Podcasts about Data science

What happens when a technology provider can see patterns across 244 million account holders and thousands of financial institutions? In this episode of Technovation, Peter High speaks with Keith Fulton, Chief Data Officer at Jack Henry, about how data science and AI are transforming community banking. Keith explains how Jack Henry is leveraging industry-scale data to help banks reduce fraud, predict customer churn, identify hidden revenue opportunities, and prepare for an agentic AI future. He also shares how the company is democratizing innovation through citizen development, AI adoption programs, and enterprise-grade governance for employee-built applications. Key Highlights: Building the data foundation required for agentic AI Using consortium-scale data to uncover insights individual banks cannot see Predicting customer churn and identifying hidden commercial customers Applying AI to fraud prevention and customer protection Scaling innovation through AI adoption and citizen development This episode is presented by Celonis — Give AI the context it needs. Learn more at celonis.com/technovation

Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an bonus 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: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

This episode is part of our special series on the India AI Impact Summit, examining the conversations, decisions, and debates that are shaping global AI governance. Professor Ravindran addresses early on the perception that the India summit sidelined safety. More than 60% of the summit's events and discussions were focused on safety, trust, and cross-border collaboration. The framing shifted, and deliberately so. When the summit came to the Global South, leading with existential risk, rather than the very real opportunity AI presents to improve healthcare, education, and public services for hundreds of millions of people, would have been the wrong entry point. The two key deliverables from his working group reflect that balance: the Trusted AI Commons, a repository of benchmarks, testing protocols, and best practices designed for AI deployment in resource-constrained settings, and a high-level governance guidance note endorsed by 22 countries, that calls out the issues every national AI policy should address without being prescriptive enough to limit how different countries approach it. On frontier risks, Professor Ravindran notes that the landscape has shifted in ways that would have seemed speculative even a year ago, and that the frameworks being built to manage these risks will need to keep pace with that change. He also reflects on what the growing concentration of the most capable AI models means for countries like India, and why that conversation may need to move from being a company-to-country dialogue to a country-to-country one. His overall view is one of cautious optimism: there will be disruption in the short term, but there will also be a new equilibrium, and the work is to make sure the transition is managed well.Episode Contributors Professor Balaraman Ravindran heads the Department of Data Science and AI at IIT Madras. He is also the Founding Head of the Wadhwani School of Data Science and AI (WSAI), Robert Bosch Centre for Data Science and AI (RBCDSAI), and Centre for Responsible AI (CeRAI) at IIT Madras. He has more than three decades of experience working in reinforcement learning, and his research interest spans responsible AI and deep RL.  Nidhi Singh is an associate fellow at Carnegie India. Her current research interests include data governance, artificial intelligence and emerging technologies. Her work focuses on the implications of information technology law and policy from a Global Majority and Asian perspective. She has previously contributed to the Indian Express, The Secretariat, Medianama and HinduBusiness Line. Every two weeks, Interpreting India brings you diverse voices from India and around the world to explore the critical questions shaping the nation's future. We delve into how technology, the economy, and foreign policy intertwine to influence India's relationship with the global stage.As a Carnegie India production, hosted by Carnegie scholars, Interpreting India, a Carnegie India production, provides insightful perspectives and cutting-edge by tackling the defining questions that chart India's course through the next decade.Stay tuned for thought-provoking discussions, expert insights, and a deeper understanding of India's place in the world.Don't forget to subscribe, share, and leave a review to join the conversation and be part of Interpreting India's journey.

Maria Dykstra an AI Visibility Architect who diagnoses why B2B companies are invisible to AI systems joins Enterprise Radio. She is the creator of … Read more The post Data, Science and the new AI of Marketing appeared first on Top Entrepreneurs Podcast | Enterprise Podcast Network.

Pünktlich zum Pride Month widmen sich Mira und Liel der Frage, was bei der Arbeit mit sensiblen personenbezogenen Daten am Beispiel queerer Daten zu beachten ist. Sie gehen die drei Phasen Datenerfassung, -bereinigung und -analyse durch und zeigen, wie schon die Wahl von Kategorien die Realität beeinflusst und wie sich Diskriminierung in Daten und Algorithmen fortschreibt. Ein Schwerpunkt liegt auf dem Umgang mit sehr kleinen Gruppen, für die sich statistisch oft wenig ableiten lässt, und auf möglichen Lösungen wie Oversampling oder qualitativen Methoden. Die Episode macht deutlich, dass es keine einzelne richtige Lösung gibt, sondern bewusste Entscheidungen und Mitdenken gefragt sind. Die besprochenen Überlegungen gelten über Queerness hinaus auch für andere Kategorien sozialer Ungleichheit und das Thema Intersektionalität.   **Zusammenfassung** Begriffsklärung: Was "queer" bedeutet, von der ursprünglichen Beleidigung zur positiven Selbstbezeichnung, und der Bezug zu LGBTQIA+ Datenerfassung: Was man erfasst, hängt vom Kontext ab (Sex in der Medizin, Gender beim Verhalten, sexuelle Orientierung im Marketing) Kategorien sind nicht neutral: Sie prägen, wie Menschen sich wahrnehmen, wie Umfragen ankommen und ob man Diskriminierung überhaupt messen kann Repräsentativität: Wie prüft man sie, wenn die Gruppengröße unbekannt ist – etwa über bayesianische Ansätze mit Annahmen, die durch Daten aktualisiert werden Datenbereinigung: Schon wenige Fehleingaben verzerren kleine Gruppen stark, wie das Beispiel der US-Zensusdaten zeigt Umgang mit kleinen Gruppen: Optionen sind große Datenmengen, Oversampling, qualitative Methoden oder zumindest transparentes Berichten Analyse: Algorithmen reproduzieren und skalieren bestehende Biases und sind nicht automatisch neutral; das Weglassen einzelner Merkmale löst das Problem nicht (Proxy-Variablen) Fazit: Es gibt keine technische Patentlösung gegen Diskriminierung – entscheidend sind bewusste Entscheidungen, Mitdenken und der Blick auf Intersektionalität   **Links** Buch "Queer Data" von Kevin Guyan: https://kevinguyan.com/queer-data/ Buch "Rainbow Trap" von Kevin Guyan: https://kevinguyan.com/rainbow-trap/ Buch "Data Feminism" von Catherine D'Ignazio und Lauren F. Klein (MIT Press, frei verfügbar): https://data-feminism.mitpress.mit.edu/ Episode #40: Sonderfolge: Frauen in Data Science und Tech mit Catrin & Isa von Mind the Tech https://www.podbean.com/eas/pb-ypy32-15747e6 Episode #93: Bayesianische Statistik: Vorwissen und Daten kombinieren https://www.podbean.com/eas/pb-crgji-1ab8218

Como é que podemos identificar uma imagem ou um vídeo criado por IA? Teremos ferramentas para controlar a (des)informação gerada artificialmente? Bernardo Caldas e Hugo van der Ding analisam os impactos da inteligência artificial na forma como nos relacionamos, e na sociedade que queremos construir.Pela primeira vez, as máquinas conseguem imitar capacidades humanas complexas, produzir conteúdos e comunicar de forma cada vez mais convincente. Mas o que significa esta transformação para a sociedade?No último episódio dedicado ao tema, Bernardo Caldas e Hugo van der Ding analisam os desafios da IA, dos fenómenos de desinformação e de «deepfakes» à ineficácia dos sistemas de deteção de conteúdos gerados artificialmente. A partir das limitações que a ciência ainda enfrenta, o especialista explora outras abordagens para lidar com as fragilidades da IA.  Como combater os usos indevidos das máquinas? Que regulamentação existe atualmente? E como conciliar a necessidade de inovação e competitividade com a proteção das pessoas?A conversa aborda ainda a influência da IA na política e na administração pública, e na perda de transparência e confiança nas instituições. Mas, além dos desafios, há também oportunidades para o desenvolvimento, participação e bem-estar dos cidadãos.Por fim, a dupla reflete sobre a questão que se impõe: como podemos utilizar a inteligência artificial para construir uma sociedade mais livre, justa e informada?Entre riscos reais e questões em aberto, fica uma certeza: este é um episódio [IN]Pertinente a não perder.LINKS E REFERÊNCIAS ÚTEISOpenAI «Detetor de texto gerado por IA desligado por baixa fiabilidade» (2023)«Democracy, and National Security» (California Law Review, 2019)CHESNEY, B. & CITRON, D. «Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security» (Boston University School of Law, 2019)Reuters Institute, «Portugal: confiança nas notícias em 54%» (Digital News Report 2025)EU AI Act — Artigo 50.º (marcação de conteúdo gerado por IA)KUNNERT, P. «Microsoft admits it 'cannot guarantee' data sovereignty» (The Register, 2025)IEA — «Energy and AI» (2025): procura de eletricidade dos data centers«Trabalho XXI» — IA e decisões algorítmicas no Código do Trabalho (2026)«Air Canada responsabilizada pela informação errada do seu chatbot» (CBC, 2024)BIOSBernardo CaldasEspecialista em inteligência artificial e cofundador da associação «Data Science for Social Good Portugal», uma associação que desenvolve projetos de ciência de dados e inteligência artificial com impacto social positivo.Hugo van der Ding Locutor, criativo e desenhador acidental. Criador de personagens digitais de sucesso como a «Criada Malcriada» e «Cavaca a Presidenta», autor de um dos podcasts mais ouvidos em Portugal, «Vamos Todos Morrer», também escreve para teatro e, atualmente, apresenta o programa «Duas Pessoas a Fazer Televisão», na RTP, com Martim Sousa Tavares. 

OpenAI just acquired Astral, the company behind uv, Ruff, and ty. And if your first thought was "wait, is uv toast?", you are not alone. But here's the twist Charlie Marsh shared with me: he thinks they may ship more open source at OpenAI than they ever did at Astral. On this episode, we get into the acquisition, the mixed feelings, the future of your favorite Python tools, and what it's like to build right at the center of the AI universe. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show Guest Charlie Marsh: github.com The announcement: astral.sh OpenAI: openai.com uv: github.com ty: github.com Ruff: github.com pyx: astral.sh Codex team: openai.com Anthropic did something similar by acquiring Bun: www.anthropic.com Daily Stars Explorer: emanuelef.github.io Agentic AI Programming for Python: training.talkpython.fm Python Web Security: OWASP Top 10 with Agentic AI: training.talkpython.fm Episode #552 deep-dive: talkpython.fm/552 Episode transcripts: talkpython.fm Theme Song: Developer Rap

In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint.In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts.In this episode, you'll learn:Why a single observation reveals more than you think [01:58]How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25]The Rule of Five and what it reveals about small sample statistics [12:08]The simplest and most overlooked technique for reducing measurement uncertainty [14:07]Guest BioDouglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years' experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It's Broken and How to Fix It.LinksHow to Measure Anything websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

City leaders are on the front lines of data use, but most lack visibility into the federal data landscape, what's available, what's changing, and how federal policy decisions affect local outcomes. This gap delays emergency response, misdirects resources away from high-need neighborhoods, and undermines AI systems that depend on accurate data and community trust. Host Stephen Goldsmith speaks with Denice Ross,  Director of Federal Data Policy at the Federation of American Scientists, about the relationship between local and federal data, what city CDOs should prioritize, and why cities have untapped power to shape federal data policy. In this episode, you'll learn: The often-hidden relationship between local data needs and federal data infrastructure How to identify and access the federal data your city should be using Why now is the time to prepare for Census 2030 and protect funding How community participation in data decisions prevents disparities and builds legitimacy for AI systems How local data leaders can advocate effectively during federal policy windows Guest: Denice Ross –  Director of Federal Data Policy at the Federation of American Scientists; former United States Chief Data Scientist Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

Each year WNYC hosts a "health convening," with support from the Alfred P. Sloan Foundation, as an opportunity for healthcare experts and practitioners to inform WNYC's health reporting. This year, the topic is ultra-processed foods and how they affect our health.  Fang Fang Zhang, M.D., Ph.D., cancer epidemiologist and chair of the Division of Nutrition Epidemiology and Data Science at the Friedman School of Nutrition Science and Policy at Tufts University discusses her population‑based research on how ultra-processed foods influence cancer prevention, cancer survivorship and long‑term health outcomes. Photo: Packets of chips are on display at a supermarket in Mumbai, India, on September 7, 2025. (Photo by Indranil Aditya/NurPhoto via Getty Images)   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Topics covered in this episode: pi + superpowers Terminal: Warp.dev + OhMyZSH {Blink,kitty} + mosh + tmux Claude code MacWhisper or Handy Tailscale Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Six Feet Up is hosting a LinkedIn Live Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Calvin: @calvinhp@sixfeetup.social / @calvinhp.com (bsky) Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am 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. Calvin #1: pi + superpowers terminal-first, open-source coding agent Session management is a first-class citizen Extension model is what makes pi special — it's aggressively composable Superpowers brings a structured software development methodology as loadable skills Steps back and asks you what you're really trying to do “hand you the keys to the car” mode vs guardrails might not be for everyone Michael #2: Terminal: Warp.dev + OhMyZSH If you're using the base terminal with default settings, you have so much head-room for improvement. I've been using Warp.dev since Elvis talked me into it. ;) Remarkable terminal but the AI side of things is a bit junky, can be turned off OhMyZSH gives better autocomplete e.g. git branch [HTML_REMOVED] lists all branches in the local repo! Commandbookapp.com is excellent to keep the terminal focused on terminal things and more server commands and other automation in Command Book. Calvin #3: {Blink,kitty} + mosh + tmux Kitty Terminal — GPU-accelerated terminal emulator for macOS, Linux, and Windows with support for graphics, ligatures, and a powerful tiling layout system built right in. Blink Shell — The go-to terminal for iPad/iPhone power users; full SSH and Mosh client with a gorgeous interface built specifically for mobile professional workflows. Mosh — Mobile Shell replaces SSH for remote connections, surviving network switches, sleep cycles, and flaky Wi-Fi with zero dropped sessions — essential for staying connected to long-running agentic jobs. tmux — Terminal multiplexer that keeps sessions alive on your Linux server indefinitely; detach from a Mosh session on your Mac, reconnect from your iPad, and your agent is right where you left it. The combo — Kitty or Blink + Mosh + tmux creates a "persistent remote brain" pattern: your beefy Linux homelab runs the compute-heavy agent sessions 24/7, and any device becomes a thin client to drop in and out at will. Michael #4: Claude code I prefer the IDE experience, the new PyCharm + Claude integration is really good. VS Code too. Why IDE? Because we should still be present with our code and managing context is much easier. Use the best/latest models on high thinking. “Speed” is not your friend, it's just shortcuts. Create skills and agents and use them. Curate your own rules (e.g. Talk Python's Claude.md) Works well on non-coding things. Just create a folder, put a ton of files in there and it's like NotebookLM + Chat + more. Calvin #5: MacWhisper or Handy Transcribes your speech using your choice of Whisper or Parakeet models. All transcription is done on your device, no data leaves your machine. Automatic Speaker Recognition with local models. Handy is more basic, but open source and runs on all platforms. Michael #6: Tailscale No need to open ports at all, Tailscale makes machines inside the same network accessible to each other Works great for laptops, desktops, etc. But also available for servers. Though I still use cloud firewalls for servers. How I use it: My dev database server, preloaded with QA data, is always running on my home mac mini m4 pro. All my apps look for that server before looking locally and tailscale makes them always accessible to each other My local LLMs expose OpenAI API compatible APIs. Tailscale makes these accessible even while traveling or at a coffee shop. Use my mini as an exit node. All traffic is routed outbound from my local fiber network. Great to restricted IPs like accessing my servers without caring about the local IP. Screen share back to my home machines even while traveling. Listen to the Talk Python episode with Alex for a deeper conversation. Extras Calvin: Telescopo great Mac Markdown viewer/editor. Michael: One more: Typora markdown editor. Created formal documentation for many of my open source packages using Great Docs. Via Mark Little: Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Joke: No second date

Andreas Rotenberg is Co-founder and COO of Pulley, an AI-powered permitting platform helping developers and operators move projects through approvals faster. Before Pulley, he was part of the team at Honest Buildings through its acquisition, then served as Chief of Staff at Procore through its IPO. Pulley has supported over $15 billion in projects approved across the U.S. Live from ICSC+Proptech in Las Vegas.(0:00) - First ever ICSC+Proptech live podcast(1:47) - Why Permitting Is a Growing Bottleneck(2:41) - What's Happening During Permitting Timelines(4:13) - Jurisdictional Complexity Across the U.S.(5:08) - What CRE Teams Underestimate About Permitting(7:35) - Why Pulley(8:18) - The Origin Story(10:53) - Combining Technology with Local Expertise(14:26) - Where AI Creates Real Value in Permitting(17:36) - Trust, Hallucinations & Accuracy(19:07) - Municipalities & Public Sector Modernization(20:40) - Second & Third Order Effects of Faster Permitting(22:41) - Collaboration Superpower: Vaclav Smil

America is at war — again. The U.S. military has launched strikes against Iran following the downing of an Army Apache helicopter in the Strait of Hormuz. Both pilots are safe. The bombs, however, are very real — and your gas prices are already telling the story. Meanwhile, ICE is pushing facial recognition surveillance that should have every American paying attention. And if that weren't enough, Donald Trump showed up to Game 3 of the NBA Finals at Madison Square Garden — and got booed by an entire arena. His response? Calling sports media giant Stephen A. Smith "low IQ." NBA Hall of Fame Kinfolk and Activist Joshua Zeke Thomas and Data Science and Analytics Leader Germar Reed join Dr. Nii-Quartelai to break down a week where every headline felt like a five-alarm fire.

Episode Summary Neil Bawa, former Silicon Valley tech executive turned "mad scientist of multifamily," breaks down how he went from building custom campuses for his healthcare company to managing 4,400+ rental units worth hundreds of millions. This isn't your typical "I flipped a house and quit my job" story—Neil backed into real estate through depreciation strategy, made an $800,000 mistake on his first project, and now uses heavy data analytics to target growth markets and build affordable housing across middle America. What You'll Learn How to enter commercial real estate backwards—and why building your own company's campus might teach you more than any fix-and-flip ever could Why depreciation is the "cheat code" that tech executives with fat California salaries discover first—and how it drove a 40-50% sales increase for Neil's core business The data scientist approach to market selection—what "location magic" looks like when you strip out gut feel and let the numbers talk Why 2009 timing made all the difference—and how Neil transitioned from dozens of single-family homes to full-scale multifamily investing by 2013 Episode Highlights [00:00] Introduction—Neil Bawa joins to discuss his unconventional path from Silicon Valley to affordable housing development [02:15] The $800,000 mistake—how building a custom campus from scratch in 2003 taught expensive lessons but created unfair competitive advantage [04:30] Discovering the depreciation cheat code—why most high-income tech workers stumble into real estate for tax reasons, not investment strategy [06:45] Perfect timing meets preparation—buying dozens of single-family homes in 2009 while simultaneously developing the "location magic" framework [08:20] From single-family to multifamily—the 2013 pivot that led to a 4,400-unit portfolio and the Mission 10K affordable housing initiative Resources Mentioned Grow Capitas—Neil's investment company Mission 10K—initiative to build 10,000 affordable rental homes across middle America Location Magic—Neil's data-driven market analysis framework (used by 50,000+ people) About the Host I'm JP Fluellen, a real estate professional working the Springfield, Missouri market. I've seen enough transactions to know that the best lessons come from people who've made expensive mistakes and lived to systematize around them. Subscribe & Review Subscribe to the Success Agent Podcast wherever you're listening right now. If this episode gave you a different way to think about market selection or commercial real estate, leave a review—it helps other agents find conversations that actually move the needle.

If you've ever been to PyCon, you know one of the best parts of the expo hall is Startup Row, a stretch of booths where early-stage companies built on Python show off what they're creating. But only attendees get to walk that lane, so let's bring it to everyone. In this episode, we stroll down Startup Row together. We kick things off with the organizers, Jason and Shay, who share the program's origin story going back to Paul Graham and the PSF, plus some surprising stats, including two unicorns among the alumni. Then we meet five startups: Tetrix, bringing AI to institutional investing in private markets. Arcjet, security that lives inside your app as an SDK. Phemeral.dev, serverless hosting built for Python web apps. CapiscIO, an identity and authority layer for AI agents. And Pixeltable, a multimodal database from Marcel Kornacker, co-creator of Apache Parquet. See if you can spot the theme running through them all. Let's go for a walk. Episode sponsors AgentField AI Talk Python Courses Links from the show Guests Naunidh Bhalla: linkedin.com Grant Gittes: linkedin.com Marcel Kornacker: linkedin.com Beon de Nood: linkedin.com Chinmaya Joshi: linkedin.com David Mytton: linkedin.com Shea Tate-Di Donna: linkedin.com Jason Rowley: linkedin.com Azul Garza: github.com Renée Rosillo: linkedin.com Tetrix: tetrix.co Tetrix Jobs: tetrix.co Arcjet: arcjet.com Pixeltable: pixeltable.com Phemeral.dev: phemeral.dev CapiscIO: capisc.io Episode #551 deep-dive: talkpython.fm/551 Episode transcripts: talkpython.fm Theme Song: Developer Rap

The World Health Organization defines health equity as a public health concept describing equity of access to health resources for genetic, socio-environmental, and economic determinants of health, varying according to individuals, families, and social or societal groups. Concerns about data equity have surfaced, which may result in many populations, including those in rural areas with disabilities, experiencing homelessness or living in low and middle-income regions of the world, being underrepresented in health data sets. This can lead to biased findings and suboptimal health outcomes for certain subgroups, which is the focus of this episode of Stats+Stories with guest Bhramar Mukherjee. Dr. Bhramar Mukherjee is the inaugural Senior Associate Dean of Public Health Data Science and Data Equity and the Anna M. R. Lauder Professor of Biostatistics, as well as Professor of Epidemiology and of Statistics and Data Science at Yale University. Among her many honors, she was elected to the US National Academy of Medicine in 2022.

What happens when the evidence of injustice is buried in messy, redacted, or inaccessible data? Mathematician and data scientist Chad Topaz joins Breaking Math to discuss his book Unlocking Justice. Together, we explore policing, sentencing, public records, Rikers Island, algorithmic risk, and the limits of quantifying human lives. This is a conversation about math, power, transparency, and the small acts of hope that can change systems. Chapters00:00 Introduction and Context of the Conversation01:11 Chad's Journey from Mathematics to Social Justice03:50 The Personal Nature of Chad's Book04:40 Challenges in Data Collection and Access08:03 The Impact of Data on Policing and Surveillance09:51 Humorous Yet Tragic Data Collection Experiences12:55 The Importance of Data Preparation and Cleaning14:40 Navigating Imperfect Data and Its Consequences17:48 The Balance Between Quantification and Human Stories22:25 Incarceration and Public Health: The Rikers Island Case Study31:36 Mathematics and Social Justice: Secrets of the Elite39:03 Hope and Action: A Personal Journey in Data for JusticeFollow Chad Topaz onBluesky(https://bsky.app/profile/chadtopaz.bsky.social) Book (https://amzn.to/3S21pKb)Follow Breaking Math onSubstack (https://breakingmath.substack.com/)X (https://x.com/breakingmathpod)Instagram (https://www.instagram.com/breakingmathmedia/)Bluesky (https://bsky.app/profile/breakingmath.bsky.social)Website (https://www.breakingmath.io/)YouTube (https://www.youtube.com/@BreakingMathPod)Follow Noah onInstagram (https://www.instagram.com/profnoahgian/)X (https://x.com/ProfNoahGian)Bluesky (https://bsky.app/profile/profnoahgian.bsky.social)Follow Autumn onX (https://x.com/1autumn_leaf)Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social)Instagram (https://www.instagram.com/1autumnleaf/)Substack (https://substack.com/@1autumnleaf)email: breakingmathpodcast@gmail.com

Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for handling them.In this episode, Douglas Hubbard joins Dr Genevieve Hayes to share how combining techniques from statistics, economics and decision theory can help data scientists tackle the problems that matter most.In this episode, you'll discover:What Applied Information Economics is and how it works in practice [03:17]Why organisations are systematically measuring the wrong things [09:23]How the Lens Model can make expert judgment more reliable than the expert themselves [13:44]How AI can turbocharge the Applied Information Economics approach [21:10]Guest BioDouglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years' experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It's Broken and How to Fix It.LinksHow to Measure Anything websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: Vulnerability and malware checks in uv HTTP GET requests with the Python standard library Millions of AI agents imperiled by critical vulnerability in open source package alembic-git-revisions Extras Joke Watch on YouTube About the show Goodbye and Thanks Brian Thanks Calvin for being part of this and future episodes! Also new time for the live show. Thanks Brian for all the hard work over the years. Calvin #1: Vulnerability and malware checks in uv release just yesterday by Astral https://astral.sh/blog/uv-audit uv audit scans dependencies for known vulnerabilities and abandoned packages via the OSV database — runs 4–10x faster than pip-audit Malware check runs on every install/sync, catching actively malicious packages (credential stealers, etc.) before they execute — including ones PyPI quarantined but lockfiles can still reference Enable malware scanning with UV_MALWARE_CHECK=1 — it's opt-in and in preview Future roadmap includes a resolver that steers toward vulnerability-free versions and install-time warnings scoped to newly added deps only Michael #2: HTTP GET requests with the Python standard library If you're doing HTTP in Python, you're probably using one of three popular libraries: requests, httpx, or urllib3. There have been issues with httpx lately. Niquest is another option: Drop-in replacement for Requests. Automatic HTTP/1.1, HTTP/2, and HTTP/3. WebSocket, and SSE included. But maybe less is more, especially in the age of agentic AI A good candidate needs two things to be true at once, not one: the used surface is small, and the behavior behind that surface is shallow. Calvin #3: Millions of AI agents imperiled by critical vulnerability in open source package "BadHost" (CVE-2026-48710) is a critical vulnerability in Starlette — the ASGI framework underlying FastAPI — with 325 million weekly downloads; also affects vLLM, LiteLLM, and most MCP server tooling The exploit is trivial: injecting a single character into an HTTP Host header bypasses path-based authentication, and can lead to credential theft, SSRF, and in some cases remote code execution MCP servers are a prime target since they store credentials for external services (email, databases, cloud accounts) — exposed data in the wild includes biopharma clinical trial DBs, full mailboxes, HR/PII pipelines, and AWS topology Fix is available — patch to Starlette 1.0.1 immediately; use the free scanner at mcp-scan.nemesis.services to check if your servers are still running a vulnerable version Open source sustainability footnote: the maintainer triages near-daily security reports solo, in his free time — most are AI-generated noise, and real ones like this still compete for the same evenings and weekends Michael #4: alembic-git-revisions By Julien Danjou from Mergify Automatic Alembic migration chaining based on git commit history. No more Multiple head revisions are present for given argument 'head'. See the introductory article Caused by two migrations landed with the same down_revision, and Alembic doesn't know which one comes first. The fix is always the same: someone manually edits the migration file to re-chain the revisions. The insight: git already knows the order Extras Calvin: GNU make can do pattern matching in the target. Not new at all, mentioned in the 1994-era docs. just and task don't have this super power on the target name yet. train-%: uv run ./train.py $* --save-hyper-params --overwrite $(TRAIN_ARGS) Michael: Updated my HTTP client using packages from httpx to httpx2: listmonk, umami, and memberful. For motivation, see this reddit thread. Joke: Accurate

AI vam neće uzeti posao... osim ako niste osrednji u onome što radite. Evo šta se zapravo dešava u industriji.

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Bet the Process, Ron Yurko joins to discuss his role at the Department of Statistics & Data Science at Carnegie Mellon. He teaches a course on sports betting where students place bets on a fake sportsbook, using statistical models and probability theory.

My conversation with Andrea starts at about 22 minutes in to today's show after headlines and clips Subscribe and Watch Interviews LIVE : On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous soul On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Andrea Jones-Rooy, Ph.D., is a data and social scientist, science educator, standup comedian, and circus performer. They are a professor and the Director of Undergraduate Studies at the NYU Center for Data Science, where they teach the flagship undergraduate course, Data Science for Everyone, as well as advanced courses on Natural Language Processing. Andrea is also a research consultant and keynote speaker for global Fortune 500 and tech companies of all sizes on how to thoughtfully integrate data science into achieving their goals, especially in the people analytics space. When they aren't doing those things, they perform standup, trapeze, and fire all over the world. Andrea hosts the podcast Majoring in Everything and is working on a book about why focusing on just one thing is overrated. Get in touch after the interview… • @jonesrooy on Twitter, Instagram, and TikTok www.jonesrooy.com  jonesrooy@gmail.com Listen rate and review on Apple Podcasts Listen rate and review on Spotify Pete On Instagram Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on Twitter Pete Personal FB page Stand Up with Pete FB page Gift a Subscription https://www.patreon.com/PeteDominick/gift Send Pete $ Directly on Venmo All things Jon Carroll  Buy Ava's Art  Subscribe to Piano Tuner Paul Paul Wesley on Substack Listen to Barry and Abigail Hummel Podcast Listen to Matty C Podcast and Substack Follow and Support Pete Coe Hire DJ Monzyk to build your website or help you with Marketing

AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place.In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice.In this episode, you'll discover:Why looking for problems to solve with AI is a warning sign [02:05]What happens when you use AI before you have the expertise to direct it [05:51]Why your AI interactions should be conversations rather than one-way requests [06:54]How to use AI to become a better thinker not just a faster worker [08:40]Guest BioTim Dietrich is an independent software developer with over 25 years' experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one.LinksConnect with Tim on LinkedInTim's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: CVE-2026-48710: A Maintainer's Perspective daily-stars-explorer Markdown to pdf with pandoc and typst postman2pytest Extras Joke Watch on YouTube About the show Brian #1: CVE-2026-48710: A Maintainer's Perspective Marcelo Trylesinski suggested by Lee Luocks Short version: users of Starlette: upgrade to Starlette 1.0.1 security professionals: we can't treat open source projects like corporations This top link is a Starlette security advisory with the title Missing Host header validation poisons request.url.path, bypassing path-based security checks The CVE apparently caused some negative press targeting starlette. However, “the vulnerability came from the application pattern and the deployment, never from something Starlette intended.” A quote from an OSTIF article: “This bug is a classic “responsibility gap” where if this maintainer didn't patch, thousands of exposed projects would have to individually secure their projects. In doing this work, they've voluntarily taken on the responsibility to protect the ecosystem from long-term systemic harm. As with all open source projects, they owed us nothing and could have left this to be everyone else's problem and took the extraordinary steps of helping the ecosystem.” Both X40 D-Sec and Ars Technica expected immediate fixes and responses from Starlette. That's not good. We can do better. Michael #2: daily-stars-explorer Explore the full history of any GitHub repository.

You wake up, brew the coffee, open GitHub, and there it is. Another pull request on your open source project. Thirteen thousand lines added. No issue filed first. No discussion. Just "here, please review this for me." Over the past year, GitHub activity has spiked roughly twelve times in a few short months, and a huge chunk of that signal is landing on the same small group of maintainers who were already stretched thin. The curl bug bounty got buried under AI-generated noise. Jazzband, the home of Django classics like pip-tools and the Django debug toolbar, hit what its maintainer called an "apocalypse" and started sunsetting. Even CPython just shipped fresh guidelines on AI-assisted contributions this week. So what does all of this actually look like from the receiving end of the pull request? On this episode, Paolo Melchiorre joins us to tell that story from inside the maintainer's chair. Paolo is a director of the Django Software Foundation, an organizer of PyCon Italy, a Django Girls coach, and he has spent the past year carefully collecting examples of how AI is reshaping open source contributions. The good, the bad, and the extra fingers. We dig into his PyCon US talk on AI-assisted contributions and maintainer load, why AI is best understood as an amplifier rather than a new kind of contributor, the wildly different policies across 86 open source foundations, whether projects banning AI today are reacting to last year's models. Episode sponsors AgentField AI Talk Python Courses Links from the show Guest Paolo Melchiorre: github.com DSF: www.djangoproject.com djangonaut-space: djangonaut.space PyCon Italia: 2026.pycon.it uDjango: github.com My PyCon US 2026 post: www.paulox.net AI-Assisted Contributions and Maintainer Load: www.paulox.net Senior Engineer Tries Vibe Coding: www.youtube.com Code Rabbit AI PR Reviews: www.coderabbit.ai GitHub Usage Graphs: github.blog Update on CPython's AI Policies: fosstodon.org High-Quality Chaos from Curl: daniel.haxx.se The Generative AI Policy Landscape in Open Source: redmonk.com Watch this episode on YouTube: youtube.com Episode #550 deep-dive: talkpython.fm/550 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Modern propaganda isn't random noise. It's a repeatable, engineered algorithm that starts with ideology, weaponizes identity, and manufactures conflict. Once you see the pattern, you can't unsee it. What happens with AI?      Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

The power of choice is in full effect! How you can leverage GitLab to publish your next Quarto document online, how to bring key R functional paradigms to a Python session, and adding a larger safety net with your unit tests with {mutagen} 0.2.0. Episode Links This week's curator: Jon Carroll - @jonocarroll@fosstodon.org (Mastodon) & @jonocarroll.fosstodon.org.ap.brid.gy (Bluesky) & @carroll_jono (X/Twitter)Deploying Quarto documents with GitLabFunctions over Idioms - Writing R in Python with rfunsmuttest 0.2.0: More Mutators, Better Reporting, and Parallel ExecutionEntire issue available at rweekly.org/2026-W22Supplement ResourcesData Science at the Command Line https://datascienceatthecommandline.com/DevOps for Data Science https://do4ds.com/{pak} System Requirements https://pak.r-lib.org/reference/sysreqs.htmlSupporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemix Wrestling with Double Bass - Street Fighter II - Malcos - https://ocremix.org/remix/OCR01270A Simple Flip can Change Fate - Final Fantasy VI - Level 99 - https://ocremix.org/remix/OCR02692

The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a virtual team of over 100 AI specialists to dramatically expand what he is able to achieve.In this episode, Tim joins Dr Genevieve Hayes to share the principles and practicalities behind building a virtual AI team, and what data scientists can learn from his experience.In this episode, you'll discover:How Tim went from being the "world's most negative person on AI" to building a virtual team of over 100 specialists [03:08]What a virtual team of AI specialists can do that a human team can't [06:11]How to build your first AI agent and what to delegate to it [14:19]Why the human in the middle is still the most important person on the team [17:11]Guest BioTim Dietrich is an independent software developer with over 25 years' experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one.LinksConnect with Tim on LinkedInTim's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Will Parrish is the Co-Founder and Chief Customer Officer of Lula, a Kansas City-based proptech platform built to streamline property maintenance for property managers and their residents. Will co-founded Lula alongside CEO Bo Lais with a mission to make property maintenance smarter — pivoting the business during the pandemic to focus on property managers in the single-family rental space, a move that fueled rapid growth. Lula recently closed a $28 million Series A round and is expanding from 42 markets to 60, with heavy investment in AI and automation. Before co-founding Lula, Will spent nearly two decades in enterprise sales and business development, including a long tenure at Thomson Reuters. (00:53) - How Lula Started(02:34) - Trading Corporate for Startup Life(03:29) - Is Maintenance Archaic(05:49) - Where Work Orders Fail(07:30) - Scaling 100K Work Orders(12:28) - Building Vendor Trust & Quality(13:19) - Expanding Markets(16:16) - Flat Rate Pricing Playbook(19:15) - Ideal Rental Customers(21:54) - Integrations(25:47) - AI In Maintenance(30:21) - Future of Lula(32:14) - ROI for Property Owners & Operators(35:49) - Hardware play ahead?(39:12) - Collaboration Superpower: MacGyver

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Five years ago I made the scariest decision of my life. Here's the full story.

Gareth McGlynn speaks with Nathan Schafer, Estimating Manager at Cornerstone General Contractors, at Advancing Preconstruction 2026 in Phoenix. A self-performing GC based in Alaska, Cornerstone gives Nathan a hands-on perspective on preconstruction that is grounded in real field conditions.Key Topics Covered:Nathan & Cornerstone: Working across healthcare, hospitality, and federal military projects, with a focus on vertical commercial construction.New Estimators & Value Engineering: The shortage of talent entering the field, their understanding of value engineering, and professional development through ASPE and AACE.Lean Construction: Its growing impact on the estimating process and the role of AI takeoff tools as a lean principle in action.Data Science in Preconstruction: How Cornerstone is incorporating data science into its workflow, including labor productivity tracking and predicting quantity growth risk as a function of design maturity.Value Management in Practice: Cornerstone's formal pilot on a $90M project: 13 propositions totaling $9M, with $7M accepted.You can connect with Nathan via his LinkedIn: https://www.linkedin.com/in/nathan-schafer-cpe-b75991178/Or reach him through his blog: https://www.preconomics.com/blog

Your documentation has two audiences now - humans reading the rendered HTML, and AI agents trying to make sense of your library. Rich Iannone and Michael Chow from Posit are back on Talk Python with a brand new Python documentation tool called Great Docs that takes both seriously. Rich is the creator of Great Tables, and before that the R package GT, the man has a serious eye for design, and he's pointed that energy at the Python docs ecosystem. We'll talk about how Great Docs spins up a polished site in three commands, why every page ships as Markdown for your favorite LLM, how it leans on Quarto for executable code blocks and tabbed install sections, and where it lands against Sphinx, MkDocs, and Zensical. Plus, you'll meet Tablin. Here we go. Episode sponsors Sentry Error Monitoring, Code talkpython26 Temporal Talk Python Courses Links from the show Guests Michael Chow: github.com Rich lannone: github.com Python Web Security with OWASP Top 10 and Agentic AI Course: talkpython.fm Great Docs: posit-dev.github.io/great-docs Great Tables: posit-dev.github.io GT Episode: talkpython.fm Sphinx: www.sphinx-doc.org mkdocs: www.mkdocs.org Zensical: zensical.org Hugo: gohugo.io Ghost: ghost.org Rs pkgdown: pkgdown.r-lib.org Quarto: quarto.org quickstart: posit-dev.github.io llms.txt file: llmstxt.org llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm Watch this episode on YouTube: youtube.com Episode #549 deep-dive: talkpython.fm/549 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Dumb Ways for an Open Source Project to Die How to create a pylock.toml lockfile https://github.com/facebook/Lifeguard Choosing a Python Logging Library in 2026 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: Dumb Ways for an Open Source Project to Die Core categories The maintainer left The maintainer is still there Sabotage and capture The release pipeline broke Force majeure The world moved on The project split - Examples Bulma PRs still from 2023, issues and PRs with no maintainer response for years, last release 1.5 years ago diskcache Similar, got hired by OpenAI, crickets after that Brian #2: How to create a pylock.toml lockfile Tim Hopper Tim walks through using uv, pip and pdm to create pylock.toml files. Recommendation: use uv export --format pylock.toml -o pylock.toml He also has How to install from a pylock.toml lockfile with pip but the short version is: use -r because tools treat it like a requirements file Michael #3: https://github.com/facebook/Lifeguard Lifeguard is a static analyzer to detect Lazy Imports incompatibilities and ease the adoption overhead for Lazy Imports in Python. I'm more excited about lazy imports after my Cutting Python Web App Memory Over 31% experience Some Python patterns depend on imports executing immediately. For example: Module-level side effects — a module that registers a handler or modifies global state at import time will behave differently if that import is deferred. The registry pattern — a module that registers itself (e.g., adding to a global dict) when imported will silently fail to register under Lazy Imports. sys.modules manipulation — code that reads or writes sys.modules assumes prior imports have already executed. Metaclasses and __init_subclass__ — class creation side effects may depend on imports being resolved. Project Stage: Beta Lifeguard is in active development. We are aiming to be ready for general use by the Python 3.15 final release. Brian #4: Choosing a Python Logging Library in 2026 Ayooluwa Isaiah " which libraries matter, how they compare, where they overlap with the standard module, and when each one makes sense.” The slant with this article is the need to log json output, which seems reasonable as things like API entry and exit point logging will include json. Covered libraries standard library logging with a hat tip to python-json-logger Same site has a guide to setting up python-json-logger structlog Loguru Logbook picologging Some benchmarks with structlog, stdlib+json, and Loguru, with structlog coming out faster I liked the Loguru example I'm going to have to try @logger.catch and logger.exception() for easily logging exceptions and serialize=True to enable JSON output. Extras Brian: When Women Stopped Coding - Planet Money segment , spotted on BlueSky from Savannah Ostrowski Lean TDD is now leaner Still working on audio version, but some great changes in 0.7.1 version Ch 6, TDD Interpretations, move ATDD and some of BDD to chapter Ch 7, Change name to TDD with Teams: BDD and ATDD Ch 9, Lean TDD, streamline steps and chapter Ch 10, Change name to Lean TDD with Teams: Lean ATDD Ch 11, Lean TDD with AI, Add short discussion about guardrails and security Michael: New course: Python Web Security: OWASP Top 10 with Agentic AI All courses now with Spanish subtitles, see announcement Joke: Stop texting me

So I think we're really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it's mostly viewed as a cost center in most companies. I think we can really now fulfill that original promise with agentic data science. Thomas Wiecki, Co-creator of PyMC and Founder at PyMC Labs, joins Hugo to talk about how agentic data science is finally fulfilling the promise of Decision Intelligence.We Discuss:* Decision Engines: Transform data science from a cost center providing cryptic answers into a real-time decision intelligence hub delivering actionable outcomes;* Tame the “Garden of Forking Paths”: Overcome human shortcuts by running parallel analyses to provide an honesty check, revealing the true robustness of business conclusions;* Multiplayer Data Science: Foster organizational learning by moving agents into team chats, democratizing “what-if” questions and reducing context-switching friction;* The Full Agentic Data Science Stack: Beyond harness and skills, the full stack includes orchestration for parallel analyses and a causal eval layer to measure actual outcome improvement;* Agentic Dashboards: Move beyond static BI; use chat interfaces to inquire into models and generate real-time, custom visualizations for specific follow-up questions;* Encode Professional Judgment as Skills: Elevate agent performance by encoding expert domain standards and high-fidelity workflows into specific Agent Skills, rather than relying on LLM pre-training;* Ground Decisions in Generative Processes: Prevent hallucinations by forcing agents to model underlying physical or behavioral processes, providing a programmatic guardrail aligned with market realities;* Scripted Causal-Bayesian Workflows: Their methodologically structured nature—from prior elicitation to posterior predictive checks—makes Causal-Bayesian workflows inherently automatable for agents;* Iterative Autonomy via Skills: Achieve autonomy iteratively: verify workflows with human oversight, then encode verifiable parts as skills to hand off trusted tasks;You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

In this episode, Cherise is joined by Sam Miller, Partner, and Stephen DeMayo, Principal at LMN Architects in Seattle, Washington. They discuss the Stanford Computing and Data Science Building at Stanford University in Palo Alto, California.You can see the project here as you listen along.At the heart of Stanford University, where historic arcades meet the evolving ambitions of a research-driven campus, the Computing and Data Science (or CoDa) building emerges as both a physical landmark and an intellectual crossroads. The Hive stair, rendered in Stanford's signature red, is more than circulation—it is a symbol. Its perforated guardrails subtly encode 8-bit binary patterns, transforming a foundational language of computing into a tactile architectural expression. As users move through the space, the stair animates the building, embodying the dynamic, interconnected nature of data science itself.If you enjoy this episode, visit arcat.com/podcast for more.If you're a frequent listener of Detailed, you might enjoy similar content at Gābl Media.Mentioned in this episode:Social Channel Pre-rollPromotes the YouTube channel, ARACTemy, and social handle.

From years in the SEO trenches, today's guest knows what it takes to run successful strategies. Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B, SaaS and marketing agencies.Adrian Dahlin is the Founder & CEO of Search to Sale, an SEO analytics SaaS company providing automatic content intelligence for B2B SaaS and marketing agencies. He began his entrepreneurial journey in 2020 after leaving corporate marketing to launch a startup consultancy, later evolving it into Search to Sale in 2023. Previously, Adrian worked in data science and marketing analytics after earning a Master's in Applied Data Science from NYU, and earlier in his career founded and led sustainability-focused ventures. CONTACT DETAILS:Email: gerardo@searchtosale.io Business: Search to SaleWebsite: https://www.searchtosale.io/ Social Media:LinkedIN: https://www.linkedin.com/in/adriandahlin/ LinkedIN Company: https://www.linkedin.com/company/search-to-sale-seo-revenue-generation-software/ Remember to SUBSCRIBE so you don't miss "Information That You Can Use." Share Just Minding My Business with your family, friends, and colleagues. Engage with us by leaving a review or comment. https://g.page/r/CVKSq-IsFaY9EBM/review Your support keeps this podcast going and growing.Visit Just Minding My Business Media™ LLC at https://jmmbmediallc.com/ to learn how we can help you get more visibility on your products and services. 

In this episode, host Josh interviews entrepreneur Rolando Rosas about his journey from office technology to Amazon selling and founding Circuit Com. Rolando shares his advanced PPC strategy, using a year's worth of sales data and heat maps to optimize Amazon ad scheduling for better ROAS. He offers practical tips for sellers: enhance product images, respond to customer questions with videos, and use data tools like Seller Labs Data Hub to identify peak buying times. Rolando encourages starting small with data-driven ad adjustments to boost efficiency and sales.Chapters:Introduction to Rolando Rosas and His Journey (00:00:00)Josh introduces Rolando, his entrepreneurial background, and the founding of Global Tech Worldwide and Circuit Com.Podcast Sound Effects and Stream Deck Tips (00:01:15)Rolando shares his experience setting up podcast sound effects and encourages using a stream deck.Introduction to Innovative Amazon PPC Strategy (00:01:38)Josh prompts Rolando to share his unique PPC strategy, setting the stage for the main discussion.Data-Driven Ad Scheduling and Heat Maps (00:02:13)Rolando explains using 12 months of order data and Seller Labs Data Hub to create heat maps for ad scheduling.Key Insights from Data: Golden Hours and Days (00:02:59)Discovery of optimal times and days for ads, including patterns like low Friday evening and weekend sales.Challenging Weekend Ad Spend Myths (00:04:12)Rolando debunks the idea that weekends are best for ads, showing most sales occur Monday–Friday.Impact on ROAS and Sales Performance (00:06:03)Discussion of improved ROAS and sales by focusing ad spend on high-performing days and times.Layering Day Parting and Low Bid Strategies (00:07:02)Exploring advanced ad scheduling, including low bid strategies during off-peak hours.Manual vs. Automated Campaign Management (00:08:31)Rolando discusses the manual nature of their current process and the use of portfolio grouping for easier management.Leveraging Seller Labs Data Hub for Insights (00:09:36)How to use Seller Labs Data Hub for actionable business insights, even for non-data experts.The Importance of Data Science and AI for Sellers (00:10:53)Emphasizing the future role of data analytics and AI in Amazon selling success.Three Actionable Takeaways for Amazon Sellers (00:11:56)Josh summarizes three key takeaways: main image optimization, customer Q&A engagement, and data-driven ad scheduling.Encouragement to Start Small and Test Strategies (00:15:20)Advice to implement changes gradually, testing on a few campaigns or SKUs before scaling.Closing Remarks and Appreciation (00:16:18)Josh and Rolando wrap up the episode, express mutual appreciation, and end the conversation.Links and Mentions:Tools and Websites"Global Teck Worldwide": "00:00:00""Seller Labs Data Hub": "00:02:59""Google Sheets": "00:10:08"Strategies and Concepts"Day Parting": "00:02:13""Heat Map": "00:02:59"Actionable Takeaways"Adjust Main Images": "00:11:56""Respond to Customer Questions": "00:12:07"Transcript:Josh 00:00:00  Today I'm super excited to introduce you all to Rolando Rosas. Rolando never could have predicted that a college computer, a printer, and an old school wall phone in his kitchen would lead him down the path of entrepreneurship. But that's exactly how it happened. In 2002, he founded Global Tech Worldwide with the goal of making it easy for businesses to use the right office technologies for better and frictionless customer interactions that help businesses elevate their customer interactions and turn them into rich, meaningful discussions. Fast forward to today, and after spending ten years selling on Amazon, he is on his third startup circuit. Com because he was frustrated with the lack of transparency and outdated methods of buying broadband, wireless and fiber internet for small and medium sized businesses. So with that introduction, welcome to the show, Rolando.Rolando 00:00:53  Woo! Woo woo woo woo. Woo woo. Let me try. Let me try.Josh 00:00:56  Hey, there you go. Hey.Rolando 00:00:57  There we go.Josh 00:00:58  You got the audio work?Rolando 00:00:59  I got it, I got it I got him to work.Josh 00:01:02  Rolando has his own podcast and we recorded an episode last week I was on, I was in the reverse side. I was the guest there. And that I told you, Rolando, I love the sound effects that you have going on in your podcast.Rolando 00:01:15  You know what? I'm here. You know what? Go get a stream deck, go get it and call me, and I'll help you set it up. Because it took me a while. I left it in the box for quite some time before I actually started using it, because I was a little intimidated. I'm not an Avi guy or anything like that, but, you know, I was like, all right, let me add one, two, three. And I was like, ooh. And now I've got a couple of those buttons set up for it.Josh 00:01:38  I love it, I love it. All right, Rolando, there's another really wicked smart strategy that I want you to share with our audience that you shared with me prior to hitting the record button.Josh 00:01:48  And this is your amazing PPC strategy that I have never heard anybody else talk about this other than yourself. everybody's always heard of de parting, right? And that's kind of the new hot PPC term, but this isn't Dave Harding. This is something, I think, even more intelligent than what De parting is. So I've laid out the red carpet for you there, Rolando. Give us the gold nugget.Rolando 00:02:13  Yeah, right. So de parting is just simply ad scheduling. You know, run an ad on a schedule. Nothing new there. But what if. Chad. Chad, I was just talking to Chad. What if Josh. We could map or have ads show up when we have our ideal customers on Amazon? How can we do that? Can we pull it off? And can we save money while we're doing that? That's really what we wanted to find out. Turns out there is a way to do it. Not easy, not clean. But there was. So we went and pulled data from our orders for 12 months, and we used, Seller Labs product that they have or service that's called Data Hub.Rolando 00:02:59  and it pulled in all that data, right? It's our own data. So we didn't have to do all these crazy reports from Amazon. Pulled it all in. Once they pulled that in I said, wait a minute, guys. I'm not a mathematician here. This is just a spreadsheet with a bunch of numbers. Can we do something better? So then we put together something that anybody could easily use in the organization. We put together a heat map so that you can visually see the data. And, you know, dark green means good, red is bad. And guess what? We found golden hours every day of the week. Also golden months also patterns within those months. For example summertime for our products which are mostly office related products. After 4 p.m. on a Friday, we've virtually had no orders on the summer months. So if I'm a betting man, Why would I run PPC after 4 p.m. if we're not getting any orders? Another one was when? on the weekends, you hear people say this all the time.Rolando 00:04:12  And now that I have the data for our stuff, I know it's totally wrong. You got to run ads on Saturday and Sunday because people browse Saturday and Sunday and buy on Monday. The evidence does not hold that up in our case, because in our case, most of our activity, nearly 85 to 90% of the purchases c...

Is artificial intelligence creating a helpful resource for your customers, or is it building a wall between them and your sales team? We caught up with Dr. John Coles, Vice President of Data Science and Analytics at ACV, for an exclusive sneak peek at the machine learning and vehicle valuation strategies he is bringing to the VADA '26 Convention at the Marriott Virginia Beach Oceanfront . Register for VADA '26: https://vada.com/convention/ In this bonus "Convention Sneak Peek" episode, Dr. Coles explains that modern consumers demand absolute transparency . He explores how to effectively utilize machine learning in the back office, the critical necessity of multi-source information fusion, and how to stop overwhelming your staff with too many software tools . In this episode: The "Zero Surprises" Consumer — Modern buyers are fiercely protective of their time . As Dr. Coles notes regarding his own car buying experience, "The thing that I look for as a consumer when I walk in is zero surprises on a cost side" . The New Normal — With lease returns growing and margin compression remaining a stark reality, dealers must utilize data to quickly position each vehicle for the right consumer . As Dr. Coles warns, "We're never going back to an old normal" . Speed to a Human — If you introduce AI as a friction point between your dealership and the customer, you put the relationship at risk . AI should be used in the back office because, as Dr. Coles puts it, "Right now, for me, it's all about speed to a human" . Stop Software Overload — Dr. Coles breaks down the change management strategies needed to actually implement data-driven tools without burning out staff . "If you lob nine software solutions in and see what works... I'll give you a hint. None of them will work" .

These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer. In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't the answer, how to recognise them, and how to have the conversation with stakeholders who are convinced it is.In this episode, you'll discover:The types of problems where AI consistently falls short [01:36]How to recognise when AI is the wrong tool for the job [04:46]Why most AI conversations eventually lead back to data, people and processes [06:25]How to push back on an AI solution without losing stakeholder confidence [09:43]Guest BioSantosh Kaveti is the CEO and Founder of ProArch, a technology consultancy that helps enterprises operationalise AI securely and at scale. His expertise spans critical infrastructure industries, including power generation, manufacturing and healthcare, where he has seen firsthand how AI can drive business transformation in complex regulatory environments.LinksConnect with Santosh on LinkedInProArch websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

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Topics covered in this episode: Using Django Tasks in production Co-authored with Claude? PyPI packages are increasing rapidly httpx2 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: Using Django Tasks in production Tim Schilling shares how the Djangonaut Space website has been using Django's new tasks framework and some of the info missing from the official Django docs. Tasks require a third party package, django-tasks-db to actually run the tasks. Article walks through all changes necessary to get an email process running to notify admins of new testimonials. Cool simple example. With the db backend, you can monitor progress of tasks in the admin, to see which tasks are scheduled, completed, or have errors. Some wishes for the community to implement new tutorial in the Django docs Django Debug toolbar panel for tasks test/mock backend Great title for wish list: Thinks I'd like to see, but I'm too lazy to implement myself. Michael #2: Co-authored with Claude? Via Nik T. We don't put “executed on macOS”, “edited with PyCharm”, etc. in our commits. Why Claude? Seems like a growth hack to me, that I don't really care to participate in. Some projects that have formalized their thoughts on this: The Generative AI Policy Landscape in Open Source Adjust to turn off in ~/.claude/settings.json see the docs. { "attribution": { "commit": "", "pr": "" } } Brian #3: PyPI packages are increasing rapidly Artem Golubin There's been an increase of published packages per week on PyPI A pretty big increase in the last handful of months. 30% increase since 2025, clearly due to AI Artem is building hexora, a malicious Python code detector. Cool package too, it can: Audit project dependencies to catch potential supply-chain attacks Detect malicious scripts found on platforms like Pastebin, GitHub, or open directories Analyze IoC files from past security incidents Audit new packages uploaded to PyPi. Artem is using hexora to analyze recently published pypi packages and many are obviously vibecoded and trigger false positives for abuses of eval, exec, and subprocess Side note: I don't think that's necessarily a false positive. Not malicious, but maybe a stupid-code-detector? Lots are LLM related, Lots have bots contributing code Publishing rate is crazy, dozens to hundreds of published versions in a day is a bug, not a feature Brian's proposal, PyPI should limit releases per day for any package to something a sane human would do, even if they make a mistake on a release, to maybe like 2-3, definitely under 10, in a day. And if the repo has obvious agent contributors listed, maybe lower to the limit to 1-2 a day? Honestly, “move fast and break things” doesn't apply to breaking the commons. Michael #4: httpx2 More on the httpx, httpxyz, etc changes: Pydantic people started their own fork, httpx2. Michiel says “while we think httpxyz was definitely needed, we welcome httpx2 and think it should be the ‘blessed' fork.” Kludex, who is among other things maintainer of Starlette, was considering a fork As it stands, httpx2 is lacking the performance improvements they added to httpxyz. But it will not be long before they will add those, too. Also they already made some smart decisions: they are switching from certifi to truststore they are switching to compression.zstd on Python 3.14+, enabling zstd compression by default they merged httpcore and vendored it in their repository Discussion on Hacker News Extras Brian: The Four Horsemen of the LLM Apocalypse - Anarcat Django/JetBrains 2026 developer survey is open Pyrefly 1.0 : “meaning we are confident that Pyrefly is ready for production use.” Michael: Just about ready to release Python Web Security: OWASP Top 10 with Agentic AI course. Be sure to be on the courses newsletter to get notified. Joke: Proud Parents

What if your database worked more like Git? Every change captured as an immutable event you can replay, instead of a single mutating row that quietly forgets its own history. That's event sourcing, and Chris May is back on Talk Python, fresh off our Datastar panel, to walk us through what it actually looks like in Python. We'll cover the core patterns, the libraries to reach for, when not to use it, and why event sourcing turns out to be a surprisingly good fit for AI-assisted coding. Episode sponsors Sentry Error Monitoring, Code talkpython26 Temporal Talk Python Courses Links from the show Guest Chris May: everydaysuperpowers.dev Intro to event sourcing e-book: everydaysuperpowers.gumroad.com Domain-Driven Design: The Power of CQRS and Event Sourcing: How CQRS/ES Redefine Building Scalable System: ricofritzsche.me DDD: www.amazon.com Understanding Eventsourcing (Martin Dilger): www.amazon.com Event Sourcing Explained using Football Video: www.youtube.com Why I finally embraced event sourcing and why you should too article: everydaysuperpowers.dev valkey: valkey.io diskcache: talkpython.fm eventsourcing package: github.com eventsourcing docs: eventsourcing.readthedocs.io John Bywater: github.com Datastar: data-star.dev Microconf: microconf.com Event Modeling & Event Sourcing Podcast: podcast.eventmodeling.org Python Package Guides for AI Agents: github.com Iodine tablets AI joke: x.com KurrentDb: www.kurrent.io Watch this episode on YouTube: youtube.com Episode #548 deep-dive: talkpython.fm/548 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: httpxyz one month in Learn concurrency - a deep dive into multithreading with Python pip 26.1 - lockfiles and dependency cooldowns Python 3.15 sentinal values from PEP 661 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: httpxyz one month in First version of httpxyz contained just the fixes to get zstd working, and the fixes to get the test suite running on python 3.14, some ‘housekeeping' changes related to the renaming End of March: a compatibility shim that allows you to use httpxyz even with third-party packages that import httpx themselves, as long as you import httpxyz first. Importing httpxyz automatically registers it under the httpx name in sys.modules , see https://httpxyz.org/httpx-compatibility/ Fixed a WHOLE bunch of performance related issues by forking httpcore Brian #2: Learn concurrency - a deep dive into multithreading with Python Nikos Vaggalis “Whenever you are trying to speed up code using multiple cores, always ask yourself: “Do these threads need to talk to each other right now?” If the answer is yes, it will be slow. The best parallel code splits a big job into completely isolated chunks, processes them separately, and merges the results at the finish line.” Good overview of thread concurrency with Python and how that's been improved dramatically with free-threaded Python Defines lots of terms you come across, including “embarrassingly parallel multithreading” There's a counter example that's nice Start with a shared resource, a counter, and multiple threads updating it Attempt to fix with threading.Lock(), which fixes it, but slows things down Good explanation of why Proper fix with concurrent.futures and separating the work of different threads so that they can be independent and their results can be combined when they're all finished. Michael #3: pip 26.1 - lockfiles and dependency cooldowns Python 3.9 is no longer supported Experimental: installing from pylock files Dependency cooldowns (see my post about this) Lifting several 2020 resolver limitations Brian #4: Python 3.15 sentinal values from PEP 661 MISSING = sentinel("MISSING") def next_value(default: int | MISSING = MISSING): ... if default is MISSING: ... Take a name str as a constructor parameter Intended to be compared with is operator, similar to None Sentinal objects can be used as a type, also similar to None and can be combined with other types with |. Unlike None, sentinal values are truthy. (Elipses ... are also truthy) This seems like a strange choice. but I guess it must have made sense to someone. It does force you to use is instead of depending on False-ness, so I guess it'll make code using sentinels more readable. Interesting that the PEP was started in 2021, and we're finally getting it this year. Extras Brian: Before GitHub - Armin Ronacher tenacity - cross-platform multi-track audio editor/recorder learned about it from Armin's article Joke: Joke option Make it myself Seems similar to what people think about software now Links httpxyz one month in httpxyz.org/httpx-compatibility Learn concurrency - a deep dive into multithreading with Python pip 26.1 - lockfiles and dependency cooldowns my post about this Python 3.15 sentinal values from PEP 661 Before GitHub tenacity Make it myself

For decades, organizations have talked about paying for skills instead of jobs.  The idea is simple. Reward people based on what they can do, not just the role they hold.  But in practice, it has always been difficult to execute.  Skills are hard to define, harder to measure, and nearly impossible to track consistently across a workforce.  At the same time, the market is shifting fast.  AI-related skills are in high demand, showing up in job postings across industries. But new data shows those skills don't always translate into higher pay.  So organizations are facing a disconnect.  They know skills matter more than ever. But they don't yet have the systems or structures to consistently pay for them.  In this episode of Comp and Coffee, Ruth Thomas is joined by Sara Hillenmeyer, VP of AI and Data Science at Payscale, to explore why skills-based pay has remained out of reach and why that may finally be changing.  Together they unpack how AI is reshaping demand for skills, why the market isn't consistently rewarding them yet, and what needs to happen for skills-based pay to become a reality at scale.  This conversation looks at the data, the technology gap, and the structural shifts required for organizations to move from jobs-based to skills-based compensation. 

When OpenAI trained GPT-3, they didn't roll their own orchestration layer. They used Ray, an open source Python framework born out of the same Berkeley research lab lineage that gave us Apache Spark. And here's the twist: Ray was originally built for reinforcement learning research, then quietly faded as RL hit a wall. Until ChatGPT showed up. Suddenly reinforcement learning was back, as the post-training step that turns a raw language model into something genuinely useful. Edward Oakes and Richard Liaw, two founding engineers behind Ray and Anyscale, join me on Talk Python to tell that story. We'll trace Ray from its RISE Lab origins at UC Berkeley to powering some of the largest training runs in the world. We'll talk about what Ray actually is, a distributed execution engine for AI workloads, and how a few lines of Python become work running across hundreds of GPUs. We'll cover Ray Data for multimodal pipelines, the dashboard, the VS Code remote debugger, KubRay for Kubernetes, and where Ray fits alongside Dask, multiprocessing, and asyncio. If you've ever stared at a single-machine Python script and thought, "there has to be a better way to scale this", this one's for you Episode sponsors Sentry Error Monitoring, Code talkpython26 AgentField AI Talk Python Courses Links from the show Guests Richard Liaw: github.com Edward Oakes: github.com Ray: www.ray.io Example code (we used for walk-through): docs.ray.io Getting Started with Ray: docs.ray.io Ray Libraries: docs.ray.io kuberay: github.com Watch this episode on YouTube: youtube.com Episode #547 deep-dive: talkpython.fm/547 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Matt Ober, Managing Partner at Social Leverage, joins Jake & Gino to discuss venture capital, fintech investing, data-driven investing strategies, AI, entrepreneurship, and the future of finance. Previously Chief Data Scientist at Third Point and Head of Data Strategy at WorldQuant, Matt shares valuable insights into startup investing, identifying market opportunities, and how technology is transforming the financial world. In this episode: Venture capital & fintech trends Data science in investing Startup growth strategies AI in finance Entrepreneurship & scaling businesses Long-term investing insights Looking to grow your real estate investing business with proven systems and education?  Visit Wheelbarrowprofits.com and start building long-term wealth today. timestamps 0:05 - Introduction by Jake Stenziano 0:13 - Gino responds to Jake 0:18 - Jake's comment on gratitude 0:21 - Gino talks about yesterday's conversation 0:49 - Gino acknowledges Jake's support 1:07 - Discussion about the weather 1:23 - Introduction of guest Matt Ober 1:52 - Matt Ober's introduction 2:01 - Matt shares his career journey 2:30 - Matt talks about his hedge fund experience 2:58 - Discussion on venture firm building 3:28 - Matt talks about his partners 3:37 - Matt discusses the hedge fund space 4:05 - Jake comments on the hedge fund space 4:31 - Matt talks about his current company 5:11 - Discussion on investment thesis 5:30 - Matt explains investment focus 6:29 - Matt talks about investing in people 7:06 - Discussion on adversity and entrepreneurship 7:39 - Jake asks about investing in trust funds 8:28 - Matt discusses work atmosphere 9:05 - Discussion on investment backgrounds 9:35 - Matt talks about global team experience 10:24 - Discussion on competition and relationships 11:00 - Discussion on wealth management 12:16 - Discussion on gambling and prediction markets 13:28 - Discussion on prediction markets as media 14:05 - Discussion on tax loss harvesting 15:17 - Discussion on investment strategies 16:02 - Discussion on borrowing against stock portfolios 17:10 - Discussion on interest rates and loans 18:04 - Discussion on democratizing financial tools 19:24 - Discussion on data and AI 20:55 - Discussion on company adaptation to AI 22:06 - Discussion on layoffs and efficiency 23:26 - Discussion on AI and job skills 24:09 - Discussion on investment lifecycle 25:13 - Discussion on venture scale 26:24 - Discussion on raising capital 27:45 - Discussion on investment success rates 29:10 - Discussion on investment distribution 30:18 - Discussion on timing and product success 31:14 - Discussion on founding teams 32:09 - Discussion on founder challenges 33:25 - Discussion on business similarities 34:25 - Discussion on AI and creativity 35:24 - Discussion on creativity and skills 36:27 - Discussion on AI usage 37:49 - Discussion on sales and networking 38:26 - Discussion on commercial real estate 39:16 - Discussion on loan processes 40:38 - Discussion on real estate debt space 41:06 - Discussion on mortgage processes 42:32 - Discussion on financial planning 43:00 - Discussion on 401k transfers 43:59 - Matt's bold prediction 44:42 - Closing remarks We're here to help create real estate entrepreneurs... About Jake & Gino: Jake & Gino are multifamily investors, operators, and owners who have created a vertically integrated real estate company. They control over $350M in assets under management. Connect with Jake & Gino here --> https://jakeandgino.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In 1973, a bizarre encounter allegedly unfolded on the Isle of Wight, involving two children who claimed to meet an odd, clown-like humanoid figure near Sandown, England. Speaking in odd phrases and appearing to inhabit a strange, makeshift dwelling, the being called itself "All Colors Sam," and despite the obscure origins of the tale, it would eventually gain a cult following within the annals of UFO and high-strangeness lore, remembered today as the story of Sam "The Sandown Clown." Joining us this week on The Micah Hanks Program to discuss this case from ufology's "Odd Files" is Ryan Whalen, a Brooklyn-based researcher, science reporter, and college instructor who holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. Whalen, who also co-hosts the podcast "Cease to Exist", reveals what he and his colleagues recently uncovered about this bizarre 1973 urban legend. What new details have emerged about the case, and one of the alleged witnesses to these eerie events that have since become a mainstay in modern UFO folklore? Want to advertise/sponsor The Micah Hanks Program? We have partnered with the AdvertiseCast to handle our advertising/sponsorship requests. If you would like to advertise with The Micah Hanks Program, all you have to do is click the link below to get started: AdvertiseCast: Advertise with The Micah Hanks Program Show Notes Below are links to stories and other content featured in this episode: NEWS: Trump unharmed after shooting incident at White House correspondents' dinner  WILCOCK UPDATE: UPDATE: (Police and Family Statement) Death Investigation Near Ridge Road Death Investigation Near Ridge Road - Boulder County   SANDOWN CLOWN: The Mystery of the Sandown Clown: Britain's Answer to Bigfoot  CEASE TO EXIST PODCAST: https://ceasetoexistpod.com/  RYAN WHALEN: Ryan Whalen (@mdntwvlf) / Posts / X BECOME AN X SUBSCRIBER AND GET EVEN MORE GREAT PODCASTS AND MONTHLY SPECIALS FROM MICAH HANKS. Sign up today and get access to the entire back catalog of The Micah Hanks Program, as well as "classic" episodes, weekly "additional editions" of the subscriber-only X Podcast, the monthly Enigmas specials, and much more. Like us on Facebook Follow @MicahHanks on X. Keep up with Micah and his work at micahhanks.com.

The connectome is the wiring diagram of a brain, a big matrix that tells us what neurons talk to what other neurons. Understanding it is an important step to understanding how brains work, but a long way from the final answer. A big next step is understanding how neuronal circuits connect to and guide bodily behavior. Very recent work on mapping the fruit-fly connectome has brought us closer to that goal. I talk with neuroscientist Bing Brunton about the connectome, how we can study it to understand bodily motion in flies and other creatures, and where it's all taking us. Chubbies is here to keep you comfy and looking good year-round. Get 20% off with code MINDSCAPE at chubbiesshorts.com/MINDSCAPE! #chubbiespod Upgrade your denim game with Rag & Bone! Get 20% off sitewide with code MINDSCAPE at www.rag-bone.com. #ragandbonepod Support Mindscape on Patreon. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/04/27/352-bing-brunton-on-connecting-the-connectome-to-the-body/ Bing Wen Brunton received her Ph.D. in neuroscience from Princeton University.. She is currently a Professor of Biology and the Richard & Joan Komen University Chair at the University of Washington, with affiliations at the eScience Institute for Data Science, the Paul G. Allen School of Computer Science & Engineering, and the Department of Applied Mathematics. Web site University of Washington web page Google Scholar publications YouTube channel Bluesky Artworks (Instagram)