Podcasts about Data science

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

In this episode, we took a deeper dive into some of the clubs on campus! We had the opportunity to chat with the president of the Data Science and Machine learning Club, Evan Poulson, as well as the president of Robogals UCalgary, Orin Zaman. They both their clubs and shared their experiences navigating clubs on campus!If you enjoyed today's episode, make sure to subscribe on whatever platform you're listening on. We encourage you to reach out to us, ask us questions about the show, or even suggest topics of interest to you! You can do so by following us on Instagram @uofc_cpsc.Music: Intro / Outro Nowhere Land by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4148-nowhere-land  || License: CC BY http://creativecommons.org/licenses/by/4.0/ Background Loopster by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4991-loopster || License: CC BY http://creativecommons.org/licenses/by/4.0/  Funkorama by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3788-funkorama || License: CC BY http://creativecommons.org/licenses/by/4.0/ I Knew a Guy by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3895-i-knew-a-guy || License: CC BY (http://creativecommons.org/licenses/by/4.0/) Cool Vibes by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3553-cool-vibes || License: CC BY (http://creativecommons.org/licenses/by/4.0/)  Thinking Music by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4522-thinking-music || License: CC BY (http://creativecommons.org/licenses/by/4.0/) Funk Game Loop by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/3787-funk-game-loop || License: CC BY http://creativecommons.org/licenses/by/4.0/ Umbrella Pants by Kevin MacLeod || Link: https://incompetech.filmmusic.io/song/4559-umbrella-pants || License: CC BY (http://creativecommons.org/licenses/by/4.0/)

In episode two of our AI & Antibodies mini-series, we speak to Ryan Emerson, Senior Vice President of Data Science at A-Alpha Bio, to discuss AlphaBind, A-Alpha Bio's antibody-antigen binding-affinity prediction model.We discuss how this model was trained, how it operates and how it has enabled researchers to test mutations designed to optimize an antibody candidate for critical quality attributes computationally, assessing their likely impact on binding affinity, before returning to the wet lab. The conversation also explores the future of AI in antibody engineering and the critical role of high-quality data in advancing the field.Contents[02:20] Current challenges in antibody sequence design [04:20] Presenting AlphaBind[08:40] Demonstrating AlphaBind's effectiveness[11:40] Benefits of AlphaBind and it's applications[16:05] How to make the most of AlphaBind[19:25] Current use of AlphaBind [23:00] Predictions for the impact of AI in antibody engineering[25:40] A brief detour into the uses of AI in drug design (See this story for more detail)[26:45] What wish could be granted to improve AI in antibody design? Hosted on Acast. See acast.com/privacy for more information.

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

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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. 

A large-scale analysis of Grokipedia, the world's first AI-written encyclopedia, has found that while many Grokipedia articles closely resemble their Wikipedia counterparts, a substantial subset diverged markedly in style, sourcing, and political leaning. Conducted by researchers at Trinity College Dublin and Technological University Dublin, the study compared nearly 18,000 of the most-edited English-language Wikipedia pages with articles on the same topic on the new Grokipedia platform. The study is the largest academic analysis of Grokipedia since it was launched by Elon Musk last October with a promise that the AI-written encyclopedia systematically "fixes" left-leaning biases alleged to exist in the widely used online encyclopedia Wikipedia. Wikipedia's content is written and maintained by volunteer editors, while Grokipedia is an AI-generated encyclopedia using the xAI's Grok large language model. What did the study find? Using computational text analysis and machine learning methods, the team analysed articles on the same topic across Wikipedia and Grokipedia. Selection of topics was based on Wikipedia's most-edited English-language pages. The team compared differences in writing style, structure, and the political orientation of external sources referenced in the paired articles. The researchers found a profound split – while many Grokipedia articles closely mirror Wikipedia, a substantial proportion (66%) of the 18,000 analysed are more extensively rewritten – they are longer, more complex, and rely on fewer references. As a whole, articles on Grokipedia show similar political leaning to those on Wikipedia, drawing on left-leaning news sources. However, when it comes to the politically and culturally sensitive topics of religion, history, literature and art, Grokipedia shows a consistent shift toward referencing more right-leaning news sources compared to Wikipedia. The study analysed Wikipedia's most-edited English-language pages, a selection that likely overrepresents high-profile and contentious topics. That said the study, according to the authors, provides useful evidence of emerging differences between AI-generated and human-edited encyclopedic knowledge systems. Details of the research, conducted at the joint Centre for Sociology of Humans and Machines (SOHAM) in Trinity and TU Dublin, have been published in the peer-reviewed journal Proceedings of the National Academy of Sciences (PNAS). What is the impact of this research? Lead author of the study, Saeedeh Mohammadi, PhD candidate at SOHAM and Research Ireland's Centre for Research Training in Foundations of Data Science said: "Online encyclopedias are central to public knowledge. They are also being used to train future generations of large language models. Our findings raise important questions about how public knowledge is produce, reproduced, verified, and governed. "Unlike Wikipedia, where biases are visible and contested through human editing, AI-generated systems operate largely opaquely. This means shifts in perspective or sourcing may occur without clear accountability or editorial oversight. Simply put AI generation does not remove bias – it changes how and where bias enters the system, often making it less visible." Professor Taha Yasseri Director of SOHAM and Principal Investigator of the study said: "Rather than systematically 'correcting' Wikipedia's alleged biases, as claimed when first launched, our findings suggest that AI-generated encyclopedias such as Grokipedia selectively reshape existing knowledge. This creates a patchwork system in which some content is copied, while other content is reinterpreted in ways that are less transparent and harder to scrutinise." "There is a dire need for transparency, oversight, and regulation in this space. Our information landscape is changing rapidly. We have already seen how the lack of editorial responsibility on social media platforms has enabled the generation and circulation of misinformation and ...

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

In this episode, Charlie Samolczyk, Global Technology Sales Leader, is joined by guest speaker André Balleyguier, Applied AI Leader at Anthropic, and WTW's Pardeep Bassi, Global Proposition Leader for Data Science, to discuss how AI is transforming the insurance industry.

Some of the most asked questions on the channel. Here answered.   Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

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

In this episode, Curtis and Joanie sit down with Mahmoud Harding from Data Science 4 Everyone (www.ds4e.com)  to explore the growing role of data science in K-12 education.Mahmoud breaks down the key distinction between data science and data literacy — two terms that are often used interchangeably but carry very different meanings for educators and students alike. The conversation dives into why data science matters for all educators right now, regardless of subject area or grade level, and why the time to act is today. And taking action doesn't mean you need math expertise or to steer away from the standards and curriculum your students need to know!Mahmoud also shares practical, accessible ways teachers can get started with data-centered lessons in their classrooms — regardless of grade level or content area.Whether you're a curious educator or ready to dive in, this episode will leave you inspired to bring data to life for your students.Resources:●      https://www.datascience4everyone.org/about   (DS4E Homepage)●      https://www.datascience4everyone.org/resources  (DS4E Resources)●      https://ds4e-org.github.io/CPN_rubric/   (DS4E Content Partner Network)●      https://ds4e-org.github.io/technologytoolkit/   (DS4E Technology Tools for working with data)●      https://datasciencelearning.org/ (K12 Data Science Learning Progressions) ●      https://datasciencelearning.org/blog/five-basic-concepts-for-teachers-new-to-data-science  (DS4E Blog: Five basic concepts for teachers new to data science)●      https://hkurzweil.github.io/ds4e-teacher-pd/frontmatter.html (DS4E Data Science Starter Kit)

Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value.For data scientists trying to get AI projects off the ground, understanding why that gap exists is as important as the technical work itself.In this episode, Santosh Kaveti joins Dr Genevieve Hayes to share what organisations consistently get wrong when embarking on AI initiatives, and what data scientists can do to help get it right.In this episode, you'll discover:Why organisations with great AI ideas still fail to deploy them [02:16]What history tells us about where the current AI wave is heading [09:48]The real cost of bolting AI onto systems that weren't designed for it [13:42]How to forge the cross-functional partnerships that get AI projects off the ground [22:21]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

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. 

Welcome back to the show! This week, I sit down with three co-authors of the Atlas of Macroscopes—Katy Borner, Elizabeth Record, and Todd Theriault from the Cyberinfrastructure for Network Science Center at Indiana University—to explore what a macroscope actually is and how it differs from a standard interactive visualization. We trace the 20-year journey of the Places and Spaces: Mapping Science exhibit, from two-dimensional wall maps to the 40 richly interactive pieces featured in this stunning 11×14-inch MIT Press book. Along the way, we talk about design strategies for making complex systems legible to general audiences, the role of AI in data visualization, and what it takes to grab and hold attention on a museum floor. Each guest shares a personal favorite from the book—ranging from Smelly Maps to an Appalachian opioid overdose tool to a skills-landscape explorer—and we close with a look at the exhibit's exciting third decade, focused on visualizing intelligences.Keywordsdata visualization, macroscope, atlas of macroscopes, interactive visualization, Katy Borner, Indiana University, Places and Spaces, complex systems, information visualization, scrollytelling, AI and data visualization, opioid epidemic mapping, data communication, science exhibit, data science podcastSubscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast (https://patreon.com/policyviz) for as little as a buck a monthFind the Atlas of Macroscopes and explore the Places and Spaces exhibit at scimaps.org. Follow Katy Borner, Elizabeth Record, and Todd Theriault through Indiana University's CNS Center.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

Can NPCs in videogames leverage new LLM-based tech? What are the benefits? What are the costs?   Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

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

Send us Fan MailDan Jenkins, Ph.D., is Professor of Leadership & Organizational Studies at the University of Southern Maine. Co-author of The Role of Leadership Educators: Transforming Learning and author of over 75 peer-reviewed publications, his scholarship spans leadership pedagogy, artificial intelligence (AI), followership, critical thinking, and curriculum design. A pioneer in integrating AI into development, training, and education, he develops innovative courses preparing students for digital-age leadership challenges. Dan serves as Co-Founder of the International Leadership Association's Leadership Education Academy, Associate Editor of the Journal of Leadership Studies, and co-host of The Leadership Educator and Leaders in the Loop podcasts. An award-winning international speaker and facilitator, he engages thousands of leadership educators, scholars, students, and professionals worldwide on innovative teaching approaches and AI integration.Gaurav Khanna, Senior Manager, Data Science and Digital Journeys, Cisco Systems, has 25 years of experience in technology and entrepreneurship. During the past five years, he has led efforts to automate business workflows using machine learning and deep learning techniques. His work focuses on using large language models and generative AI to transform how users interact with sales acceleration platforms. Khanna is passionate about demystifying complex subjects and is a frequent speaker on AI/ML topics. He received a BS in physics from Yale and an MS and a PhD in materials science and engineering from Stanford.A Couple of Quotes From This Episode“About The International Leadership Association (ILA)The ILA was created in 1999 to bring together professionals interested in studying, practicing, and teaching leadership. Attend The Global Conference in Toronto, October 28-31.About  Scott J. AllenWebsiteWeekly Newsletter: Practical Wisdom for LeadersMy Approach to HostingThe views of my guests do not constitute "truth." Nor do they reflect my personal views in some instances. However, they are views to consider, and I hope they help you clarify your perspective. Nothing can replace your reflection, research, and exploration of the topic. ♻️ Please share with others and follow/subscribe to the podcast!⭐️ Please leave a review on Apple, Spotify, or your platform of choice.➡️ Follow me on LinkedIn for more on leadership, communication, and tech.

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.

Topics covered in this episode: profiling-explorer Reverting the incremental GC in Python 3.14 and 3.15 VSCode AI Co-author defaults to on, then off django freeze 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: profiling-explorer Adam Johnson And intro post Python: introducing profiling-explorer “profiling-explorer is a tool for exploring profiling data from Python's built-in profilers, which are stored in pstats files. ” Features Dark mode Click the calls, internal ms, or cumulative ms column headers to sort by that column. Use the search box to filter by filename or function name. Hover by a filename + line number pair to reveal the copy button, which copies the location to your clipboard for faster opening. Click the callers or callees links on the right of a row (not pictured above) to see the callers or callees of that function. Michael #2: Reverting the incremental GC in Python 3.14 and 3.15 Python 3.14 shipped with a new incremental garbage collector, but production reports of severe memory pressure (Neil Schemenauer measured up to 5× peak RSS on pathological cyclic workloads) have pushed the core team and Steering Council to revert it in both 3.14 and 3.15 - returning to the 3.13-era generational GC. This is the second time the inc GC has been pulled back: it was also reverted right before 3.13.0 final, and it shipped in 3.14 without going through the PEP process. The tradeoff is real: Neil's benchmarks showed max GC pause times of 1.3ms with inc GC versus 26ms with the generational one - great for latency-sensitive apps, terrible for memory-constrained ones. Release manager Hugo van Kemenade will ship 3.14.5 early with the revert, and Gregory Smith floated the idea of a 3.14.5rc1 - the first patch-release RC since 3.9.2 back in 2021. Tim Peters spent the thread doing live forensics on Windows, running a toy deque program that should cap at 1GB and watching it balloon to 15.6GB on a 16GB machine - and discovered the gen0 collector effectively never fires under the new scheme. Tim's bigger meta-point: CPython has a chronic shortage of real-world GC benchmarks, pyperformance has "basically no interesting" cyclic workloads, and users almost never share real data - so core devs keep flying blind on changes like this. Django maintainer Adam Johnson published a blog post mid-thread documenting a real memory "leak" in Django's migration system caused by inc GC, with a manual gc.collect() workaround - the listener-facing receipt that this wasn't just theoretical. If the inc GC comes back for 3.16, it'll go through a proper PEP, and the discussion is already shifting toward keeping both collectors available via a startup flag - which Neil and Sergey Miryanov have both prototyped. Brian #3: VSCode AI Co-author defaults to on, then off VSCode merges Enabling ai co author by default - 3 week ago Ton's of “why would you do this” and related comments VSCode merges Change default for git.addAICoAuthor to off - yesterday Take-away, don't rely on default, set addAICoAuthor to off yourself Michael #4: django freeze Convert your dynamic django site to a static one with one line of code. Just run python manage.py generate_static_site :) Features Generate the static version of your Django site, optionally compressed .zip file Generate/download the static site using urls (only superuser and staff) Follow sitemap.xml urls Follow internal links founded in each page Follow redirects Report invalid/broken urls Selectively include/exclude media and static files Custom base url (very useful if the static site will run in a specific folder different by the document-root) Convert urls to relative urls (very useful if the static site will run offline or in an unknown folder different by the document-root) Prevent local directory index Extras Brian: Thinking Less, Trusting More: GenAI's Impacts on Students' Cognitive Habits Michael: Vercel breached, employee to blame Introducing the new Talk Python web player GitHub uptime (a couple of views 1, 2) Joke: Friends in tech

In this special episode of Tangent Proptech, Edward Cohen is on the red carpet at one of the most exclusive commercial real estate events of the year: the Real Estate Gala in New York City. This episode features rapid-fire conversations with founders, investors, brokers, developers, and operators across the proptech and commercial real estate ecosystem. A big focus of the evening was on AI. Namely, this question: how is AI being used in real estate right now? And possibly more front-and-center: what's hype and what's here to stay? From leasing and marketing to underwriting and financial modeling, this episode explores where artificial intelligence is already driving value in real estate, where it's falling short, and how we can close the gap.(00:00) - Welcome to the Real Estate Gala Red Carpet Interviews (02:30) - Cyrus Claffey (ButterflyMX): AI Across Product, Marketing, & Operations (06:00) - Zach Molzer (Molzer Development) & Madi Bremer (CBRE): Networking & AI in Leasing (08:30) - Gabe Einhorn (VryfID): Content, Consistency, and AI Efficiency (10:00) - Kaylan Knitowski (Franklin Street): AI Workflows and Competing with Experience (13:30) - David Auerbach (Hoya Capital): Driving Tech Adoption in Real Estate (14:45) - Adam Steiner (Rick, Steiner, Fell, and Benowitz): Document Automation & Bridging Tech and Business (16:45) - Humberto Lopes (HL Dynasty, Gotham Housing Alliance): A Human-First Real Estate Perspective (19:15) - Jovian Lopes (Gotham Housing Alliance): AI for Research vs Human Relationships (21:00) - Lauren O'Breza (Foresite CRE): AI in Brokerage & Underwriting (24:30) - Pablo Barreiro (Fortec): Simplifying Tech Adoption & the Future of Financing (26:00) - Shanti Ryle (Crexi): AI Data Enrichment & Storytelling Advantage (30:30) - Rameen Inayat (Ryan): AI for Admin & Property Tax Insights (32:00) - Collaboration Superpower: Priya Parker 

Send us Fan MailJoin Brandon Seigel on the Private Practice Survival Guide Podcast as he speaks with cybersecurity expert Yves Martin about the critical importance of cybersecurity for private practices. Discover how a single click can lead to devastating data breaches and ransomware attacks, and learn the essential strategies to protect your business. Yves Martin, president and founder of MQual, shares real-world insights and actionable advice on preventing, detecting, and responding to cyber threats in the healthcare industry. This episode is a must-listen for any private practice owner looking to fortify their digital defenses and ensure compliance. What You'll Learn:The prevalent dangers of phishing and social engineering in healthcare cybersecurity.The crucial difference between IT support and dedicated cyber protection.Why staff training is your most potent defense against cyberattacks.The benefits and ease of implementing multi-factor authentication across your systems.Urgent steps to take if you suspect your practice has been compromised.Don't let your practice become another statistic. Tune in to understand the cybersecurity landscape and empower your team.#Cybersecurity #PrivatePractice #DataProtection #HealthcareIT #RansomwareYves Martin has been programming since age twelve, starting with BASIC on a TRS-80. He studied Industrial Engineering at Lehigh University and holds a Professional Certificate in Artificial Intelligence from MIT, along with a certification in Designing and Building AI Products and Services. He also holds certificates in Statistics, Data Analysis, Data Science, and Analyzing and Visualizing Data. With over twenty years of experience designing and building data systems—including business intelligence platforms—he combines technical depth with practical insight. As an author, he writes about artificial intelligence and the use of technology to automate business processes.https://www.mqual.comhttps://www.facebook.com/mqualtech/Welcome to Private Practice Survival Guide Podcast hosted by Brandon Seigel! Brandon Seigel, President of Wellness Works Management Partners, is an internationally known private practice consultant with over fifteen years of executive leadership experience. Seigel's book "The Private Practice Survival Guide" takes private practice entrepreneurs on a journey to unlocking key strategies for surviving―and thriving―in today's business environment. Now Brandon Seigel goes beyond the book and brings the same great tips, tricks, and anecdotes to improve your private practice in this companion podcast. Get In Touch With MePodcast Website: https://www.privatepracticesurvivalguide.com/LinkedIn: https://www.linkedin.com/in/brandonseigel/Instagram: https://www.instagram.com/brandonseigel/https://wellnessworksmedicalbilling.com/Private Practice Survival Guide BookThis show is proudly produced at PS Studios — learn more https://www.psstudios.co

How do you add agent skills to your data science workflow? How can a coding agent assist with data wrangling and research? This week on the show, Trevor Manz from marimo joins us to discuss marimo pair.

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)

The cloud is convenient until it isn't. You upload your photos, sync your contacts, click through the cookie banners. Then prices go up again or you read about a family that lost their entire Google account over a medical photo sent to a doctor. At some point, the question shifts from "why would I run this myself?" to "why aren't I?" My guest this week is Alex Kretzschmar, head of DevRel at Tailscale, longtime host of the Self-Hosted podcast, and co-founder of Linuxserver.io. We cover what self-hosting really means in 2026, the apps worth running yourself like Immich and Home Assistant, why Docker Compose ties it all together, and how Tailscale lets you reach any of it from anywhere, without opening a single port. If you've been thinking about pulling your digital life back behind your own walls, this is your roadmap. Episode sponsors Temporal Talk Python Courses Links from the show Guest Alex Kretzschmar: alex.ktz.me Bitflip podcast: bitflip.show Self-Hosted podcast (Alex's previous show): selfhosted.show Perfect Media Server: perfectmediaserver.com KTZ Systems on YouTube: youtube.com/@ktzsystems Linuxserver.io (co-founded by Alex): linuxserver.io "How Tailscale Works" blog post: tailscale.com/blog/how-tailscale-works https://tailscale.com/: tailscale.com Self-hosted apps discussed Awesome Self-Hosted (GitHub list): github.com Immich (Google Photos alternative): immich.app Home Assistant: home-assistant.io Open Home Foundation: openhomefoundation.org Plausible Analytics: plausible.io Umami Analytics: umami.is Python integration for umami: pypi.org Pi-hole: pi-hole.net AdGuard Home: adguard.com NextDNS: nextdns.io Coolify: coolify.io Docker + ufw: docs.docker.com Storage, backup & filesystem OpenZFS: openzfs.org ZFS.rent (offsite ZFS replication): zfs.rent Backblaze: backblaze.com Hetzner Storage Box: hetzner.com DigitalOcean: digitalocean.com Secrets management mentioned OpenBao (open-source Vault fork): openbao.org HashiCorp Vault: hashicorp.com Bitwarden: bitwarden.com 1Password: 1password.com Hardware mentioned Proxmox VE: proxmox.com Minisforum MS01: minisforum.com Zima Board / Zima OS: zimaspace.com Other references Cory Doctorow on "enshittification" (Cory's blog where he coined the term): pluralistic.net Linus Tech Tips' WAN Show (Linus mentioned NAS-building going mainstream): linustechtips.com Watch this episode on YouTube: youtube.com Episode #546 deep-dive: talkpython.fm/546 Episode transcripts: talkpython.fm Theme Song: Developer Rap

My conversation with Andrea starts at about 41 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