Podcasts about Web development

How should development teams adopt AI without sacrificing code quality or collaboration? In this Talking Drupal Cafe, Stephen Cross is joined by Mike Miles and Jim Birch to discuss practical strategies for integrating AI into Drupal development teams. They explore AI coding assistants, team policies, code review, agent workflows, governance, and real-world lessons from using tools like Claude Code and GitHub Copilot in production environments. For show notes visit: https://www.talkingDrupal.com/cafe020 Topics Why AI Matters Now Team Introductions From Experiments to Workflows Standards and Guardrails Skills and Automation Examples Taming Verbose AI Code Adoption and Tool Choices Governance and Training Measuring Productivity Gains Keeping Up Without FOMO AI for Editors and Site Features Red Teaming and AI Security Jim Birch Jim Birch is Director of Engineering and AI Practice Lead at Kanopi Studios, where he leads engineering teams and oversees the company's responsible adoption of AI. Jim is also a Drupal CMS committer, and Recipes Initiative Coordinator, and is a Google Cloud Certified Generative AI Leader. Michael Miles Mike Miles is a technical leader and speaker with more than 20 years of experience in web engineering, open-source development, and digital platform delivery. As the Director of Web Development at MIT Sloan, he leads the team responsible for the development, maintenance, and growth of the school's public digital properties. Mike regularly speaks at technical conferences on topics including modern web development, Drupal, technical leadership, testing, delivery practices, and practical AI adoption. He is also one of the organizers of New England Drupal Camp. Stephen Cross Stephen Cross has been a Drupal developer for over 20 years and founded Talking Drupal in 2013. As founder of Second Signal Media, he combines his passion for open source and media production to share conversations that help the Drupal community learn and grow. Guests Michael Miles - mikemiles86 Jim Birch - thejimbirch Stephen Cross - stephencross Resources Courses https://anthropic.skilljar.com/ https://academy.openai.com/pages/courses Skills https://kanopi.github.io/cms-cultivator/ https://kanopi.github.io/delivery-record/  

Passer de 5 à 500 personnes, ce n'est pas juste une question de recrutement. C'est accepter de se réinventer en permanence, de lâcher prise sur ce qu'on maîtrise, et de construire des équipes capables d'avancer sans friction.Jean-Marc Charles a vécu cette transformation de l'intérieur chez BlaBlaCar pendant 10 ans. Il est passé par 18 postes différents, a managé des équipes tech grandissantes, et a appris à identifier ce qui fait vraiment fonctionner une équipe performante.Dans cet extrait, il partage les règles non-négociables qu'il applique aujourd'hui : focus, leadership et autonomie. Mais aussi pourquoi la culture du "fail learn succeed" n'est pas qu'un buzzword, et comment s'entourer de la bonne communauté pour continuer à grandir.————— JEAN-MARC CHARLES —————Retrouvez Jean-Marc :Sur son site web : https://antlice.fr/Sur LinkedIn : https://www.linkedin.com/in/jmcharles/————— 5 ÉTOILES —————Si cet épisode vous a plu, pensez à laisser une note et un commentaire sur Spotify ou Apple Podcast. Ça ne vous coûte rien et ça m'aide beaucoup !————— COACHING —————Vous êtes leader tech ou product face à des défis majeurs ?

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

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

Rémi a fait un parcours atypique : des neurosciences à Paris 6 au développement web, puis de développeur à engineering manager chez Wakeo.Pendant 7 ans dans la même boîte, il a tout vu : la croissance d'une startup, le doublement d'une équipe tech, la mise en place de process, les recrutements, la dette technique qui s'accumule... et ces moments de doute qui accompagnent chaque transition.Aujourd'hui, après 3 ans en tant qu'EM, Rémi se pose LA question : est-ce que le management, c'est vraiment fait pour lui ? Entre l'appel du code qu'il n'a jamais quitté et cette dimension humaine qu'il a appris à maîtriser, il cherche son équilibre.Dans cet échange, on parle de syndrome de l'imposteur, de culture d'entreprise, de shift produit vs tech, de recrutement, de feedback... et de ce moment où on réalise qu'on a peut-être besoin de remettre les mains dans le cambouis.————— RÉMI LEBIGRE —————Retrouvez Rémi sur LinkedIn : https://www.linkedin.com/in/remi-lebigre-6a102a53/————— PARTIE 1/3 : PARCOURS —————(01:04) Introduction à un parcours atypique(09:04) Découverte de la reconversion vers le développement(16:33) Transition progressive vers le management(42:30) Évolution vers le rôle d'Engineering Manager(45:16) Le quotidien d'un Engineering Manager(48:13) Gestion de l'agenda et priorisation(49:35) Développement et accompagnement de l'équipe(52:16) Suivi des projets et mesure de l'impact(56:30) Prise de décision et légitimité(1:00:26) Recrutement et culture d'entreprise chez Wakeo(1:05:07) Processus de recrutement détaillé(1:09:11) Structuration de l'équipe et chapters(1:10:02) Partage de connaissances : press days et R&D(1:16:18) Team Health : évaluation et amélioration continue(1:24:39) Syndrome de l'imposteur en tant que manager(1:31:56) Évolution salariale et reconnaissance(1:34:47) Réflexions sur la carrière et remise en question(1:38:17) Le podcast comme source d'inspiration(1:39:43) L'importance des entretiens et de la confrontation au marché(1:45:07) Valeurs et choix professionnels————— PARTIE 2/3 : ROLL-BACK —————(1:53:20) Le rollback : dette technique et stop-the-line produit(1:59:35) Culture et processus de management(2:10:59) Le rôle humain du manager————— PARTIE 3/3 : STAND-UP —————(2:16:19) Apprentissage et développement personnel(2:18:31) Ressources incontournables pour managers et devs(2:20:47) Passion et épanouissement dans la tech————— RESSOURCES —————Stay Sassy (SaaS) : ressources management pour heads of et EMsMartin Fowler : blog de référence sur l'architecture logicielleJosh W. Comeau : tutoriels interactifs React et CSSCharity Majors : article "The Engineer/Manager Pendulum"Communauté EM France————— 5 ÉTOILES —————Si cet épisode vous a plu, pensez à laisser une note et un commentaire - c'est la meilleure façon de faire découvrir le podcast à d'autres personnes !Envoyez-moi une capture de cet avis (LinkedIn ou par mail à dx@donatienleon.com) et je vous enverrai une petite surprise en remerciement.

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

————— COACHING —————Vous êtes leader tech ou product face à des défis majeurs ?

A 15-year-old caller asks Kent about his advice for the next 5 years if he were 15 again, knowing everything he knows today. Kent suggests exploring different technologies and AI coding tools to broaden horizons and avoid focusing on specific frameworks too early. He emphasizes the importance of understanding what AI is doing and using it as a tool to learn. Exploring Interests at 15

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

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

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

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

Welcome to the final episode of Season #12 of the PolicyViz Podcast! Thanks so much for checking out the show this season and I hope you enjoy your summer. I'll be back this fall with more great episodes.On this episode of the show, I'm joined by David Aerne, a freelance developer and designer based in Zurich who has spent years building a remarkable collection of open-source color tools. We dig into the difference between color models, color spaces, and gamuts, and David explains why thinking about color in three dimensions—like navigating a cylinder—can make choosing palettes so much more intuitive. We talk through several of his projects, including Color Names (a curated list of nearly 32,000 community-contributed color names), Rampensau for generating color ramps, and the playful RYB-inspired explainer that simulates how physical, “printy” colors look on screen. David shares his philosophy that we should integrate the systems that generate colors into our work, rather than freezing a static palette and losing the creativity along the way. Whether you build dashboards, design slides, or just love playing with color, this conversation will change how you think about picking your next palette.Keywords: color theory, color palettes, data visualization, color tools, HSL, HSV, RGB, color models, color spaces, open source, generative art, color ramps, design tools, JavaScript, web development, pixel art, PolicyViz Podcast, David AerneSubscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast (https://patreon.com/policyviz) for as little as a buck a monthFollow David Aerne on Bluesky and X (@meodai) and explore his open-source projects at elastiq.chFollow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

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

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

Jacques Spitzer is a 4x Emmy® award-winning creative agency founder who was named to AdWeek's Agency Vanguard as one of the top 20 leaders shaping the future of advertising. His agency, Raindrop, has generated billions in campaign sales for powerhouse brands like Dr. Squatch, Native and Grüns and insurgent brands like Good Culture, Hello Panda, Magic Spoon and more. Raindrop's creative force has been showcased by their work on three Super Bowl campaigns and their recent execution of the largest brand launch in Procter & Gamble history for Spruce. As a champion for the next generation of disruptive companies, Jacques serves as a strategic advisor to high-growth CPG brands that Raindrop Ventures has uniquely helped launch and invested in, including Grüns, Laundry Sauce, ForAll, VitaWild, Maeva and Magic Mind. With a trophy case boasting over 50 advertising awards, Jacques' work is consistently recognized for its rare blend of viral creativity and massive ROI. His insights have been featured in Forbes, AdAge, and Entrepreneur Magazine.  He was recently named one of the “most influential people in San Diego” by the San Diego Business Journal and one of “California's most visionary CEOs” by the Los Angeles Times, who noted: “Raindrop's creative success and results have put San Diego on the map for creative work across the country.” In addition to his work in advertising, Spitzer helped produce the full-length documentary Wampler's Ascent, which won over 38 international film festival awards. In This Conversation We Discuss:  [00:00] Intro [02:43] Scaling Ecommerce through storytelling  [04:41] Maximizing current growth channels first  [08:14] Managing multiple priorities as a founder [10:11] Shifting from product to customer worth [15:26] Callouts [15:36] Overcoming a leader's limiting beliefs  [24:03] Taking balanced risks to protect equity  [25:17] Combining math with strategic stories Resources: Subscribe to Honest Ecommerce on Youtube Marketing that people love raindrop.agency/    Follow Jacques Spitzer linkedin.com/in/jspitzer5/    If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

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…

Angela Clark - Mubarak is a senior digital and eCommerce executive with 30 years of experience building and transforming digital businesses at some of the world's most recognized consumer brands — including Patagonia, Levi Strauss, eBay, elf Cosmetics, Williams-Sonoma, True Religion, and Eddie Bauer.  Most recently VP of Digital at Patagonia, Angela now leads Eclipse Advisory Group, a consultancy focused on helping PE-backed brands, legacy retailers, and DTC startups unlock digital growth. She serves on the board of the California State Park Foundation, is an incoming Fellow at the Graham School at the University of Chicago, sits on the Total Retail Advisory Board, and has been recognized as a Direct 60 Honoree and CommerceNext 2024 Leader to Watch. She is based in LA, where is an avid cycler and dog mom to Maximus and Chloe and super auntie to her 12 yr nephew Evan. In This Conversation We Discuss: [00:00] Intro [02:31] Adapting old strategies to new mediums  [07:33] Sponsor: Klaviyo [09:39] Measuring success beyond simple revenue [14:23] Sponsor: Intelligems [16:24] Resisting trends that mismatch your brand [19:14] Sponsor: Electric Eye [20:19] Investing resources where they matter most [24:25] Moving away from the promotional drug [29:27] Callouts [29:37] Defining your target market sweet spot  Resources: Subscribe to Honest Ecommerce on Youtube Retail Legacy Meets Digital Disruption eclipsemedia365.com/ Follow Angela Clark - Mubarak linkedin.com/in/angclrk/ Book a demo today at intelligems.io/ Get your free demo klaviyo.com/honest   Schedule an intro call with one of our experts electriceye.io/connect If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

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

Topics covered in this episode: pi + superpowers Terminal: Warp.dev + OhMyZSH {Blink,kitty} + mosh + tmux Claude code MacWhisper or Handy Tailscale Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Six Feet Up is hosting a LinkedIn Live Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Calvin: @calvinhp@sixfeetup.social / @calvinhp.com (bsky) Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: pi + superpowers terminal-first, open-source coding agent Session management is a first-class citizen Extension model is what makes pi special — it's aggressively composable Superpowers brings a structured software development methodology as loadable skills Steps back and asks you what you're really trying to do “hand you the keys to the car” mode vs guardrails might not be for everyone Michael #2: Terminal: Warp.dev + OhMyZSH If you're using the base terminal with default settings, you have so much head-room for improvement. I've been using Warp.dev since Elvis talked me into it. ;) Remarkable terminal but the AI side of things is a bit junky, can be turned off OhMyZSH gives better autocomplete e.g. git branch [HTML_REMOVED] lists all branches in the local repo! Commandbookapp.com is excellent to keep the terminal focused on terminal things and more server commands and other automation in Command Book. Calvin #3: {Blink,kitty} + mosh + tmux Kitty Terminal — GPU-accelerated terminal emulator for macOS, Linux, and Windows with support for graphics, ligatures, and a powerful tiling layout system built right in. Blink Shell — The go-to terminal for iPad/iPhone power users; full SSH and Mosh client with a gorgeous interface built specifically for mobile professional workflows. Mosh — Mobile Shell replaces SSH for remote connections, surviving network switches, sleep cycles, and flaky Wi-Fi with zero dropped sessions — essential for staying connected to long-running agentic jobs. tmux — Terminal multiplexer that keeps sessions alive on your Linux server indefinitely; detach from a Mosh session on your Mac, reconnect from your iPad, and your agent is right where you left it. The combo — Kitty or Blink + Mosh + tmux creates a "persistent remote brain" pattern: your beefy Linux homelab runs the compute-heavy agent sessions 24/7, and any device becomes a thin client to drop in and out at will. Michael #4: Claude code I prefer the IDE experience, the new PyCharm + Claude integration is really good. VS Code too. Why IDE? Because we should still be present with our code and managing context is much easier. Use the best/latest models on high thinking. “Speed” is not your friend, it's just shortcuts. Create skills and agents and use them. Curate your own rules (e.g. Talk Python's Claude.md) Works well on non-coding things. Just create a folder, put a ton of files in there and it's like NotebookLM + Chat + more. Calvin #5: MacWhisper or Handy Transcribes your speech using your choice of Whisper or Parakeet models. All transcription is done on your device, no data leaves your machine. Automatic Speaker Recognition with local models. Handy is more basic, but open source and runs on all platforms. Michael #6: Tailscale No need to open ports at all, Tailscale makes machines inside the same network accessible to each other Works great for laptops, desktops, etc. But also available for servers. Though I still use cloud firewalls for servers. How I use it: My dev database server, preloaded with QA data, is always running on my home mac mini m4 pro. All my apps look for that server before looking locally and tailscale makes them always accessible to each other My local LLMs expose OpenAI API compatible APIs. Tailscale makes these accessible even while traveling or at a coffee shop. Use my mini as an exit node. All traffic is routed outbound from my local fiber network. Great to restricted IPs like accessing my servers without caring about the local IP. Screen share back to my home machines even while traveling. Listen to the Talk Python episode with Alex for a deeper conversation. Extras Calvin: Telescopo great Mac Markdown viewer/editor. Michael: One more: Typora markdown editor. Created formal documentation for many of my open source packages using Great Docs. Via Mark Little: Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Joke: No second date

In this episode, Laura Cantor shares key takeaways from her experience at Vendors in Partnership, including emerging trends in retail, the growing importance of meaningful partnerships, and how brands can cut through the noise in a tech-saturated landscape.  She dives into why people—and the partnerships they build—are still the foundation of innovation and growth, even as AI continues to transform the industry.  Laura also highlights tactical approaches that are driving real results today, including insights on high-impact ecommerce solutions like AfterSell, a platform helping brands maximize revenue through post-purchase optimization.  In This Conversation We Discuss: [00:00] Intro [02:38] Learning the value of brand building [06:20] Sponsor: Migrate [08:19] Prioritizing learning over job titles [12:46] Sponsor: Intelligems [14:46] Overcoming organizational status quo [17:08] Streamlining operations for future tech [21:06] Sponsor: Electric eye [22:14] Optimizing brands for agentic AI search [23:43] Monetizing traffic through retail networks [25:34] Callouts [25:44] Leveraging partnerships for mutual wins [28:00] Emphasizing human strategy alongside AI  Resources: Subscribe to Honest Ecommerce on Youtube Women's apparel specialty retailer nyandcompany.com/ Follow Laura Cantor linkedin.com/in/lauracantor/ Migrate and grow more klaviyo.com/honest Book a demo today at intelligems.io/ Schedule an intro call with one of our experts electriceye.io/connect If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

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

Kevin McLaughlin is a Google Analytics and Tag Manager expert specializing in building custom Google Analytics implementations that give you consistent, accurate, and easy to use results that actually help you make better business and product decisions.  Because of his years of experience in product development and management, he knows how to implement your marketing analytics tools so you can derive new insights from your data. As a developer and engineer, Kevin can deal with any level of technical-detail, from quick audits to in-depth, custom javascript setups and maintenance.  He has worked at both large companies and small startups and have setup analytics for both as well as many blogs, small businesses, and non-profits.  Kevin is currently developing several web-applications myself, which keeps me up to date on the latest web technologies and how to implement analytics effectively with them. In This Conversation We Discuss:  [00:30] Intro [01:30] Solving messy data gaps in business [03:33] Building tools to fix your own pain [04:50] Rebuilding analytics for a new internet era [06:20] Adapting to a more privacy-first internet [06:56] Moving beyond session-centric measurement [08:15] Aligning analytics with real shopping sessions [09:24] Shifting from plug-and-play to custom reporting [10:25] Callout [10:36] Overcoming the GA4 learning curve shock [12:37] Unlocking power in custom GA4 explorations [13:13] Fixing tracking before analyzing performance [14:37] Breaking down how GA4 actually receives data [16:53] Understanding why GA4 misses real orders [18:23] Fixing missing orders with server-side tracking [20:44] Choosing build vs buy analytics tools [21:31] Keeping analytics simple for early-stage stores [22:37] Avoiding over-optimization too early [25:01] Staying grounded in real customer acquisition [25:47] Combining clean data with real interpretation [26:49] Making GA4 implementation simple for merchants Resources: Subscribe to Honest Ecommerce on Youtube The leading GA4 integration for Shopify slideruleanalytics.com/ Follow Kevin McLaughlin https://www.linkedin.com/in/kevin-mclaughlin-1900/ If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

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

Catherine Hayden is the Chief Marketing Officer at Kate Farms, the #1 doctor-recommended plant-based nutrition brand. Since joining the company in 2018, she has helped scale Kate Farms through rapid growth, multiple funding rounds, and its acquisition by Danone, while building an omnichannel business spanning healthcare, direct-to-consumer, subscription, Amazon, and retail. Catherine began her career as a Registered Dietitian, giving her a unique perspective at the intersection of healthcare, nutrition, and consumer behavior. Today, she leads brand strategy, commercial growth, innovation, and integration across both healthcare and consumer channels. Kate Farms was founded to solve a deeply personal problem. After being diagnosed with cerebral palsy at age five, Kate struggled to tolerate existing nutrition formulas and relied on a feeding tube for nourishment. What began as a solution for one child has since grown into a company that has nourished more than 600,000 people. In this episode, Catherine shares how Kate Farms evolved from a healthcare-focused company into a high-growth Ecommerce and omnichannel brand, including lessons on building DTC alongside Amazon, uncovering customer insights that reshaped the business, and expanding awareness and access without sacrificing growth. In This Conversation We Discuss: [00:29] Intro [01:42] Serving customers across every life stage [02:02] Scaling impact from one success story [03:36] Validating demand before scaling [05:48] Episode Sponsor: Klaviyo [07:55] Learning complex channels through partnerships [10:36] Balancing trust with Ecommerce growth [12:32] Episode Sponsor: Intelligems [14:32] Using customer insights to guide strategy [17:40] Connecting brand awareness to conversions [19:13] Expanding reach while maintaining growth [22:13] Episode Sponsor: Electric Eye [23:20] Creating loyalty beyond product discounts [26:45] Winning customers through better products [27:17] Callout [27:27] Making great products easier to access Resources: Subscribe to Honest Ecommerce on Youtube Plant-based tube feeding formulas and shakes katefarms.com/ Follow Catherine Hayden linkedin.com/in/catherine-hayden-28233816 Migrate and grow more klaviyo.com/honest  Schedule an intro call with one of our experts electriceye.io/connect Book a demo today at intelligems.io/ If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

Today's podcast is a recording of a call that Rich had with a company in the midwest who RIch had already spent two solid hours on the phone with, auditing their website and explaining in detail why their current website and SEO was terrible, and how Level 10 could help them do it much, much better. But even after two hours, they were… understandably… still skeptical. Their trust with web companies was non-existent, and they needed a real boost to get over the finish line.

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

Bob Verlaat and Nick Nijhof are Amsterdam-based entrepreneurs and Co-Founders of Hears, the fast-growing hearing protection brand redefining earplugs through premium design and industry-leading sound clarity. Prior to Hears, the duo successfully scaled luxury sleep wellness brand Dore & Rose to $30M in revenue, building deep expertise in branding, Ecommerce, and consumer behavior. Their entrepreneurial journey has been shaped by creating products that solve real consumer problems while building emotionally resonant brands. After Bob experienced hearing damage and persistent tinnitus from loud music, the pair became increasingly aware of the global problem of noise-induced hearing loss and the lack of earplugs people actually wanted to wear. Existing products compromised sound quality, looked unattractive, and failed to fit seamlessly into modern lifestyles. Driven by that personal frustration, Bob and Nick spent 1.5 years researching and developing Hears from scratch, investing in patented filter technology and an award-winning heart-shaped design focused on preserving natural sound while protecting hearing. Since launching in 2024, Hears has generated $7M in first-year revenue, won the Red Dot Design Award, and partnered with globally recognized brands and venues including Yves Saint Laurent and Pacha Ibiza. In This Conversation We Discuss: [00:32] Intro [00:58] Launching products with clear positioning  [01:31] Solving everyday problems through Ecommerce  [03:14] Leveraging past mistakes to scale faster  [06:33] Episode Sponsor: Klaviyo [08:32] Finding product ideas through personal pain  [09:49] Testing creatives to accelerate growth  [11:01] Balancing brand building with direct sales  [11:57] Leveraging organic content before paid scaling  [13:51] Episode Sponsor: Intelligems [15:52] Optimizing products for global scalability  [19:14] Episode Sponsor: Electric Eye [20:23] Designing products customers instantly notice  [22:20] Protecting products through patented innovation  [23:25] Callout [23:34] Using social proof to increase conversions  Resources: Subscribe to Honest Ecommerce on Youtube Engineered for maximum sound blocking, reduce disruptive noise, helping you fall asleep faster, stay asleep longer and wake up fully rested hears.com/ Follow Bob Verlaat linkedin.com/in/bobverlaat/ Follow Nick Nijhof https://www.linkedin.com/in/nicknijhof/ Book a demo today at intelligems.io/ Migrate and grow more klaviyo.com/honest  Schedule an intro call with one of our experts electriceye.io/connect If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

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

Jason Zigelbaum is the solo founder behind Zigpoll—the zero-party data platform trusted by Sony, HP, Kraft Heinz, and Hallmark.  Zigpoll collected over 100 million survey responses and counting. Third-party cookies are going away. Ad platforms are losing signal. Brands that don't collect first-party data are flying blind. Zigpoll fixes that.  Zigpoll makes it dead simple to launch contextual surveys that ask the right questions, at the right time, in the right channel so brands can stop guessing and start knowing.  How brands use Zigpoll:  - Discover how customers found you with post-purchase surveys - Improve products with real customer feedback  - Boost sales with on-site CRO surveys - Recover lost sales with abandoned cart & exit intent surveys  - Segment audiences by demographics and psychographics for higher-ROI campaigns  What makes it easy:  - No code. Installs on Shopify in seconds  - Surveys in any language with built-in translation  - Conditional logic and follow-up questions that dig deeper  - Triggers for post-purchase, abandoned cart, fulfillment, exit intent  - Deliver via SMS, email, or on-site  - Pipes data directly into Klaviyo, ActiveCampaign, Gorgias & more In This Conversation We Discuss:  [00:00] Intro [02:31] Starting with what you already know  [04:35] Uncovering your business blind spots  [07:38] Lowering mental friction for your users  [09:06] Eliminating the guesswork from strategies  [11:07] Callouts [11:07] Catching errors with your users' feedback  [13:35] Segmenting buyers to understand habits  [17:24] Using AI as a powerful force multiplier [22:21] Testing concepts without real users  Resources: Subscribe to Honest Ecommerce on Youtube Survey & feedback platform.zigpoll.com/ Follow Jason Zigelbaum LinkedIn linkedin.com/in/jason-zigelbaum If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Saša Jurić, Elixir mentor and author of Elixir in Action, to discuss software craftsmanship in the age of AI. As AI coding tools become increasingly capable, Saša argues that the real challenge isn't generating code, it's maintaining quality, clarity, and shared understanding within a codebase. We explore the difference between correct code and good code, and why code is more than a set of instructions for a machine to execute. Code is also documentation, communication, and a long-term investment that future developers must be able to understand and maintain. Saša shares his concerns about the growing "theater of pull requests," where teams go through the motions of code review without creating meaningful opportunities for learning, feedback, or knowledge sharing. The hosts and Saša talk about practical ways to work effectively with AI, including taking smaller steps, carefully reviewing AI-generated code, and using AI as a collaborative tool rather than an autonomous developer. Throughout the discussion, Saša challenges the industry's obsession with speed and makes the case that the principles of good software development (incremental progress, clear communication, and human judgment) remain important in the age of AI. Key Topics Discussed The difference between correct code and good code Code as communication, documentation, and shared understanding The "theater of pull requests" and ineffective review practices How AI is changing software development workflows Using AI as a collaborator rather than a replacement Why smaller, incremental changes lead to better outcomes Human oversight in AI-assisted development Balancing development speed with maintainability Pull request size and review effectiveness Commit history as a tool for storytelling and context The risks of accumulating technical debt faster with AI Testing and validating AI-generated code Refactoring AI-generated solutions for clarity Applying agile principles to AI-assisted workflows The role of experience and judgment in software design Why software craftsmanship still matters in the age of AI Links mentioned Code Complete by Steve McConnell https://khmerbamboo.wordpress.com/wp-content/uploads/2014/09/code-complete-2nd-edition-v413hav.pdf Harness AI for DevOps, Testing, and AppSec https://www.harness.io/ Claude Code https://claude.com/product/claude-code Claude Code GitHub https://github.com/anthropics/claude-code Pull Request for Oban https://github.com/oban-bg/oban/pull/331 SMPP https://en.wikipedia.org/wiki/Short_Message_Peer-to-Peer OpenAI Codex https://chatgpt.com/codex/ Opus AI https://opus.ai/ Tidewave https://tidewave.ai/ Credo Static Code Analysis https://github.com/rrrene/credo https://smartlogic.io/podcast/elixir-wizards/s11-e09-static-code-analyzer-elixir-credo-ruby-rubocop/ Link to Sasa's X post https://x.com/sasajuric/status/2029522378196238503 Saša Jurić “Tell Me A Story” at Goatmire https://www.youtube.com/watch?v=GOrKfCs-mr0 https://meks.quest/blogs/the-theatre-of-pull-requests-and-code-review Looks Good to Me: Constructive Code Reviews by Adrienne Braganza https://www.manning.com/books/looks-good-to-me Towards Maintainable Elixir: Testing https://medium.com/very-big-things/towards-maintainable-elixir-testing-b32ac0604b99 TDD, Where Did It All Go Wrong (Ian Cooper) https://youtu.be/EZ05e7EMOLMSpecial Guest: Saša Jurić.

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

Topics covered in this episode: Dumb Ways for an Open Source Project to Die How to create a pylock.toml lockfile https://github.com/facebook/Lifeguard Choosing a Python Logging Library in 2026 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Dumb Ways for an Open Source Project to Die Core categories The maintainer left The maintainer is still there Sabotage and capture The release pipeline broke Force majeure The world moved on The project split - Examples Bulma PRs still from 2023, issues and PRs with no maintainer response for years, last release 1.5 years ago diskcache Similar, got hired by OpenAI, crickets after that Brian #2: How to create a pylock.toml lockfile Tim Hopper Tim walks through using uv, pip and pdm to create pylock.toml files. Recommendation: use uv export --format pylock.toml -o pylock.toml He also has How to install from a pylock.toml lockfile with pip but the short version is: use -r because tools treat it like a requirements file Michael #3: https://github.com/facebook/Lifeguard Lifeguard is a static analyzer to detect Lazy Imports incompatibilities and ease the adoption overhead for Lazy Imports in Python. I'm more excited about lazy imports after my Cutting Python Web App Memory Over 31% experience Some Python patterns depend on imports executing immediately. For example: Module-level side effects — a module that registers a handler or modifies global state at import time will behave differently if that import is deferred. The registry pattern — a module that registers itself (e.g., adding to a global dict) when imported will silently fail to register under Lazy Imports. sys.modules manipulation — code that reads or writes sys.modules assumes prior imports have already executed. Metaclasses and __init_subclass__ — class creation side effects may depend on imports being resolved. Project Stage: Beta Lifeguard is in active development. We are aiming to be ready for general use by the Python 3.15 final release. Brian #4: Choosing a Python Logging Library in 2026 Ayooluwa Isaiah " which libraries matter, how they compare, where they overlap with the standard module, and when each one makes sense.” The slant with this article is the need to log json output, which seems reasonable as things like API entry and exit point logging will include json. Covered libraries standard library logging with a hat tip to python-json-logger Same site has a guide to setting up python-json-logger structlog Loguru Logbook picologging Some benchmarks with structlog, stdlib+json, and Loguru, with structlog coming out faster I liked the Loguru example I'm going to have to try @logger.catch and logger.exception() for easily logging exceptions and serialize=True to enable JSON output. Extras Brian: When Women Stopped Coding - Planet Money segment , spotted on BlueSky from Savannah Ostrowski Lean TDD is now leaner Still working on audio version, but some great changes in 0.7.1 version Ch 6, TDD Interpretations, move ATDD and some of BDD to chapter Ch 7, Change name to TDD with Teams: BDD and ATDD Ch 9, Lean TDD, streamline steps and chapter Ch 10, Change name to Lean TDD with Teams: Lean ATDD Ch 11, Lean TDD with AI, Add short discussion about guardrails and security Michael: New course: Python Web Security: OWASP Top 10 with Agentic AI All courses now with Spanish subtitles, see announcement Joke: Stop texting me

Hilary Dubin is co-CEO and head of Jones' digital product & behavioral support program. She graduated from the University of Pennsylvania magna cum laude, majoring in cognitive science with a concentration in computation and cognition, an honors thesis on the effects of gender, realism, and role of virtual agents, and a minor from Wharton in consumer psychology.  She worked in David Brainard's visual neuroscience lab for 3 years and published 4 papers and supplementary materials on illumination discrimination (color perception). After Penn, she was selected as one of ten Americans to be a Ventures Fellow in the Excel Ventures incubator program in Tel Aviv, and continued on to be the inaugural member, and later program lead, of the US Associate Product Manager Program at Atlassian.  She worked as a product manager at Atlassian for 5 years, ultimately as Head of Confluence Editions & Admin Experience where she launched Confluence Premium & Free into multi-million dollar product offerings with 2M+ users. She hired & managed two PMs and lead a team of over 30 developers.  Prior to founding Jones, she and Caroline founded Cozier together, a sleep & loungewear brand designing ethical, effortlessly chic garments for every/body. Hilary started vaping casually in 2017 when the JUUL seemed relatively harmless and fun.  When the world went on lockdown in 2020, her casual vaping habit became a daily crutch for coping with stress and working from home. After over a year of unsuccessful cold-turkey quit attempts, she finally kicked her vaping habit in 2022 when Caroline suggested she try NRT.  Outside of work, Hilary loves hiking, backcountry skiing, trying to find the best burger in NYC, and playing with other people's dogs. In This Conversation We Discuss: [00:00] Intro [02:34] Creating products from personal pain points [06:52] Sponsor: Klaviyo  [08:59] Meeting potential customers where they are [10:47] Adapting products based on user feedback [13:48] Testing market demand with waitlists [16:02] Sponsor: Electric Eye [17:10] Maximizing personal networks for growth [18:34] Gathering behavioral data in early days [19:52] Callouts [20:02] Launching a product to engaged audiences  [22:09] Sponsor: Intelligems [24:09] Pivoting marketing to bridge early limitations [26:24] Driving organic traffic with relatable content  [30:33] Adding modern value to traditional products Resources: Subscribe to Honest Ecommerce on Youtube Nicotime mints and social app to quit vaping quitwithjones.com/ Follow Hilary Dubin linkedin.com/in/hilary-dubin-374156b4/ Follow Caroline Vasquez Huber linkedin.com/in/caroline-vasquez-huber Book a demo today at intelligems.io/ Schedule an intro call with one of our experts electriceye.io/connect Get your free demo klaviyo.com/honest If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

Topics covered in this episode: Using Django Tasks in production Co-authored with Claude? PyPI packages are increasing rapidly httpx2 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Using Django Tasks in production Tim Schilling shares how the Djangonaut Space website has been using Django's new tasks framework and some of the info missing from the official Django docs. Tasks require a third party package, django-tasks-db to actually run the tasks. Article walks through all changes necessary to get an email process running to notify admins of new testimonials. Cool simple example. With the db backend, you can monitor progress of tasks in the admin, to see which tasks are scheduled, completed, or have errors. Some wishes for the community to implement new tutorial in the Django docs Django Debug toolbar panel for tasks test/mock backend Great title for wish list: Thinks I'd like to see, but I'm too lazy to implement myself. Michael #2: Co-authored with Claude? Via Nik T. We don't put “executed on macOS”, “edited with PyCharm”, etc. in our commits. Why Claude? Seems like a growth hack to me, that I don't really care to participate in. Some projects that have formalized their thoughts on this: The Generative AI Policy Landscape in Open Source Adjust to turn off in ~/.claude/settings.json see the docs. { "attribution": { "commit": "", "pr": "" } } Brian #3: PyPI packages are increasing rapidly Artem Golubin There's been an increase of published packages per week on PyPI A pretty big increase in the last handful of months. 30% increase since 2025, clearly due to AI Artem is building hexora, a malicious Python code detector. Cool package too, it can: Audit project dependencies to catch potential supply-chain attacks Detect malicious scripts found on platforms like Pastebin, GitHub, or open directories Analyze IoC files from past security incidents Audit new packages uploaded to PyPi. Artem is using hexora to analyze recently published pypi packages and many are obviously vibecoded and trigger false positives for abuses of eval, exec, and subprocess Side note: I don't think that's necessarily a false positive. Not malicious, but maybe a stupid-code-detector? Lots are LLM related, Lots have bots contributing code Publishing rate is crazy, dozens to hundreds of published versions in a day is a bug, not a feature Brian's proposal, PyPI should limit releases per day for any package to something a sane human would do, even if they make a mistake on a release, to maybe like 2-3, definitely under 10, in a day. And if the repo has obvious agent contributors listed, maybe lower to the limit to 1-2 a day? Honestly, “move fast and break things” doesn't apply to breaking the commons. Michael #4: httpx2 More on the httpx, httpxyz, etc changes: Pydantic people started their own fork, httpx2. Michiel says “while we think httpxyz was definitely needed, we welcome httpx2 and think it should be the ‘blessed' fork.” Kludex, who is among other things maintainer of Starlette, was considering a fork As it stands, httpx2 is lacking the performance improvements they added to httpxyz. But it will not be long before they will add those, too. Also they already made some smart decisions: they are switching from certifi to truststore they are switching to compression.zstd on Python 3.14+, enabling zstd compression by default they merged httpcore and vendored it in their repository Discussion on Hacker News Extras Brian: The Four Horsemen of the LLM Apocalypse - Anarcat Django/JetBrains 2026 developer survey is open Pyrefly 1.0 : “meaning we are confident that Pyrefly is ready for production use.” Michael: Just about ready to release Python Web Security: OWASP Top 10 with Agentic AI course. Be sure to be on the courses newsletter to get notified. Joke: Proud Parents

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

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