Podcasts about Data science

Dr. Lauren Ponisio is an Assistant Professor in the Department of Biology at the University of Oregon. Lauren's research revolves around preserving and restoring bee populations in agricultural areas and other natural habitats. She is interested in understanding the distribution and health of different populations of native bees. When she's not working, you can often find Lauren in her garden. She has been an avid gardener since childhood, and she currently has a thriving garden with lots of vegetables and plants to attract bees and other pollinators. She received her B.S. degree in biology with honors in ecology and evolution, as well as her M.S. degree in biology, from Stanford University. Lauren was awarded her Ph.D. from the Department of Environmental Science Policy and Management at the University of California, Berkeley. She conducted postdoctoral research at UC, Berkeley afterwards, and she served on the faculty University of California, Riverside before recently accepting her current position at the University of Oregon. Lauren received graduate fellowships from the National Science Foundation and the National Institute for Food and Agriculture, as well as a Postdoctoral Fellowship from the Berkeley Institute for Data Science. She was also named among the Global Food Initiative's "30 Under 30" in Food Systems in 2016. In our interview, Lauren shares more about her life and science.

Welcome to episode #1051 of Thinking With Mitch Joel (formerly Six Pixels of Separation). Dr. Sebastian Wernicke has built his career at the intersection of data science, artificial intelligence, economics, and decision-making. He leads the Data Science and AI team at Oxera, one of Europe's leading economics consultancies, and has become widely recognized for his ability to make complex ideas about data both accessible and deeply human. Many people first encountered Sebastian through his memorable TED Talk, which cleverly used data to satirize TED Talks themselves, but his work has always been driven by a much bigger question: how can organizations use data to challenge assumptions instead of reinforcing them? His new book, Data Inspired - Building An Organizational Culture Of Inquiry For Lasting Transformation, argues that businesses have become too focused on being "data-driven" and not focused enough on becoming "data-inspired." In this episode, Sebastian explains why more data doesn't automatically lead to better decisions, why confirmation bias often causes us to use data to strengthen existing beliefs, and why the greatest value of data lies not in optimization but in transformation. We also discuss the promises and disappointments of big data, the rise of generative AI, synthetic data, AI agents, and why the hardest challenge facing organizations isn't technological... it's cultural. Along the way, Sebastian makes a compelling case that leadership is ultimately about designing environments where people are encouraged to ask better questions, embrace uncertainty, and use evidence to change their minds rather than defend them. It is a thoughtful conversation about data, intelligence, curiosity, and why the organizations that thrive in the AI era will be the ones that build cultures of inquiry instead of cultures of certainty. Enjoy the conversation... Running time: 57:16. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Thinking With Mitch Joel. Feel free to connect to me directly on LinkedIn. Check out ThinkersOne. Here is my conversation with Dr. Sebastian Wernicke. Data Inspired - Building An Organizational Culture Of Inquiry For Lasting Transformation. Sebastian's viral TED Talk. Oxera. Follow Sebastian on LinkedIn. Chapters: (00:00) - Introduction to Sebastian Wernicke and Data Inspired. (02:52) - The Meta Nature of TED Talks and Storytelling. (06:12) - Confirmation Bias and Data Interpretation. (09:03) - The Limits of Data-Driven Decision Making. (12:10) - The Concept of Data Inspired. (14:58) - The Role of AI in Data Utilization. (18:07) - The Promise and Pitfalls of Big Data. (21:12) - Cultural Elements in Data Transformation. (24:02) - Trust in AI and Machine Learning. (26:52) - Human Nature and the Quest for Perfection. (29:30) - The Balance of Autonomy and Control in AI. (30:30) - The Rise of Synthetic Data. (32:00) - Skepticism Towards Synthetic Data. (34:54) - Exploring Personas and Assumptions. (37:40) - The Role of Data in Decision Making. (39:27) - The Complexity of Uncertainty in Business. (41:52) - Rethinking Intelligence and Creativity. (46:54) - Building Beautiful Questions with Data. (51:30) - Navigating the Data Dilemma. (54:46) - Cultural Attitudes Towards Privacy and Surveillance.

What are the key characteristics of complex systems, and what are practical patterns for tackling complex coding problems? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

In 2020, a gastroenterologist in Glasgow did the math on his new research study and came up with 30,000 samples, arriving over two years from three cities and a dozen hospitals. He asked around about how researchers keep track of that. The answer was Microsoft Excel. Shaun Chuah had written some HTML by hand in Notepad back in high school and that was about the whole of his programming experience, so he opened the Django tutorial and started reading. Six years later that app is Foundry120, holding 10 terabytes of clinical and genomics data with an agentic AI running on top of it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Talk Python Courses Links from the show Guest Shaun Chuah: github.com Up and Running with Rust Course: talkpython.fm Foundry120: www.foundry120.com Designing Data Intensive Applications: www.oreilly.com Microsoft Foundry: ai.azure.com ChatIBD: www.chatibd.com Blog: shaunchuah.github.io @drshaunchuah: x.com github.com/shaunchuah: github.com Watch this episode on YouTube: youtube.com Episode #560 deep-dive: talkpython.fm/560 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Web UIs for your reverse proxy Wagtail 8.0 is hot off the presses RISC-V is now officially supported by CPython Django's annual releases make every version an LTS Extras Joke Watch on YouTube About the show Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an 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: Web UIs for your reverse proxy Traefik, nginx, and Caddy all sit in front of a lot of self-hosted infrastructure, and all three are configured by hand-editing files. Three active projects put a control plane on top: Traefik Manager (Python + Flask), Nginx UI (Go + Vue), and caddy/ui (React + Node). All three are additive rather than replacements - none of them take ownership of your config away from you - which is the part that matters when the thing has write access to production routing. Traefik Manager is the Python one: Flask 3.1 and Gunicorn for the control plane, a lightweight Go agent for remote instances, currently v1.10.0 with an Android companion app. Nginx UI is a single Go binary at 11.3k stars, with a block-style config editor, an Ace editor doing LLM completion on nginx syntax, and an MCP server so agents can drive it. caddy/ui runs as two containers next to your existing Caddy, reads and writes your Caddyfile directly, and uses Caddy's /adapt API to validate before reload - no Docker socket required. Each one edits the config the underlying server already reads, so your files stay the source of truth and you can drop the UI without unwinding anything. Undo is a first-class feature across all three - timestamped backups with optional Git history, config version compare and restore, Caddyfile snapshots with one-click rollback. Observability is where they diverge: Traefik Manager does CrowdSec and a visual route map, Nginx UI does server metrics, caddy/ui streams access logs over SSE and pulls p50/p95/p99 off Caddy's Prometheus endpoint. Maturity spread is wide - Nginx UI has 11.3k stars, caddy/ui has 4 and was built in a single Claude session - and caddy/ui ships with auth off by default, so set CADDY_UI_USER and JWT_SECRET before it goes anywhere near a public interface. Calvin #2: Wagtail 8.0 is hot off the presses Link: https://github.com/wagtail/wagtail/releases/tag/v8.0 Custom base page models are now supported, so projects aren't locked into subclassing Wagtail's Page as shipped (Matt Westcott). New v3 REST API handles both read and write CMS operations, a first for Wagtail's API. A global registry for permission policies, plus full customizability for the remaining page views via PageViewSet. AVIF and WebP images are no longer auto-converted to PNG by default, a real behavior change to watch on upgrade. Five security fixes: page admin API restrictions, document identification by SHA1 hash, descendant collections in the Documents/Images API, snippet copy permissions, and the page translation endpoint. Formalized Django 6.1 support, and CI now runs on uv with a lockfile. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: RISC-V is now officially supported by CPython Link: https://blog.python.org/2026/08/riscv-now-officially-supported/ CPython added RISC-V as a tier 3 platform under PEP 11, specifically the 64-bit Linux target riscv64-unknown-linux-gnu. RISC-V is an open ISA anyone can implement, unlike x86 and ARM, and its market is projected to quadruple by 2032. The RISE Project donated real RISC-V machines for buildbots; the author's work was funded by a Sovereign Tech Agency fellowship. What changes: the port is now a maintained compatibility target, so CPython changes are less likely to quietly break it. What doesn't: no python.org installers, no binary wheel parity for native extensions. Next up: RISC-V runners in CPython CI for pre-merge feedback, then a push toward tier 2, plus architecture-specific optimizations. The ask is testing. If you have RISC-V hardware, build CPython, run your test suite, file what breaks. Tier 3 is the weakest support tier. PEP 11 tier 3 requires a core developer contact and a buildbot, but failures on tier 3 platforms explicitly do not block a release. Saying "ongoing CI/testing expectations" oversells it. The honest bit is "someone is now on the hook for it, and breakage gets noticed," not "it's guaranteed working." Worth the caveat that this is Linux SBCs, not microcontrollers. A VisionFive 2 counts, an ESP32-C6 or Pico 2 does not. Those are 32-bit non-Linux parts where MicroPython is still the answer. Michael #4: Django's annual releases make every version an LTS Starting with Django 2028, Django will move to one January feature release per year, adopt calendar-based version numbers, and support every release for three years. The old distinction between standard and LTS releases disappears, giving teams a predictable annual upgrade path that aligns more closely with Python's own release and support cadence. Every Django release becomes the safe, long-supported choice, so teams no longer need to wait for a specially designated LTS version or absorb two years of changes at once. Each release gets one year of mainstream bug fixes followed by two years of security and data-loss fixes. New releases support the three latest Python versions and add the next Python release during their first year. Calendar versioning begins with Django 2028, followed by Django 2029 and so on. Three Django versions will be supported at any time, giving third-party packages a clearer rolling target. Nothing changes before 2028, and existing commitments for Django 5.2 LTS and 6.2 LTS remain in place. Extras Calvin: The Python docs now document the time complexity of built-in types https://docs.python.org/3.16/library/time-complexity.html Thinking in Python - Bruce Eckel's free book https://thinkinginpython.com/ Michael: prune_uv_pythons.py - Prune uv-managed Python installs, keeping only the newest patch per minor version Runs automatically in my system “upgrade” script: upgrade-output-2026.png Started using Ollama cloud models for my Hermes assistant. Thanks to Jeff Triplett I learned they are not just local models. Joke: The Tao of Programming - Book Seven: Corporate Wisdom

In this episode of Robots and Red Tape, host Nick Schutt sits down with Austin Keller, Director of Data Science at IntelliDyne, for a grounded conversation on why critical systems still run on DOS, floppies, and “version 15 final redo,” and why most AI projects start in the wrong place.Drawing on manufacturing, Navy, DOJ, and VA work, Austin argues you start with the business problem—not the shiny tool—and that security is step zero.Key topics:Why “if it works, don't mess with it” leaves agencies on 20–30-year-old stacksStarting with the job to be done, not “we want AI”Data that will not talk to other data—and why that is by designCoding agents that can read COBOL and Fortran when the last developer is goneWhere AI ROI actually shows up: repetitive tasks, small agents, and UI that saves keystrokesPodcast: Robots and Red Tape | Host: Nick Schutt | YouTube: @RobotsandRedTapeAI

Dr. Hooman Rashidi is Associate Dean of AI in Medicine at the University of Pittsburgh and Executive Director of CPACE. Before Pitt, he founded Cleveland Clinic's Center for AI and Data Science and directed AI at UC Davis Medical Center. Ed asks him whether medical staff are prepared for AI. His answer is that even the ones who feel ready are not, because people bundle every kind of AI together and the newer generative tools don't behave like the predictive models the field has been using for decades. In this episode of DGTL Voices, he explains what's missing from tumor boards, why he leads with education before deployment, how Pitt runs a hybrid strategy rather than committing to vendors alone, and why his center puts junior contributors on patent filings when most institutions don't. https://bio.marxadvisory.com/

Your site is down. It's 3am. Is it a bug, a bill, or a breach? You can't tell yet, and everyone is watching you find out. Matt Lea has spent fifteen years being the person companies call when an outage is costing them real money per hour, and his whole argument is that everything you'd want in that moment gets decided months earlier, on ordinary afternoons, when someone chose the convenient thing. We walk his top twelve dos and don'ts in AWS - infrastructure as code, IAM roles instead of access keys, private subnets, no wildcards, no public buckets - and I push on which of them actually matter if you're one person on a small VPS. Then we get to Cloud War Games, where Matt breaks things on purpose so your team's first real incident isn't their first incident. Let's get into it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Talk Python Courses Links from the show Guest Matt Lea: linkedin.com Talk Python Certificates: training.talkpython.fm/certificates Schematical: schematical.com CloudWarGames.com: cloudwargames.com Zero to Hero on AWS Security: www.oreilly.com Repo: github.com Custom Wheel Offset: customwheeloffset.com 2012 TechCrunch Disrupt Hackathon: techcrunch.com tech comics: schematical.com shhgit: github.com Zero Trust in 200ms: Implementing Identity-Per-Transaction: us.pycon.org Coolify: coolify.io returned to full GA Nov 2025: aws.amazon.com Signed URLs/cookies: docs.aws.amazon.com Cloudflare: www.cloudflare.com Bunny Shield: bunny.net Cloud War Games One: www.youtube.com Cloud War Games Two: www.youtube.com LinkedIn: linkedin.com YouTube: youtube.com KnocKnoc: knocknoc.io Watch this episode on YouTube: youtube.com Episode #559 deep-dive: talkpython.fm/559 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Python 3.12.14, 3.11.16, 3.10.21 - security releases Codeberg's AI-code ban tests its role as a GitHub alternative Brett Cannon: what's missing for reproducible builds on PyPI nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. Extra extra extra, hear all about it Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic pythonbytes.fm/logfire This episode is brought to you by Pydantic Logfire. It's observability for AI apps from the team behind Pydantic - agents, LLMs, APIs, database, and infrastructure in a single trace, queried with Postgres-compatible SQL. Your coding agent can query it too, through their MCP server. I'll tell you more later. Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Python 3.12.14, 3.11.16, 3.10.21 - security releases https://blog.python.org/2026/08/python-31214-31116-31021/ Source-only security releases for the three branches now in security-fix-only mode; release team blamed the European solar eclipse for the timing. tarfile hardening. Multiple path-traversal bypasses of the data filter closed, including a symlink escape that bypassed the CVE-2025-4330 fix; extract() now applies the filter to link targets too. Four fresh CVEs: CVE-2026-2297 (SourcelessFileLoader not using io.open_code() for .pyc), CVE-2026-4224 (expat crash on deeply nested content models), CVE-2026-3644 (control chars in http.cookies.Morsel), plus the completed CVE-2021-4189 fix in ftplib.ftpcp. Quadratic-complexity DoS cleanup across the stdlib: HTMLParser, configparser regexes, unicodedata.normalize(), csv.Sniffer.sniff(), and ElementTree XPath index predicates. Header/injection fixes: CR/LF rejected in HTTPConnection.set_tunnel(), control chars blocked in wsgiref.handlers status, and webbrowser now rejects leading dashes (plus a %action prefix bypass). http.client now caps chunked trailer lines and 1xx interim responses at 100 each - a hostile server could previously hang the client forever despite a socket timeout. Memory-safety odds and ends: stale pointers in lzma/bz2/zlib decompressors after MemoryError, a bz2 stack overflow on reuse-after-error, and bundled libexpat bumped to 2.8.3. If you're still on 3.10, 3.11, or 3.12 - and you extract tarballs from anywhere you don't fully control - this one's not optional. Michael #2: Codeberg's AI-code ban tests its role as a GitHub alternative Armin's article “Codeberg Divides” Armin Ronacher argues that Codeberg's new terms, which prohibit projects mostly written with generative AI, create a vague and difficult-to-enforce boundary. His larger concern is that a democratically governed host can still be unpredictable or ideologically narrow, weakening Codeberg's potential as a broad European alternative to GitHub. The strongest question for Python developers is whether repository hosting should judge legal open source by how code was produced, or focus on behavior and resource abuse. “Mostly generated” is hard to measure in modern codebases where developers mix handwritten code, completions, agents, and generated refactors. Ronacher suggests clearer alternatives: ban all LLM involvement, or target autonomous repository spam, abusive resource use, and low-quality generated contributions directly. Codeberg is free to choose a values-driven community, but that may conflict with being predictable, neutral infrastructure and a serious GitHub competitor. Worth discussing: can open-source communities set meaningful AI boundaries without driving maintainers and projects into opposing camps? Very first search for these terms lands on this page. Codeberg looked like a viable alternative. … Unfortunately, the latest update to its terms of service seems to mark a first step in changing one part I moved there for, namely the “freedom” part. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: Brett Cannon: what's missing for reproducible builds on PyPI Framing came out of his 2026 Python Packaging Council nomination - the secure-supply-chain gap he found is that Python has no defined way to do reproducible builds at all. Design goal is zero friction: producers uploading to PyPI shouldn't have to do anything. The work lands on build backends and installers. Gap #1: nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. Gap #2: recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. The replay mechanism already exists: [build-system] in pyproject.toml is a defined entry point, so if backends recorded their own environment, you could reinstall and re-run the build. Payoff idea: trusted third parties report successful reproductions back to PyPI, which displays "independently reproduced by X" - surfaced in the index API so installers could prefer reproduced files. Explicitly framed as a perk, not a requirement - roughly SLSA build level 1, no shaming projects that don't opt in. Verbal kicker option: "And don't think pure-Python wheels are off the hook. Something built that wheel, and if that something was compromised, so is your wheel. SolarWinds was a build-process attack." Michael #4: Extra extra extra, hear all about it Python 3.14.7 Upgraded the MCP servers to 2026-07-28 v2 protocols (talk python, python bytes) Got agentsview running synced via postgres Talk Python courses, teams trial offering Talk Python courses, government procurement offering Lean TDD audio book is out Extras Calvin: uv now prefers post-quantum key exchange - https://github.com/astral-sh/uv/releases/tag/0.12.4 Joke: Beware of dog

Steven Song is the founder and CEO of Diald, an AI-powered decision intelligence platform for commercial real estate. Before founding Diald, Steven worked on both the investing and development sides of real estate, and built his career across architecture and urban planning, training at Carnegie Mellon and the University of Pennsylvania. He was a founding principal at SCAAA, a global strategy, planning, and design firm, and is a partner at Axle Companies, a family office focused on real estate investment and social impact ventures. Steven is based in Los Angeles.(02:26) Why CRE Decisions Are Still Judgment-Driven (04:41) The Signal That Killed an Atlantic City Deal (07:44) Contextual Drift: The Risk Nobody Models(10:20) Diald's approach (11:59) AI Token Costs and Asking Better Questions (14:27) Tools vs. Workflows (15:52) Diald's Underwriting (17:58) Killing Bad Deals Earlier (19:18) How AI Upgrades the Analyst Role (20:44) Where General Purpose AI Fails at Underwriting (24:23) Does AI Make CRE More Efficient or More Competitive (26:02) What Underwriting Looks Like in 5 Years (27:14) Where Human Judgment Still Matters (29:06) The Local Signals Investors Miss (31:15) Collaboration Superpower: Denise Scott Brown and Reyner Banham

0:30 - Mike Koolidge filling in for Dan 9:11 - DSA 27:59 - Can the Republicans Beat Socialism in the Midterms? 46:10 - WRFH 101.7 FM General Manager and Hillsdale College journalism lecturer Scot Bertram gives his on-the-ground view of the Michigan Senate race. Follow Scot on X @ScotBertram 01:00:19 - The Heritage Foundation's Steven Bucci dismisses the alleged complaints from the USS Lincoln: "It's a shame because they are, frankly, whining." 01:15:50 - More reaction to the USS Lincoln 01:31:34 - Judge Glock, director of research at the Manhattan Institute, makes the case for data centers. Follow Judge on X @judgeglock 01:49:50 - Professor of the Practice of Data Science at Washington University, Liberty Vittert Capito, analyzes Why nutty candidates are underperforming their polling so badly. Follow Liberty on X @libertycapitoSee omnystudio.com/listener for privacy information.

In this episode of Ethnocynology, I sit down with dog science communicator Alexandra the Average for a wide-ranging conversation about dogs, science, identity, and the ways our own cultural biases shape how we understand dogs themselves. Alexandra discusses her approach to navigating misinformation in the dog and animal-welfare world, including why she relies on multiple studies rather than individual papers. But also the importance of reading beyond the abstract, and how scientific findings can become messy once they reach social media. The conversation dives into some of the most persistent debates surrounding dogs, including pit bull stereotypes, breed and temperament, dog-bite statistics, veterinary bias, and how race and socioeconomic inequality can affect people's experiences with animal welfare and adoption. We also explore the deeper history behind our relationships with dogs, like the use of dogs as status symbols to the effects of colonialism on Indigenous dog populations. We  discuss how dogs can become wrapped up in human identity, prejudice, politics, and even colonial violence, illustrating why the history of dogs can never really be separated from the history of people. Alexandra also discusses her upcoming podcast, Pod Reviewed Research, which explores the human-animal connection, animal welfare, legislation, history, and culture through detailed, research-heavy conversations. Transcripts For a rough transcript head over to: https://www.archaeologypodcastnetwork.com/ethnocynology/43 Links: The racialization of pit bulls: What dogs can teach us about racial politics Ancestry-inclusive dog genomics challenges popular breed stereotypes Human–dog bond cross-cultural study Once-daily feeding is associated with better health in companion dogs" "Implicit weight bias exists among veterinary professionals," Visual breed identification vs. DNA. Dog-bite fatality risk factors Woolly Dog “Possessions and the Extended Self," Bronwen Dickey, Pit Bull: The Battle over an American Icon Michael Luo, Strangers in the Land: Exclusion, Belonging, and the Epic Story of the Chinese in America (2025) CARE: Companions and Animals for Reform and Equity Dog Aging Project Darwin's Ark davidianhowe.com Davidianhowe.com/store ArchPodNet APN Website: https://www.archpodnet.com APN on Facebook: https://www.facebook.com/archpodnet APN on Twitter: https://www.twitter.com/archpodnet APN on Instagram: https://www.instagram.com/archpodnet APN Shop Affiliates Motion Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In dieser Folge des Performance Manager Podcasts spricht Peter Bluhm mit Florian Bliefert, Trainer und Berater bei der CA Akademie AG, über sein neues Buch „Künstliche Intelligenz und Data Science im Controlling – verstehen und anwenden". „Es ist interessanterweise gar keine technische Frage" – so beschreibt Florian Bliefert, was erfolgreiche von erfolglosen KI-Projekten im Controlling unterscheidet. Künstliche Intelligenz und Data Science im Controlling sind in aller Munde – doch wie kommt man als Controller wirklich zu einem soliden Einstieg, jenseits von Hype und Buzzwords? Themen der Episode: Was Unternehmen mit erfolgreichen KI-Investitionen im Controlling anders machen  Warum man Probleme zuerst definieren und dann nach KI-Lösungen suchen sollte  Warum eine solide, einheitliche Datenbasis das Fundament jedes KI-Projekts ist  Wie Machine Learning auch ohne Mathestudium verständlich und anwendbar wird  Wofür sich Sprachmodelle im Controlling eignen – und wofür nicht  Was Agentic AI ist und wie sie sich von klassischen Chatbots unterscheidet  Wie sich die Rolle des Controllers durch KI verändert 

Topics covered in this episode: Claude Code /insights Post-quantum crypto lands in Python MCP goes stateless — and FastMCP gets renamed inshellisense - IDE style command line auto complete Extras Joke Watch on YouTube About the show Sponsored by Xweather Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Michael will tell you more about them later in the show. Get started for free at pythonbytes.fm/xweather Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal 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: Claude Code /insights Michael's Insights: michael-kennedy-claude-code-insights-2026-08-09.html Be careful sharing these outputs, they include details references to your projects, errors, security findings, etc. ;) /insights reads your last 30 days of local session transcripts and hands back an interactive HTML report on how you actually work. One command, zero setup: type /insights in a session, or run claude -p "/insights" from the shell for a non-interactive version that just prints the path Reads what's already on disk: pulls session logs from ~/.claude/projects/, skipping agent sub-sessions and anything under 2 messages or 1 minute Project areas: clusters your sessions into themes like "CLI Tooling" or "Documentation" with session counts Friction analysis: categorizes where things went wrong by root cause - and quotes your own prompts back at you Interaction style: tells you whether you're a delegator or a micromanager, plus which workflows are worth doubling down on Actually actionable: suggests concrete CLAUDE.md additions and Claude Code features you're not using The catch: Haiku does the per-session classification, so the first run takes several minutes; results cache to ~/.claude/usage-data/facets/ and the report lands at ~/.claude/usage-data/report.html Calvin #2: Post-quantum crypto lands in Python pyca/cryptography 48 ships ML-KEM (key establishment) and ML-DSA (signatures) — NIST's post-quantum standards, now one pip install away. Big deal because it's the 11th most-downloaded package on PyPI (~1.2B downloads/month) and sits under Ansible, Certbot, Airflow, and paramiko. No PQ there, no PQ anywhere in Python. Trail of Bits did the work (Rust bindings, cross-backend API, tests, AWS-LC backend support), funded by the Sovereign Tech Agency. Timing tracks a June 22 White House order setting federal deadlines: PQ key establishment by end of 2030, PQ signatures by end of 2031. Not a drop-in swap — the wire sizes explode. ML-DSA-65 signatures are 3,309 bytes vs Ed25519's 64; ML-KEM-768 public keys are 1,184 bytes vs X25519's 32. Hardcoded field sizes and length prefixes will bite. API looks like the existing asymmetric primitives, except ML-KEM is encapsulate/decapsulate rather than a Diffie-Hellman exchange. SLH-DSA (the hash-based conservative backstop) is still in progress. The primitives are here, but protocols haven't caught up — so you won't be running post-quantum Certbot this week. Sponsor: Xweather You're using agents that can write code, summarize documents, and automate workflows. But they're missing one thing: awareness of the world around them. This is where today's sponsor, Xweather comes in. Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server built for tools like Claude, Codex, Copilot, and modern IDEs – so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Backed by Vaisala, whose instruments fly on NASA missions to Mars, Xweather delivers trusted data and unique insights that go beyond conditions to actual impact – from real-time lightning strikes to road surface forecasts. Start with 15,000 free API calls each month and pay only for what you use as you grow. Xweather is your full weather stack, for developers by developers. Start building for free today at pythonbytes.fm/xweather. The link is in your podcast player's show notes and on the episode page. Thanks so much to Xweather for supporting Python Bytes. Calvin #3: MCP goes stateless — and FastMCP gets renamed From Philipp Acsany over at Real Python The 2026-07-28 spec landed July 28 and the Python SDK shipped 2.0.0 the same day. Biggest rewrite since MCP launched, and it's breaking on purpose. Context for scale: the Tier 1 SDKs are pulling close to half a billion downloads a month, with TypeScript and Python each past a billion total. The headline is the stateless core. The initialize/initialized handshake and the Mcp-Session-Id header are both retired — protocol version, client identity, and capabilities now ride in _meta on every request, with an optional server/discover RPC if a client wants capabilities up front. Any request can land on any instance behind plain round-robin, no shared storage. Server-initiated calls are the hard part of the migration. Sampling, elicitation, and roots/list no longer call back to the client; instead the server returns resultType: "input_required" and the client retries with inputResponses attached. Multi Round-Trip Requests, MRTR. Also: Mcp-Method and Mcp-Name are now required headers so gateways route on headers instead of cracking JSON bodies, and missing-resource errors move to standard 32602. Deprecation sweep with an actual policy behind it — Roots, Sampling, Logging, and the legacy HTTP+SSE transport all deprecated with a twelve-month minimum offramp. Tasks graduated out of the experimental core into a real extension, which is what the formalized extensions framework was for. MCP Apps is now an official extension too, so a tool call can return sandboxed interactive HTML. Auth picked up RFC 9207 issuer validation, issuer-bound credentials, and a shift from DCR toward CIMD. Python SDK 2.0 is where it gets personal: FastMCP is now MCPServer, no alias, no shim. McpError → MCPError. Wire types went snake_case (is_error, input_schema) and moved to a standalone mcp_types package, with mcp.types kept as a permanent alias. One Client object replaces the old transport + ClientSession + initialize() stack. httpx became httpx2. Sync handlers run on worker threads now, so asyncio.get_running_loop() raises inside them. The good news: one MCPServer serves both protocol eras, so 2025-era clients keep working with nothing to configure, and a Resolve(fn) parameter lets one tool body cover MRTR and the old path. 1.x is maintenance-and-security-fixes only — pin mcp>=1.28,

A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have anticipated.In this Value Boost episode, Cynthia Dunlop joins Dr Genevieve Hayes to explore how data scientists can convert blog writing into bigger opportunities and what it actually takes to make the leap from blog post to book.You'll discover:How conference organisers actually find their speakers — and why blogging is the answer [02:35]How acquisitions editors scout for authors and why you don't need a huge following [03:39]The low risk way to find out if you're ready to write a book [06:57]How each new opportunity compounds the authority you've already built [08:10]Guest BioCynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack.LinksConnect with Cynthia on LinkedInFollow Cynthia on SubstackConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

#144Josh and Mike sit down with Kenny Warner, VP of Data Science and Engineering at meez, for a conversation that starts with Kenny's unlikely path into tech leaving college after his sophomore year to launch a cause-marketing startup and winds up deep inside the machinery of modern AI. Kenny breaks down what actually happens when a language model reads your data, explaining tokens, embeddings, attention, and inference in plain terms, and why you can't simply point a chatbot at a hundred-million-row database and expect good answers.He and Josh dig into the real difference between building a model and fine-tuning one, why clean and trusted data is the true competitive advantage, and how smaller, purpose-built models can beat the giants at specific jobs. They also get into where this is all heading for restaurants, from turning recipes, costs, and margins into something a system can reason about to the rise of MCP and agentic tooling, with a detour through Nobu, a Miami chef tour, and a few fun facts along the way. It's a rare, jargon-free look under the hood of AI from someone who builds it every day.Links and resources

Clinicians and medical providers rely on data to tailor care plans to their patients. Aartik Sarma, MD, University of California, San Francisco, Pat Lyons, MD, Oregon Health & Science University, and host Lekshmi Santhosh, MD, the University of California, San Francisco, discuss the use of data science in critical care medicine and how it has evolved over time. They dive into how new technologies benefit ICU patients, and how the medical infrastructure can be further improved.

In this episode, I talk with Ning Leng, Ph.D., Director II, Data and AI Acceleration Group, Data & Statistical Sciences at AbbVie, about the growing role of the R Consortium in the pharmaceutical industry. Ning brings extensive experience in statistics, computational genomics, open-source technology, and the adoption of R across the pharmaceutical industry. Before joining AbbVie, she spent 10 years at Roche Genentech, where she helped drive the adoption of R, cloud technologies, Git, and Shiny. We discuss how the R Consortium creates a platform for statisticians, programmers, pharmaceutical companies, and regulators to collaborate on practical challenges—and how that collaboration is changing the way we approach regulatory submissions.

Key topics Impact of AI and data science on category management Role of retail media and private label in retail strategy Changing shopper behaviors and professional shoppers The evolution of store formats and online integration The importance of human touch and education in retail The role of nutrition and health in category planning The influence of private label on market dynamics The future of store formats and the role of physical stores Chapters 00:00Introduction to James Jackson and episode overview 02:28The evolution of retail events and networking 04:01The changing landscape of retail stakeholders 05:42Educational focus and industry innovation 07:04The role of data science and AI in category management 09:07Career journey into category management 10:40Impact of technology on the role of category managers 12:36Balancing art and science in retail decision-making 14:54Online shopping trends and consumer behavior 17:25The rise of professional shoppers and store formats 22:48Private label strategies and market impact 27:22The influence of Aldi and private label on retail 32:03The role of nutrition and health in retail 36:34Data-driven education and consumer trust 37:43The future of health and wellness in retail 39:47The impact of retail media and monetization 40:01Lightning round: retail trends and strategies

IKünstliche Intelligenz und Data Science verändern die Arbeitsweise im Controlling grundlegend – aber wie gelingt der Einstieg, ohne gleich Informatik oder Statistik studieren zu müssen? Florian Bliefert, Manager und Berater bei der CA Akademie, hat dazu das Buch „Künstliche Intelligenz und Data Science im Controlling verstehen und anwenden" geschrieben. Im Gespräch erklärt er, warum solide und erklärbare Methoden im Controlling-Alltag oft mehr bewirken als komplexe Algorithmen, welche Rolle Datenqualität und klare Verantwortlichkeiten für den Projekterfolg spielen und wo die Grenzen von KI-Systemen liegen, die Controller kennen sollten. Außerdem wirft er einen Blick auf Sprachmodelle und Agentic AI – und was diese Entwicklungen für die Rolle des Controllers bedeuten. Das Buch ist im Haufe Verlag erschienen (1. Auflage 2026, 222 Seiten, ISBN 978-3-7775-0073-7) und als Hardcover sowie eBook erhältlich: https://shop.haufe.de/prod/kuenstliche-intelligenz-und-data-science-im-controlling-verstehen-und-anwenden   Der Performance Manager Podcast ist der führende deutschsprachige Podcast für Controlling, Data & AI und Performance Management. Er richtet sich an alle, die mit Daten besser entscheiden und ihr Unternehmen erfolgreicher steuern wollen. Regelmäßig begrüßt Peter Bluhm Führungskräfte, Experten und Persönlichkeiten, die in ihrem Fachgebiet zu den Besten zählen. Mit ihnen spricht er über Strategien, Inspirationen und Impulse für glasklare Entscheidungen und Erfolg im Business. Peter Bluhm ist Experte für Controlling und Data & AI sowie Buchautor. 2004 gründete er die ATVISIO Consult, eine Data- & AI-Beratung, die 2014, 2019 und 2025 mit dem Gütesiegel "Top Consultant – Die besten Berater des deutschen Mittelstandes" ausgezeichnet wurde. Neben dem "Performance Manager Podcast" ist auch Peter Bluhms Buch "Business Intelligence ganz einfach" als kostenloser Podcast verfügbar.

Every company has one. The little internal tool that Jane built back in 2021, and then Jane left. Nobody understands it, nobody will touch it. There are two unwritten rules around it: don't change it, it's working. And if you break it, you bought it. That's dark-matter enterprise software. For every app you can actually see, there are ten of these sitting in the shadows, frozen. Michael Booth thinks that just changed. He read my article on hyper-personal software and ran with it, writing about hyper-team software: small teams inside big companies finally building the tools that were never going to get built. We cover where this works, where it quietly goes wrong, and the guardrails that keep it from turning into a mess. Let's get into it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Python in Production Talk Python Courses Links from the show Guest Michael Booth: github.com Talk Python AI Integrations: talkpython.fm/blog From Hyper-Personal to Hyper-Team Software: Small Team-Built, AI-Assisted Tools Inside the Enterprise: www.databooth.com.au What hyper-personal software looks like (MK's article): mkennedy.codes Databooth Site: www.databooth.com.au Wall Street just lost $285 billion because of 13 markdown files: martinalderson.com SaaSpocalypse is real but everyone is panicking about the wrong thing: www.reddit.com Warp Terminal: www.warp.dev Watch this episode on YouTube: youtube.com Episode #558 deep-dive: talkpython.fm/558 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable. Series: "Data Science Channel" [Science] [Show ID: 41487]

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable. Series: "Data Science Channel" [Science] [Show ID: 41487]

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable. Series: "Data Science Channel" [Science] [Show ID: 41487]

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable. Series: "Data Science Channel" [Science] [Show ID: 41487]

On this Summer Friday, we've put together some of our favorite recent interviews, including: Each year WNYC hosts a "health convening," with support from the Alfred P. Sloan Foundation, as an opportunity for healthcare experts and practitioners to inform WNYC's health reporting. This year, the topic is ultra-processed foods and how they affect our health.  First, Kevin Hall, Ph.D., former senior investigator at the National Institutes of Health (NIH) , discusses his groundbreaking, tightly controlled metabolic ward trials and mathematical models tracking how human bodies respond to ultra-processed foods. Then, Fang Fang Zhang, M.D., Ph.D., cancer epidemiologist and chair of the Division of Nutrition Epidemiology and Data Science at the Friedman School of Nutrition Science and Policy at Tufts University, discusses her population‑based research on how ultra-processed foods influence cancer prevention, cancer survivorship and long‑term health outcomes. Then, David Kessler, MD, former FDA Commissioner, breaks down a citizen petition he filed with the Food and Drug Administration last summer which would, among other things, declare that some of the core ingredients in ultra-processed foods were no longer “generally recognized as safe (GRAS)” -- a classification  that means ingredients are exempt from strict premarket approval process. Plus, Ilan Stavans, publisher of Restless Books, professor of humanities and Latin American and Latino culture at Amherst College and the author of A Nation Wrestles with God: American Prophets, Philosophers, and Firebrands (Restless Books, 2026), talks about the broad spectrum of American religious thought, from Cotton Mather to Lana Del Rey.   These interviews were lightly polished up and edited for time, the original versions are available here: What Are Ultra-Processed Foods and What Are They Doing to Us? (June 16, 2026) Ultra-Processed Foods and Colon Cancer (June 16, 2026) Ultra-Processed Foods Policy Proposals (June 16, 2026) American Believers (July 9, 2026) photo: Potato chips and other junk food in Walmart, Wenatchee Washington (Thayne Tuason, CC BY-SA 4.0, via Wikimedia Commons) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

For data scientists who want to build authority beyond their organisation, writing is one of the most powerful tools available. But in a world flooded with AI-generated content, simply publishing is no longer enough. The data scientists who stand out are the ones writing things no AI could have written.In this episode, Cynthia Dunlop joins Dr Genevieve Hayes to share practical frameworks for writing blog posts that stand out, build genuine authority and actually get read.You'll discover:Why AI-generated content has made personal experience more valuable than ever [05:10]The three Ps test for finding topics you can write about with genuine authority [08:56]The blog post patterns that work best for demonstrating expertise [11:03]How to use AI to improve your writing without letting it replace your voice [16:33]Guest BioCynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack.LinksConnect with Cynthia on LinkedInFollow Cynthia on SubstackConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Oz y Tito Neira ponen a la universidad en el banquillo junto a Santiago Basulto, fundador de DataWars y ex-cofundador de RMOTR (vendido a INE). Santiago argumenta que la universidad está bajo asedio desde 2010, mucho antes de la IA, y que su verdadero problema es haber apostado todo a una value prop que ya no existe: que el conocimiento es escaso. Hablan de los $1.7 billones de deuda estudiantil, por qué el modelo de evaluación está roto, por qué las universidades top adoptan IA más rápido que las pequeñas, y la pregunta que ninguno logra responder: ¿el conocimiento fundacional todavía te hace mejor profesional?00:00 Intro y bienvenida03:00 La universidad bajo asedio desde 201006:22 El paralelo con la banca: reinventarse o morir08:02 Las value props: conocimiento, credibilidad, network12:01 $1.7 billones de deuda y la caída de matrícula14:26 Cuando el mercado pedía perfiles que no existían19:12 Qué gana la universidad si adopta la IA23:00 El modelo industrial: push vs pull de conocimiento27:01 El problema real: cómo evaluar en la era de la IA32:03 "No es sin IA, es con IA": el error de prohibirla33:56 Universidades grandes vs chiquitas: el ego39:42 De Assembler a los prompts: capas de abstracción44:35 ¿El conocimiento fundacional te hace mejor?49:17 Lo que la universidad realmente te deja54:52 Cierre✩ CURSOS DISPONIBLES

Send us Fan MailUnderstanding HIPAA compliance is more critical than ever for private practices. In this Private Practice Survival Guide episode, Brandon Seigel sits down with technologist Yves Martin to uncover the essential shifts and ongoing requirements in HIPAA regulations. They explore what it truly means to be compliant in an evolving digital landscape, emphasizing strategies to protect patient data and avoid common pitfalls. Learn about new mandates, practical security assessments, and how to foster a culture of privacy within your organization. This episode is packed with invaluable insights for any healthcare professional looking to strengthen their data security and navigate HIPAA with confidence. What You'll Learn:The current state and future landscape of HIPAA complianceWhy avoiding 'gross negligence' is your top priorityMandatory changes like multi-factor authentication and data encryptionHow to conduct effective security risk assessmentsA critical, often-overlooked HIPAA update regarding substance abuse recordsCommon scenarios leading to HIPAA breaches and how to prevent themThe importance of a 'culture of privacy' in your practiceEquip yourself with the knowledge to maintain robust HIPAA compliance and ensure patient trust.#HIPAACompliance #PrivatePractice #DataSecurity #PatientPrivacy #HealthcareRegulationsAmazon Bestselling Author and Founder and CEO of MQUAL Advanced IT Engineering and Support. Yves Martin has been programming computers 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 IT and data systems—including business intelligence platforms—he combines technical depth with practical insight. As an author, he writes about artificial intelligence, cyber security and compliance and the use of technology to automate business processes.https://www.mqual.com https://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

Mark McDonald, Head of AI and Data Science, assesses the dominant market narratives around artificial intelligence.Click here for appropriate Disclosures, including analyst certifications, and Disclaimers that must be viewed with this podcast: https://www.research.hsbc.com/R/101/fdLXmWmStay connected and access free to view reports and videos from HSBC Global Investment Research follow us on LinkedIn https://www.linkedin.com/feed/hashtag/hsbcresearch/ or click here: https://www.gbm.hsbc.com/insights/global-research.

Security has always been the vegetables of software. Everyone agrees it matters, and somehow it never quite makes it onto the plate. At PyCon US this year, that changed. For the first time ever, security got its own dedicated, day-long track, one of just two at the whole conference, sitting right next to AI. And the room was packed to the back wall. On this episode, I'm joined by the three people at the center of it. Seth Larson, Security Developer in Residence at the Python Software Foundation and, very recently, a CPython core developer. Juanita Gomez, a PhD researcher at UC Santa Cruz in open source security, who co-chaired the track. And Mike Fiedler, PyPI's Safety and Security Engineer, one of the very few people paid full-time to keep the packages you install safe. We use the arc of the track's talks to take the temperature of Python security right now: supply chain attacks, dependency cooldowns, zero trust, SBOMs, and the push to bring Rust into CPython. And why not one of us thinks security is anywhere close to solved. Turns out that's the good news. It's why the room was full. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show Guests Juanita Gomez: linkedin.com Mike Fiedler: miketheman.dev Seth Michael Larson: sethmlarson.dev Trailblazing Python Security: us.pycon.org Everything Security at PyCon US 2026 (PSF blog): pyfound.blogspot.com Dependency Cooldowns: cooldowns.dev Anatomy of a Phishing Campaign (Mike Fiedler) Recording: www.youtube.com FedRAMP: www.gsa.gov Zero Trust in 200ms: Implementing Identity-Per-Transaction with Python & Serverless-Tristan McKinnon: www.youtube.com Rust for CPython project: blog.python.org pre-PEP: discuss.python.org Rust for CPython: Making Python Safer and More Robust for Everyone - Emma Smith: www.youtube.com SBOMit: github.com Asleep at the Wheel: Getting your SBOMs to pay attention... - Sanchit Sahay, Abhishek Reddypalle: www.youtube.com Volatility: volatilityfoundation.org Post Incident Runtime SBOM Generation from Python Memory - Hala Ali: www.youtube.com zizmor: docs.zizmor.sh GitHub Actions security in Python packages (Andrew Nesbitt write-up): nesbitt.io andrew/pycon: data & analysis for the GitHub Actions security talk: github.com GitHub Actions Security in Python Packages - Andrew Nesbitt: www.youtube.com gh-profiler: examine a GitHub user's profile to gauge their contributions: github.com PyCon US YouTube channel: www.youtube.com SBOMit: adding verification to SBOMs (OpenSSF): openssf.org Ecosystems: ecosyste.ms Watch this episode on YouTube: youtube.com Episode #557 deep-dive: talkpython.fm/557 Episode transcripts: talkpython.fm Theme Song: Developer Rap

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

Prediction markets are becoming an increasingly visible part of sports, politics, business, and culture. But what makes them different from traditional betting?Abraham Wyner, Wharton Professor of Statistics and Data Science and Co-Director of the Wharton Sports Analytics and Business Initiative, explains the wisdom-of-crowds theory behind prediction markets, how these platforms make money, and why their structure creates a different relationship between bettors and the market. He also examines regulation, the risks of gambling, and whether prediction markets and sportsbooks can continue growing alongside one another. Hosted on Acast. See acast.com/privacy for more information.

How will artificial intelligence reshape the future of work—and will it create enough opportunities for the next generation?Over the next decade, 1.2 billion young people are expected to enter the workforce, but only around 400 million jobs are currently projected to be available. In this episode, host Tanvir Gill explores whether AI could help close that gap—or make the challenge even greater.Entrepreneur and content creator, Marina Mogilko, explains how workers can build an advantage through AI systems, agents and “vibe coding.”Mohamed Eissa, IFC Chief Investment Officer and Global Head of Venture Capital and Direct Technology Investments, examines how AI could transform businesses and create opportunities across emerging markets.Sharat Raghavan, Director of Data Science and Research at LinkedIn, shares what their data reveals about changing jobs, rising skills and the future of career progression.Timestamps[00:00] Will AI make or take the next billion jobs?[00:48] What Nairobi's tech community thinks[02:51] Marina Mogilko on vibe coding and AI as opportunity[05:06] AI and the future of content creation[08:00] The top skill for an AI economy[09:29] Introductions: Sharat Raghavan from LinkedIn and Mohamed Eissa from IFC[13:04] Human skills and AI entrepreneurship[14:40] AI opportunities in emerging markets[16:25] Where jobs are growing[17:25] AI for development: healthcare in Africa[18:35] The changing career ladder[21:44] AI disruption: reality or hype?[25:27] AI, SMEs and the missing middle[26:23] How to future-proof a career

Right now, AI is cheap. But the companies providing it are losing billions of dollars a year and are expected to go public within the next 12 months. When they do, the price of intelligence is going up - and any organisation that has built its AI future entirely on rented intelligence is going to face a very unpleasant surprise.In this Value Boost episode, Nicholas Kelly joins Dr Genevieve Hayes to explore why organisations shouldn't be building their AI future entirely on frontier models, and what data professionals need to understand to be the strategic voice in the AI infrastructure conversation.You'll discover:1. Why relying entirely on frontier AI models is a strategic error [02:00]2. How the economics of AI are about to change [03:34]3. The case for owning at least some of your AI infrastructure [07:53]4. What data professionals need to know to become the strategic voice in AI infrastructure decisions [11:02]Guest BioNicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect 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: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

Is the reverse mortgage industry's biggest opportunity still ahead? According to ATTOM's Head of Data Science, Aaron Wagner, the data says yes. In Part Two of this Industry Leader Insights series, Andrew Montesi sits down with Aaron to explore the future of housing equity, the biggest forces shaping property wealth over the next five years, and why America's enormous equity base continues to support a strong long-term outlook for reverse mortgages. Aaron explains why tenure-driven equity, built through decades of homeownership, is fundamentally different from appreciation-driven equity, why housing supply may be the most underestimated force supporting home values, and how AI is transforming the future of property intelligence. In this episode: • Has America reached peak housing equity? • Why the reverse mortgage opportunity remains exceptionally strong • The difference between tenure-driven and appreciation-driven equity • The biggest drivers of housing wealth over the next five years • Which regions are best positioned for future equity growth • Why housing supply may be the market's most underestimated force • How AI is changing the way property data is analysed and accessed If you work in the reverse mortgage industry, understanding where housing equity is heading, and why, has never been more important.

For years, "Django and async" came with an asterisk. The docs themselves warned you off it. Scary performance notes, a story that felt half-finished. Well, that story just got rewritten, literally, and the person who rewrote it is here to tell you why the old framing was wrong. Carlton Gibson is a former Django Fellow, sat on the security team for eight years, and he's on the steering council. On this episode we get into the async topic doc rewrite, what actually remains versus what was just fear, the new Tasks framework in 6.0, DB-level cascades and fetch modes landing in 6.1, and why free-threading is the bet that's about to pay off big for Django. If you've been told Django's async story isn't ready, this is the episode that puts that myth to bed. Episode sponsors Sentry Error Monitoring, Code talkpython26 Python in Production Talk Python Courses Links from the show DjangoCon Europe: djangocon.eu PyCon Italia: pycon.it Django on the Med: djangomed.eu Django Mantle: noumenal.es PyPI: pypi.org release notes: docs.djangoproject.com on_delete: docs.djangoproject.com Fetch modes: docs.djangoproject.com HttpRequest.multipart_parser_class: docs.djangoproject.com async topic doc: docs.djangoproject.com docs: docs.djangoproject.com DEP 14: github.com django-tasks: github.com django-tasks-local: github.com Celery: docs.celeryq.dev PEP 703: peps.python.org free-threading HOWTO: docs.python.org PEP 779: peps.python.org ASGI: docs.djangoproject.com PGBouncer: www.pgbouncer.org Channels: channels.readthedocs.io sync_to_async / async_to_sync: docs.djangoproject.com noumenal.es: noumenal.es Django Chat: djangochat.com @carlton@fosstodon.org: fosstodon.org Article: Cutting Python Web App Memory Over 31%: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #556 deep-dive: talkpython.fm/556 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Recorded live, this episode features Bruce Martin, former Sr. Vice President of Account Development at NielsenIQ, who shares insights from a career that spans every angle of the retail ecosystem—from an apprentice meat cutter at Kroger to the head of category management at Kellogg and managing director for Nielsen Chile.  Bruce breaks down why consumer packaged goods (CPG) represent the absolute highest standard for high-intensity, repeat-transaction data analytics.  He dives into the modern retail paradigm shift, detailing how direct-to-manufacturer data sales and media networks pose an existential threat to third-party data companies. Bruce also pulls back the curtain on how AI is accelerating historical quarterly projections into rapid weekly cycles, the massive challenge of managing omnichannel metadata on platforms like Amazon, and how up-and-coming brands can leverage fact-based selling to secure category leadership.   

PHP Podcast – July 23, 2026 Hosts: Joe Ferguson, Sara Golemon, and Holly Schilling Eric got ousted from his own show (no pants required), Holly’s elephants are lost somewhere in the U.S., and the crew talks SIMD, PHP 8.6 feature freeze, and whether AI can actually create. Also, socks. A Show Without Pants: The Guest Takeover This week Eric got booted from his own podcast while John was off doing something more important, leaving Holly, Sara, and Producer Joe to run the show. The gang wasted no time reminding everyone that the correct Discord URL is discord.phparch.com — not phparch.com/discord — a mistake that ended up as tomorrow’s task list item for Joe. There was plenty of housekeeping to get through: PHP Tek 2027 lands April 27th through 29th in Chicago, the CFP is open, and the call for papers closes August 31st. Holly, ever the deadline enthusiast, admitted she loves “the sound deadlines make as they whoosh by,” which set the tone for much of the episode. And yes, the swag store now has PHP Architect socks. There was extensive debate about whether the socks are safe for large-footed individuals, whether banana-for-scale photos are needed, and whether Eric should post feet pics. DuckNizzle bought some socks. We are also, apparently, SOC 2 certified. The Great Elephant Migration Sara’s elephant herd is currently scattered across the United States, most of them somewhere between Chicago and California courtesy of U-Haul. With import forms to Portugal not yet ready, the plush pachyderms are taking a scenic detour west before eventually making their way overseas. Only six elephants are currently on hand, and technically none of them are PHP Architect elephants — though one is a PHP Roundtable elephant, prompting the recurring wish that someone should bring that podcast back. Spoiler: they might, more on that later. Sara also shared she’s been in intensive foreign language classes every day, getting up at 7 a.m. while not falling asleep until 2 a.m., all while assembling a Prusa Core One+ 3D printer kit with roughly 300 pages of instructions and hundreds of tiny pieces. SIMD and Data Science in PHP Joe brought a blog post from Mitchell Hashimoto arguing that everyone should know SIMD — Single Instruction, Multiple Data. It’s a form of parallel computing that can dramatically speed up large data processing, offering 8x, 10x, even 100x improvements when you’re working with hundreds of thousands or millions of records. It won’t help you loop over a 100-item array, but it might supercharge a FlowPHP parquet file with millions of rows. Sara pushed back on how deep into the processor this discussion goes, noting that floating point math already flows through the SIMD registers — so in a very narrow sense, you’re already getting SIMD support in current PHP. Reordering instructions safely is another matter entirely (see: Intel’s history of getting it wrong). The conversation tied back to Florian Englehart’s “One Billion Rows” talk at PHP Tek 2025, and Holly mentioned her monomorphic generics work now supports variadic types — meaning a typed vector structure could, with a bit of JIT work, make SIMD-style patterns possible in PHP’s near future. PHP 8.6 Feature Freeze Is Coming Code freeze for PHP 8.6 lands August 11th — less than three weeks out. The release managers admitted they flubbed and missed the five-week warning email that should have gone out July 6th, though the four-week email did go out July 13th and a two-week warning is coming next Monday. Sara, one of PHP’s previous release managers, was philosophical about people panicking at the deadline: you’ve had twelve months, your lack of planning is not the release team’s problem. An idea to move the feature freeze date was floated and quickly shot down — the cadence has been established for years, and one missed email doesn’t change that. Larry got a shout-out for suggesting internals push all new business to September so 8.6 can get properly tested. And Holly declared, in full Benevolent Dictator mode, that the next release simply has to be 9.0 — too many big features to ship for a mere 8.7. AI, Copyright, and Whether Machines Can Create The crew dug into AI, starting with the news that a judge approved Anthropic’s $1.5 billion copyright lawsuit settlement — with roughly two-thirds going to lawyers. On the brighter side, Anthropic is giving a million dollars to Code Crew, a Memphis nonprofit building a physical location to offer less-predatory career training. Holly described using AI to implement a new module dereference operator (colon greater-than) that exists nowhere but her own machine, arguing the AI isn’t stealing when it follows precise instructions to generate novel syntax. Sara countered that the LLM isn’t creating so much as blending existing functionality — following instructions rather than inventing. The debate turned philosophical: how much truly new code have humans written in the last decade? We stand on the shoulders of giants, we’re “thinking meat sacks,” and Sara cheerfully accepted the label of “biased anti-clanker bigot.” The Terminator is coming for Larry first, apparently. Foundation Transparency and Household Jira The PHP Foundation published a quarterly progress report authored by Elizabeth, detailing what every contractor and employee has been working on. Holly praised Elizabeth for bringing the transparency the Foundation had long lacked, since devs have a lot of autonomy and asking them what they’re doing is, apparently, the worst. This spiraled into a discussion of household project management — one host’s wife is a project manager, another family installed a Jira instance for their home that the programmer husband absolutely hates, and there’s a gamified house-cleaning app that Holly refuses to adopt for fear she’ll either become obsessed or start a fight over losing. The episode wrapped with talk of finally reviving PHP Roundtable (scheduling permitting), Longhorn PHP acceptances, and gentle ribbing that Eric and John are “getting up in years” and can’t perform with their usual podcast consistency. There’s a pill for that. Links from the show: PHP Tek 2027 — Chicago, April 27–29, CFP closes August 31 PHP Architect Swag Store — now with socks Join our Discord Hosts: Joe Ferguson Mastodon: @joepferguson@social.social PHPArch.me: @svpernova09 Sara Golemon Mastodon: @pollita@phpc.social Holly Schilling Mastodon: @TheCodeLorax@tech.lgbt Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ OurCVEs Your security posture, on autopilot with OurCVEs CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. PHP Architect Consulting Your PHP codebase deserves a partner, not a contractor PHP Architect provides long-term technical partnerships for organizations that need senior-level PHP expertise that you can depend on https://www.phparch.com/consulting/ Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.07.23 appeared first on PHP Architect.

Renée Cummings channels the spirit of due process while envisioning a radically imaginative world in which the benefits of ethical AI innovation are available to all. Kimberly and Renée discuss her radical optimism; finding joy in justice; AI's criminal misadventures; bringing an ethical imagination to AI; creative innovation; our responsibility to the future; AI as a public health challenge; the social contract and duty of care; how governance, data, and trust intertwine; due process as core to intelligence; child protection and the AWARE (algorithmic awareness and responsible engagement) campaign; giving love a break; and why responsibility requires honesty.   Renée Cummings is a Professor of Practice at the University of Virginia School of Data Science as well as a Nonresident Senior Fellow and Co-Director of the AI Equity Lab at the Brookings Institution. Renee also serves as the Co-Chair of the Global Academic Network at the Centre for AI and Digital Policy (CAIDP). Related Resources: Michigan Technology Journal: An Invitation to Partner (article) The A.W.A.R.E Initiative (Press Kit) Who is Raising Your Child? (A.W.A.R.E Initiative publication)  A transcript of this episode is here. 

The AI era presents a choice for data professionals: wait to see what happens to your role, or get ahead of it and decide what it becomes. Nicholas Kelly made that choice two years ago, when a client told him ChatGPT could do 50% of what he did. What followed is a masterclass in proactive career evolution.In this episode, Nick joins Dr Genevieve Hayes to explore how data professionals can evolve their skills for the AI era, what that evolution looks like in practice, and why their existing expertise puts them in a stronger position than they might think.You'll discover:1. How Nick evolved from dashboard consultant to AI architect [02:35]2. Why the data team's role is staying the same even as everything around it changes [06:43]3. How AI is enabling data professionals to build software that used to require a development team [12:11]4. The best first project to build if you want to develop your AI skills [25:08]Guest BioNicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect 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: django-orjson Best Django Redis configuration for speed and size Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Django Steering Council backs the Triptych Project Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Michael #1: django-orjson Adam Johnson dropped django-orjson - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib json for orjson, the Rust-based library. Headline numbers: 10x faster serialization, 2x faster deserialization. The interesting question is why this needs to be a package at all. pip install orjson is the easy part. Adam's actual pitch: adopting it "isn't easy, especially when your framework uses json in many different parts." Django scatters JSON across JsonResponse, the test client and test case classes, the json_script template tag, and more. There's no single hook to grab, so you get a library that catches them all. Adam is refreshingly honest about the scale of the win. His words: "While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant." He calls it "a nearly free performance win" - not "this will 10x your app." That's a claim about cost, not magnitude, and it's worth keeping those straight. Worth flagging what the post doesn't cover: caveats. There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (Wed, 15 Jul 2026 12:00:00 GMT); orjson does ISO 8601. It can't do ensure_ascii, it rejects NaN and Infinity (which stdlib happily emits), and it raises on Decimal. If you've got a JS client parsing dates, that's a wire-format change. Who should actually take this? If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero. The problem Adam's package solves doesn't exist in Flask or Quart. They already centralize every JSON operation - jsonify, request.get_json(), the test client, the |tojson filter - behind one provider object at app.json. So there's no library to install. It's about ten lines: import orjson from quart.json.provider import JSONProvider # or flask.json.provider class OrjsonProvider(JSONProvider): def dumps(self, obj, **kwargs) -> str: return orjson.dumps(obj).decode() # provider must return str def loads(self, s, **kwargs): return orjson.loads(s) app.json = OrjsonProvider(app) The numbers on talkpython.fm Evaluated it, measured it, and skipped it. The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves 0.119 milliseconds per request. That total response takes 1.1 ms We got 4.1x, not 10x - and the reason is the good lesson. Payload shape decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy Calvin #2: Best Django Redis configuration for speed and size Peter Bengtsson revisits a classic: his 2017 "Fastest Redis configuration for Django" benchmark now has a 2026 update posted this week. The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was msgpack + zlib as the sweet spot - avoid the json serializer, it's fat and slow. The 2026 update narrows focus to just compressors: default (no compression), zlib, lzma, and newcomer zstd. New results: lzma compresses best but is slowest; zstd is the fastest compressor on Ubuntu; differences between them are very small. Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint. Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload. Michael #3: Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Write up on Ars. Really good coverage by Maximillian: Time to wake up (for some) Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.” I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won't be working professionally in software development in the coming years. The statement came amid a lengthy thread arguing about the use of Sashiko, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. “We're not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said. “Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote. Calvin #4: Django Steering Council backs the Triptych Project Django Steering Council issued a Letter of Collaboration backing Carson Gross & Alex Petros's funding bid for the Triptych Project - three proposals to make HTML more expressive natively, in every browser. The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement. Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain. Current focus is button actions (WHATWG #12330): Logout instead of wrapping a button in a form. Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns. How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues. Extras Calvin: DOOMQL - A playable first-person shooter whose framebuffer is a SQL query. Michael: Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling Welcome Calvin post Joke: Solving all bugs

What is the state of AI and videogames? Who is considering it? What are the big fails so far? This and much more is covered in this 1st episode of AI and videogames.   Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

In so-called "red states" and "blue states" around the US, from rural areas to urban centers, the explosion of new data center projects is impacting residents in poor, working-class, and middle-class communities alike. In this episode of Working People, we speak with a diverse panel of residents fighting new and proposed data center projects in Texas and Maryland about what it means for you and your community when one of these loud, polluting, energy- and water-guzzling behemoths comes to your town—and how to fight back. Panelists include: Cheryl Shadden, a registered nurse anesthetist who lives right next to the site of a Marathon bitcoin mine in Granbury, Texas; Karen Pearson, a licensed professional counselor and a resident of Granbury who also lives right next to the Marathon bitcoin mine; Dr. Shannon Wolf, Precinct Chair in Hood County, who lives three miles from the Marathon bitcoin mine; Craig Jackson, a resident of Granbury who lives 300 feet from the site of a new proposed data center, and who is also a plaintiff in a current lawsuit residents have filed against the city of Granbury; Micaiah Lloyd, a grassroots organizer and resident of Charles County, Maryland, who lives near the site of a proposed data center; Hillary Gonzalez, an eco-poet, author, founder of Sacred Parks & Waterways, and a resident of Baltimore, Maryland, who lives right next to the site of Johns Hopkins University's future Data Science and Artificial Intelligence complex. Additional links/info: Protect Hood County website and Facebook page Sacred Parks & Waterways website and Instagram Viral MS Now clip featuring Cheryl Shadden of Granbury, Texas Maximillian Alvarez, Working People / TRNN, "A bitcoin mine in Texas is 'killing us slowly,' local residents say" Maximillian Alvarez, Working People / TRNN, "'Like being tortured': Texas residents living next to bitcoin mine are getting sick and being ignored" Maximillian Alvarez, Working People / TRNN, "Toxic Avengers: America's poisoned and abandoned communities must stand together or die" Christine Condon, Maryland Matters, "Data center frustrations cause upheaval in county primary elections" Emmanuel Rivas Valenzuela, KERA News, "Granbury residents sue city over data center plan, allege Texas Open Meetings Act violations" Featured Music: Jules Taylor, Working People Theme Song Credits: Audio Post-Production: Jules Taylor

In so-called “red states” and “blue states” around the US, from rural areas to urban centers, the explosion of new data center projects is impacting residents in poor, working-class, and middle-class communities alike. In this episode of Working People, we speak with a diverse panel of residents fighting new and proposed data center projects in Texas and Maryland about what it means for you and your community when one of these loud, polluting, energy- and water-guzzling behemoths comes to your town—and how to fight back.Panelists include: Cheryl Shadden, a registered nurse anesthetist who lives right next to the site of a Marathon bitcoin mine in Granbury, Texas; Karen Pearson, a licensed professional counselor and a resident of Granbury who also lives right next to the Marathon bitcoin mine; Dr. Shannon Wolf, Precinct Chair in Hood County, who lives three miles from the Marathon bitcoin mine; Craig Jackson, a resident of Granbury who lives 300 feet from the site of a new proposed data center, and who is also a plaintiff in a current lawsuit residents have filed against the city of Granbury; Micaiah Lloyd, a grassroots organizer and resident of Charles County, Maryland, who lives near the site of a proposed data center; Hillary Gonzalez, an eco-poet, author, founder of Sacred Parks & Waterways, and a resident of Baltimore, Maryland, who lives right next to the site of Johns Hopkins University's future Data Science and Artificial Intelligence complex.Additional links/info: Protect Hood County website and Facebook pageSacred Parks & Waterways website and InstagramViral MS Now clip featuring Cheryl Shadden of Granbury, TexasMaximillian Alvarez, Working People / TRNN, “A bitcoin mine in Texas is ‘killing us slowly,' local residents say”Maximillian Alvarez, Working People / TRNN, “‘Like being tortured': Texas residents living next to bitcoin mine are getting sick and being ignored”Maximillian Alvarez, Working People / TRNN, “Toxic Avengers: America's poisoned and abandoned communities must stand together or die”Christine Condon, Maryland Matters, “Data center frustrations cause upheaval in county primary elections”Emmanuel Rivas Valenzuela, KERA News, “Granbury residents sue city over data center plan, allege Texas Open Meetings Act violations”Featured Music: Jules Taylor, Working People Theme SongCredits: Audio Post-Production: Jules TaylorBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-real-news-podcast--2952221/support.Help us continue producing radically independent news and in-depth analysis by following us and becoming a monthly sustainer.Follow us on:Bluesky: @therealnews.comFacebook: The Real News NetworkTwitter: @TheRealNewsYouTube: @therealnewsInstagram: @therealnewsnetworkBecome a member and join the Supporters Club for The Real News Podcast today!

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It only saw the files. Never the live state. This episode, that wall comes down. marimo pair drops a coding agent right inside a running notebook, with full access to every variable Python is holding in memory. The notebook becomes a shared canvas. You point, it runs the code. You tell it to zoom in on the Picasso paintings, and the chart just updates. No MCP tools to wire up, no schema to describe. Just Python, and an agent that can finally see what you see. Trevor Manz is back to walk us through it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show marimo pair: marimo.io/pair Course transcripts announcement: talkpython.fm/blog anywidget: Jupyter Widgets made easy: talkpython.fm marimo: marimo.io blog: marimo.io GitHub: github.com given this: martinalderson.com llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm open issues: github.com Discord: marimo.io Marimo Pair: marimo.io OpenCode: opencode.ai AI Tooling for Software Engineers in 2026: newsletter.pragmaticengineer.com Watch this episode on YouTube: youtube.com Episode #555 deep-dive: talkpython.fm/555 Episode transcripts: talkpython.fm Theme Song: Developer Rap

You ask an AI a question and it answers with total confidence. Most of the time, a confidently wrong answer is just an annoyance. But what if the question is medical, and there's a real patient on the other end? In that world, a hallucination isn't a bug, it's a patient-safety event. Sumit Gundawar is a London-based software engineer who builds the clinical platform for a UK longevity and aesthetic-medicine clinic, and his whole argument is that in high-stakes AI, the model is the easy part. Earning trust is the real engineering. We dig into grounding, refusal logic, human-in-the-loop design, and the messy frontier of longevity and biohacking, plus a live demo of an assistant that refuses to answer when it can't back up the claim. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Guest Sumit Gundawar: linkedin.com Course transcripts announcement: talkpython.fm/blog Sumit Gundawar - JAX London Speaker: jaxlondon.com Anthropic: anthropic.com OpenAI Platform: platform.openai.com Anthropic: anthropic.com LangChain: langchain.com OWASP: owasp.org Pydantic: pydantic.dev EU AI Act - Regulatory Framework: digital-strategy.ec.europa.eu HIPAA - HHS: www.hhs.gov NHS: www.nhs.uk Llama: llama.com Qwen - QwenLM on GitHub: github.com OpenAI Platform: platform.openai.com Hugging Face: huggingface.co Llama: llama.com Granola: www.granola.ai HIPAA - HHS: www.hhs.gov CodeRabbit: www.coderabbit.ai Cursor Origin: cursor.com GitHub Status: www.githubstatus.com Midjourney Medical: www.midjourney.com Neko Health: www.nekohealth.com CERN: home.cern ATLAS Experiment: atlas.cern Watch this episode on YouTube: youtube.com Episode #554 deep-dive: talkpython.fm/554 Episode transcripts: talkpython.fm Theme Song: Developer Rap