Podcasts about Data science

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.

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

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.

Which master's degree actually pays more in Kansas, data science or analytics? We break down the thirty-thousand-dollar salary gap, the technical divide, and why program names don't always match what you'll actually learn. Newman University City: Wichita Address: 3100 McCormick Website: https://newmanu.edu/

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. 

In this episode, we dive deep into the world of autonomous AI for data science with Doudou BA, CEO and founder of Octopus. I explore how Octopus is transforming raw data into actionable business decisions using advanced time series foundation models and agentic AI. We discuss the unique reasoning capabilities of Octopus, its intuitive approach for non-technical users, and how it stands apart from traditional AutoML solutions. Along the way, I share insights from VivaTech in Paris, including thought-provoking moments with industry leaders like Yann LeCun and Jeff Bezos. Join us as we reimagine the data science workflow and uncover what's next for AI-powered business intelligence.

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

In the second episode of Season 5 on the Neuroethics of Psychedelics, we speak with Eiko Fried about psychedelic research, the facts, the myths, and the hype. Professor Fried is a professor of Mental Health & Data Science at Leiden University. He is the co-Founder of the local Open Science Community, and a previous member of the Young Academy Leiden. In this episode, we discuss the limitations of existing psychedelics research, the implications of exaggerated claims, and the role of hype in moving this research forward, at a risk. Relevant references:• Checklist to vet psychedelic science - Eiko Fried BlogEpisode contributorsHost: Dr. Lavinia Uscatescu Audio editor: Sarah Schultz

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

Housing equity remains one of the biggest drivers of opportunity for the reverse mortgage industry—but are the headlines telling the full story? In Part One of this Industry Leader Insights series, HECM World's Andrew Montesi sits down with Aaron Wagner, Head of Data Science at ATTOM, to look beyond the latest housing equity headlines and explore what the data is really saying. Aaron explains why today's moderation in home equity is better viewed as market normalization, why the Midwest has become one of America's strongest equity regions, and how reverse mortgage professionals should think differently about housing and property data. In this episode: • Why housing equity is moderating, but remains historically strong • What “equity rich” actually means and how ATTOM measures it • Why Florida, Arizona and other pandemic boom markets are cooling • Why the Midwest is now home to many of America's most equity-rich counties • How reverse mortgage professionals can use local property data to uncover opportunities If you work in the reverse mortgage industry, housing market intelligence has never been more important. ▶️ Next week in Part Two: We look ahead to the future of housing equity, the biggest drivers of property wealth over the next five years, the role of AI in property intelligence, and why ATTOM believes the long-term opportunity for the reverse mortgage industry remains incredibly strong.

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! https://opencv.org/

What is "wrong" with the EU AI Act? Watch the full episode and get in touch.   Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

Julia is a Senior Research Scientist at Bigelow Laboratory, where she has been working on ocean science research since 2016. Her current research aims to characterize the diversity and roles of marine viruses and microbes in order to better understand their activity within climate relevant ecosystems, such as the rapidly warming Gulf of Maine and large marine oxygen minimum zones. Julia was one of this year's 5 Minute Genius speakers at the 2026 Maine Science Festival. The New York Times featured Julia's work in 2020: https://www.nytimes.com/2020/09/24/science/virus-eaters-protists.html. This conversation was recorded in May 2026.   ~~~~~The Maine Science Podcast is a production of the Maine Discovery Museum. It is recorded at Discovery Studios, at the Maine Discovery Museum, in Bangor, ME. The Maine Science Podcast is hosted and executive produced by Kate Dickerson; edited and produced by Scott Loiselle. The Discover Maine theme was composed and performed by Nick Parker. To support our work: https://www.mainediscoverymuseum.org/donate. Find us online:Maine Discovery MuseumMaine Discovery Museum on social media: Facebook Instagram LinkedIn Bluesky YouTubeMaine Science Podcast on social media: Facebook Instagram YouTubeMaine Science Festival on social media: Facebook Instagram LinkedIn YouTube© 2026 Maine Discovery Museum

In this episode, Kelly Schuster-Paredes speaks with Mahmoud Harding about his work in data science education and the way he thinks about teaching Python, R, and statistics. Mahmoud explains that he is the instructional design director at Data Science for Everyone, where the goal is to make data science available to more students and to connect it to meaningful, real-world contexts. A major part of the conversation focuses on how students learn best through curiosity and project-based work. Mahmoud describes the ADAPT model, including its emphasis on project-based learning and common learning elements, and he argues that students should begin working with their own data early in a course. Kelly and Mahmoud discuss how choosing their own datasets helps students become more engaged, notice mistakes, and ask better questions. The discussion also compares R and Python as tools for data science. Mahmoud explains that R was designed by statisticians for statistical analysis, while Python became popular as a general-purpose language that later grew into a strong data science ecosystem through libraries like NumPy and pandas. He also describes Jupyter Everywhere, a browser-based notebook environment designed to reduce barriers for schools and allow students to use R or Python without complicated setup. Later, the conversation turns to judgment, nuance, and the role of data in learning. Mahmoud argues that students need domain knowledge and human judgment to interpret data responsibly, and that data projects can help them develop those skills. Kelly extends this idea to other subjects, suggesting that books, history, and other classroom materials can also be treated as data for analysis and discussion. The episode closes with Mahmoud sharing ways to connect with him through Data Science for Everyone and with mention of an upcoming Data Science Education K–12 event in Atlanta in February.Special Guest: Mahmoud Harding.

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!

For data scientists, getting a project approved is a sale. It might not feel like one and money might not change hands, but the dynamics are exactly the same. And like any sale, it goes a lot better if you go in with a plan.In this Value Boost episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use the Four Conversations framework to sell their expertise more effectively, whether as independent consultants or as employees within organisations.You'll discover:Why reputation is the most powerful sales tool a data professional has [04:33]How to stop behaving like a vendor when you're trying to sell expertise [05:09]Why the value of your work resides in your stakeholder not in you [09:53]The single question that unlocks what your stakeholder truly values [12:13]Guest BioBlair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship.LinksConnect with Blair on LinkedInBlair's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: 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.

Exploring the Social Life of Urban Spaces through AI (Arianna Salazar-Miranda) Welcome to the fifth season of Densely Speaking! Subscribe today for automatic delivery of future interviews, and please take a moment to rate and review us—it helps others find the show and become introduced to wonderful authors doing cutting-edge work on cities, economics & law. Arianna Salazar-Miranda is an Assistant Professor of Urban Planning and Data Science at the Yale School of the environment. She is the author of Exploring the Social Life of Urban Spaces Through AI (with Zhuangyuan Fan, Michael Baick, Keith N. Hampton, Fabio Duarte, Becky P. Y. Loo, Edward Glaeser, and Carlo Ratti). Appendices: Arianna Salazar-Miranda: The World in the Year 2000 Greg Shill: Sidewalk Government and Sidewalk Nation: The Life and Law of America's Most Overlooked Resource by Michael Pollack. Jeff Lin: What Holly Whyte's “Social Life” Film Can Teach Us Today by Anne Tan-Detchkov at Project for Public Spaces discusses the film in question and Statistical Models and Shoe Leather by David Freedman discusses regression analysis and advocates for broader, holistic observational work within the social sciences. Follow us on the web and on Bluesky at @jeffrlin.bsky.social and @gregshill.com. Producer: Nathan Spindler-Krage The views expressed on the show are those of the participants, and do not necessarily represent the views of the Federal Reserve Bank of Philadelphia, the Federal Reserve System, or any of the other institutions with which the hosts or guests are affiliated.

Dan Mosher is the CEO of DealGround, an AI-native platform helping commercial real estate brokers and investors find deals faster by organizing property data, identifying opportunities, and connecting directly with property owners. A veteran Silicon Valley entrepreneur, operator, and investor, Dan previously built and led the Merchant team at Postmates through its acquisition by Uber, served as President of Presto, and helped scale BrightRoll tenfold before its acquisition by Yahoo. Live from ICSC+Proptech in Las Vegas.(01:09) AI Survey with FirstAmerican (03:07) How DealGround Helps Brokers Find More Deals (06:08) Why AI Adoption Outpaces Trust (10:14) MCPs, Integrations & Fitting Into Broker Workflows

AI takes over (kinda). Regulators look for a new job. Watch the full episode and get in touch.   Buy me a coffee https://ko-fi.com/datascience Discord Channel: https://discord.gg/4UNKGf3   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com

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

Learn to build AI systems that companies actually pay for: RAG, agents, production LLM apps. Not YouTube tutorial toys. Real projects with a dedicated mentor who's shipped this stuff at scale. Spots are capped at 10 per cohort: https://parsity.io/ai-devWho should software engineers marry? Not another software engineer, apparently.Samir Ranjan is the CTO and Director of Data Science at Catenate, where he built a patented platform that measures soft skills and personality to predict career outcomes. Translation: he has receipts on what actually gets engineers hired, promoted, and passed over.We get into:The 2 traits that separate good engineers from average ones (spoiler: it's not knowing every tool)Why "learning ability" beats technical knowledge in the AI eraThe age group struggling most with AI adoption (it's not who you think)What the data really says about junior devs getting replacedThe new job roles coming by 2027: AI orchestrators and agent managersWhy engineers should learn an art form (seriously)His weirdly practical advice for introverts: dress up and go to TargetWho software engineers should actually marrySamir went from mining engineering a thousand feet underground to building AI career tools. If you're trying to make sense of a chaotic job market, this one's full of stuff you can actually use.Connect with Samir on LinkedIn and check out Catenate.Connect with me:LinkedIn: https://www.linkedin.com/in/brianjenneyYouTube: https://www.youtube.com/@brianjenney

You might remember Katryn from episode 292, where we dove deep into the behavioral blueprint for inclusion. Well, a lot can happen in a year—or in our current case, since the wild ride of the 2026 election—and the DEI landscape is shifting beneath our feet. I'll admit, when I first started this work ten years ago, I thought we could just shout from the rooftops that inclusion matters and everyone would just magically get it. But as we look at the headlines today, it's clear that "shiny object syndrome" has left us with a lot of noise and not enough real, systemic change. Katryn and I sit down to unpack what global leaders are actually doing right now to push past the performative and get to the heart of what makes workplaces genuinely fair. Key Themes from the Conversation Moving from Noise to Systemic Change. Organizations frequently focus on highly public, performative declarations of inclusivity rather than restructuring the underlying processes that perpetuate bias. "Organizations were doing a lot of the shiny stuff... doing what we would call noisy things, right? Proclaiming, saying, being public... but obviously that not necessarily translating to real-world change." — Katryn Wright The Problem with Unconscious Bias Training. Treating broad, one-size-fits-all training modules as a standalone solution is ineffective and can create artificial metrics that trigger cultural backlash. "An awful amount of money was spent on something that the science shows is... ineffective at best, counterproductive at worst." — Katryn Wright Inclusion as an Aspiration, Not a Default Value. Framing inclusion as a predefined company value mistakenly implies that the work is already complete, whereas framing it as an ongoing aspiration invites employees to actively participate in closing the gap. "When we talk about inclusion as a value, it is not as effective as when we talk about inclusion as an aspirational goal... it suggests that we've been missing a trick to be bringing people on as much as we can." — Katryn Wright Precision and Data Science in Workplace Fairness. True progress requires identifying the exact inflection points in employee experiences—like hiring, promotion, and retention stages—where disparities emerge, and applying targeted behavioral interventions. "Let's go and be as precise as possible about changing behavior in that exact situation... when we are able to be as precise as possible about which specific behaviors need to change, we can get to those outcomes." — Katryn Wright Actionable Takeaway for Listeners Stop trying to de-bias your entire team all at once with sweeping declarations. Instead, pick one specific process in your daily workflow—whether it's how you audit resumes, run performance reviews, or distribute project assignments—and analyze the data to find where the equity gaps lie. Designing small, targeted interventions at precise moments is how real cultural evolution happens. Follow Katryn at https://www.morethannow.co.uk/

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

Die Episode behandelt den Einsatz von Legal Data Science in der juristischen Forschung und Lehre. Themen sind die statistische Analyse von Rechtskomplexität, der neue Bucerius-Bachelor „Märkte digitaler Gesellschaft“, die Bedeutung von Daten für KI-Anbieter wie LexisNexis sowie aktuelle examensrelevante Rechtsprechung im Strafrecht.

Building genuine expertise takes years. And in the age of AI, losing it can happen gradually and almost invisibly, through small delegations that each seem reasonable in isolation but add up to something significant over time.In this episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use AI without compromising their hard-earned expertise and reputation.You'll discover:Why delegating to AI is always a trade-off [03:00]The crucial difference between writing to communicate and writing to think [08:40]Why you should orient yourself around the problems you solve  [14:28]How to decide which skills are worth protecting and which to let go [17:49]Guest BioBlair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship.LinksThe Experts' AI ManifestoConnect with Blair on LinkedInBlair's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: dust - a better du Hermes Agent: The AI agent that grows with you llm-coding-agent 0.1a0 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. Michael #1: dust - a better du du + Rust = dust - a fast, visual, intuitive disk-usage CLI Run dust and immediately see the biggest directories and files without piping through sort, head, or awk Smart recursive output focuses on what matters instead of dumping every folder Colored bars show relative size and parent/child hierarchy, making “where did the space go?” obvious Perfect for Python projects bloated by .venv, caches, Docker volumes, downloaded datasets, and local AI models Install via brew, cargo install du-dust, conda-forge, Scoop, Snap, deb-get, or GitHub releases Calvin #2: A Way better ARchive format for Python packaging war - new archive format spec from Astral (same team as uv/ruff), v0.0.2, still no binary encoding defined yet Header-Index-Store layout: header IDs the file, index maps names to store offsets, store holds compressed data Index uses a finite-state transducer (FST) to dedupe common path prefixes across entry names Supports three entry types (file, directory, link) and three compression modes (store/DEFLATE/zstd), plus an "executable" metadata flag Unpacking is atomic - writes to a temp dir, then renames into place, so a failed extract never leaves a half-unpacked directory Strict name-segment rules (no NUL/control chars, no leading/trailing whitespace, blocks Windows-reserved names like CON/PRN) to avoid path traversal and cross-platform footguns Michael #3: Hermes Agent: The AI agent that grows with you Hermes Agent is an open-source, Python-built AI agent framework from Nous Research - think ChatGPT-style assistant, but connected to your tools, files, shell, browser, calendar, memory, and messaging apps I'm using it in Discord as a long-running agent conversation, not just a one-off chatbot session Hermes can connect through a gateway to platforms like Discord, Telegram, Slack, WhatsApp, email, webhooks, and more - so the same assistant can follow you across surfaces In my setup, I can send Hermes voice/text from Discord, keep project context across turns as threads, and ask it to actually do things: read GitHub repos, run commands, edit files, schedule calendar events, generate drafts, and verify results A fun workflow: I can trigger one-shot actions from an Apple Watch shortcut - dictate a request, send it to Hermes, and have the agent execute it asynchronously Hermes has persistent memory, so it can remember durable preferences and facts - for example, how I like my research formatted It also has “skills,” which are reusable procedures the agent can load later, so Hermes can self-improve over time instead of rediscovering the same workflow repeatedly It supports scheduled jobs / cron-style automations, so it can proactively watch for releases, send summaries, run checks, or remind you about things It's provider-agnostic: OpenRouter, Anthropic, Google, xAI, local models, Nous Portal, and others The big idea: Hermes turns an LLM from “a chat box I visit” into “an agent I can reach from anywhere that knows my workflows and can take real actions and learns over time.” Calvin #4: llm-coding-agent 0.1a0 Simon Willison built a Claude/Codex-style coding agent on top of his llm library, using an alpha of the llm package plus his python-lib-template-repo Built almost entirely via prompted TDD - asked an agent to write a spec.md, then commit + implement with red/green tests, occasionally hitting a real OpenAI key to sanity-check Shipped to PyPI as an alpha: uvx --prerelease=allow --with llm-coding-agent llm code Tool set mirrors familiar coding-agent primitives: read_file, edit_file (exact string replace + diff), write_file, list_files, search_files, execute_command Also exposes a Python API - CodingAgent(model="gpt-5.5", root=..., approve=True).run(...) - which Simon didn't ask for but got anyway Demo: llm code --yolo told GPT-5.5 to build a SwiftUI CLI clock; model correctly noted SwiftUI isn't really CLI-friendly and still produced an ASCII-art time display Extras Calvin: Slides, but for developers https://sli.dev/ Wanna reduce your token usage…. only issue is that its lossy https://github.com/teamchong/pxpipe PEP 772 - Python Packaging Council inaugural election dates set, nominations open July 28, voting September 1-15 Michael: What the pls? revisited! Joke: Min requirements for Linux

https://clearmeasure.com/developers/forums/ Matthew Renze is an AI researcher, consultant, and author, and the founder of Renze Consulting, where he has trained over 500,000 software developers and IT professionals worldwide. He has delivered over 200 keynotes, presentations, and workshops on every continent — including Antarctica — for clients ranging from tech startups to Fortune 500 companies. A nine-time Microsoft MVP in AI, Matthew is also the president of the Renze AI Research Institute, where he studies how self-reflecting large language model agents improve problem-solving performance, trustworthiness, and value alignment. Most recently he was accepted into the Doctor of Engineering program at Johns Hopkins University, and he featured as an interview subject in the 2026 documentary "AI Everywhere." Website: https://matthewrenze.com/ LinkedIn: https://www.linkedin.com/in/matthewrenze/ Twitter/X: @matthewrenze GitHub: https://github.com/matthewrenze (via profile links) Our OpenClaw agent ("Bob") - Bob's website: https://bobrenze.com/ - Bob's blog: https://blog.bobrenze.com/ - Bob's book: https://a.co/d/014kieQI - Agent ranking site: https://agentfolio.io/ Stage 1 - Communicating in steps with an AI assistant - ChatGPT - https://chatgpt.com/ - Claude Chat - https://claude.ai/ Stage 2 - Collaborating on tasks with an AI agent - GitHub Copilot - https://github.com/features/copilot - Claude Code - https://claude.com/product/claude-code - OpenAI Codex - https://openai.com/codex/ Stage 3 - Supervising processes with an agentic workflow - LangChain / LangGraph - https://www.langchain.com/langgraph - Microsoft Agent Workflows - https://learn.microsoft.com/en-us/agent-framework/workflows/ Stage 4 - Managing a project with an autonomous agent - OpenClaw: https://openclaw.ai/ - Hermes: https://hermes-agent.nousresearch.com/ Stage 5 - Leading a mission with an autonomous agency - PaperClip AI: https://paperclip.ing/ Fireworks AI: https://fireworks.ai/ ---------------------------------- Previous Appearances on the Azure & DevOps Podcast: Episode 44 — Matthew Renze on Data Science for Developers https://azuredevopspodcast.clear-measure.com/matthew-renze-on-data-science-for-developers-episode-44 Episode 220 — Matthew Renze: Developing Your AI Strategy https://azuredevopspodcast.clear-measure.com/matthew-renze-developing-your-ai-strategy-episode-220 Episode 249 — Matthew Renze: AI Ethics https://azuredevopspodcast.clear-measure.com/ai-ethics-with-matthew-renze-episode-249 --------------------------------------- Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Pankaj Rai | Group Chief Data and Analytics Officer, Aditya Birla Management Corporation Pvt LtdPankaj Rai, Chief Data and Analytics Officer at Aditya Birla Group, has been enabling the analytics maturity journey across group companies by solving problems leveraging Data Science, creating horizontal data platforms and developing people capabilities.He completed his graduation from IIT Delhi and Post Graduate Programme in Management from IIM Ahmedabad. He went on to spearhead teams across diverse portfolios including consulting, credit, six-sigma, strategy, analytics and digital across financial services and offshoring.During his career, he has been associated with companies like GE Capital, ICICI Bank, Standard Chartered Bank and Feedback Ventures. He has served as Director (Strategy) at Dell, Bangalore for their offshore business and built the internal Analytics team. He also led Wells Fargo's Global Delivery Strategy as Senior Vice President. He is a speaker in various leading platforms including TEDx.  

AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe.In this Value Boost episode, Derek Gibson joins Dr Genevieve Hayes to explore how data professionals can help their stakeholders become better data sceptics and avoid being duped by misleading data long before it ever reaches an AI.In this episode, you'll discover:1. The timeless data traps that catch even experienced decision makers [01:56]2. How to arm your stakeholders with the right questions to push back on data [07:57]3. Why confirmation bias is the most dangerous data vulnerability in any organisation [09:20]4. What it means when an analytics team is asked to confirm a decision rather than inform one [13:24]Guest BioDerek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms.LinksConnect with Derek on LinkedInDerek's websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: Free-threaded Python: past, present, and future django-admin-site-search Qwen 3.6 27B is the sweet spot for local development A large batch of PEPs are finalized Extras Joke Watch on YouTube Show Intro 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: Free-threaded Python: past, present, and future The GIL has prevented true multi-threaded parallelism in CPython since the beginning — multiple past attempts to remove it failed on performance grounds Sam Gross at Meta finally solved it; his work became PEP 703 and ships as free-threaded CPython today Python 3.13 was experimental with 20–40% single-threaded slowdown; 3.14 brought that to 0–10% Python 3.15 (October 2026) delivers a unified ABI — one extension binary works on both GIL and free-threaded builds Already >50% of the top PyPI binary wheels support free threading Wouters predicts free-threaded becomes the default between 3.16–3.20 (2027–2031), with the GIL eventually disappearing next decade Michael #2: django-admin-site-search via Adam Parkin A global/site search modal for the Django admin, by Ahmed Aljawahiry. Hit cmd+k anywhere in the admin and you get a command-palette-style search window, kind of like the one in VS Code. It doesn't just search one model's list page. It searches your entire site in one box: App labels Model labels and field attributes Actual model instances (your data) Two ways to search the instances: model_char_fields (the default): runs an __icontains across every CharField (and subclasses) on the model. Zero config, works out of the box. admin_search_fields: defers to each ModelAdmin's existing get_search_results(), so it respects the search_fields you've already set up. The part I like: it's permission-aware out of the box. Users only see results for the apps and models they actually have view permission on, so you're not leaking anything through search. Results appear as you type, with throttling/debouncing so you're not hammering the server on every keystroke, and it's full keyboard nav: cmd+k to open, up/down to move, enter to go. It's responsive, does dark and light mode, and it pulls Django's built-in admin CSS variables so it just matches whatever admin theme you're running. Under the hood it's Alpine.js, but bundled into static so there's no external CDN dependency. Setup is about what you'd expect: pip install django-admin-site-search, add it to INSTALLED_APPS, mix the AdminSiteSearchView into your AdminSite, and drop a few template includes into base_site.html. Supports Python 3.8 through 3.14 and Django 3.2 through 6.0, MIT licensed, and everything is overridable if you want to skip certain models, add TextField matching, etc. Calvin #3: Qwen 3.6 27B is the sweet spot for local development Qwen 3.6 27B is being called the first local model that genuinely competes as a general-purpose intelligence — benchmarks put it at roughly mid-2025 frontier level (comparable to GPT-5 / Claude Sonnet 4.5) Runs locally via llama.cpp; on an M5 MacBook Max with 8-bit quantization + multi-token prediction, it hits ~32 tokens/sec using ~42GB RAM 4-bit quantization gets it under 18GB, runnable on 32GB devices; Nvidia RTX cards run it even faster The dense 27B is recommended over the faster MoE 35B A3B — author prefers higher quality output over raw speed Privacy and reliability are the pitch: fine-tunable, can't be taken down, suitable for sensitive/proprietary data Author sees this as a stepping stone — frontier open-weight models like GLM 5.2 are now locally runnable with company-grade hardware, and smarter-still local models are coming Michael #4: A large batch of PEPs are finalized A bunch of PEPs went from accepted to final. 668, 687, 691, 699, 701, 703, 728, 770, 773, 829 But this wasn't them making their way into CPython. It's an admin sorta thing. (Thanks PyCoders) See the commit. Extras Calvin: More fun bling for your terminal this time - https://charm.land/ Michael: Follow up from pls, What the pls? Thanks Pito. Joke: BEMoji A production-grade utility and component framework built entirely on emoji class names via Jeff Triplett

Artificial intelligence is everywhere. The question is no longer whether AI will change work. The question is whether humans will continue to think critically while using it. In this episode, Dr. Wayne Pernell sits down with Sameer Ranjan, CTO and Director of Data Science at Catenate, to discuss how AI can be used to enhance human potential rather than replace human judgment. Sameer shares how his company helps organizations identify talent, improve workforce mobility, reduce turnover, and create measurable learning outcomes. Together they explore behavioral economics, Ikigai, career transitions, critical thinking, creativity, and the growing importance of curiosity in an AI-driven world. They also discuss: Human-centered AI• Workforce intelligence• Career transitions and upskilling• Learning and development measurement• Emotional intelligence and data• Curiosity as a leadership advantage• Critical thinking in the age of AI• The future of work and talent development If you're a leader, educator, founder, HR executive, or simply curious about how humans and AI can work together more effectively, this conversation is for you. Sameer Ranjan's LinkedIn: linkedin.com/in/sameerranjan009 Hosted by Dr. Wayne Pernell.

What happens when a philosopher walks into Wall Street? Richard Taylor, SVP of People Experience at Nasdaq, left a near-complete PhD in Buddhist studies to find out. In this episode, he joins Culture Amp's Justin Angsuwat to reveal how one of the world's most powerful financial institutions built a culture of empathy, data-driven people decisions, and radical transparency – with turnover 3x lower than the industry average.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hustle fails. Use physics-style leverage for premium deals with Jake Shannon. Our guest built his career on a brutal question every founder eventually has to face: if you stepped away for 30 days, would your business grow — or would it stall?He's a two-time 10X Performance Coach of the Year and the founder of No1 Coaching, where he installs operating systems that turn founder-led chaos into companies that run, scale, and sell without the owner glued to the controls. As Chief Data & AI Officer for the 10X Performance Coaching Program, he's also at the front edge of using AI to engineer revenue — not as a gimmick, but as leverage built into the system.Here's the twist: he learned all of it on the mat. He's the founder of Scientific Wrestling, the man who revived Catch-as-Catch-Can — the most ruthless grappling style in history — and he coaches alongside UFC legends Pat Miletich and Frank Shamrock. He takes the same principles that win on the mat — leverage, position, pressure, control — and installs them in the boardroom.If you're ready to stop being the bottleneck in your own business, please welcome Coach Jake Shannon.Tagspodcast for creatives,creative podcast,podcast creator interviews,professional podcast,creative podcasts,podcast host interviews,creative podcast ideas,AI & Data Science,Business Growth,Business Strategy,Coaching & Mentoring,Entrepreneurship,Leadership,Mindset,Sales,Small Business,WrestlingLinkshttps://sportify.ai/audithttps://www.instagram.com/scientificwrestling/Support PEG by checking out our Sponsors:Download and use Newsly for free now from www.newsly.me or from the link in the description, and use promo code “GHOST” and receive a 1-month free premium subscription.The best tool for getting podcast guests:https://podmatch.com/signup/phantomelectricghostSubscribe to our Instagram for exclusive content:https://www.instagram.com/expansive_sound_experiments/Subscribe to our YouTube https://youtube.com/@phantomelectricghost?si=rEyT56WQvDsAoRprRSShttps://anchor.fm/s/3b31908/podcast/rssSubstackhttps://substack.com/@phantomelectricghost?utm_source=edit-profile-page

This episode is a fun crossover from our Python news and tips podcast, Python Bytes. We have had some big changes over there. Brian Okken has moved on and Calvin Hendryx-Parker has joined the show as the new co-host. To kick off this new era, we decided to do a longer and more personal episode called "All Our Tools". The idea is both of us talk about some of our most useful day-to-day developer and business owner tools that we think you all would find useful. It was so well received, that I'm bringing it to you all as a crossover episode. Enjoy and we hope you find something new and awesome to help you with your software and data science day to day. Episode sponsors Sentry Error Monitoring, Code talkpython26 Python in Production Talk Python Courses Links from the show @calvinhp@sixfeetup.social: sixfeetup.social @calvinhp.com: bsky.app calvinhp.com: calvinhp.com Original airing on Python Bytes: pythonbytes.fm pi: pi.dev superpowers: github.com Warp.dev: Warp.dev OhMyZSH: ohmyz.sh Commandbookapp.com: Commandbookapp.com Blink: blink.sh kitty: sw.kovidgoyal.net mosh: mosh.org tmux: github.com Claude code: www.anthropic.com Claude.md: Claude.md MacWhisper: goodsnooze.gumroad.com Handy: handy.computer Tailscale: tailscale.com Talk Python episode with Alex: talkpython.fm Telescopo: www.telescopo.app Typora markdown: typora.io formal documentation for many of my open source packages: mkennedy.codes Great Docs: posit-dev.github.io Statement on the US government directive to suspend access to Fable 5 and Mythos 5: www.anthropic.com No second date: x.com Watch this episode on YouTube: youtube.com Episode #553 deep-dive: talkpython.fm/553 Episode transcripts: talkpython.fm Theme Song: Developer Rap

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

Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an bonus digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

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

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

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