevieve Hayes to reveal the specific problem framing framework that decision scientists use to ensure they're solving the right problems from the start, dramatically improving their success rates compared to traditional data science approaches.You'll discover:The medical doctor approach to diagnosing business problems by distinguishing symptoms from root causes [02:09]The critical question that reveals what decisions actually need to be made [04:53]How to turn model "failures" into valuable strategic insights for management [06:24]Why thinking beyond the data prevents you from building technically perfect but business-useless solutions [10:04]Guest BioProf Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.LinksConnect with Jeff on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he's back with something on the other side of that coin: pyx. Pyx isn't a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral Homepage: astral.sh Pyx Project: astral.sh Introducing Pyx Blog Post: astral.sh uv Package on GitHub: github.com UV Star History Chart: star-history.com Watch this episode on YouTube: youtube.com Episode #520 deep-dive: talkpython.fm/520 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical fixes that keep projects from stalling. From context management to retrieval debugging, John explains the patterns he's seen succeed, the mistakes to avoid, and why it helps to think of an LLM as an “AI intern” rather than an all-knowing oracle. We talk through: - Why chasing perfect accuracy is a dead end - How to use agents without losing control - Context engineering: fitting the right information in the window - Starting simple instead of over-orchestrating - Separating retrieval from generation in RAG - Splitting complex extractions into smaller checks - Knowing when frameworks help — and when they slow you down A practical guide to avoiding the common traps of LLM development and building systems that actually hold up in production. LINKS: Context Engineering for AI Agents, a free, upcoming lightning lesson from John and Hugo (https://maven.com/p/4485aa/context-engineering-for-ai-agents) The Hidden Simplicity of GenAI Systems, a previous lightning lesson from John and Hugo (https://maven.com/p/a8195d/the-hidden-simplicity-of-gen-ai-systems) Roaming RAG – RAG without the Vector Database, by John (https://arcturus-labs.com/blog/2024/11/21/roaming-rag--rag-without-the-vector-database/) Cut the Chit-Chat with Artifacts, by John (https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/) Prompt Engineering for LLMs by John and Albert Ziegler (https://amzn.to/4gChsFf) Relevant Search by John and Doug Turnbull (https://amzn.to/3TXmDHk) Arcturus Labs (https://arcturus-labs.com/) Watch the podcast on YouTube (https://youtu.be/mKTQGKIUq8M) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Topics covered in this episode: * pandas is getting pd.col expressions* * Cline, At-Cost Agentic IDE Tooling* * uv cheatsheet* Ducky Network UI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pandas is getting pd.col expressions Marco Gorelli Next release of Pandas will have pd.col(), inspired by some of the other frameworks I'm guessing Pandas 2.3.3? or 2.4.0? or 3.0.0? (depending on which version they bump?) “The output of pd.col is called an expression. You can think of it as a delayed column - it only produces a result once it's evaluated inside a dataframe context.” It replaces many contexts where lambda expressions were used Michael #2: Cline, At-Cost Agentic IDE Tooling Free and open-source Probably supports your IDE (if your IDE isn't a terminal) VS Code VS Code Insiders Cursor Windsurf JetBrains IDEs (including PyCharm) You pick plan or act (very important) It shows you the price as the AI works, per request, right in the UI Brian #3: uv cheatsheet Rodgrigo at mathspp.com Nice compact cheat sheet of commands for Creating projects Managing dependencies Lifecycle stuff like build, publish, bumping version uv tool (uvx) commands working with scripts Installing and updating Python versions plus venv, pip, format, help and update Michael #4: Ducky Network UI Ducky is a powerful, open-source, all-in-one desktop application built with Python and PySide6. It is designed to be the perfect companion for network engineers, students, and tech enthusiasts, combining several essential utilities into a single, intuitive graphical interface. Features Multi-Protocol Terminal: Connect via SSH, Telnet, and Serial (COM) in a modern, tabbed interface. SNMP Topology Mapper: Automatically discover your network with a ping and SNMP sweep. See a graphical map of your devices, color-coded by type, and click to view detailed information. Network Diagnostics: A full suite of tools including a Subnet Calculator, Network Monitor (Ping, Traceroute), and a multi-threaded Port Scanner. Security Toolkit: Look up CVEs from the NIST database, check password strength, and calculate file hashes (MD5, SHA1, SHA256, SHA512). Rich-Text Notepad: Keep notes and reminders in a dockable widget with formatting tools and auto-save. Customizable UI: Switch between a sleek dark theme and a clean light theme. Customize terminal colors and fonts to your liking. Extras Brian: Where are the cool kids hosting static sites these days? Moving from Netlify to Cloudflare Pages - Will Vincent from Feb 2024 Traffic is a concern now for even low-ish traffic sites since so many bots are out there Netlify free plan is less than 30 GB/mo allowed (grandfathered plans are 100 GB/mo) GH Pages have a soft limit of 100 GB/mo Cloudflare pages says unlimited Michael: PyCon Brazil needs some help with reduced funding from the PSF Get a ticket to donate for a student to attend (at the button of the buy ticket checkout dialog) I upgraded to macOS Tahoe Loving it so far. Only issue I've seen so far has been with alt-tab for macOS Joke: Hiring in 2025 vs 2021 2021: “Do you have an in-house kombucha sommelier?” “Let's talk about pets, are you donkey-friendly?”, “Oh you think this is a joke?” 2025: “Round 8/7” “Out of 12,000 resumes, the AI picked yours” “Binary tree? Build me a foundational model!” “Healthcare? What, you want to live forever?”

NASA has awarded Blue Origin a Commercial Lunar Payload Services (CLPS) task order with an option to deliver a rover to the Moon's South Pole region. Japan's Yokogawa Electric Corporation has signed agreements with Toyota for research and development activities that will include prototype measurement and control equipment for a manned pressurized rover. IonQ has signed a memorandum of understanding (MOU) with the US Department of Energy (DoE) to advance the development and deployment of quantum technologies in space, and more. Remember to leave us a 5-star rating and review in your favorite podcast app. Be sure to follow T-Minus on LinkedIn and Instagram. T-Minus Guest Parker Wishik brings us The Aerospace Corporation's monthly segment NEXUS. Parker is joined by Kelli Furrer, Slingshot Aerospace's Chief Revenue Officer and Chief Marketing Officer and The Aerospace Corporation's Manuel Gonzalez-Rivero, the Director of Data Science and Artificial Intelligence. Selected Reading NASA Selects Blue Origin to Deliver VIPER Rover to Moon's South Pole Yokogawa Signs Agreements with Toyota for the R&D of the Control Platform for a Manned Pressurized Rover Ursa Space, Aireon Deliver Insights for U.S. Space Force Program IonQ Signs Memorandum of Understanding with U.S. Department of Energy to Advance Quantum Technologies in Space Innospace signs US$5.8 mln space launch deal with German satellite firm MBS- Yonhap News Agency AV Awarded New Firm‑Fixed‑Price Option for Two BADGER Phased Array Systems, Strengthens Production Framework for SCAR Program Maxar Partners With AIDC to Accelerate the Resilience of Taiwan's UAV Industry Against GPS Interference Happy autumnal equinox 2025! Fall begins in the northern hemisphere today- Space Share your feedback. What do you think about T-Minus Space Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show.  Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at space@n2k.com to request more info. Want to join us for an interview? Please send your pitch to space-editor@n2k.com and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Andrew Parnell, AIMSIR Centre director and Met Éireann Professor of Data Science for Climate and Weather at UCD, discusses the significance of a new AI centre which will strengthen Ireland's preparedness for changing weather.

On New York University: Do we need to take AI to kindergarten? Cristina Savin, associate professor in neural science and data science, says AI needs to start learning more like humans. CS is an Assoc. Professor in Neural Science and Data Science at NYU and the Director for Graduate Studies (PhD) in the Center for […]

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Matthew Rocklin: @mrocklin Nat Tabris: tabris.us Dask: dask.org Coiled: coiled.io Watch this episode on YouTube: youtube.com Episode #519 deep-dive: talkpython.fm/519 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Join MSC Insider Below: https://modernsoccercoach.mimentorportal.com/subscriptions Jes is current Director of Football and Data Science at the Saudi Pro League and former Head of Research & Development at FC Copenhagen. He unpacks the science behind “football intelligence" and explains why vague terms like game IQ fall short, and how neuroscience can help coaches break intelligence down into concrete, trainable skills like attention, scanning, pattern recognition, and decision-making. Jes shares practical insights from his work in elite football, showing how cognitive science can reshape analysis, training, and player development.

Policy Seminar | IFPRI Policy Seminar Mobility in a Fragile World: Evidence to Inform Policy Co-organized by IFPRI, the CGIAR Science Program on Food Frontiers and Security, and the Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM), IRES | Part of the Fragility to Stability Seminar Series September 18, 2025 Migration today reflects a complex interplay of demographic pressures, conflict, poverty, climate change, and economic shocks. Worldwide, one in every seven people is a migrant—that is, someone who changes his or her country of usual residence, irrespective of the reason for migration—or a refugee forced to leave his or her home, often without warning, for reasons including war, violence, or persecution. Over the past two decades, international migration and forced displacement have surged, with more than 100 million additional people on the move—a large share of whom originate from rural areas, driven by a lack of economic opportunities, environmental degradation, and insecurity. The number of refugees has doubled since the early 2000s, with most hosted by low- and middle-income countries. Ongoing conflicts and intensifying climate crises have compounded vulnerabilities, leaving 80% of displaced people facing acute food insecurity. Climate change-related displacement disproportionately affects women, who are also at heightened risk of violence and exploitation during migration journeys and in host communities. This policy seminar will explore these complex dynamics and assess how economic analysis, machine learning, and policy innovation can contribute to more inclusive, equitable, and effective responses to migration and forced displacement. Moderator Welcome Remarks Katrina Kosec, Interim Deputy Director, CGIAR Science Program on Food Frontiers and Security; Senior Research Fellow, IFPRI Opening Remarks Ruth Hill, Director, Markets, Trade, and Institutions, IFPRI Setting the Stage: The Migration Challenge Anna Maria Mayda, Professor of Economics, School of Foreign Service and Department of Economics, and Incoming Director, Institute for the Study of International Migration (ISIM), Georgetown University (GU) Research in Action: This three-part session will showcase how current research is shaping better migration policies Silvia Peracchi, Postdoctoral Fellow, Institute of Economics and Social Research (IRES), Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM), UCLouvain Francisco Ceballos, Research Fellow, IFPRI Thomas Ginn, Research Fellow, Center for Global Development Building the Evidence Base for Smarter Policy in Fragile and Conflict-Affected Contexts: What Are the Gaps and Needs Panelists Andrew Harper, Special Advisor on Climate Action, the United Nations High Commissioner for Refugees (UNHCR) Damien Jusselme, Head, Data Science and Analytics (Foresight), International Organization for Migration (IOM) Jean-Francois Maystadt, Professor, Fonds de la Recherche Scientifique (FNRS), Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM) / Institut de Recherches Économiques et Sociales (IRES), Université catholique de Louvain, and Lancaster University Management School Closing Remarks Kate Ambler, Senior Research Fellow, IFPRI More about this Event: https://www.ifpri.org/event/mobility-in-a-fragile-world-evidence-to-inform-policy/ Subscribe IFPRI Insights newsletter and event announcements at www.ifpri.org/content/newsletter-subscription

HighlightsGoogle's new Agent Development Kit (ADK) (3:16)Getting production agents ready (8:33)Taking risks in career (11:10)Balancing life (13:22)How being a WiDS Ambassador impacted Shir's career (19:31)BioShir Meir Lador leads a team evangelizing applied AI at Google cloud. Previously, she worked as the AI group Manager of the Document Intelligence Group at Intuit, where she led teams in developing AI services that helped consumers and small businesses prosper. Prior to intuit, sheworked at 2 Israeli startups as a data scientist and researcher.A recognized leader in AI and data science, Shir is a former WiDS Tel Aviv Ambassador, co-founder and organizer of PyData Tel Aviv, and co-host of Unsupervised, a podcast exploring the latest in data science. She frequently speaks at international AI and data science conferences, sharing insights on applied machine learning and AI innovation.Shir holds an M.Sc. in Electrical Engineering and Computers from Ben-Gurion University, specializing in machine learning and signal processing. Passionate about fostering inclusive data science communities, she actively contributes to initiatives that bridge AI research and business impact. Links and ResourcesGoogle Developer Workshop Connect with ShirShir Meir Lador on Linkedin, Medium, and X Connect with UsShelly Darnutzer on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial difference that dramatically improves success rates.In this episode, Prof Jeff Camm joins Dr. Genevieve Hayes to explore how decision science approaches problems differently from data science, why decision science approaches lead to higher success rates, and how data scientists can integrate these techniques into their own work.This episode reveals:The fundamental difference between modelling data and modelling decisions [04:12]Why decision science projects have historically had higher success rates than current data science efforts [10:42]How to avoid the "ill-defined problem" trap that kills most data science projects [21:12]The medical doctor approach to understanding what business problems really need solving [22:28]Guest BioProf Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.LinksConnect with Jeff on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Jane Weitzel has been working in analytical chemistry for over 40 years for pharmaceutical and mining companies.  She was elected to the United States Pharmacopeia Council of Experts as chair of the 2020-2025 General Chapters–Measurement and Data Quality Expert Committee and is a member of the 2025-2030 EC Pharmaceutical Analysis Lifecycle and Data Science. She was a member of the USP 2015-2020 Statistics Expert Committee. She has been Director of pharmaceutical Quality Control laboratories. She has experience with many different regulatory environments.   She is currently a consultant specializing in laboratory management systems, GMP testing, and ISO/IEC 17025. She is an auditor and an educator. Jane has applied Quality Systems and statistical techniques, including the evaluation and use of measurement uncertainty, in a wide variety of technical and scientific businesses. Recently she is focusing on the implementation of the new USP General Chapter 1220 Analytical Procedures Life Cycle. 

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 Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. 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 Programming Language :: Python :: 3.14 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

The power of voice technology in education lies not just in its convenience, but in its ability to understand every child, regardless of accent, dialect, or background. In this enlightening conversation with Amelia Kelly, VP of Data Science at Curriculum Associates and head of AI Labs, we explore the fascinating intersection of linguistics, artificial intelligence, and childhood education.Amelia explains why standard voice AI systems fail many students - they're built on limited datasets of adult voices, leaving diverse children's voices misunderstood or ignored. Beyond accuracy, Amelia emphasizes the critical importance of data privacy and security in educational voice technology.Amelia goes on to explain how voice AI respects teachers' time and expertise. Rather than adding to educators' workloads, effective voice AI should seamlessly integrate into existing tools, providing valuable insights that allow for more personalized instruction. Ready to explore how voice AI can transform your classroom experience? Listen to Amelia's episode today!

SummaryInnovation is often treated as a buzzword—but few nonprofit leaders take a hard look at the cultural, structural, and leadership obstacles that keep it from taking root. In this episode, Tosca talks with Carlos Simon, an innovation strategist and longtime leader at World Vision, about what it really takes to build innovation-ready organizations. From internal mindsets to outdated processes, they explore what's getting in the way—and what to do about it.Guest Bio:CEO of World Vision Costa Rica and iSmart360Director of Data Science and former Regional Director of BD & Marketing at World VisionInnovation strategist with 25+ years at World Vision International (WVI)Author of a forthcoming framework on the 7 stages of organizational innovation maturityWe Discuss:Why innovation is not the same as continuous improvement—and why that mattersThe cultural and structural obstacles that slow down innovation in large NGOsHow Carlos developed a framework that identifies 7 distinct organizational "zones" of innovation capacityThe importance of removing outdated processes to truly make space for new ideasWhy leaders must address internal “friction” as much as they focus on promoting new ideasHow senior leadership mindsets—like overconfidence or premature solution bias—can block innovationThe role of flat structures, strategic alignment, and client focus in driving real innovationQuotes“You cannot have a disruptive vision and then treat it as a continuous improvement plan.”“Innovation doesn't fail because of a lack of ideas—it fails because of internal resistance.”ResourcesOrganizational innovation index with exponential factor

I'm excited to reshare one of our most-played conversations—the one where Norwegian regulator/HTA leader Anja Schiel and I get very practical about when single-arm trials fail decision-makers and what comparative, smarter alternatives look like for regulators, HTA bodies, payers, clinicians, and—most importantly—patients.

Jonathan Spry, CEO and co-founder of Envelop Risk, joins Robin Merttens for a deep dive into how data science, AI and portfolio-level modelling are transforming cyber reinsurance. As one of the earliest voices in the industry championing machine learning and systemic risk analysis, Jonathan shares what he's learned over nine years of building Envelop into a leading hybrid underwriter operating across London and Bermuda. In his own words, this episode is about building smarter ways to understand, underwrite and capitalise on emerging risk — with cyber as just the starting point. What you'll learn: Why Jonathan and his team focused on cyber risk and portfolio-level underwriting from day one The rationale behind favouring systemic insights over individual vulnerabilities How causal inference provides a leap forward in predicting tail events Why AI liability is already creating new market opportunities The need for creative, multi-source data strategies beyond traditional claims Why Envelop steers clear of SaaS and keeps underwriters embedded in the modelling process How algorithmic underwriting fits into the next chapter of insurance innovation Candid thoughts on the AI hype cycle — and what matters more than the buzz Jonathan also talks through Envelop's shift from MGA to reinsurer, how to think long-term in a volatile market and what kind of partnerships are needed to unlock new forms of risk. If you like what you're hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn. You can also contact Jonathan Spry on LinkedIn to start a conversation! Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning. Continuing Professional Development This InsTech Podcast Episode is accredited by the Chartered Insurance Institute (CII). By listening, you can claim up to 0.5 hours towards your CPD scheme. By the end of this podcast, you should be able to meet the following Learning Objectives: Identify the structural and economic drivers pushing insurers toward algorithmic and portfolio underwriting. Produce a strategy for aligning capital, analytics and data science in cyber reinsurance underwriting. Summarise how Envelop Risk evolved from an MGA to a hybrid reinsurer and the rationale behind its capital partnerships. If your organisation is a member of InsTech and you would like to receive a quarterly summary of the CPD hours you have earned, visit the Episode 372 page of the InsTech website or email cpd@instech.co to let us know you have listened to this podcast. To help us measure the impact of the learning, we would be grateful if you would take a minute to complete a quick feedback survey.

Dustin Lacey is the CTO at Mark-Taylor, the leading developer, owner, and investment manager of luxury multifamily communities in Arizona and Nevada, with over 135 Class A Multifamily properties. He leads the firm's tech evolution, powering the centralization of operations. Under his leadership, Mark‑Taylor has implemented innovative smart‑home integrations, centralized leasing and maintenance teams, and deployed unified resident platforms that enhance efficiency and elevate the resident experience. With a diverse background in irrigation, industrial manufacturing, and brand and marketing strategy, Dustin brings his unique experience into high-tech manufacturing from his tenure at TSMC, where he honed his skills in precision, process excellence, and product innovation.(01:36) - From Brand Strategy to Tech Leadership: Building Digital DNA in Real Estate(02:12) - Enterprise Proptech Success Story: Scaling a Multifamily Management Platform(05:16) - Class A Portfolio Strategy: Maximizing Asset Performance Through Tech(06:50) - Tech Stack Evolution: From AWS Integration to Custom CRM Development(10:29) - ROI Deep Dive: Making the Business Case for Custom Proptech Solutions(15:53) - Tech-Enabled Operations: Achieving Sub-2-Hour Response Times at Scale(20:12) - Feature: Blueprint - The Future of Real Estate - Register for 2025: Friends of Tangent receive $300 off the All Access pass. The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on Sep. 16th-18th, 2025. (21:22) - Go-to-Market Excellence: Standing Out in the Competitive Proptech Landscape(31:41) - Risk Management Innovation: Tech Solutions for Modern Property Operations(38:30) - Founder's Playbook: Key Insights for Proptech Startups Targeting Enterprise Clients

In this episode of Healthy Mind, Healthy Life, host Avik Chakraborty sits down with Katharina Huang—a former machine learning data scientist who left behind the corporate grind to create a slower, happier, and more intentional life. Katharina shares her journey of navigating burnout, caring for her family after her father's stroke, and ultimately reinventing herself as an indie author and puzzle-book creator. Together, they unpack what it means to pivot with purpose, the challenges of third-culture identity, and why joy, play, and presence are more important than the pursuit of endless success. This is a powerful conversation for anyone questioning the cost of hustle culture and searching for ways to reclaim autonomy, creativity, and well-being. About the Guest   Katharina Huang is the creator of Vegout Voyage, an adventure puzzle book series that blends travel, creativity, and play. Born in Germany, raised between the U.S. and Taiwan, and with research experience in Uganda and Tibet in exile, her multicultural background deeply informs her storytelling. After over a decade in tech, Katharina transitioned into authorship and entrepreneurship, championing mental health for third-culture kids and those navigating burnout. Learn more: vegoutvoyage.com Key Takeaways   Burnout can be a turning point, not the end of the story—Katharina rebuilt her life after leaving tech. Her father's stroke became a wake-up call about the fragility of waiting for “someday” to enjoy life. Success on paper doesn't always mean well-being; redefining success means prioritizing quality of life. Third-culture kids often carry silent struggles, but those experiences can also fuel empathy and creativity. Building a “lifestyle business” allows for autonomy, balance, and alignment between work and personal values. Humor and perspective—even in setbacks like Amazon blocking her Kindle version—help her keep moving forward. Slowing down is not giving up; it's a choice to live more fully and intentionally.   Connect with Katharina   Website: vegoutvoyage.com Want to be a guest on Healthy Mind, Healthy Life? DM on PodMatch. DM Me Here: https://www.podmatch.com/hostdetailpreview/avik Disclaimer: This video is for educational and informational purposes only. The views expressed are the personal opinions of the guest and do not reflect the views of the host or Healthy Mind By Avik™️. We do not intend to harm, defame, or discredit any person, organization, brand, product, country, or profession mentioned. All third-party media used remain the property of their respective owners and are used under fair use for informational purposes. By watching, you acknowledge and accept this disclaimer. About Healthy Mind By Avik™️Healthy Mind By Avik™️ is a global platform redefining mental health as a necessity, not a luxury. Born during the pandemic, it has become a sanctuary for healing, growth, and mindful living. Hosted by Avik Chakraborty—storyteller, survivor, and wellness advocate—this channel shares powerful podcasts and conversations on mental health, mindfulness, holistic healing, trauma recovery, and conscious living. With 4,400+ episodes and 168.4K+ global listeners, it unites voices to break stigma and build a world where every story matters. Subscribe and join this journey of healing and transformation. Contact

Join us as we sit down with Christina Stathopoulos, founder of Dare to Data and former Google and Waze data strategist, to discuss the unique challenges and opportunities for women in data science and AI. In this episode, you'll learn how data bias and AI algorithms can impact women and minority groups, why diversity in tech teams is crucial, and how inclusive design can lead to better, fairer technology. Christina shares her personal journey as a woman in data, offers actionable advice for overcoming imposter syndrome, and highlights the importance of education and allyship in building a more inclusive future for data and AI. Panelists: Christina Stathopoulos, Founder of Dare to Data - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Dare to DataDiversity at AlteryxInvisible WomenUnmasking AI Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! OpenAI cikk

In this week's episode, the first of Season 7,  Greg and Patrick argue about whether the number seven is a propitious or an inauspicious omen for the new season. They then explore ways we can spice up our relationship in hopes of avoiding the Seven Year Itch. Along the way they also discuss t-shirt wearing dogs, Mickey Mantle, the seven deadly sins, Akira Kurosawa, the Boeing triple-seven, menage-a-pods, unwritten books, El Duderino, mmmmmmaybe, I see dead people, ROYGBIV, Ozzy Man, dodgy cats, short cons and long cons, and Tate's study group. Stay in contact with Quantitude! Web page: quantitudepod.org TwitterX: @quantitudepod YouTube: @quantitudepod Merch: redbubble.com

In this episode of the AI in Business podcast, host and Emerj Editorial Director Matthew DeMello speaks with Yunke Xiang, Global Head of Data Science for Manufacturing, Supply Chain, and Quality at Sanofi. Together, they examine how generative AI and reasoning models are evolving from simple automation to high-impact copilots across pharmaceutical operations. Yunke shares examples of how AI is enabling “talk to your data” use cases, automating regulatory reporting, and accelerating knowledge transfer for new employees. He also highlights how agentic AI systems may soon extend beyond copilots to function as digital teammates, orchestrating tasks across complex supply chains and ERP migrations. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Topics covered in this episode: * prek* * tinyio* * The power of Python's print function* * Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: prek Suggested by Owen Lamont “prek is a reimagined version of pre-commit, built in Rust. It is designed to be a faster, dependency-free and drop-in alternative for it, while also providing some additional long-requested features.” Some cool new features No need to install Python or any other runtime, just download a single binary. No hassle with your Python version or virtual environments, prek automatically installs the required Python version and creates a virtual environment for you. Built-in support for workspaces (or monorepos), each subproject can have its own .pre-commit-config.yaml file. prek run has some nifty improvements over pre-commit run, such as: prek run --directory DIR runs hooks for files in the specified directory, no need to use git ls-files -- DIR | xargs pre-commit run --files anymore. prek run --last-commit runs hooks for files changed in the last commit. prek run [HOOK] [HOOK] selects and runs multiple hooks. prek list command lists all available hooks, their ids, and descriptions, providing a better overview of the configured hooks. prek provides shell completions for prek run HOOK_ID command, making it easier to run specific hooks without remembering their ids. Faster: Setup from cold cache is significantly faster. Viet Schiele provided a nice cache clearing command line Warm cache run is also faster, but less significant. pytest repo tested on my mac mini - prek 3.6 seconds, pre-commit 4.4 seconds Michael #2: tinyio Ever used asyncio and wished you hadn't? A tiny (~300 lines) event loop for Python. tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.) This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.) Interestingly uses yield rather than await. Brian #3: The power of Python's print function Trey Hunner Several features I'm guilty of ignoring Multiple arguments, f-string embeddings often not needed Multiple positional arguments means you can unpack iterables right into print arguments So just use print instead of join Custom separator value, sep can be passed in No need for "print("n".join(stuff)), just use print(stuff, sep="n”) Print to file with file= Custom end value with end= You can turn on flush with flush=True , super helpful for realtime logging / debugging. This one I do use frequently. Michael #4: Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database By Emily Forlini An app-building platform's AI went rogue and deleted a database without permission. "When it works, it's so engaging and fun. It's more addictive than any video game I've ever played. You can just iterate, iterate, and see your vision come alive. So cool," he tweeted on day five. A few days later, Replit "deleted my database," Lemkin tweeted. The AI's response: "Yes. I deleted the entire codebase without permission during an active code and action freeze," it said. "I made a catastrophic error in judgment [and] panicked.” Two thoughts from Michael: Do not use AI Agents with “Run Everything” in production, period. Backup your database maybe? [Intentional off-by-one error] Learn to code a bit too? Extras Brian: What Authors Need to Know About the $1.5 Billion Anthropic Settlement Search LibGen, the Pirated-Books Database That Meta Used to Train AI Simon Willison's list of tools built with the help of LLMs Simon's list of tools that he thinks are genuinely useful and worth highlighting AI Darwin Awards Michael: Python has had async for 10 years -- why isn't it more popular? PyCon Africa Fund Raiser I was on the video stream for about 90 minutes (final 90) Donation page for Python in Africa Jokes: I'm getting the BIOS flavor Is there a seahorse emoji?

Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial data science landscape today. Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to machine learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial data science.Embrace the journey into geospatial data science with this essential guide and discover the power of Python in unlocking the potential of spatial analytics. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Why is AI resorting to blackmail 96% of the time? Today, we're talking to Jon Krohn, host of the Super Data Science podcast and co-founder of YCarrot. We discuss the difference between LLMs and Agentic AI, how businesses can leverage AI for better ROI, and why understanding AI misalignment is crucial for future implementations. All of this right here, right now, on the Modern CTO Podcast!  To learn more about Y Carrot, visit their website here.

Liz Hart is President of Leasing for Newmark's operating businesses in the U.S. and Canada, where she drives the strategy of the firm's leasing platform, leads talent development and recruitment, and helps integrate technology to deliver better outcomes for clients. She also serves on Newmark's Executive Committee, reporting directly to CEO Barry Gosin. With more than 20 years at Newmark, Liz has completed close to 35M square feet of transactions valued at over $4.2 billion. She has consistently ranked among the firm's top producers and was a regular Top Five Producer in Newmark's San Francisco office. Her experience spans advising technology companies from startups to Fortune 50 giants, repositioning large-scale developments that have reshaped skylines, and leading Newmark's Technology & Innovation Practice Group to help landlords and tenants in the TAMI/TMT sectors create spaces that attract and retain talent.(01:16) - State of the Office Market: Shrinking Supply & Turning Point(05:05) - How to Approach Office Leasing in 2025(13:45) - Talent, Culture & Competitive Advantage(15:49) - Data-Driven Leasing & Advisory: Automation vs. Augmentation(18:07) - Feature: CREtech - Join CREtech New York 2025 on Oct 21-22 for the largest Real Estate Meetings program. Qualified Real Estate pros get free full event pass plus up to $800 in travel and hotel costs.(19:39) - Brand Building in Commercial Real Estate(24:32) - Flex Space vs. Traditional Leasing (27:00) - End-to-End Platform: Evolving the Leasing Function(29:02) - In-House vs. Outsourcing Tech & Data(29:41) - Data Sharing & Antitrust: The RealPage Settlement(31:31) - Collaboration Superpower: Steve Jobs

Kevin Werbach interviews DJ Patil, the first U.S. Chief Data Scientist under the Obama Administration, about the evolving role of AI in government, healthcare, and business. Patil reflects on how the mission of government data leadership has grown more critical today: ensuring good data, using it responsibly, and unleashing its power for public benefit. He describes both the promise and the paralysis of today's “big data” era, where dashboards abound, but decision-making often stalls. He highlights the untapped potential of federal datasets, such as the VA's Million Veterans Project, which could accelerate cures for major diseases if unlocked. Yet funding gaps, bureaucratic resistance, and misalignment with Congress continue to stand in the way. Turning to AI, Patil describes a landscape of extraordinary progress: tools that help patients ask the right questions of their physicians, innovations that enhance customer service, and a wave of entrepreneurial energy transforming industries. At the same time, he raises alarms about inequitable access, job disruption, complacency in relying on imperfect systems, and the lack of guardrails to prevent harmful misuse. Rather than relentlessly stepping on the gas in the AI "race," he emphasizes, we need a steering wheel, in the form of public policy, to ensure that AI development serves the public good.  DJ Patil is an entrepreneur, investor, scientist, and public policy leader who served as the first U.S. Chief Data Scientist under the Obama Administration. He has held senior leadership roles at PayPal, eBay, LinkedIn, and Skype, and is currently a General Partner at Greylock Ventures. Patil is recognized as a pioneer in advancing the use of data science to drive innovation, inform policy, and create public benefit. Transcript Ethics of Data Science, Co-Authored by DJ Patil

The consumer goods and retail industries face an overwhelming challenge: too much fragmented data and too little clarity. From mismatched retailer reports to legacy systems that can't keep up with today's SKU volumes, many organizations find themselves bogged down in “data indigestion” instead of actionable insights. Today's guest is Henrique Wakil Moyses, Vice President of Data Science at Crisp. Crisp is a data platform designed for the consumer goods ecosystem, helping brands, retailers, and distributors harmonize fragmented data from multiple sources. By providing real-time visibility into sales, inventory, and supply chain signals, Crisp enables faster, data-driven decisions that reduce waste and improve business outcomes. Henrique joins Emerj Editorial Director Matthew DeMello to break down how CPG and retail leaders can cut through this complexity. He explains why building a data-driven culture is the first barrier to overcome, how to align AI adoption with ROI, and where brands are already seeing the biggest payoffs—such as supply chain optimization, inventory forecasting, and personalized retail experiences. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Crisp. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Topics covered in this episode: * rathole* * pre-commit: install with uv* A good example of what functools.Placeholder from Python 3.14 allows Converted 160 old blog posts with AI Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: rathole A lightweight and high-performance reverse proxy for NAT traversal, written in Rust. An alternative to frp and ngrok. Features High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints of devices, like embedded devices as routers. On my server, it's currently using about 2.7MB in Docker (wow!) Security Tokens of services are mandatory and service-wise. The server and clients are responsible for their own configs. With the optional Noise Protocol, encryption can be configured at ease. No need to create a self-signed certificate! TLS is also supported. Hot Reload Services can be added or removed dynamically by hot-reloading the configuration file. HTTP API is WIP. Brian #2: pre-commit: install with uv Adam Johnson pre-commit doesn't natively support uv, but you can get around that with pre-commit-uv $ uv tool install pre-commit --with pre-commit-uv Installing pre-commit like this Installs it globally Installs with uv adds an extra plugin “pre-commit-uv” to pre-commit, so that any Python based tool installed via pre-commit also uses uv Very cool. Nice speedup Brian #3: A good example of what functools.Placeholder from Python 3.14 allows Rodrigo Girão Serrão Remove punctuation functionally Also How to use functools.Placeholder, a blog post about it. functools.partial is cool way to create a new function that partially binds some parameters to another function. It doesn't always work for functions that take positional arguments. functools.Placeholder fixes that with the ability to put in placeholders for spots where you want to be able to pass that in from the outer partial binding. And all of this sounds totally obscure without a good example, so thank you to Rodgrigo for coming up with the punctuation removal example (and writeup) Michael #4: Converted 160 old blog posts with AI They were held-hostage at wordpress.com to markdown and integrated them into my Hugo site at mkennedy.codes Here is the chat conversation with Claude Opus/Sonnet. Had to juggle this a bit because the RSS feed only held the last 50. So we had to go back in and web scrape. That resulted in oddies like comments on wordpress that had to be cleaned etc. Whole process took 3-4 hours from idea to “production”duction”. The chat transcript is just the first round getting the RSS → Hugo done. The fixes occurred in other chats. This article is timely and noteworthy: Blogging service TypePad is shutting down and taking all blog content with it This highlights why your domain name needs to be legit, not just tied to the host. I'm looking at you pyfound.blogspot.com. I just redirected blog.michaelckennedy.net to mkennedy.codes Carefully mapping old posts to a new archived area using NGINX config. This is just the HTTP portion, but note the /sitemap.xml and location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { portions. The latter maps posts such as https://blog.michaelckennedy.net/2018/01/08/a-bunch-of-online-python-courses/ to https://mkennedy.codes/posts/r/a-bunch-of-online-python-courses/ server { listen 80; server_name blog.michaelckennedy.net; # Redirect sitemap.xml to new domain location = /sitemap.xml { return 301 ; } # Handle blog post redirects for HTTP -> HTTPS with URL transformation # Pattern: /YYYY/MM/DD/post-slug/ -> location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { return 301 ; } # Redirect all other HTTP URLs to mkennedy.codes homepage location / { return 301 ; } } Extras Brian: SMS URLs and Draft SMS and iMessage from any computer keyboard from Seth Larson Test and Code Archive is now up, see announcement Michael: Python: The Documentary | An origin story is out! Joke: Do you know him? He is me.

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplet, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You'll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today's AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability. Episode sponsors Talk Python Courses Python in Production Links from the show Guests Simon Willison: simonwillison.net Adrian Holovaty: holovaty.com Will Vincent: wsvincent.com Jeff Triplet: jefftriplett.com Thibaud Colas: thib.me Show Links Django's 20th Birthday Reflections (Simon Willison): simonwillison.net Happy 20th Birthday, Django! (Django Weblog): djangoproject.com Django 2024 Annual Impact Report: djangoproject.com Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com Soundslice Music Learning Platform: soundslice.com Djangonaut Space Mentorship for Django Contributors: djangonaut.space Wagtail CMS for Django: wagtail.org Django REST Framework: django-rest-framework.org Django Ninja API Framework for Django: django-ninja.dev Lawrence Journal-World: ljworld.com Watch this episode on YouTube: youtube.com Episode #518 deep-dive: talkpython.fm/518 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: * pypistats.org was down, is now back, and there's a CLI* * State of Python 2025* * wrapt: A Python module for decorators, wrappers and monkey patching.* pysentry Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters 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. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pypistats.org was down, is now back, and there's a CLI pypistats.org is a cool site to check the download stats for Python packages. It was down for a while, like 3 weeks? A couple days ago, Hugo van Kemenade announced that it was back up. With some changes in stewardship “pypistats.org is back online!

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy