Podcasts about Cloud computing

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

Domo and AWS reveal how AI agents freed sales reps from 20 hours of weekly busywork, turning scattered data into real-time coaching and forecasting.Topics Include:Domo and AWS teams introduce today's session on AI agents in sales.Topic: using AI agents to transform sales operations, from insight to action.IT teams increasingly asked to turn data into actionable outcomes, not just access.Domo's CRO wanted AI agents to boost sales rep efficiency significantly.Reps act like "archaeologists," digging through scattered systems for basic context.This digging eats roughly 20 hours weekly, half of reps' time.Goal: personal AI agent per rep, understanding their book of business.Live demo begins: agent app surfaces urgent items needing attention.Agent tracks deal milestones, timelines, and forecasts from call and email data."Deal coach" feature grades rep performance and suggests next actions.Agent tone can be tuned from gentle to direct, aiding tough feedback.Architecture overview begins: building an AI-ready data foundation first.Data from CRM, calls, and emails flows into a cloud warehouse.Two agents built: automated deal analysis and personalized deal coach.Agents write insights back to CRM, preserving human edit control.Recipe: build foundation, activate with agents, distribute to people.Governance must be embedded throughout, not bolted on afterward.Second example: Fogo do Chão uses AI to analyze restaurant reviews.AWS architecture explained: Domo runs on Bedrock, defaulting to Anthropic models.Q&A: sales team adoption was immediate and enthusiastic post-rollout.Participants:Jason Longhurst – Head of Product Marketing, DomoAman Tiwari - Sr Solutions Architect, ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Comments/ideas: ACFpod@outlook.comIs India's net zero push real, or just clever PR? Arun Kumar of Asia Research and Engagement joins the podcast to explain why the greenwashing era is ending and why decarbonisation now wins on cost, not sentiment. We dig into the hard-to-abate sectors, green steel and cement, alongside power, coal, renewables, carbon markets, corporate PPAs and how Indian banks are pricing climate transition risk. Essential listening for anyone in climate finance, energy transition and sustainable investment across India and the wider Asia Pacific.Ref.: Asia Research and Engagement Group, ARE's Asia Transition PlatformABOUT ARUN: Arun Kumar is a Strategic Advisor at Asia Research and Engagement (ARE), a Singapore-headquartered organisation whose collaborative platform connects institutional investors with large listed companies to accelerate the energy transition. His focus spans the highest market-cap companies in hard-to-abate sectors, Power, Cement, Steel and Automobiles, plus Banks and financial institutions. A recognised expert in India's power sector with over 30 years of experience, Arun brings deep expertise across power trading, management consulting and equity research. He has held senior positions at PTC India, HSBC, KPMG and CRISIL, advising investors, policymakers, regulators and corporates on critical aspects of the energy sector. His work spans Asia, the US, Europe and India. Arun holds a Master's in Economics from the Delhi School of Economics and an MBA from IMI Delhi, plus an advanced certification in Cloud Computing, Data Analytics and Blockchain from IIT Madras.Recommendations:Climate Capitalism by Akshat Rathi: A Bloomberg journalist's ground-level account of how clean technologies, from China's electric cars and batteries to solar, wind and green steel, are becoming commercially viable and profitable.  https://www.hachette.co.uk/titles/akshat-rathi/climate-capitalism/9781529329957/ Value(s): Building a Better World for All by Mark Carney: The former Bank of England governor and current Canadian Prime Minister argues that finance and markets must be steered by human values rather than price alone. https://books.google.com/books/about/Value_s.html?id=Jz6XzQEACAAJHOST, PRODUCTION, ARTWORK: Joseph Jacobelli  |  MUSIC: Ep76 onward excerpts from Vivaldi's La Follia, played by Luca Jacobelli.

Prediction market Kalshi moves into biotech, new study findings show no clear electromagnetic wave cancer risk in mobile phone use, Microsoft's 13-inch Surface Laptop struggles with 8GB of RAM. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible. IfContinue reading "SpaceX In Talks To Provide Cloud Computing Power For DoD AI Projects – DTH"

Quantum materials discovery shows how quantum computing can create real value in industry by working alongside AI, advanced computing, and experiments to better understand materials, improve decision-making, and accelerate innovation at scale, ultimately helping deliver practical, measurable progress for the energy transition.This week, Dave, Esmee, and Rob are joined by co-host and quantum expert Phalgun Lolur, together with Jonathan Owens, Senior Scientist in Computational Materials Physics at GE Vernova to explore how quantum computing could reshape materials discovery and why that matters for the future of energy.  TLDR00:00 – Introduction01:50 – Hang out: The wet-bulb thermometer03:20 – Dig in: Technology Convergence and the Link to Quantum11:30 – Conversation with Jonathan Owens44:26 – Exciting to see how the quantum landscape matures and the magic wand for magnetismGuestJonathan Owens: https://www.linkedin.com/in/jonathan-r-owens-phd/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/Co-host Phalgun Lolur:  https://www.linkedin.com/in/phalgun-lolur/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

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.

Curious how AI can query your enterprise data without moving it or making things up? AWS and Teradata break down a trustworthy analyst agent built for real production use.Topics Include:Neha Wadhera (AWS) introduces Trinath Yarlagadda and the Teradata Analyst AgentEnterprise AI data prep is costly, stalling most orgs at experimentationAgent answers plain-English questions via traceable SQL, zero data movementBarrier removal drives 3.7x ROI and 40% productivity gainsHealthcare demo setup: hospital COPD readmissions, ~$10K cost per incidentFour design principles: traceability, no data movement, deterministic-first, governance as codeMain orchestrator agent plans, writes SQL, calls Teradata MCP serverComplex questions escalate to a context-isolated data scientist agentBuilt on Claude Agent SDK, running Bedrock Claude Sonnet/Haiku/OpusLive demo: COPD readmission rates explored through iterative agent reasoningDelegation demo: data scientist agent runs in-database analysis, surfaces factorsPre/post tool hooks log every step and cost to CloudWatchAgent hosted on Amazon Bedrock AgentCore, fully serverless and scalableAgentCore delivers runtime, memory, identity, and observability out of the boxLessons learned: guardrails first, deterministic ops, multi-agent registry, ongoing evaluationParticipants:Trinath Yarlagadda – Principal Solution Architect – Agentic AI, TeradataNeha Wadhera – Sr Solutions Architect, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

Life sciences are at a turning point, where scientific innovation, regulatory pressure, and patient expectations collide with unprecedented advances in data, AI, and digital platforms. IT is no longer a supporting function but a critical driver of how therapies are discovered, developed, scaled, and delivered safely and at speed.This week, Dave and Rob wrap up our State of Life Sciences mini-series with Thorsten Rall, Global Industry Lead for Life Sciences at Capgemini and together, they connect the dots across the series, exploring how AI, data and innovation are accelerating drug discovery, transforming med tech, modernising manufacturing and improving patient outcomes, all built on a strong digital foundation. TLDR00:27 – Introduction and conclusion of the Life Sciences mini-series 02:16 – Key insights and lessons from the previous episodes on the Life Sciences landscape 21:18 – Building resilient, efficient and future-ready operations 34:35 – Creating integrated, patient-centric healthcare experiences 43:15 – Why the Digital Core is the foundation for transformation and innovation 53:21 – Final reflections: the future of Life Sciences and the key takeaways 54:53 – Weekend BBQs, Thorsten's daughter's theatre performance, and the role of R&D HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/with co-host Thorsten Rall: https://www.linkedin.com/in/thorsten-alexander-rall-b232185/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

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

Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.Topics Include:Hedieh Zandi (Vercel) and Manbeen Kohli (AWS) introduce prompt-to-production sessionVercel powers 18 million developers, maintains Next.js and AI SDKVercel's agentic infrastructure runs on AWS Lambda, CloudFront, and S3AI now generates frontend, APIs, and workflows for small teamsBackend friction remains: credentials, provisioning, database configuration still hardVercel envisions "self-driving infrastructure" that adapts automatically to appsNew AWS partnership brings native Aurora DSQL and Postgres integrationManbeen explains databases now built into Vercel Marketplace and v0Aurora Serverless database creation sped up from minutes to secondsAurora Postgres, DynamoDB, and DSQL scale prototypes without rewritesPre-configured templates help builders start RAG or shopping AI appsDatabase security uses OIDC and IAM tokens, no stored passwordsAWS chosen for agents: low latency, autonomy, one-click simplicityskills.sh gives agents reusable instructions, mirrors AWS Kiro's "powers"v0 lets users build full-stack apps using natural language promptsv0 uses Bedrock models and deploys directly on Vercel infrastructureLive demo: v0 builds restaurant app, provisions database, adds Stripe checkoutDemo ends at AWS console; Rauch quote and hackathon close sessionParticipants:Hedieh Zandi - Product Lead, VercelManbeen Kohli - Director of Product Management, Aurora and RDS Databases, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Amy MacIver speaks to Rob Rose about why South Africa is attracting major investment in data centres and what it means for AI, technology and the economy. Presenter John Maytham is an actor and author-turned-talk radio veteran and seasoned journalist. His show serves a round-up of local and international news coupled with the latest in business, sport, traffic and weather. The host’s eclectic interests mean the program often surprises the audience with intriguing book reviews and inspiring interviews profiling artists. A daily highlight is Rapid Fire, just after 5:30pm. CapeTalk fans call in, to stump the presenter with their general knowledge questions. Another firm favourite is the humorous Thursday crossing with award-winning journalist Rebecca Davis, called “Plan B”. Thank you for listening to a podcast from Afternoon Drive with John Maytham Listen live on Primedia+ weekdays from 15:00 and 18:00 (SA Time) to Afternoon Drive with John Maytham broadcast on CapeTalk https://buff.ly/NnFM3Nk For more from the show go to https://buff.ly/BSFy4Cn or find all the catch-up podcasts here https://buff.ly/n8nWt4x Subscribe to the CapeTalk Daily and Weekly Newsletters https://buff.ly/sbvVZD5 Follow us on social media: CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

Life sciences are at a critical inflection point, where scientific innovation, regulatory demands, and patient expectations converge with advances in data and artificial intelligence, positioning IT as a central driver of faster and more effective drug discovery and clinical development.This week, Dave and Rob continue with part 3 off the Life Sciences mini-series with Predrag Angelovski, VP, CTO at Healthcare Informatics at Philips to exploring how MedTech products are more and more becoming connected platforms, combining hardware, software and services.TLDR00:21 – Introduction with co-host Thorsten Rall01:00 – Hang out: Esmee joins and Rob is lost at a train station03:00 – Dig in: Life Sciences mini-series, Part 304:57 – Conversation with Predrag Angelovski50:56 – Travelling to Europe, agents vs. agentic, and the age of intelligence GuestPredrag Angelovski: https://www.linkedin.com/in/predrag-angelovski/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Malcolm Matalka joins William and Eyvonne to challenge the narrative that Infrastructure as Code (IaC) is dead. Malcolm argues that the real value of IaC was never the syntax, but state and governance. Together they examine whether the state was a file problem at all, or a distributed systems problem in a JSON costume. Episode... Read more »

Malcolm Matalka joins William and Eyvonne to challenge the narrative that Infrastructure as Code (IaC) is dead. Malcolm argues that the real value of IaC was never the syntax, but state and governance. Together they examine whether the state was a file problem at all, or a distributed systems problem in a JSON costume. Episode... Read more »

Voor wie dacht dat Nike nu eindelijk met goed nieuws zou komen, zat mis. Het herstel (waar beleggers al jaren op wachten) laat nóg langer op zich wachten. Dat is de conclusie na het zien van de kwartaalcijfers. Vooral China springt eruit: de omzetdaling wordt daar alleen maar erger. Je raadt het dus al: de vooruitblik ziet er niet goed uit. De komende zes maanden zullen de omstandigheden niet verbeteren, denkt de directie. Deze aflevering kijken we of er nog lichtpuntjes in de cijfers vindbaar zijn. Wat voor jou als aandeelhouder (of potentiële aandeelhouder) het houvast is in deze resultaten. En wanneer dan ein-de-lijk dat herstel gaat plaatsvinden. Hoor je ook alles over de plannen van Basic-Fit. Dat heeft namelijk wéér een bedrijf overgenomen. Dit keer een in Duitsland. We kijken of de fitnessketen nu ook zelf breder wordt en of ze het aandeel meer gaan oppompen. Het aandeel Meta wordt ook opgepompt. Je hoort waarom het aandeel even 10 procent (!) in de plus staat. Praten we je ook bij over de crypto-inkomsten van president Trump. Die heeft even een miljard dollar verdiend met zijn belangen in de digitale munt. En Maxim heeft een prachtig verhaal over de gekke hobby van Mark Zuckerberg. Te gast: Thomas Pellegrom van ABN Amro MeesPierson BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. See omnystudio.com/listener for privacy information.

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

Kaltura's Ruthie Eisenberg and Yair Neumann reveal how the video giant is reinventing itself as an agentic digital experience company built on AI avatars and hyper-personalized content.Topics Include:Kaltura founded 2006, went public on NASDAQ in 2021.Kaltura reinventing itself from video company to agentic digital experience company.Shift from static content delivery to hyper-personalized conversational experiences.Partners and customers now demand intelligence, not just video infrastructure.Kaltura's mission: powering agentic experiences across customer and learner journeys.AWS co-sell motion strengthened as Kaltura runs on AWS AI infrastructure.Camille used a Kaltura avatar to scale her own presentations.Most enterprise websites bury content behind thousands of static links.Kaltura builds personalised web pages on the fly, in real time.Over 80% of content users see is surfaced for the very first time.Acquisitions of eSelf.ai and PassFactory complete Kaltura's agentic content flywheel.PassFactory answers: what should this specific person see next?eSelf.ai enables multimodal conversational avatars that guide users emotionally.20 years of behavioral data underpins Kaltura's content intelligence advantage.GPU scarcity and compute costs shape every AI architecture decision Kaltura makes.Kaltura optimises model tiers — strongest for planning, lighter models for execution.Fidelity, speed, and cost form a constant triangle in every AI product decision.Go-to-market and product teams now work closer together than ever before.Pricing shifting from seat-based SaaS to consumption and outcome-based models.Kaltura co-creating pricing frameworks with customers across different verticals.Internal product agent now handles research, stories, and data analysis autonomously.Small two-to-three person squads move fastest in the current AI environment.Yair's advice: fail at least once a week, succeed once a quarter.Kaltura scaled its CEO via avatar for a live investor earnings call.Ruthie's advice: keep the customer at the centre of every single decision.Participants:Ruthie Eisenberg – Vice President, Strategic Partnerships, KalturaYair Neumann – Senior Vice President of Product, KalturaKamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web ServicesJohan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

Resilience is the recognition that in today's highly interconnected and unpredictable world, disruption cannot always be anticipated or prevented and therefore requires a shift from traditional risk avoidance toward designing systems that can absorb shocks, adapt in real time, and recover quickly, ultimately emerging stronger and turning uncertainty into a source of advantage.This week, Dave, Esmee, and Rob are joined by Benjamin Trump, President Society for Risk Analysis and Cedrick Moriggi, Chief Resilience Officer and co-found the CCRO network under the United Nations Office for Disaster Risk Reduction, to explore what resilience means in a world shaped by systemic risk, fragile supply chains, climate shocks, cyber threats and human decision-making. TLDR00:30 – Introduction01:29 – Hang out: Heatwave weather and the perfect pub temperature03:18 – Dig in: What is resilience, and how do you deal with it?10:35 – Conversation with Benjamin Trump and Cedrick Moriggi48:32 – Ben is a writer and Cedrick teaches children GuestBenjamin Trump: https://www.linkedin.com/in/benjamin-trump-ba062523/Cedrick Moriggi: https://www.linkedin.com/in/cedrickmoriggi/HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Join us this week for a Tech Leaders Podcast Special, where Gareth sits down with John Abel, MD, Office of the CTO, and Alex Rutter, EMEA MD for AI, fresh from the Google Cloud Summit in London.On this episode Gareth, John and Alex discuss how organisations can effectively deploy Agents, future skills the workforce will need to use Agentic AI, and how to simulate a virtual board meeting.Timestamps:John Abel Introduction (1:25)Agentic AI Adoption and Data Readiness (3:39)Future Skills – John's take (12:09)How to move beyond “Pilot Mode” (15:50)Alex Rutter Introduction (19:16)The Integrated Google Tech Stack (22:50)AI Automation vs Human Oversight (27:55)Is Agentic AI Adoption Maturing? (30:48)Future Skills – Alex's take (35:48)Data Quality (38:35)The UK's AI Position (41:01)https://www.bedigitaluk.com/

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

This Day in Legal History: Title IXOn June 23, 1972, President Richard Nixon signed the Education Amendments of 1972, a sweeping federal education law that included what became one of the most consequential civil rights provisions in American history: Title IX. Title IX stated that no person in the United States, on the basis of sex, could be excluded from participation in, denied the benefits of, or subjected to discrimination under any education program or activity receiving federal financial assistance. The language was brief, but its legal effect was enormous because it tied sex-equality obligations to the federal funding received by schools, colleges, and universities. That structure gave the federal government a powerful enforcement tool: institutions that accepted federal education money also had to comply with anti-discrimination rules.Although Title IX is often remembered for transforming women's and girls' athletics, the law was never limited to sports. It also affected admissions, scholarships, hiring, classroom access, pregnancy discrimination, and later legal debates over sexual harassment and institutional responsibility. Before Title IX, many educational institutions openly limited opportunities for women, including through quotas, unequal athletic resources, and restricted access to professional programs. The statute helped turn those practices into legal liabilities rather than accepted traditions. In later decades, courts and federal agencies would shape Title IX's meaning through regulations, enforcement actions, and major cases interpreting what counts as sex discrimination in education. Its influence reached far beyond individual lawsuits because schools had to rethink policies, reporting systems, athletic budgets, and equal-access obligations.Title IX also became a model for how civil rights law can operate through spending power, using federal money as the hook for national anti-discrimination standards. Its passage showed that a single sentence in a larger statute could become a foundation for generations of legal, political, and cultural change. On June 23, 1972, the federal government did more than amend education law; it created a durable legal framework for challenging sex discrimination wherever public money supported educational opportunity.A federal judge in California dismissed the Trump administration's lawsuit challenging Los Angeles's limits on cooperation with federal immigration enforcement. The administration had argued that the city's ordinance was unconstitutional because it restricted the use of city resources to support federal immigration operations and limited the collection of citizenship-status information. U.S. District Judge Fernando Olguin rejected that argument, finding that Los Angeles was regulating the conduct of its own employees and agencies rather than trying to control the federal government. The dismissal was not necessarily the end of the case, because the judge allowed the administration to file an amended complaint. Los Angeles City Attorney Hydee Feldstein Soto praised the ruling, saying it confirmed that local governments can decide how to use their own personnel and resources. The lawsuit was filed after immigration-related protests in Los Angeles and after Trump sent troops to the city in response to unrest over deportation operations. The case is part of a broader Trump administration effort to challenge local “sanctuary” policies in Democratic-led jurisdictions. Similar administration lawsuits against Boston and Chicago have also been dismissed by federal judges. The White House did not immediately comment on the ruling. The decision leaves Los Angeles's ordinance intact for now while giving the federal government another chance to revise its legal claims.US court dismisses Trump administration lawsuit over Los Angeles immigration policy | ReutersA federal judge in Washington, D.C., blocked the Trump administration from using a revised immigration database to help states check voter rolls. The database, known as SAVE, is used by the Department of Homeland Security to verify citizenship and immigration status, but the administration had changed it to make bulk searches easier for state and local officials reviewing voter eligibility. U.S. District Judge Sparkle Sooknanan sided with voting-rights and privacy groups that argued the changes made the system less reliable and could wrongly remove eligible voters from registration lists. The challengers said the database can be outdated, especially when naturalized citizens are still incorrectly listed as noncitizens. The judge also found that the revamped system raised serious privacy concerns because it gave users access to sensitive information, including Social Security numbers. DHS criticized the ruling and framed the case as part of its effort to prevent noncitizen voting. The ruling comes as the Trump administration has tried to expand the federal government's role in election administration before the November 2026 midterm elections. Courts have already blocked several related efforts, including parts of executive orders involving proof-of-citizenship requirements and mail-ballot restrictions. The administration has also faced setbacks in lawsuits seeking full voter-roll data from states. For now, the decision limits how the federal government can use immigration records in voter-roll checks.Judge blocks Trump's use of revamped immigration database for voter checks | ReutersIn my Bloomberg column this week, I wrote about OpenAI's request that Treasury update an outdated R&D tax credit rule for computer-related research expenses. My argument is that OpenAI's position should not be dismissed as just another technology company asking for a more generous tax benefit. The problem is that the existing rule was designed for an older world of identifiable physical computers, not modern cloud computing, data centers, GPUs, and reserved compute capacity. Section 41 allows a research credit for certain amounts paid to another person for computer use in qualified research, but Treasury regulations narrow that benefit by requiring that the computer be owned and operated by someone else, located off the taxpayer's premises, and not be a computer for which the taxpayer is the “primary user.” That “primary user” test made more sense when a taxpayer could point to a discrete machine, but it becomes unstable when a company is buying access to capacity inside a provider-owned cloud or data center.I argue that reserved or exclusive use of computing capacity should not automatically be treated as ownership or abuse, because modern AI research may require dedicated capacity for security, speed, and performance reasons. The real question should be whether the taxpayer is buying a third-party service or has effectively acquired, operated, or taken control of the infrastructure. Treasury can still protect against abuse without treating ordinary commercial cloud arrangements as disguised ownership. I suggest that a practical safe harbor could presume service treatment where the provider owns, operates, maintains, and houses the equipment off the taxpayer's premises while bearing the incidents of ownership. That presumption should remain rebuttable where the taxpayer bears ownership-like risks or is simply routing its own equipment through another entity to claim the credit.The broader point is that modernizing the rule would not need to turn the R&D credit into an AI subsidy machine, but it would prevent an old regulatory framework from excluding a major category of modern research. The column closes with the idea that tax rules meant to police fake outsourcing should not end up penalizing real outsourcing just because the computing world no longer looks like it did when the rule was written.OpenAI's Call for Modernized R&D Credit Rule Makes Perfect Sense This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

Episode 4 of Rethinking EHS, Season 3 focuses on the fast-growing data center sector and the need to balance speed, innovation, and sustainability. The episode explores how global demand for digital infrastructure is accelerating rapidly, driven by cloud adoption, AI, and increasing digital consumption, while physical constraints such as power, space, and water are shaping where and how data centres are developed. Emerging hubs like Milan are gaining prominence as traditional markets reach capacity, supported by evolving regulatory frameworks that are beginning to recognise data centers as strategic infrastructure. Looking ahead, the industry's future will depend on improving safety maturity, strengthening collaboration across the supply chain, and ensuring data centers are developed as responsible “neighbours” that minimise environmental impact. Ultimately, global collaboration, combined with local knowledge, will be key to scaling the sector sustainably and building a more resilient digital infrastructure. --- Guest quotes: Julie Kreger-King: “There's a real tension between the need for speed and the need to put strong systems and processes in place.” Alessandro Intile: “We are not building warehouses or chemical plants—we are exactly in the middle, with risks that must be carefully managed.” --- Timestamps: 00:00:00 – Introduction & data centre growth overview 00:01:10 – What's driving global demand (cloud, AI, digitalisation) 00:02:31 – Emerging hubs and regulatory developments in Europe 00:04:25 – Regulatory differences between regions 00:05:51 – Why data centres are a critical EHS focus area 00:08:08 – Safety maturity across the sector 00:10:10 – Balancing speed vs systems and processes 00:12:21 – Technology evolution and new risk factors 00:14:03 – Supply chain and quality challenges 00:16:06 – Brownfield development and environmental risks 00:20:13 – Overlooked risks: noise, fuel storage, permitting 00:22:35 – Achieving global consistency vs local requirements 00:28:24 – Advice for EHS professionals entering the sector 00:32:31 – Future ESG priorities and industry maturity 00:36:03 – The role of global collaboration 00:38:01 – Closing reflections --- Sponsor Copy Rethinking EHS is brought to you by the Inogen Alliance. Inogen Alliance is a global network of 70+ companies providing environment, health, safety, and sustainability services, working together to provide one point of contact to guide multinational organizations to meet their global commitments locally.  Visit inogenalliance.com to learn more. --- Links https://Inogenalliance.com/resources https://Inogenalliance.com/podcast Julie on LinkedIn: https://www.linkedin.com/in/julie-kreger-king/  Charlotte on LinkedIn: https://www.linkedin.com/in/charlotte-buffoni-a42b9629/ Alessandro on LinkedIn: https://www.linkedin.com/in/alessandro-intile-5730a2124/?skipRedirect=true  Produced by https://madcontent.co.nz/  

With 250,000 customers and $1.2B in revenue, Monday.com's CPTO explains why they threw out their roadmap and rebuilt everything around agentic AI.Topics Include:Daniel Lereya joined Monday.com when it had just 30 people and five engineers.He grew the R&D org from five engineers to roughly 900 over a decade.Three years ago Daniel became Monday.com's first ever CPTO.Monday.com initially approached AI by adding small features across the product.They called this early phase "sprinkling AI dust" — helpful but not transformative.A pivotal board meeting made Daniel realise AI hadn't changed Monday's core value.Monday.com decided to rethink its mission from first principles around AI.The new mission: AI agents that actually execute work, not just manage it.AI gives businesses an "infinite workforce" regardless of company size.Agents can now do hyper-personalised work at a scale humans simply cannot.Monday's platform puts agents at the centre, replacing boards and dashboards.Shared context and human-in-the-loop handoffs make their agents uniquely powerful.Monday ran an "AI month" — pausing the entire 900-person builder org to transform.The month rebuilt team mindset and energy, reminding staff of early startup days.Monday also ran an "agentic week" where every department built their own agents.Finance built agents to automatically match incoming payments to customer accounts.Scaling AI adoption internally remains the biggest challenge across businesses today.Monday introduced "effective AI" — balancing capability with cost efficiency.They acquired voice AI startup One AI to add specialised model capabilities.On pricing, Monday shifted to a hybrid seats-plus-AI-credits consumption model.Participants:Daniel Lereya – Chief Product and Technology Officer, Monday.comKamil Davidov – Sales Leader Israel ISV-BizApps, Amazon Web ServicesJohan Broman – EMEA ISV Head of Solutions Architecture, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The massive data centers that AI needs require huge amounts of electricity and water. And they're popping up all over the state. Those data centers are likely to impact the electrical grid, electricity prices and the state's water infrastructure and supplies. Assemblymembers Rick Chavez-Zbur and Diane Papan are working to prevent those impacts from hurting Californians.     AB 2383 Ensures Large Energy Users Pay Their Fair Share and Strengthens Grid Reliability SACRAMENTO, CA - Democratic Caucus Chair and Assemblymember Rick Chavez Zbur's (D-Hollywood) AB 2383, legislation protecting California ratepayers from bearing the rising energy costs associated with large energy use facilities such as data centers, has passed the California State Assembly with bipartisan support and now heads to the Senate. Strongly supported by the NRDC (Natural Resources Defense Council) and the Little Hoover Commission, this bill requires the California Public Utilities Commission (CPUC) to establish a new electricity customer classification for large energy users to ensure the costs of serving these facilities are not shifted onto residential and small business ratepayers. "As California continues leading the world in innovation and artificial intelligence, we must make sure working families and small businesses are not left footing the bill for the enormous energy demands of large-scale data centers," said Assemblymember Rick Chavez Zbur. "AB 2383 ensures these facilities pay their fair share, protects ratepayers from cost shifts, and helps California plan responsibly for the future of our electrical grid." California is home to a rapidly expanding technology and artificial intelligence sector, driving increased demand for data centers that power cloud computing, AI systems, and digital infrastructure used worldwide. The California Energy Commission projects statewide peak electricity demand could exceed 66 gigawatts by 2040, with data centers accounting for approximately 6.7 gigawatts of new demand — roughly equivalent to the electricity use of more than 4 million households. As utilities receive increasing requests from large-load facilities seeking transmission-level service, regulators have identified significant gaps in how these customers are classified and charged for service. While the CPUC recently approved interim rules for large-load customers within Pacific Gas & Electric's territory, statewide long-term planning and ratepayer protections remain unresolved. AB 2383 requires the CPUC to establish a new classification for large energy use customers by 2028 designed to appropriately assign costs, avoid shifting infrastructure expenses onto other ratepayers, support grid reliability, and promote equitable contributions to state energy programs. The bill also requires utilities serving these facilities to enter into long-term service agreements with large energy users to help avoid stranded infrastructure costs and ensure financial responsibility remains with the facilities driving the demand. "Californians are one step closer to being protected from paying extra for energy-hogging data centers," said Victoria Rome, director of California government affairs at NRDC (Natural Resources Defense Council.) "Requiring data centers to pay for their energy usage makes sense for all ratepayers and helps keep electricity affordable across the board." "This bill is an important step toward protecting California ratepayers while enabling responsible economic growth," said Ethan Rarick, executive director of the Little Hoover Commission. "By requiring the creation of a separate rate classification for large energy use facilities, AB 2383 helps ensure that costs are appropriately allocated, and reflects our Commission's core finding that ratepayer protection must be the state's foremost priority in addressing large-load growth."    

Life sciences are at a critical inflection point, where scientific innovation, regulatory demands, and patient expectations converge with advances in data and artificial intelligence, positioning IT as a central driver of faster and more effective drug discovery and clinical development.This week, Dave and Rob continue with part 2 off the Life Sciences mini-series with Dr. Alex Zhavoronkov founder and CEO of Insilico Medicine to exploring how drug discovery and clinical development can become faster and more effective, and the role of AI in that process.  TLDR00:40 – Introduction01:00 – Hang out: Kill Bill Vol. 1 & 2 03:07 – Dig in: Life Sciences mini-series, Part 2 06:43 – Conversation with Dr Alex Zhavoronkov 42:12 – The future of AI in drug discovery and a new paradigm for pharma GuestDr. Alex Zhavoronkov: https://www.linkedin.com/in/zhavoronkov/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

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

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The Pope issued a recent encyclical on AI, urging developers to safeguard human agency in the age of artificial intelligence. Eyvonne and William explore this encyclical, moving beyond the headlines to the core message regarding human dignity. They examine how the document provides a values-based framework for evaluating technology and the need for a balanced... Read more »

The Pope issued a recent encyclical on AI, urging developers to safeguard human agency in the age of artificial intelligence. Eyvonne and William explore this encyclical, moving beyond the headlines to the core message regarding human dignity. They examine how the document provides a values-based framework for evaluating technology and the need for a balanced... Read more »

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

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

Innovation isn't about funding, it's about how organisations are built and led. Progress comes from cutting bureaucracy, empowering mission-led teams, and asking the right questions to unlock bold breakthroughs. This week, Dave, Esmee and Rob are joined again by André Loesekrug-Pietri, Chair and Scientific Director of the Joint European Disruptive Initiative (JEDI, Europe's ARPA) to explore how Europe can turn moonshot ambitions into reality by building the right people, culture and operating models for future-shaping organisations. TLDR00:41 – Introduction01:14 – Hang out: Esmee returns and the missing API has been found!05:14 – Dig in: Staying in step with global innovation12:57 – Conversation with André Loesekrug-Pietri1:02:26 – Roland Garros tennis, and unlocking creative energy GuestAndre Loeskrug-Petri: https://www.linkedin.com/in/andrepietri/X: @eurojediwww.jedi.foundation HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Modern cloud environments are evolving faster than traditional security models can keep up. In this episode, we sit down with Yarin Pinyan, VP Products at Upwind, to explore how real-time runtime visibility and behavioral baselining are reshaping how organizations detect and respond to threats, especially zero-day and supply chain attacks that emerge before signatures or CVEs exist. We'll also discuss how AI is enabling a new generation of cloud security, where detection, investigation, and response happen continuously and automatically. The conversation highlights how organizations can reduce risk, improve operational efficiency, and protect critical workloads in dynamic, cloud-native environments.AWS MP offering: https://aws.amazon.com/marketplace/pp/prodview-ff3am62vjukrw?sr=0-1&ref_=beagle&applicationId=AWSMPContessaWebsite: https://www.upwind.io/Customer success story: https://www.upwind.io/case-studiesAWS Hosts: Nolan Chen & Ashok MahajanEmail Your Feedback: rethinkpodcast@amazon.com

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

PagerDuty SVP Rukmini Reddy explains why AI is making software operations exponentially more complex — and why the companies that learn and recover fastest will be the ones that win.Topics Include:PagerDuty powers critical digital operations for enterprises and AI-native companies.Founded by early AWS employees who experienced always-on system failures firsthand.The platform evolved from simple alerting into a full operational intelligence platform.Complexity exploded with microservices, cloud-native infrastructure, and multi-cloud environments.Reliability must be a core value — not an operational afterthought.PagerDuty's culture champions the customer above everything else.Employee recognition extends beyond sales to celebrate the whole business.AI is accelerating software creation but making operations far more complex.AI fails differently — silently, unpredictably, with a much larger blast radius.Enterprises should leverage their operational history as a competitive AI asset.AI-native companies must build operational resilience early, not bolt it on later.The winners won't build fastest — they'll learn and recover fastest.Participants:Rukmini Reddy – Senior Vice President of Engineering, PagerDutySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Life sciences are at a turning point, where scientific innovation, regulatory pressure, and patient expectations collide with unprecedented advances in data, AI, and digital platforms. IT is no longer a supporting function but a critical driver of how therapies are discovered, developed, scaled, and delivered safely and at speed.This week, Dave and Rob kick off the Life Sciences mini‑series with Thorsten Rall, Global Industry Lead for Life Sciences at Capgemini, to exploring the current state of the sector, the key themes shaping the episodes ahead, and what it takes to drive better patient outcomes. TLDR00:30 – Introduction to Life Sciences and co‑host Thorsten Rall04:37 – Hang‑out: Navigating Waterloo Station07:50 – Deep dive with Thorsten Rall into the Life Sciences landscape28:03 - What are the main challenges in the sector and main themes45:31 – BBQ season is starting HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/with co-host Thorsten Rall: https://www.linkedin.com/in/thorsten-alexander-rall-b232185/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

William and Eyvonne discuss recent tech news, including the growing political and community opposition to AI data centers driven by fears over power and water usage. They also analyze the “AI Chip War” as hyperscalers such as AWS and Google invest in specialized silicon for training and inference.  Episode Links: Amid backlash, O'Leary Digital CEO... Read more »

William and Eyvonne discuss recent tech news, including the growing political and community opposition to AI data centers driven by fears over power and water usage. They also analyze the “AI Chip War” as hyperscalers such as AWS and Google invest in specialized silicon for training and inference.  Episode Links: Amid backlash, O'Leary Digital CEO... Read more »

From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.Topics Include:Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.Starburst is built on Trino, a fast SQL engine for federated data queries.Their platform lets users query data across lakes, stores, and databases seamlessly.Governed "data products" give organizations access to their full data estate in context.A strong data foundation is essential before any AI use case can succeed.AI doesn't create data problems — it exposes the cracks already there.Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.More data isn't always better — getting the right data is what matters.Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.AWS partnership spans technical support, SLA reliability, and proactive product briefings.Advice for product leaders: always anchor new technology back to the customer problem.2026 will be defined by specialized multi-agents working together autonomously.Participants:Matt Fuller – Co-Founder, Vice President of Product, Starburst DataSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

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

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