Podcasts about Data science

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns 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: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b

Your brain doesn't just run on chemistry. It runs on time.Every day your body broadcasts signals through sleep timing, light exposure, body temperature, hormones, and circadian rhythms—yet most people ignore these patterns while chasing pills, supplements, and productivity hacks.In this episode of the Crackin' Backs Podcast, we sit down with Benjamin Smarr to explore a new frontier of human biology: how time-series biology and wearable data may unlock powerful, non-drug ways to improve brain health, mood, and performance.Dr. Smarr's research looks at the body not as a snapshot—but as a movie, where continuous biological signals reveal patterns that traditional medicine often misses.In this episode, we explore:Why “normal” is a misleading concept in human biologyHow circadian rhythms and sleep timing shape mental performance and moodWhat wearable devices can reveal about your hidden biological patternsWhy body temperature rhythms may be linked to depression and mental healthThe overlooked role of light timing, temperature regulation, and daily rhythmsHow “social time” vs biological time affects cognition, sleep, and productivityWhere self-tracking and wearable data help—and where they can backfireWhether the future of medicine could include “time prescriptions” instead of drugsThis conversation reframes how we think about health, performance, and mental well-being—not as something fixed, but as something that shifts with how we live in time.If you're interested in sleep science, circadian biology, wearables, mental performance, precision health, and the future of non-drug brain optimization, this episode will challenge how you think about your own body.About Dr. Benjamin SmarrBenjamin Smarr is an Associate Professor of Bioengineering and Data Science at the University of California, San Diego (UCSD). He earned his PhD in Neurobiology from the University of Washington, and later served as an NIH fellow at UC Berkeley in Psychology.His research focuses on biological rhythms, neuroendocrinology, wearable health data, and HealthAI, developing technologies that improve precision medicine while reducing algorithmic bias for diverse populations.The Smarr Lab works at the intersection of women's health, aging, circadian biology, and data science, aiming to accelerate the future of personalized healthcare and population-level health insights.Dr. Smarr's work and insights have been featured in global media outlets including NPR, BBC, Forbes, and many others. He is also a strong advocate for science communication and community empowerment in discovery and health innovation.Learn more about his research and work HERE:  We are two sports chiropractors, seeking knowledge from some of the best resources in the world of health. From our perspective, health is more than just “Crackin Backs” but a deep dive into physical, mental, and nutritional well-being philosophies. Join us as we talk to some of the greatest minds and discover some of the most incredible gems you can use to maintain a higher level of health. Crackin Backs Podcast

Monorepos -- you've heard the talks, you've read the blog posts, maybe you've seen a few tantalizing glimpses into how Google or Meta organize their massive codebases. But it's often in the abstract and behind closed doors. What if you could crack open a real, production monorepo, one with over a million lines of Python and over 100 of sub-packages, and actually see how it's built, step by step, using modern tools and standards? That's exactly what Apache Airflow gives us. On this episode, I sit down with Jarek Potiuk and Amogh Desai, two of Airflow's top contributors, to go inside one of the largest open-source Python monorepos in the world and learn how they manage it with uv, pyproject.toml, and the latest packaging standards, so you can apply those same patterns to your own projects. Episode sponsors Agentic AI Course Python in Production Talk Python Courses Links from the show Guests Amogh Desai: github.com Jarek's GitHub: github.com definition of a monorepo: monorepo.tools airflow: airflow.apache.org Activity: github.com OpenAI: airflowsummit.org Part 1. Pains of big modular Python projects: medium.com Part 2. Modern Python packaging standards and tools for monorepos: medium.com Part 3. Monorepo on steroids - modular prek hooks: medium.com Part 4. Shared “static” libraries in Airflow monorepo: medium.com PEP-440: peps.python.org PEP-517: peps.python.org PEP-518: peps.python.org PEP-566: peps.python.org PEP-561: peps.python.org PEP-660: peps.python.org PEP-621: peps.python.org PEP-685: peps.python.org PEP-723: peps.python.org PEP-735: peps.python.org uv: docs.astral.sh uv workspaces: blobs.talkpython.fm prek.j178.dev: prek.j178.dev your presentation at FOSDEM26: fosdem.org Tallyman: github.com Watch this episode on YouTube: youtube.com Episode #540 deep-dive: talkpython.fm/540 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Michael Feldman is the former CEO and Co-Founder of Choice New York Companies, a group of firms providing property management, building staffing, and brokerage services to medium and large residential buildings across New York City. He built the company from zero revenue to roughly $27M in revenue and $5.1M in EBITDA before selling the business to Associa Corp. in 2021. Today, Michael remains active in the real estate industry as a real estate and tech investor and advisor, bringing decades of operational experience building and scaling service platforms in the New York multifamily market and beyond.(01:17) - From Hollywood to New York Real Estate(03:35) - How PM Models Evolved(04:50) - Decision to Exit in 2021(05:59) - Why Few New Entrants & Barriers to Entry(09:49) - AI Use Cases in Property Management(12:00) - Feature: Blueprint: The Future of Real Estate 2026 in Vegas on Sep. 22-24(12:51) - 'War' Stories(16:05) - M&A & Industry Consolidation(20:51) - Collaboration Superpower: Winston Churchill

In this episode of 50 Shades of Green, we sit down with Cora Wyent, Head of Research & Data Science at Rewiring America, the US' leading electrification nonprofit, to demystify one of the most impactful climate and cost‑saving technologies available today: heat pumps.Cora breaks down:What a heat pump is and how it works as a highly efficient, two‑in‑one heating and cooling systemHow electrification lowers energy bills, boosts indoor air quality, and reduces carbon emissionsWhy 75% of U.S. households could save money by switching to a heat pumpTools Rewiring America offers - including incentives calculators, a personal electrification planner, and a national network of quality contractorsHow renters can electrify their homes, from portable heat pumps to induction hot platesWhy every home is a “unicorn” and what that means for your own electrification journeyWe also dive into policy, workforce readiness, energy equity, and how electrification can help address rising utility costs, especially for vulnerable communities. Whether you're a homeowner, renter, or just energy‑curious, this episode will change how you think about the appliances that shape your daily life.See below for resources mentioned in the episode:Rewiring America Savings CalculatorRewiring America's Latest Homegrown Energy ReportNational Quality Contractor NetworkResources for renters! Hosted on Acast. See acast.com/privacy for more information.

Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly. Key Moments: From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments. Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale. Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making. AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become more sophisticated, Louis discusses how the insurance industry is combating deepfake fraud and AI-driven manipulation. He shares best practices around AI risk management, vendor partnerships, and regulatory collaboration to protect policyholders and maintain trust. Unstructured Data and AI: Why Governance Still Matters (29:28): Louis explores how expanding beyond structured data is reshaping enterprise AI. He explains why incorporating unstructured data into vector databases, graph models, and knowledge systems can significantly improve model accuracy and decision confidence. At the same time, he emphasizes that stronger governance (or observability as he reframes it) is essential as organizations scale AI across regulated industries. Key Quotes: “The more data that you bring to the equation, the more elements that you have in the algorithm, the higher level of accuracy you should be able to reach with your outcomes.” - Louis DiModugno “I've tried to move away from using the word governance as much as I like to use the word observability, because I really think observability shows more aspects of what it is that we are doing with the data.” - Louis DiModugno “The underlying aspect of what ThoughtSpot's delivering to them is our insights that not only give them their answer, but also give them insights that maybe they weren't looking specifically for. One of the big benefits of ThoughtSpot is that it's trying to anticipate what you're asking for.” - Louis DiModugno “We've partnered with ThoughtSpot, which brings AI embedded within its product. By having our data available through the data sets that we populate through the ThoughtSpot products, we've got the opportunity to utilize Spotter and the natural language processing capabilities to interact with the data, so that you can ‘talk with your data'.” - Louis DiModugno Mentions From Months to Weeks: How Verisk Scaled Embedded Analytics Breaking Down Digital Media Fraud for Claims in the AI Era Randy Bean's 2026 AI & Data Leadership Executive Benchmark Survey Guest Bio  Louis DiModugno brings more than 20 years of career experience in data and analytics to his new role. He has held several leadership positions in insurance and (re)insurance at firms including The Hartford and AXA US, where he served as the company's inaugural Chief Data & Analytics Officer. Most recently, DiModugno pioneered the role of Chief Data and Technology Officer for Hartford Steam Boiler. Before entering the private sector, DiModugno served with distinction as a Colonel in the U.S. Air Force and Air Force Reserves. He has held teaching positions at Rensselaer Polytechnic Institute, and he currently serves on the Chief Data Officer Advisory Council for the George Mason University School of Business. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper explore how leaders can cut through the hype around artificial intelligence and focus on real-world impact. The conversation dives into why so many AI initiatives fail, how cognitive biases affect AI adoption, and why finance professionals must learn to ask better questions before deploying models. John Thomas is the Founder and CEO of the Global Institute of Data Science (GIDS), a consulting and professional development organization focused on helping organizations successfully implement AI and data science initiatives. He serves as a Fractional Chief AI Officer for Fortune 500 companies and teaches AI and machine learning courses at Caltech CTME and UC San Diego Extended Studies.In this episode, you will discover:Why 85% of AI projects fail and how to avoid The difference between AI hype and real implementationHow augmented intelligence improves human decision-makingWhy asking the right questions about AI models matters mostHow AI can help with risk analysis and financial decision-makingThis episode highlights that successful AI adoption is not about chasing the latest technology trends but about asking better questions, understanding assumptions, and focusing on real business problems. As AI continues to evolve, finance leaders who combine human judgment with intelligent systems will be best positioned to turn AI from hype into measurable results.Follow John:GIDS: https://gidsco.substack.com LinkedIn: https://www.linkedin.com/in/john-thomas-foxworthy-m-s-data-science-1718073/Future Alpha Event: https://www.alphaevents.com/events-futurealphaglobal/agenda-page/filter?_gl=1*1j0347f*[…]ovIhoCWOYQAvD_BwE&gbraid=0AAAAAomEzrlLzh-epjUJjbfXNnASlChgaFollow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn - https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[03:05] – Meet John Thomas[04:11] – Augmented intelligence explained[11:21] – Global Institute of Data Science[15:18] – Why AI projects fail[21:29] – Understanding AI models[24:45] – AI in portfolio risk analysis[30:16] – Best advice for finance leaders[32:15] – Rapid-fire questions & wrap-up

As organisations and societies become increasingly data-driven, data science has emerged as one of the most influential disciplines shaping decision-making, innovation and economic opportunity. With data scientists, Dr Letetia Addison, Educator, Statistician, and Researcher at the University of the West Indies, St. Augustine Campus, Labour Market Specialist, Tanisha Ash, and Entrepreneur and Agri-tech specialist, Lesley-Ann Jurawan, we discuss, among the state of data science in the caribbean region, including :    *  student enrolment in data-science-related academic programmes and the proportion of women enrolled in those programmes;   *  how we should be thinking of data science within the context of AI, and vice versa;    *  AI adoption in the public and private sectors; and   *  how individuals should be positioning themselves to establish a career in data science.   The episode, show notes and links to some of the things mentioned during the episode can be found on the ICT Pulse Podcast Page (www.ict-pulse.com/category/podcast/)       Enjoyed the episode?  Do rate the show and leave us a review!       Also, connect with us on: Facebook – https://www.facebook.com/ICTPulse/   Instagram –  https://www.instagram.com/ictpulse/   Twitter –  https://twitter.com/ICTPulse   LinkedIn –  https://www.linkedin.com/company/3745954/admin/   Join our mailing list: http://eepurl.com/qnUtj    Music credit: The Last Word (Oui Ma Chérie), by Andy Narrell Podcast editing support:  Mayra Bonilla Lopez   ---------------

Companies are running tons of AI pilots, yet most struggle to move from experimentation to scaled impact. Zero100's latest research reveals why: only 15% of organizations say their data foundation is ready to support AI at scale, while the rest wrestle with fragmentated data and meetings where teams spend more time debating which numbers to trust than making decisions. This week, Kelly Coutinho (VP, Research & Advisory), Julia “JD” Dahlgren ( Director, Data Science), and Justin Gillebo (Senior Director, Research & Advisory) explore what “decision safe data” actually means, why agentic systems raise the bar on clarity, and working from one source of truth. The data cleanliness issue that gave 500,000 free gas and electricity (01:11)Why 85% of companies aren't ready for “decision-safe” AI (02:18)How humans and AI fail differently (07:40)How "good enough" data can lead to misaligned metrics and escalating operational costs (10:04)The human in the loop – from training a replacement to becoming a collaborator (13:57)The “decision agreement” test: How to know when your AI is ready for production (15:49)The ultimate prize? Compressing the time between signal and response (17:37)

Topics covered in this episode: Setting up a Python monorepo with uv workspaces cattrs: Flexible Object Serialization and Validation Learning to program in the AI age VS Code extension for FastAPI and friends 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 11am 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: Setting up a Python monorepo with uv workspaces Dennis Traub The 3 things Give the Root a Distinct Name Use workspace = true for Inter-Package Deps Use importlib Mode for pytest Michael #2: cattrs: Flexible Object Serialization and Validation cattrs is a Swiss Army knife for (un)structuring and validating data in Python. A natural alternative/follow on from DataClass Wizard Converts to ←→ from dictionaries cattrs also focuses on functional composition and not coupling your data model to its serialization and validation rules. When you're handed unstructured data (by your network, file system, database, …), cattrs helps to convert this data into trustworthy structured data. Batteries Included: cattrs comes with pre-configured converters for a number of serialization libraries, including JSON (standard library, orjson, UltraJSON), msgpack, cbor2, bson, PyYAML, tomlkit and msgspec (supports only JSON at this time). Brian #3: Learning to program in the AI age Jose Blanca “I teach a couple of introductory Python courses and I've been thinking about which advice to give to my students, that are studying how to program for the first time. I have collected my ideas in these blog posts” Why learning to program is as useful as ever, even with powerful AI tools available. How to use AI as a tutor rather than a shortcut, and why practice remains the key to real understanding. What the real learning objectives are: mental models, managing complexity, and thinking like a software developer. Michael #4: VS Code extension for FastAPI and friends Enhances the FastAPI development experience in Visual Studio Code Path Operation Explorer: Provides a hierarchical tree view of all FastAPI routes in your application. Search for routes: Use the Command Palette and quickly search for routes by path, method, or name. CodeLens links appear above HTTP client calls like client.get('/items'), letting you jump directly to the matching route definition. Deploy your application directly to FastAPI Cloud from the status bar with zero config. View real-time logs from your FastAPI Cloud deployed applications directly within VS Code. Install from Marketplace. Extras Brian: Guido van Rossum interviews key Python developers from the first 25 years Interview with Brett Cannon Interview with Thomas Wouters Michael: IntelliJ IDEA: The Documentary | An origin story video Cursor Joined the ACP Registry and Is Now Live in Your JetBrains IDE What hyper-personal software looks like I'm doing in-person training again (limited scope): On-site, hands-on AI engineering enablement for software teams with Michael Joke: Saas is dead

Marie-Camille Achard a passé 9 ans chez Uber à San Francisco où elle était Data Science Manager.On aborde :

You're adding type hints to your Python code, your editor is happy, autocomplete is working great. But then you switch tools and suddenly there are red squiggles everywhere. Who decides what a float annotation actually means? Or whether passing None where an int is expected should be an error? It turns out there's a five-person council dedicated to exactly these questions -- and two brand-new Rust-based type checkers are raising the bar. On this episode, I sit down with three members of the Python Typing Council -- Jelle Zijlstra, Rebecca Chen, and Carl Meyer -- to learn how the type system is governed, where the spec and the type checkers agree and disagree, and get the council's official advice on how much typing is just enough. Episode sponsors Sentry Error Monitoring, Code talkpython26 Agentic AI Course Talk Python Courses Links from the show Guests Carl Meyer: github.com Jelle Zijlstra: jellezijlstra.github.io Rebecca Chen: github.com Typing Council: github.com typing.python.org: typing.python.org details here: github.com ty: docs.astral.sh pyrefly: pyrefly.org conformance test suite project: github.com typeshed: github.com Stub files: mypy.readthedocs.io Pydantic: pydantic.dev Beartype: github.com TOAD AI: github.com PEP 747 – Annotating Type Forms: peps.python.org PEP 724 – Stricter Type Guards: peps.python.org Python Typing Repo (PRs and Issues): github.com Watch this episode on YouTube: youtube.com Episode #539 deep-dive: talkpython.fm/539 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Money has always been yours to spend freely. That's about to change. This episode breaks down programmable money, the technology that turns your wallet into a permission system.   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

“The most curious person in multifamily,” Moshe Crane is the VP of Branding and Strategic Initiatives at Sage Ventures, a Maryland-based real estate investment and management firm focused on multifamily and other asset acquisitions and development in the Baltimore-Washington corridor. The company manages more than $1B in assets and over 4,000 apartment units while developing and selling new homes. Moshe also hosts the Curious Wire podcast and writes the Curious Deal newsletter, where he breaks down multifamily deals, careers, and industry trends while exploring how operators build, finance, and scale real estate businesses.(01:45) - Moshe's Real Estate Path(02:32) - Deals Returning to the Market(06:05) - Sage Ventures' Market Focus(07:27) - Defining Great Operators(08:27) - The Third-Party Talent Crunch(10:17) - Systems Beat Stars(12:36) - The Sage Operations Playbook(15:47) - Fraud Screening Tools(19:01) - The Roving Team Mindset(21:05) - Moshe's Role(23:56) - Feature: CREtech New York Oct. 20–21 (25:52) - The Accidental Self-Storage Win(26:41) - Office-to-Storage Conversions(28:21) - A Scrappy Deal Mix(28:58) - Low-Basis Development Opportunities(29:46) - Pitching Flexibility to LPs(30:26) - No Gurus, Just Operators(33:58) - Discipline Over Vertical Integration(36:19) - PropTech Ecosystem Shifts(39:38) - Proptech Adoption(44:42) - Motivation, Curiosity & Faith(49:31) - Collaboration Superpower: Bill Walsh

Donald Beshada, former litigator turned legal tech entrepreneur and CEO of Covalynt shares his journey from big-law to the forefront of using data science in litigation. Specifically, to address systemic fraud in class action settlements. The conversation explores the evolution of claims administration—from the traditional "People Magazine" notice era to the current digital landscape dominated by targeted advertising and sophisticated fraud bots. Donald explains how his company uses data science and identity resolution to bring "scientific rigor" to ensure class action settlements reach legitimate claimants while filtering out fraudulent activity. Key Takeaways: The Shift in Fraud: How class action fraud evolved from "couponing" websites to sophisticated, non-US-based bot attacks. Defensible Clarity: Why "gut feelings" about fraud don't hold up in court, and the necessity of providing an evidentiary framework for disqualifying claims. Data Science vs. Traditional Settlement Administration: A look at how the Apple Antitrust case served as an inflection point, proving that old-school matching methods are no longer sufficient for class certification or ascertainability. The Future of Notice: Moving toward a world where data science can connect retail purchases directly to individuals, potentially eliminating the need for expensive, broad-market advertising.  

No Tecnocast de hoje, a gente disseca o lançamento da linha Galaxy S26 e a nova tela de privacidade da Samsung, uma inovação genuína em um mercado de smartphones que anda meio estagnado.Mas será que uma solução ‘anti-bisbilhoteiro' é o suficiente para levar as pessoas às lojas e convencer o usuário a pagar mais de 11 mil reais em um aparelho? E entre as promessas da IA Agêntica e a volta do Exynos, o que realmente muda na prática para o consumidor brasileiro?Dá o play e vem com a gente!ParticipantesThiago Mobilon⁠⁠⁠⁠Thássius Veloso⁠⁠Ana Marques⁠Emerson Alecrim⁠Oferecimento: TripleTenNo Tecnocast 390, conversamos com Gustavo Fuga, da TripleTen, sobre a explosão de vagas internacionais para brasileiros em tech. Com as contratações remotas crescendo quase 500%, dominar IA e Data Science se tornou o caminho mais rápido para quem busca salários em dólar ou euro e quer se destacar em processos seletivos de empresas americanas e europeias.Se você busca uma vaga sênior ou de liderança global, o MBA em Data Science e IA da TripleTen oferece a formação estratégica que o mercado exige. Acesse go.tripleten.com/tecnocast e use o cupom TECNO10 para garantir 10% de desconto exclusivo para os ouvintes do Tecnocast.Créditos:Edição e sonorização: Caroline RochaArte da capa: ⁠⁠Vitor PáduaAssine o TecnocastYouTubeApple PodcastsSpotifyPocket CastsAndroid (outros apps)Feed RSS

Seth Rabinowitz uses AI with intent by studiously prioritizing learning, actively resisting dependency, promoting ethical practices, and seeing people in the data.    Seth and Kimberly discuss his shift from fearing AI to fearing (some) people using AI; expertise and critical thinking; how different cohorts use AI; resisting dependency and intentional use; the role of educators; developing soft skills; not confusing AI's learning with your own; stewarding AI; business ethics and data privacy; prioritizing AI fundamentals and putting people first.Seth Rabinowitz is pursuing a Master's degree in Data Science and Business Analytics at UNC Charlotte. A transcript of this episode is here.   

Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many data scientists are unaware of - that can be used to dramatically boost the business value of your ML outputs. This second layer transforms predictions into optimal decisions, and it's where the real impact often happens.In this episode, Dr. Tim Varelmann joins Dr. Genevieve Hayes to explain how combining machine learning with decision optimisation creates solutions that go far beyond prediction, helping stakeholders make better decisions in uncertain environments.You'll discover:How decision optimisation differs from ML parameter tuning [02:19]Why combining predictions with optimisation multiplies value [13:36]The mindset shift needed to think in optimisation terms [22:59]How to spot immediate optimisation opportunities in your work [23:42]Guest BioDr Tim Varelmann is the founder of Bluebird Optimization and holds a PhD in Mathematical Optimisation. He is also the creator of Effortless Modeling in Python with GAMSPy, the world's first GAMSPy course.LinksBluebird Optimization WebsiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

As foundation models, including large language models and multimodal models, are increasingly deployed in complex and high-stakes settings, ensuring their safety has become more important than ever. In this talk, I present a probabilistic perspective on AI safety: safety risks are treated as structured distributions to be discovered and controlled, rather than isolated failures to be patched. I first introduce probabilistic red-teaming methods that characterize distributions of failures, revealing systematic safety risks that standard evaluation often misses. I then describe probabilistic defense methods that control model behavior during deployment by adaptively steering generation toward constraint-aligned distributions. By unifying failure discovery and behavior control under a probabilistic perspective, this talk highlights a distributional approach for understanding and managing safety risks in foundation models. About the speaker: Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Her research focuses on probabilistic machine learning, generative modeling, and trustworthy AI. Prior to joining Purdue, she was a postdoctoral researcher at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin. She received her Ph.D. from Cornell University. Dr. Zhang has been a key organizer of the Symposium on Probabilistic Machine Learning. She has served as an Area Chair and Editor for ML conferences and journals, including ICML, NeurIPS, ICLR, AISTATS, UAI, and TMLR. Her contributions have been recognized with several honors, including AAAI New Faculty Highlights, Amazon Research Award, Spotlight Rising Star in Data Science, Seed for Success Acorn Award, and Ross-Lynn Research Scholar.

The Big Ten Preseeds are out and causing chaos! We react & dive into all the preseeds on today's episode of Basch & The Brain!TIMESTAMPS:0:00 - Willie's Back with a Broken Ankle06:30 - Big Ten Preseeds Reaction26:30 - The Captain Joins to talk Data Science in Seeds41:55 - Will We Get The Points?48:15 - 125lbs - 157lbs & What Will Change?01:03:42 - 165lbs - 285lbs01:13:00 - Big 12 Seeds01:19:30 - Only SoCon Champion Allocates01:23:00 - Jeremy Hunter Named Illinois Head Coach01:28:30 - Some High School Talk!01:35:00 - AftershowRokfin.com/MatScouts for all of Willie's Content!Be sure to SUBSCRIBE to the podcast. NEW EPISODES WEEKLY! Support the show & leave a 5-star rating and review on Apple Podcasts, and shop some apparel on BASCHAMANIA.com! For all partnership and sponsorship inquiries, email info@baschamania.com.BASCHAMANIA is a Basch Solutions Production. Learn more about Basch Solutions, a digital marketing agency specializing in custom websites, content creation, and digital strategy, at BaschSolutions.com.

Right now, millions of people are simultaneously chatting with a system that remembers nothing, knows nothing, and resets after every message. The engineering keeping that illusion alive is actually the impressive part.   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

Topics covered in this episode: Raw+DC: The ORM pattern of 2026? pytest-check releases Dataclass Wizard SQLiteo - “native macOS SQLite browser built for normal people” 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 11am 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: Raw+DC: The ORM pattern of 2026? ORMs/ODMs provide great support and abstractions for developers They are not the native language of agentic AI Raw queries are trained 100x+ more than standard ORMs Using raw queries at the data access optimizes for AI coding Returning some sort of object mapped to the data optimizes for type safety and devs Brian #2: pytest-check releases 3 merged pull requests 8 closed issues at one point got to 0 PR's and 1 enhancement request Now back to 2 issues and 1 PR, but activity means it's still alive and being used. so cool Check out changelog for all mods A lot of changes around supporting mypy I've decided to NOT have the examples be fully --strict as I find it reduces readability See tox.ini for explanation But src is --strict clean now, so user tests can be --strict clean. Michael #3: Dataclass Wizard Simple, elegant wizarding tools for Python's dataclasses. Features

In this episode of Practical Significance, cohosts Donna LaLonde and Ron Wasserstein welcome Real World Data Science managing editor, Annie Flynn (Royal Statistical Society), and editorial board members Monnie McGee (Southern Methodist University) and Willis Jensen (CHG Healthcare) to talk about what stories need to be told. Real World Data Science, a publication from the Royal Statistical Society in […]

Digital humanities sounds niche, until you realize it can mean a searchable archive of U.S. amendment proposals, Irish folklore, or pigment science in ancient art. Today I'm talking with David Flood from Harvard's DARTH team about an unglamorous problem: What happens when the grant ends but the website can't. His answer, static sites, client-side search, and sneaky Python. Let's dive in. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guest David Flood: davidaflood.com DARTH: digitalhumanities.fas.harvard.edu Amendments Project: digitalhumanities.fas.harvard.edu Fionn Folklore Database: fionnfolklore.org Mapping Color in History: iiif.harvard.edu Apatosaurus: apatosaurus.io Criticus: github.com github.com/palewire/django-bakery: github.com sigsim.acm.org/conf/pads/2026/blog/artifact-evaluation: sigsim.acm.org Hugo: gohugo.io Water Stories: waterstories.fas.harvard.edu Tsumeb Mine Notebook: tmn.fas.harvard.edu Dharma and Punya: dharmapunya2019.org Pagefind library: pagefind.app django_webassembly: github.com Astro Static Site Generator: astro.build PageFind Python Lib: pypi.org Frozen-Flask: frozen-flask.readthedocs.io Watch this episode on YouTube: youtube.com Episode #538 deep-dive: talkpython.fm/538 Episode transcripts: talkpython.fm Theme Song: Developer Rap

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

These days people are using AI chatbots for everything. These chatbots have a wealth of information at their metaphorical fingertips. But the accuracy of the information that they offer us is, well, questionable. But it makes sense why some people turn to AI for medical advice. They’re usually free, which gives them an upper hand when healthcare in the United States is so expensive. They’re also easy to access, so people can get their questions answered immediately, instead of waiting for an opening at their doctor’s office. And they’re trained to be empathic, which is especially appealing to patients who don’t feel valued in medical settings. In this "Ask a Doctor" segment, we explore the world of health advice and chatbots with two medical professionals. Guests: Angad Singh is a family medicine physician. He's also an Associate Chief Clinical Information Officer and Clinical Associate Professor at UW Medicine. Danielle Bitterman is an assistant Professor at Harvard Medical School and Clinical Lead for Data Science and AI at Mass General Brigham. Related links: A.I. Chatbots Are Changing How Patients Get Medical Advice - The New York Times How to Use ChatGPT for Health Advice | Right as Rain Health Advice From A.I. Chatbots Is Frequently Wrong, Study Shows - The New York Times Thank you to the supporters of KUOW, you help make this show possible! If you want to help out, go to kuow.org/donate/soundsidenotes Soundside is a production of KUOW in Seattle, a proud member of the NPR Network.See omnystudio.com/listener for privacy information.

Matei Zatreanu is the CEO and founder of System2, a data science firm that helps institutional investors make better decision. His family immigrated from Romania, but for the first few years, his parents had to leave him and his brother temporarily behind as they settled themselves in their new country.In our conversation, we explored how early hardship, like growing up separated by your parents, builds a “figure it out” mentality, why storytelling is one of the most underrated leadership skills, and how something as simple as sharing a meal can become the foundation of culture and trust inside an organization.We went deep into the meaning of profit, the tradeoffs entrepreneurs face when considering outside capital, and the importance of understanding your own motivations before chasing growth, money, or status.Contact Dino at: dino@al4ep.comWebsites:sstm2.comal4ep.comAdditional Guest Links:Podcast: on SpotifyOn YouTube: youtube.com/@system2podLinkedIn: linkedin.com/in/matei-zatreanu/Authentic Leadership For Everyday People / Dino CattaneoDino on LinkedIn: linkedin.com/in/dinocattaneoPodcast Instagram – @al4edp Podcast Twitter – @al4edpPodcast Facebook: facebook.com/al4edpMusicSusan Cattaneo: susancattaneo.bandcamp.com

Building data capability from zero is not a tooling problem, it is a trust and prioritization problem. In this episode, Laura Guerin, Head of Data and Data Science at Bevi, breaks down how she goes from blank slate to real business impact, without getting trapped in endless plumbing or endless meetings. Laura shares how she runs an early listening tour, prototypes value before asking for bigger investment, and decides when to hire scrappy generalists versus specialists. We also get practical on AI, where it helps, where it is unnecessary, and why quality data and a clean semantic layer still decide whether anything works.Key takeaways• Start with business priorities, then map data work to the actions and outcomes leaders actually care about• Prototype the end deliverable fast, even if the backend is duct tape at first, then scale after stakeholders see value• Use cases first for AI, most problems do not need AI, but the right problems can see real acceleration• Early teams win with adaptable generalists who can wear multiple hats across data, analytics, and data science• Trust is a shared responsibility, build reliability, then create a culture where users flag weirdness quicklyTimestamped highlights00:44 Bevy explained, smart bottle less dispensers and why the business context matters for data priorities02:01 The listening tour playbook, exec alignment, stakeholder map, and using AI to synthesize themes into a SWOT04:00 The MVP reality, manual prototypes to prove value, then the conversation about scalable pipelines06:33 AI without the hype, use cases, when AI is not needed, and two examples with clear business impact09:22 Hiring from zero, why generalists first, the data analytics data science spectrum, and the personality traits that matter14:21 Self service reimagined, Slack as the interface, semantic layer and permissions, and how to keep a single source of truth20:19 Keeping trust when things break, checks and balances plus a shared responsibility model22:39 Making innovation real, baking it into expectations so the team has time to learn and test new approachesA line worth stealingData on its own is not typically a priority. It is more about the action or the impact that comes out of the data.Pro tips• Run a structured listening tour early, capture themes, then pick two or three priorities you can deliver quickly• Show the business an MVP output first, then use that proof to justify the unglamorous backend work• Treat AI like any other tool, define the problem, validate the use case, then confirm the data quality inputsCall to actionIf you are building analytics, data products, or AI inside a growing company, follow the show and subscribe so you do not miss the next operator level conversation. Share this episode with one leader who is asking for data outcomes but has not funded the foundation yet.

Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There's no room for the kind of errors that might be acceptable in research settings or even standard business applications.In this Value Boost episode, Prof. Steve Stern joins Dr. Genevieve Hayes to share practical lessons from deploying the Duckworth-Lewis-Stern method in high-pressure, real-time environments where mistakes have global consequences.You'll learn:Why model simplicity matters more than you think [02:04]The two types of errors you need to understand [03:21]How to test models for extreme situations [05:50]The balance between confidence and humility [07:37]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this week's episode, Leslie Heaney sits down with Vasant Dhar—professor at NYU Stern School of Business and the Center for Data Science at New York University, founder of SCT Capital, and author of Thinking with Machines: The Brave New World of AI.Together, they explore how artificial intelligence evolved, why language prediction changed everything, and what it means now that machines can think alongside humans. The conversation examines the growing divide between those who use AI to sharpen judgment and those who rely on it to think for them, as well as the broader implications for work, education, power, and responsibility.This is a grounded, honest conversation about the power of AI—and how we choose to live with it.Hosted on Ausha. See ausha.co/privacy-policy for more information.

March 3rd, Computer History Museum CODING AGENTS CONFERENCE, come join us while there are still tickets left.https://luma.com/codingagentsChris Fregly is currently focused on building and scaling high-performance AI systems, writing and teaching about AI infrastructure, helping organizations adopt generative AI and performance engineering principles on AWS, and fostering large developer communities around these topics.Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs // MLOps Podcast #363 with Chris Fregly, Founder, AI Performance Engineer, and InvestorJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractIn today's era of massive generative models, it's important to understand the full scope of AI systems' performance engineering. This talk discusses the new O'Reilly book, AI Systems Performance Engineering, and the accompanying GitHub repo (https://github.com/cfregly/ai-performance-engineering). This talk provides engineers, researchers, and developers with a set of actionable optimization strategies. You'll learn techniques to co-design and co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems for both training and inference. // BioChris Fregly is an AI performance engineer and startup founder with experience at AWS, Databricks, and Netflix. He's the author of three (3) O'Reilly books, including Data Science on AWS (2021), Generative AI on AWS (2023), and AI Systems Performance Engineering (2025). He also runs the global AI Performance Engineering meetup and speaks at many AI-related conferences, including Nvidia GTC, ODSC, Big Data London, and more.// Related LinksAI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch 1st Edition by Chris Fregly: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/Coding Agents Conference: https://luma.com/codingagents~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Chris on LinkedIn: /cfreglyTimestamps:[00:00] SageMaker HyperPod Resilience[00:27] Book Creation and Software Engineering[04:57] Software Engineers and Maintenance[11:49] AI Systems Performance Engineering[22:03] Cognitive Biases and Optimization / "Mechanical Sympathy"[29:36] GPU Rack-Scale Architecture[33:58] Data Center Reliability Issues[43:52] AI Compute Platforms[49:05] Hardware vs Ecosystem Choice[1:00:05] Claude vs Codex vs Gemini[1:14:53] Kernel Budget Allocation[1:18:49] Steerable Reasoning Challenges[1:24:18] Data Chain Value Awareness

Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally 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 11am 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: Better Python tests with inline-snapshot Alex Hall, on Pydantic blog Great for testing complex data structures Allows you to write a test like this: from inline_snapshot import snapshot def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({}) Then run pytest --inline-snapshot=fix And the library updates the test source code to look like this: def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({ "id": 123, "name": "test_user", "status": "active" }) Now, when you run the code without “fix” the collected data is used for comparison Awesome to be able to visually inspect the test data right there in the test code. Projects mentioned inline-snapshot pytest-examples syrupy dirty-equals executing Michael #2: jolt Battery intelligence for your laptop Support for both macOS and Linux Battery Status — Charge percentage, time remaining, health, and cycle count Power Monitoring — System power draw with CPU/GPU breakdown Process Tracking — Processes sorted by energy impact with color-coded severity Historical Graphs — Track battery and power trends over time Themes — 10+ built-in themes with dark/light auto-detection Background Daemon — Collect historical data even when the TUI isn't running Process Management — Kill energy-hungry processes directly Brian #3: Markdown code formatting with ruff Suggested by Matthias Schoettle ruff can now format code within markdown files Will format valid Python code in code blocks marked with python, py, python3 or py3. Also recognizes pyi as Python type stub files. Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks. Requires preview mode [tool.ruff.lint] preview = true Michael #4: act - run your GitHub actions locally Run your GitHub Actions locally! Why would you want to do this? Two reasons: Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides. Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile! When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run. Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match what GitHub provides. Extras Michael: Winter is coming: Frozendict accepted Django ORM stand-alone Command Book app announcement post Joke: Plug ‘n Paste

How do people become addicted to social media and what are the implications of such an addiction? [ dur: 30mins. ] Ofir Turel is Professor of Information Systems (IS) Management, IS group co-lead, University of Melbourne. He has published over 250 journal papers, two of those titles include The Benefits and Dangers of Enjoyment with Social Networking Websites and Followers Problematic Engagement with Influencers on Social Media and Attachment Theory Perspective. Most of our activity on the internet interacts with posts, memes and videos that are driven by algorithms. How might algorithms be biased, racist, or sexist, and how might they amplify those biases in us? [ dur: 28mins. ]  Full length of this interview can be found here. Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern’s Network Science Institute and the Institute for Experiential AI. She is the author of Measuring Algorithmically Infused Societies and What Science Can Do for Democracy: A Complexity Science Approach. Damien Patrick Williams is Assistant Professor in Philosophy and Data Science at the University of North Carolina at Charlotte. He is the author of Why AI Research Needs Disabled and Marginalized Perspectives, Fitting the description: historical and sociotechnical elements of facial recognition and anti-black surveillance, and Constructing Situated and Social Knowledge: Ethical, Sociological, and Phenomenological Factors in Technological Design. Damien is a member of the Project Advisory Committee for the Center for Democracy and Technology’s Project on Disability Rights and Algorithmic Fairness, Bias, and Discrimination, and the Disability Inclusion Fund’s Tech & Disability Stream Advisory Committee. Henning Schulzrinne is Professor in the Department of Computer Science at Colombia University. He is the co-author of Mobility Protocols and Handover Optimization: Design, Evaluation and Application, Bridging communications and the physical world and Future internets escape the simulator. He was nominated as Internet Hall of Fame Innovator in 2013. He was Chief Technology Officer for the FCC under the Obama Administration. This program is produced by Doug Becker, Ankine Aghassian, Maria Armoudian, Anna Lapin and Sudd Dongre. Politics and Activism, Science / Technology, Computers and Internet, Racism 

You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guests Delaney Gillilan: linkedin.com Ben Croker: x.com Chris May: everydaysuperpowers.dev Datastar: data-star.dev HTMX: htmx.org AlpineJS: alpinejs.dev Core Attribute Tour: data-star.dev data-star.dev/examples: data-star.dev github.com/starfederation/datastar-python: github.com VSCode: marketplace.visualstudio.com OpenVSX: open-vsx.org PyCharm/Intellij plugin: plugins.jetbrains.com data-star.dev/datastar_pro: data-star.dev gg: discord.gg HTML-ivating your Django web app's experience with HTMX, AlpineJS, and streaming HTML - Chris May: www.youtube.com Senior Engineer tries Vibe Coding: www.youtube.com 1 Billion Checkboxes: checkboxes.andersmurphy.com Game of life example: example.andersmurphy.com Watch this episode on YouTube: youtube.com Episode #537 deep-dive: talkpython.fm/537 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Rayid Ghani, a professor at Carnegie Mellon University, who was the chief scientist of the Obama 2012 campaign, talks with Host Llewellyn King and Co-host Adam Clayton Powell III about AI literacy, machine learning, and the potential for AI to improve various sectors such as healthcare, education, and employment. He also addresses concerns about job losses due to AI, the role of AI in politics and democracy, and the challenges of detecting fake images and videos. 

Shahar Goldboim is the Founder and CEO of Boom, an AI-enabled property management platform for short-term rental portfolios built by operators. Shahar is an entrepreneur and builder, and he launched Boom with his two sibblings after identifying real-world operational pain points inside a large South Florida property management company as it scaled. Under his leadership, Boom delivers comprehensive software that simplifies workflows, increases revenue, and reduces costs for property managers.(02:15) - Why Short-Term Rentals Are the Hardest Asset Class(02:44) - Fragmentation and the Review-Driven Revenue Trap(04:13) - The Spark: A Miami Airbnb Experiment(05:30) - From Airbnb Host to Property Manager(07:31) - Software Fragmentation in STR Ops(07:55) - From SaaS to Baas (Business-as-Software)(09:09) - Boom, the AI PMS(12:47) - Enabling Proactive Ops(13:51) - The $12M+ Fundraise(14:47) - Winning Investors with Hospitality & Tech Credibility(16:53) - Feature: Blueprint Vegas 2026(17:46) - STR Market Context and the Vacasa Lesson(21:03) - Replacing Point Solutions(23:47) - ROI, AI Moats, Future of STR Ops(32:06) - Collaboration Superpower: Tony Robbins (Wiki)

In this episode, Peter Swartz, Co-Founder and Chief Science Officer at Altana, reveals how the company's AI-powered supply chain knowledge graph has helped stop hundreds of millions of dollars in forced labor goods from crossing borders and contributed to some of the largest counter-narcotics seizures in investigators' careers. Peter shares the real-world impact Altana is making across both the public and private sectors.Peter breaks down how Altana's multi-tier supply chain visibility works to trace forced labor cotton through global networks, how dual-use chemicals are being diverted into fentanyl production, and how the platform helps governments and enterprises collaborate to avoid billions of dollars in trade disruptions while saving hundreds of millions in tariff fees.Key Topics Covered- How Altana blocked hundreds of millions of dollars in forced labor goods at U.S. borders- The role of AI knowledge graphs in mapping multi-tier global supply chains- How Altana supports CBP enforcement of the Uyghur Forced Labor Prevention Act- Product passports and how they expedite legitimate goods through customs- The difference between forced labor entering legit supply chains vs. legit goods entering illicit ones- How logistics companies use Altana to prevent their networks from being misused- Proactive vs. reactive approaches to supply chain risk using probabilistic AI models- Scenario modeling for geopolitical disruptions including Taiwan and global conflicts- Saving billions in supply chain disruptions and hundreds of millions in tariff feesEpisode Timestamps00:00 - Introduction and overview of Altana's real-world impact00:41 - Understanding forced labor as a multi-tier supply chain problem03:09 - Hundreds of millions in forced labor goods stopped at borders03:45 - How the AI knowledge graph maps global supply chain connections04:15 - Working with CBP on the Uyghur Forced Labor Prevention Act04:35 - Product passports and expediting goods through customs04:51 - Counter-narcotics and the dual-use chemical problem05:45 - Helping logistics companies stop network misuse06:27 - From alert to action and the system handoff process06:49 - Responsible AI and the role of human-in-the-loop decisions07:33 - Proactive vs. reactive supply chain intelligence08:08 - Scenario modeling for geopolitical disruptions and resiliencyAbout Peter SwartzPeter Swartz is Co-Founder and Chief Science Officer at Altana. He has spoken on global trade, supply chains, and machine learning at the World Trade Organization, the World Customs Organization, the U.S. Court of International Trade, and the National Academies of Medicine. Previously, Peter was Head of Data Science at Panjiva, listed as one of Fast Company's most innovative data science companies in 2018 and later acquired by S&P Global. He holds patents in machine learning and global trade, and completed his education at Yale, MIT, and EPFL.About AltanaAltana is the world's first Value Chain Management System, providing AI-powered supply chain intelligence to governments, enterprises, and logistics providers. The platform is built on a proprietary knowledge graph comprising more than 2.8 billion shipments, tracking over 500 million companies and 850 million facilities globally. Altana covers more than 50% of global trade, making it the most comprehensive and accurate supply chain map available.Resources Mentioned- Altana Atlas platform and AI knowledge graph- U.S. Customs and Border Protection (CBP)- Uyghur Forced Labor Prevention Act (UFLPA)- Product passports for cross-border compliance- Altana's disruption and tariff scenario modeling toolsPeter's Socials:LinkedIn — https://www.linkedin.com/in/pgswartz/Partner LinksBook Enterprise Training — https://www.upscaile.com/

For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organisation, but seeing them become industry standards or influence global decisions feels completely out of reach.In this episode, Prof. Steve Stern joins Dr Genevieve Hayes to share how he transformed a mathematical critique of a cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia.This episode reveals:How a single email response changed everything [05:24]Why principles build trust where mathematics can't [13:19]The "error whack-a-mole" problem that destroys credibility [16:00]The real secret to creating work with impact [30:29]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Vaishali shares her experience leading global data teams, partnering with executive leadership, and building strategies that connect cutting-edge technology to real business value. We explore her insights on operationalizing AI, scaling analytics across enterprises, and overcoming challenges in data governance, stakeholder alignment, and innovation adoption.Key Highlights:Bridging Tech and Business: How Vaishali connects AI and analytics innovations to organizational strategy and measurable outcomes.Global Team Leadership: Lessons from managing cross-functional, geographically distributed teams and driving collaboration.Operational Optimization: Examples of initiatives that reduced operational complexity while improving efficiency.Scaling Analytics and AI: Best practices for governance, workflow, and embedding AI into enterprise decision-making.Emerging Trends: Vaishali's perspective on the next wave of AI, analytics, and enterprise data strategies.Tune in to Episode 61 to learn how Vaishali Lambe drives data-driven transformation, operational excellence, and AI innovation across global enterprises.Be sure to mark your calendars for the 10th annual ALD NYC on May 13, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE FINANCE AND BANKING. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/new-york/

Guest Full Name: Dr. R. Stacy Lindborg, PhDGuest Title: President, Chief Executive Officer, and Board DirectorCompany: IMUNONTicker: IMNNWebsite: https://imunon.com/Guest Bio:Stacy R. Lindborg, PhD, was appointed President and Chief Executive Officer of IMUNON in May 2024. Dr. Lindborg has served on IMUNON's Board of Directors since June 2021. She has nearly 30 years of experience in the pharmaceutical and biotech industries, with a particular focus on R&D, regulatory affairs, executive management, and strategy development. She has designed, hired, and led global teams, guiding long-term visions for growth through analytics and stimulating innovative development platforms to increase productivity.Prior to joining IMUNON, Dr. Lindborg was Executive Vice President and Co-Chief Executive Officer at BrainStorm Cell Therapeutics, where she remains a member of the company's Board of Directors. At BrainStorm, she was accountable for creating and executing clinical development strategies through registration and launch and progressed its novel cell therapy for ALS through a positive Phase 3 Special Protocol Assessment (SPA) study with the U.S. Food and Drug Administration. She frequently interacted with investors and analysts, represented the company in the scientific community and with the media, and played an active role in discussions with potential business partners.Dr. Lindborg previously was Vice President and Head of Global Analytics and Data Sciences, responsible for R&D and marketed products at Biogen. She began her biopharmaceutical career at Eli Lilly and Company, where, over the course of 16 years, she assumed positions of increasing responsibility, including Head of R&D strategy.Dr. Lindborg received an MA and PhD in statistics, and a BA in psychology and math from Baylor University. She has authored more than 200 presentations and 90 manuscripts that have been published in peer-reviewed journals, including 20 first-authored. She has held numerous positions within the International Biometric Society and American Statistical Association and was elected Fellow in 2008.Company Bio:IMUNON is a clinical-stage biotechnology company focused on advancing a portfolio of innovative treatments that harness the body's natural mechanisms to generate safe, effective, and durable responses across a broad array of diseases. IMUNON is developing its non-viral DNA technology across its modalities. The first modality, TheraPlas®, is developed for the gene-based delivery of cytokines and other therapeutic proteins in the treatment of solid tumors where an immunological approach is deemed promising. The second modality, PlaCCine®, is developed for the gene delivery of viral antigens that can elicit a strong immunological response.IMUNON's lead clinical program, IMNN-001, is a DNA-based immunotherapy for the localized treatment of advanced ovarian cancer. IMNN-001 is the first therapy to achieve a clinically effective response in advanced (stage IIIC/IV) ovarian cancer including benefits in both progression-free survival (PFS) and overall survival (OS) in a first-line treatment setting when used with standard of care chemotherapy. IMUNON has completed multiple clinical trials evaluating the potential of IMNN-001, including one Phase 2 clinical trial (OVATION 2), and is currently conducting a Phase 3 clinical trial (OVATION 3). The first patient was dosed in the Phase 3 study in the third quarter of 2025. IMNN-001 works by instructing the body to produce safe and durable levels of powerful cancer-fighting molecules, such as IL-12 and interferon gamma, at the tumor site. Additionally, the Company has completed dosing in a first-in-human study of its COVID-19 booster vaccine (IMNN-101).

Check out host Bidemi Ologunde's new show: The Work Ethic Podcast, available on Spotify and Apple Podcasts.Email: bidemiologunde@gmail.comIn this episode, host Bidemi Ologunde sits down with Damilola "Dammy" Gbenro, a Data Analytics and Machine Learning professional and talked about what it really means to build and maintain an online presence in the age of algorithms and AI. How do you utilize the benefits of social media without letting it consume you? What does online safety look like when your life is also your brand? And as AI reshapes trust, attention, and creativity, how do we protect our identities and our peace?Quick question: when you buy something handmade, do you ever wonder who made it, and where your money really goes? Lembrih is building a marketplace where you can shop Black and African-owned brands and learn the story behind the craft. And the impact is built in: buyers can support vendors directly, and Lembrih also gives back through African-led charities, including $1 per purchase. They're crowdfunding on Kickstarter now. Back Lembrih at lembrih.com, or search “Lembrih” on Kickstarter.Support the show

Tiffany Yeh, MD is the CEO and Co-Founder of Eztia Materials, a climate-tech venture developing energy-efficient cooling materials to protect people from extreme heat. With a mission to advance hard tech solutions at the climate-health nexus, Tiffany draws on her unique background as a physician, engineer, and public health advocate to build technologies that improve global health in a warming world.(01:13) - Dr. Ye's Background & Inspiration (01:52) - The Heat Challenge(05:20) - Singapore and the Power of Cooling(06:32) - Why Construction Has Been Slow to Adapt (07:22) - The Human Factor(08:14) - HydroVolt Technology(09:29) - Business Model, Distribution & Competition(11:19) - Worker Comfort (15:32) - Hidden Productivity Crisis Brewing(18:18) - Feature: Blueprint: The Future of Real Estate 2026 in Vegas on Sep. 22-24 (19:21) - The Secret Sauce Behind HydroVolt (20:31) - Prototyping & Real-World Applications (21:32) - Measuring Impact & ROI (23:34) - Pitching to VCs & Investors(25:31) - Product Roadmap(29:08) - Collaboration Superpower: Lionel Messi

Feb 10, 2026 – This year marks a turning point, as deepfakes reach new heights in realism and influence. FS Insider interviews Dr. Siwei Lyu, director of the Institute for AI and Data Sciences, about the rapid evolution and growing dangers of deepfakes...

You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever. Episode sponsors Command Book Python in Production Talk Python Courses Links from the show Guests Sebastián Ramírez: github.com Savannah Ostrowski: github.com Patrick Arminio: github.com Jonathan Ehwald: github.com FastAPI labs: fastapilabs.com quickstart: fastapicloud.com an episode on diskcache: talkpython.fm Fastar: github.com FastAPI: The Documentary: www.youtube.com Tailwind CSS Situation: adams-morning-walk.transistor.fm FastAPI Job Meme: fastapi.meme Migrate an Existing Project: fastapicloud.com Join the waitlist: fastapicloud.com Talk Python CLI Talk Python CLI Announcement: talkpython.fm Talk Python CLI GitHub: github.com Command Book Download Command Book: commandbookapp.com Announcement post: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #536 deep-dive: talkpython.fm/536 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI 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: Command Book App New app from Michael Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more. This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications. It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions. Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need" I'd love it if you gave it a look, shared it with your team, and send me feedback. Has a free version and paid version. Build with Swift and Swift UI Check it out at https://commandbookapp.com Brian #2: uvx.sh: Install Python tools without uv or Python Tim Hopper Michael #3: Ending 15 years of subprocess polling by Giampaolo Rodola The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago The problem with busy-polling CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries. Latency: there's always a gap between when a process actually terminates and when you detect it. Scalability: monitoring many processes simultaneously magnifies all of the above. + L1/L2 CPU cache invalidations It's interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps. Here is the merged PR for this change. Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI Samuel Colvin and others at Pydantic Still experimental “Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “ “Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” Extras Brian: Expertise is the art of ignoring - Kevin Renskers You don't need to master the language. You need to master your slice. Learning everything up front is wasted effort. Experience changes what you pay attention to. I hate fish - Rands (Michael Lopp) Really about productivity systems And a nice process for dealing with email Michael: Talk Python now has a CLI New essay: It's not vibe coding - Agentic engineering GitHub is having a day Python 3.14.3 and 3.13.12 are available Wall Street just lost $285 billion because of 13 markdown files Joke: Silence, current side project!

Dr. Ben Zweig joins the podcast from NYU's Stern School of Business to discuss what is wrong with the world of work and how to fix it. Ben is the author of the book “Job Architecture: Building a Language for Workforce Intelligence,” professor of Economics, and CEO of Revelio Labs. In this conversation, Ben discusses the challenges created when a new employee finds out after working a few months that the job that was described to them is different than what they are doing. Ben says this lack of clarity results in pendulum swings between rapid job expansions and mass layoffs. He also discusses how work can be better designed to be a source of dignity and purpose. Ben believes that management is about job reconfiguration in order to keep employees relevant so those employees are able to meet current and future needs at their organizations. Ben also shares his opinion on whether or not work - in an augmented world of robots and AI - should be saved. The interview finishes with a conversation about the future of work, how artificial intelligence will augment every job, and the likelihood AI and robots will be taxed in order to generate revenue to pay for universal basic income. Dr. Ben Zweig is the CEO of Revelio Labs, a workforce intelligence company that leverages the latest advances in AI research to create a universal HR database from public sources. Ben teaches courses on Data Science and The Future of Work at NYU Stern. His first book is “Job Architecture: Building a Language for Workforce Intelligence.”

Breaking into Cybersecurity with Shadya MaldonadoIn this episode of Breaking into Cybersecurity, Shadya Maldonado, Founder and CEO of ArcQubit, shares her journey and extensive experience in the field. With 16 years in security operations, technology modernization, and risk management, Shadya discusses her transition from a military analyst to a leader in cybersecurity and AI. She highlights her work with organizations such as CISA, DARPA, DOE, and NASA, as well as her passion for developing tools to make quantum computing accessible. Shadya also offers valuable advice for individuals looking to grow their careers in cybersecurity.00:00 Introduction and Guest Welcome01:16 Shaday's Unconventional Path to Cybersecurity01:48 From Military to Cybersecurity02:50 Exposure to Data Science and Cybersecurity03:43 Immersion in Cybersecurity and SANS Conference04:45 Founding Arc Qubit and Quantum Computing06:49 Developing Quantum-Ready Talent14:02 The Importance of Cybersecurity Knowledge21:06 Shaday's Leadership Journey24:24 Advice for Aspiring Cybersecurity Professionals26:09 Closing RemarksSponsored by CPF Coaching LLC - http://cpf-coaching.comThe Breaking into Cybersecurity: It's a conversation about what they did before, why they pivoted into cyber, what the process was they went through, how they keep up, and advice/tips/tricks along the way.The Breaking into Cybersecurity Leadership Series is an additional series focused on cybersecurity leadership and hearing directly from different leaders in cybersecurity (high and low) on what it takes to be a successful leader. We focus on the skills and competencies associated with cybersecurity leadership, as well as tips/tricks/advice from cybersecurity leaders.Check out our books:The Cybersecurity Advantage - https://leanpub.com/the-cybersecurity-advantageDevelop Your Cybersecurity Career Path: How to Break into Cybersecurity at Any Level https://amzn.to/3443AUIHack the Cybersecurity Interview: Navigate Cybersecurity Interviews with Confidence, from Entry-level to Expert roleshttps://www.amazon.com/Hack-Cybersecurity-Interview-Interviews-Entry-level/dp/1835461298/Hacker Inc.: Mindset For Your Careerhttps://www.amazon.com/Hacker-Inc-Mindset-Your-Career/dp/B0DKTK1R93/About the hosts:Renee Small is the CEO of Cyber Human Capital, one of the leading human resources business partners in the field of cybersecurity, and author of the Amazon #1 best-selling book, Magnetic Hiring: Your Company's Secret Weapon to Attracting Top Cyber Security Talent. She is committed to helping leaders close the cybersecurity talent gap by hiring from within and encouraging more people to enter the lucrative cybersecurity profession. https://www.linkedin.com/in/reneebrownsmall/Download a free copy of her book at magnetichiring.com/bookChristophe Foulon focuses on helping secure people and processes, drawing on a solid understanding of the technologies involved. He has over ten years of experience as an Information Security Manager and Cybersecurity Strategist. He is passionate about customer service, process improvement, and information security. He has significant expertise in optimizing technology use while balancing its implications for people, processes, and information security, through a consultative approach.https://www.linkedin.com/in/christophefoulon/Find out more about CPF-Coaching at https://www.cpf-coaching.comWebsite: https://www.cyberhubpodcast.com/breakingintocybersecurityPodcast: https://podcasters.spotify.com/pod/show/breaking-into-cybersecuriYouTube: https://www.youtube.com/c/BreakingIntoCybersecurityLinkedin: https://www.linkedin.com/company/breaking-into-cybersecurity/

Frank Rohde is the Founder and CEO of Ownify, a fractional homeownership platform pairing institutional and impact investors with qualified first-time buyers to make homeownership more accessible. With a 20+ year career at the intersection of finance, credit analytics, and technology, Frank previously led Nomis Solutions, scaling it into a global mortgage pricing engine used by top banks. Earlier roles include leadership at FICO, founding the early online insurer eCoverage, and launching AI models before it was trendy. Born in Germany, Frank is a former national whitewater kayaking champion, marathon runner on all seven continents, and lifelong reader—now channeling that energy into building a path between renting and owning, one Brick by Brick™.(01:51) - Why Homeownership Is Broken(04:10) - Ownify model(06:03) - How Fractional Ownership Works(13:08) - Ownify Benefits for First-time Homebuyers(16:11) - Homeowner & Investor Alignment(23:21) - Feature: CREtech New York Oct. 20–21(24:09) - Event Opportunities(25:38) - All-Cash Offers Explained(32:40) - Underwriting & Risk Management(35:47) - Investor Returns(38:48) - Market Expansion(41:37) - Policy & Regulatory Headwinds(44:04) - Collaboration Superpower: Elon Musk

Topics covered in this episode: django-bolt: Faster than FastAPI, but with Django ORM, Django Admin, and Django packages pyleak More Django (three articles) Datastar 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 11am 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: django-bolt : Faster than FastAPI, but with Django ORM, Django Admin, and Django packages Farhan Ali Raza High-Performance Fully Typed API Framework for Django Inspired by DRF, FastAPI, Litestar, and Robyn Django-Bolt docs Interview with Farhan on Django Chat Podcast And a walkthrough video Michael #2: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak. Has patterns for Context managers decorators Checks for Unawaited asyncio tasks Threads Blocking of an asyncio loop Includes a pytest plugin so you can do @pytest.mark.no_leaks Brian #3: More Django (three articles) Migrating From Celery to Django Tasks Paul Taylor Nice intro of how easy it is to get started with Django Tasks Some notes on starting to use Django Julia Evans A handful of reasons why Django is a great choice for a web framework less magic than Rails a built-in admin nice ORM automatic migrations nice docs you can use sqlite in production built in email The definitive guide to using Django with SQLite in production I'm gonna have to study this a bit. The conclusion states one of the benefits is “reduced complexity”, but, it still seems like quite a bit to me. Michael #4: Datastar Sent to us by Forrest Lanier Lots of work by Chris May Out on Talk Python soon. Official Datastar Python SDK Datastar is a little like HTMX, but The single source of truth is your server Events can be sent from server automatically (using SSE) e.g yield SSE.patch_elements( f"""{(#HTML#)}{datetime.now().isoformat()}""" ) Why I switched from HTMX to Datastar article Extras Brian: Django Chat: Inverting the Testing Pyramid - Brian Okken Quite a fun interview PEP 686 – Make UTF-8 mode default Now with status “Final” and slated for Python 3.15 Michael: Prayson Daniel's Paper tracker Ice Cubes (open source Mastodon client for macOS) Rumdl for PyCharm, et. al cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun Python Developers Survey 2026 Joke: Pushed to prod