Python Bytes
Michael Kennedy and Calvin Hendryx-Parker
Python Bytes is a weekly podcast hosted by Michael Kennedy and Calvin Hendryx-Parker. The show is a short discussion on the headlines and noteworthy news in the Python, developer, and data science space.
Episodes
#500 Episode 500!
Topics covered in this episode: A quick chat with Brian looking back Extras Joke Watch on YouTube About the show Brian #1: A quick chat with Brian looking back Nick #2: Jev and the Case for Boring ML Vincent Warmerdam from marimo benchmarked Jev, a new "System 1" decision model from TypeSafe AI. In his video It handles classification without the latency or token cost of generative LLMs, which raises a practical question for data teams: where does this fit alongside traditional machine learning? He tested Jev against classical machine learning models to see if its speed and accuracy hold up on real-world datasets. General LLMs work well for reasoning, but generating full token streams just to assign a category adds unnecessary latency and cloud cost. Jev avoids generation entirely by evaluating the input and returning only the decision, which makes it much faster and cheaper than standard chat APIs. It consistently outperformed eight-shot sentence transformer setups like SetFit out of the box, especially on datasets with dozens of target categories. Traditional models like logistic regression still matter because they run in single-digit milliseconds on basic CPUs, cost nothing per inference, and keep private data within your own network. You cannot fine-tune Jev directly, so standard models remain the better choice when your data contains internal company acronyms, product names, or domain slang. The most practical approach is using Jev to bootstrap initial labels or audit bad training data, then training a small model you can own and run locally. Brett #3: Announcing the 2026 Python Packaging Council Election Results! The inaugural Python packaging council (PPC) has been elected and started meeting! Introduced via PEP 772 5 members elected Top 2 vote-getters get two-year terms Other 3 get one-year terms All members will get two-year terms going forward, staggering the elections There have been 4 meetings far as well as a meeting with the SC Initial meeting notes As the inaugural packaging council still working out basic logistics like how to take notes Also working out what the packaging council should do as PEP 772 explicitly says packaging.python.org falls under the PPC, but otherwise tries not to dictate what the PPC is in charge of Calvin #4: Parsing JSON at tens of gigabytes per second in Python Daniel Lemire (Oct 12) benchmarks fastsimdjson 0.6.0, a Python binding for simdjson on PyPI, repo at simdjson/fastpysimdjson. Its map_many function runs a callback per JSON line across N threads. Workload: Eight hours of GH Archive data, 7.6 GB and 2.2M events. The task counts event types and looks up the target repo's language for each opened PR, and it only touches a few fields per event. Lazy trick: With lazy=True, the whole event is parsed but Python objects are created only for the fields you access. The idea comes from Tyler Kennedy's pysimdjson. Single core: stdlib json gets 0.18 GB/s (about 42 s for the whole set), orjson 0.42, msgspec 1.29, fastsimdjson lazy 2.13 (3.6 s). 64 cores: stdlib json gets 3.17 GB/s, msgspec 24.6, fastsimdjson eager 29.8, and fastsimdjson lazy 65.7. That's the full 7.6 GB in about 0.12 s, roughly 31x its own single-core speed. Requires free-threading: It ran on Python 3.14.6 free-threaded (3.14t) on a 64-core dual-Xeon box. Grain of salt: It's one machine and one dataset. It's an embarrassingly parallel job with per-thread counters. The lazy win only shows up when you read a few fields from big objects. msgspec was handed a schema declaring only the needed fields. The post itself doesn't say whether this generalizes. Michael #5: Grinberg's annual benchmark says 3.15 is flat, but the JIT finally pays off Miguel Grinberg ran his annual release-eve benchmark, comparing Python 3.15.0rc3 against every interpreter back to 3.10, plus PyPy, Node.js, and Rust, using his usual two CPU-bound programs: recursive Fibonacci and bubble sort. The standard 3.15 interpreter lands within noise of 3.14 on his workloads, while the experimental JIT build is roughly 20 to 28 percent faster and finally beats the standard interpreter, which has not happened before. The free-threaded build holds up well in the 4-thread tests. He is candid that the workloads are just Fibonacci and bubble sort, and the official release lands days after his measurements. It is an independent, workload-based counterpoint to the official 7-8 percent JIT geometric-mean claim, from a series with real trend data back to 3.10. He did the same exercise for 3.12, 3.13, and 3.14, so there is a multi-year trend line to draw on. Methodology is two hand-picked CPU-bound programs, recursive Fibonacci plus bubble sort, not a pyperformance-style suite, which cuts both ways. PyPy 3.12 still runs circles around every CPython in the single-threaded tests, and Rust is about 77x faster on Fibonacci. The JIT gain is real in these numbers, but the JIT stays experimental, so he will not use it in production yet. Free-threaded fibo at 4 threads ran about 4.5x faster than the standard interpreter with the GIL. His verdict: not worth rushing an upgrade for speed alone, though lazy imports may tempt production projects. Carol #6: Transparent and trusted AI Our Way - Satyrn AI Paul Everitt, Georgi Ker and I were discussing Python, its community and AI back in March at the JetBrains PyTV event. Python is the foundational language for data science and AI tools. We wanted something that reflects the Python community's strengths: language and learning. Mission: https://satyrn.ai/mission/ Python 3.15 trained model is on Hugging Face After Paul's Europython keynote, he asked for folks to join the community (think of it as a collective of individuals putting their talents together We used some of the organizational tips from Jupyter and JupyterHub's early days on scaling community and emphasizing leveling up individuals Paul will be keynoting in Africa and sharing progress made by a team of dedicated volunteers Putting AI tools and knowledge in the hands of Pythonistas makes good sense and over time will benefit people (A theme that I have spoken about since PyCon 2023 and most recently PyCon JP 2026 Extras Michael: Adobe Creative Suite was mostly cloned in Rust ArtCraft released seven free, open-source desktop apps that mirror the Adobe lineup: PhotoCraft for Photoshop, VectorCraft, FilmCraft, LightCraft, PrintCraft, EffectCraft, and DesignCraft. Everything is written from scratch in Rust with WebAssembly builds for the browser, and developer Brandon Thomas says the apps are AI-built clean-room reimplementations, a claim Ars Technica covered alongside the predictable skepticism. The clean-room story matters: reimplementing Adobe from public specs and behavior with AI is legally defensible, and building the apps for agents to drive, not just humans, is a genuinely different design bet. PhotoCraft already shows real depth: layers, masks, adjustment layers, layer styles, and real PSD support, with over 1,700 tests and dual CPU and GPU compositors tested against each other. The agent story: an MCP server and JSON control protocol let an agent headless-edit documents or drive the live app, useful for bulk edits and first drafts. Tradeoffs are real: reviewers call most of the apps super early alpha, and trade dress claims from Adobe remain a live legal risk even for clean-room code. Sustainability matters: the founder says token sales from their ArtCraft AI model fund the work so it does not become an orphaned small-team project. What happens when open-source alternatives that once took years of community work can be built by a model in weeks? Reminds me of the chardet story we covered awhile ago. The reason I put this topic here is I think it encapsulates a lot of what's happening in the industry for devs now. Building software has changed, creating is easy - sustaining is hard, owning an idea is more fleeting than before, Rust obviously, MCP and CLIs. It's just an interesting bridge from the first 500 episodes and the next 500 likely. Calvin: https://www.llmfit.org/) see what models can run on your hardware! uv 0.13.0 makes Python 3.15 the default! Nick: Black Python Devs: https://blackpythondevs.org/student-ambassador-program/100-campus-tour-campaign/ Brett: Python 3.15.0 final is out! Joke: Exceptional by Derrick Chambers Interviewer: It says here on your résumé that you’re an “exceptional Python developer.” Can you prove it? Me: Sure. Run any of my code. It raises exceptions a lot of the time.
#499 So many questions??
Topics covered in this episode: PEP 824 brings ?? and ??= to Python for None handling Python 3.15 gets a surprise RC3, and Python 3.10 reaches end of life asyncio.shield Pyxel: the retro game engine for Python 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: PEP 824 brings ?? and ??= to Python for None handling PEP 824, a new draft from Marc Mueller sponsored by Guido van Rossum, proposes None-coalescing operators for Python 3.16. The ?? operator returns its left-hand side unless that value is None, and ??= assigns a fallback only when the target is None. It revives the long-deferred PEP 505 from over a decade ago, trimmed to just these two operators, with None-aware access split off into PEP 823. The goal is replacing verbose is None checks with something closer to or, but keyed on None instead of truthiness. Every Python codebase has piles of if x is None fallback code, and this is the first serious move in ten years to give it real syntax. user.age ?? "unknown" is like or but only falls back on None, so 0, "", and [] pass through instead of getting clobbered user.name ??= "unknown" replaces the two-line check-then-assign pattern, with left-side subexpressions evaluated and cached exactly once Precedence sits between or and conditional expressions, matching JavaScript and C#, and ?? lands as a BoolOp in the AST next to and and or Unlike +=, ??= is a conditional assignment that skips the right side entirely, which is why it gets its own AST node rather than AugAssign Still draft status targeting 3.16, with a working reference implementation and an online demo to try, and it notably rejects a soft keyword like otherwise in favor of the familiar ?? Discussion-worthy: the PEP answers the old objection that making None easier will proliferate its use, pointing out None is already everywhere and the status quo just produces uglier code # OG way user = db.get_user() city = None if user is not None: if user.address is not None: if user.address.city is not None: city = user.address.city # Michael's preferred way user = db.get_user() city = user?.address?.city Calvin #2: Python 3.15 gets a surprise RC3, and Python 3.10 reaches end of life The release team added a surprise third release candidate for Python 3.15 to fix last-minute lazy-import release blockers. They wanted time to test the fixes properly. That pushes the 3.15.0 final release from October 2 to October 9, 2026. RC3 has about 156 fixes from 82 contributors. The headline features are explicit lazy imports (PEP 810), the new frozendict (PEP 814) and sentinel (PEP 661) built-ins, and UTF-8 as the default encoding (PEP 686). No more ABI changes are coming, so library authors should be building 3.15 wheels now. uv 0.12.23 already added CPython 3.15.0rc3. On October 1, Python 3.10.22, 3.11.17, 3.12.15, 3.13.16 and 3.14.8 shipped together with nine common security fixes. They cover SSL validation, tarfile and zipfile handling, and urllib credentials. Python 3.10.22 is the final 3.10 release. After five years it gets no more security updates, so anyone still on 3.10 should upgrade. Python 3.13.16 is the last full maintenance release of 3.13, which now moves to security-only fixes. Only 3.13.16 and 3.14.8 have Windows and macOS installers, and the older releases are source-only. Python 3.11 gets security fixes through October 2027, and 3.12 through October 2028. A natural way to tie the two together: one version is nearly out the door and one is officially gone, and the same week is the time to test your code on 3.15 and move off 3.10. Michael #3: asyncio.shield Where async code can be cancelled In async Python, every await is a point where your coroutine can be stopped. If someone cancels the task, asyncio raises CancelledError at whichever await the coroutine is paused on, and nothing after that line runs. Quart does this on purpose. When the client disconnects, it cancels the request's task. From quart/asgi.py: elif message["type"] == "http.disconnect": self._disconnected = True request.body.disconnect() request_task.cancel() That's reasonable for a slow page nobody is waiting on anymore. It's a problem for a handler that writes to the database in two steps: await downloads.insert_one(event) # 1. log the download await totals.update_one(..., {'$inc': ...}) # 2. bump the episode's total A podcast app requests the MP3, gets what it needs, and hangs up. If it hangs up while step 2 is in flight, step 1 has already landed and step 2 never runs. Nothing is logged and nothing reaches Sentry, because a cancelled request isn't an error. The two collections just drift apart. Under WSGI this couldn't happen. A disconnect didn't stop your view; the view finished both writes and the server only failed later, when it tried to send the response. The demo I ran this on Python 3.14. The "client" hangs up 15 ms into a pair of 10 ms writes: async def record_download(): await db_write('events') await db_write('total') _pending: set[asyncio.Task] = set() async def record_download_shielded(): task = asyncio.create_task(record_download()) _pending.add(task) task.add_done_callback(_pending.discard) await asyncio.shield(task) record_download events=1 total=0 record_download_shielded events=1 total=1 The unshielded version loses the increment. The shielded version finishes it, even though the request was cancelled. The full script is in the scratchpad as shield_demo.py if you want it for the show. What asyncio.shield does, and its traps shield(task) protects the inner task from a cancellation aimed at the outer one. The handler still gets CancelledError immediately, so nothing after that line runs, but the inner task keeps going until both writes finish. The traps: Keep a reference to the task. The event loop only holds weak references to tasks, so a task with no other reference can be garbage-collected mid-run. The asyncio docs warn about this. That's what _pending is for. Its errors go nowhere by default. If a shielded write fails after the handler has already been cancelled, nobody awaits the result. You only get a "Task exception was never retrieved" warning. Log errors from inside the task, or in the done callback. It doesn't survive a process shutdown. If the event loop is closing, as during a Granian worker recycle or a deploy, pending tasks are still cancelled. The window is tiny, but it isn't zero. It hides hangs. A shielded write to a stuck database lives forever. Put a timeout inside the shielded task (asyncio.timeout(...)), not outside it. It makes the pair finish, not atomic. A crash between the two writes still splits them. The real fix is one operation that can't half-happen. That's what the SQLite move gets: an insert and an upsert in one synchronous transaction, with no await in the middle. Shield is the right patch for the Mongo code until then. The general lesson: in async code, related writes made one after another are only as reliable as the client's patience. Before shipping something like that, check whether your framework cancels on disconnect. Quart does, and I believe Django 5's async views do too. I don't think Starlette cancels a plain endpoint, but I haven't checked any of these other frameworks' source, so verify them before you say it on air. Calvin #4: Pyxel: the retro game engine for Python Pyxel is a free, MIT-licensed retro game engine for Python, built and maintained by a single developer. It has been in development since 2018 and has passed 18,000 GitHub stars. It's modeled on classic game consoles and deliberately limits you to 16 colors and 4 sound channels, which keeps scope small and games finishable. Built-in editors cover pixel art, tilemaps, sound and music, so you can make the graphics, audio and game logic in one place. The engine is implemented in Rust and compiled to WebAssembly, but you write your games in plain Python. Pyxel Code Maker is a browser playground with a code editor, resource tools and a run button. It loads projects from local files, GitHub Gists or URLs, and saves back to a Gist for sharing. Games can also run on the web, so sharing one can be as simple as sending a link. It's a fun, low-barrier way into game development, good for beginners and for teaching kids. Extras Calvin: Python Lang Summit 2026 discusses a sys namespace Be Careful Updating Your Refrigerator Michael: New Siri is No Bueno Who out there is doing interesting work with sqlite and litestream? Trying to put an episode together on it. Tinymongo shoutout https://tinymongo.org Joke: I was there.
#498 A Tiny Episode
Topics covered in this episode: MemTensor / MemoryOS PyPI package hijacked via a malicious build backend TinyMongo Jev: what to know One innocent dict read makes attribute access permanently slower 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: MemTensor / MemoryOS PyPI package hijacked via a malicious build backend On Sept 23 an attacker published backdoored MemoryOS 2.0.34 on PyPI and three bad versions (0.1.21, 0.1.23, 0.1.25) of MemTensor's OpenClaw plugin on npm. PyPI had no clean release that day, so 2.0.34 was the newest. They pushed commits to MemTensor's own GitHub Actions release pipelines. On PyPI that was a custom Poetry build backend, and on npm a tweaked validation script. Both used BASH_ENV to hand the publish token to the attacker before the real publish ran. SafeDep couldn't confirm how the attacker got push access. Runs on import, not install: A Go implant called sckit starts when the library loads, so --ignore-scripts won't save you. It harvests credentials from your home directory (npm and PyPI tokens, GitHub tokens, SSH keys, cloud CLI tokens, .env files) and sends them to skyleen[.]fr servers. It's a worm: It uses stolen tokens to copy itself into other repos and packages, so the victim list could grow. If you installed it: Downgrade to MemoryOS 2.0.33 (plugin 0.1.20) and rotate every credential reachable from $HOME. Also kill any running sckit stage0 process and check repos you can push to for a stray runtime-update.yml workflow or .sckit/ directory. Michael #2: TinyMongo Want to use a MongoDB data interface, but swap out the storage engine? Memory for testing/caching JSON/TinyDB simple JSON files SQLite for durable, high-perf reads with WAL SQLIte shared for high write apps DuckDB + Parquet for analytics apps Postgres + MariaDB for multi-machine client/server Great for teaching, examples, and simple deployments Amazing story of paired AI development Will completely run talkpython.fm after weeks of shared work together (in SQLite mode). Calvin #3: Jev: what to know What it is: Jev is a model from TypeSafe AI that answers with typed results (yes/no probabilities, scores, picks from your options) instead of prose. Real Python published a hands-on tutorial on 2026-09-24 and the buzz on hacker news is almost deafening. It's proprietary: Jev is a hosted, closed-weight model. There are no weights to download and no self-hosting. Everything called "open Jev" is an independent reimplementation, not TypeSafe's model. Your data leaves your machine: Every call sends your input text to a third-party API. In the tutorial that path goes through OpenRouter to TypeSafe. Think twice before sending customer messages, tickets or anything sensitive. Cost and stability are open questions: The tutorial calls Jev "cheap, but not free" and says it's fast and cheap "at the moment." It also says whether that stays true is "something to keep an eye on." Credit to Real Python: It's a good, practical intro. It shows the Noul, Score and Choice primitives, and its point that instruction wording matters more than thresholds is useful advice for any model. The tutorial itself says similar results are possible with a well-prompted LLM. Open options to look at instead: JevK5 (https://github.com/allebee/jevk5): Apache-2.0 weights and code, 4B or 9B parameters, and it accepts TypeSafe-style requests. SemIf, formerly OpenJev (https://github.com/TheoLeeCJ/openjev): MIT-licensed, small models, and it can run CPU-only. openjev-sglang (https://github.com/ekzhang/openjev-sglang): a Jev-compatible endpoint running Qwen3.6-35B-A3B, but no license is stated, so check before commercial use. The catch: These copy Jev's interface, not its model or training. Results will differ, and I haven't run any of them. Benchmarks are self-reported, and JevK5 is English-only. Michael #4: One innocent dict read makes attribute access permanently slower Timofei Ivankov benchmarks a CPython internals surprise: since 3.11, attribute access skips the instance dict entirely. A specialized opcode reads the attri.bute at a fixed byte offset in the object's inline values array. Read obj.__dict__ once, though, and the dict gets materialized, the object loses that specialized path for the rest of its life, and a million-iteration loop goes from 33 ms to 51 ms on CPython 3.14. vars() and copy.copy() trigger the same thing, so a debugging print or a shallow copy in code touching your hot objects quietly makes every later attribute access roughly 1.5x slower. The slowdown is permanent and nothing about it looks like a performance decision: ordinary code far from the hot loop can trigger it, and the function that gets slower never changes. Materializing dict produces a split table, and the LOAD_ATTR_WITH_HINT fallback declines split tables, so the object ends up with no specialization at all vars(), 'x' in o.dict, and copy.copy() all materialize it; copy.copy is the realistic trap since nobody treats a shallow copy as a performance decision slots instances read attributes at exactly the same speed and cannot fall into the trap since there is no dict to materialize On the free-threaded build both effects grow: atomic incref on reads plus an object lock on writes push the penalty from 17.6 to 25.4 ns Credit: this item was surfaced by the PyCoder's Weekly newsletter Extras Calvin: whatsnewt - a TUI text adventure through what's new in Python 3.15; playful but niche. Joke: Shipping a button in 2026…
#497 Faster than light profiling
Topics covered in this episode: Tachyon: A sampling profiler ships in Python 3.15's stdlib Python Workers are now generally available on Cloudflare Flet 1.0 - build cross-platform apps in Python marimo-book: Build static books from marimo notebooks Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Tachyon: A sampling profiler ships in Python 3.15's stdlib Python 3.15 adds the profiling package per PEP 799: profiling.tracing (where cProfile moved) and profiling.sampling, the new sampler called Tachyon py-spy and Austin exist but copy raw interpreter bytes with no API, so every CPython release risks breaking them; one in the stdlib is a contract to stop breaking profilers Defaults: 1 kHz, main thread, wall clock, and a -live top-like view for poking at a slow server Output is flexible: pstats, -flamegraph, -diff-flamegraph against a baseline, -heatmap on source lines, -opcodes for specialized bytecode, -gecko for Firefox Profiler with GIL and GC markers Profiling modes: wall, cpu, gil (which function is starving my other threads?), and exception, plus -async-aware to see the task graph instead of just select(), -all-threads, and -subprocesses forking a profiler per child Near-zero overhead for production; guidance is 10-30 second windows on representative load, and free-threaded builds divide the rate by thread count Attach to a running PID, same minor version only; ptrace permissions are the main friction. A 3.14 backport already exists on GitHub Caveat: it only sees Python frames, so 90% in calculate() hides NumPy underneath. For native stacks there's Cronon from HRT, 200k samples/sec over DWARF, not yet open source Calvin #2: Python Workers are now generally available on Cloudflare Python Workers are out of beta - now GA, "first-class" language on Cloudflare's Developer Platform No more manual JS interop: bindings (queues, R2, D1, Durable Objects) now work natively in Python, e.g. self.env.QUEUE.send({...}) Runs on Pyodide (WASM-compiled Python), with real TCP socket support for DB connectivity Frameworks supported: FastAPI, Django, Flask; AI libs like OpenAI SDK, LangChain, MCP Underlying platform work formalized as PEP 783 (PyEmscripten), after a year of discussion Bottom line: write real Python on Cloudflare's edge, no JS glue code required Calvin #3: Flet 1.0 - build cross-platform apps in Python Flet hits 1.0 - build Flutter-backed apps from pure Python, no frontend experience needed One codebase targets six platforms: iOS, Android, Windows, macOS, Linux, web 150+ built-in UI controls, plus support for custom controls / wrapping Flutter packages Mobile now supports real Python packages: NumPy, pandas, Pillow, cryptography Comes with pytest-based UI testing and an MCP integration for AI coding assistants Milestone lands 4+ years after its first PyPI release (Sept 2022) - signals "production ready," not experimental Michael #4: marimo-book: Build static books from marimo notebooks marimo-book is a Jupyter-Book-style static site generator built specifically for marimo .py notebooks. It ships polished multi-page sites with Material for MkDocs theming, full-text search, dark mode, and code copy, plus a content-hashed incremental build cache that drops rebuilds from 100+ seconds to roughly 3 seconds on real books. Standout extras include anywidget rendering without a kernel, static reactivity for discrete sliders via pre-rendered lookup tables, an opt-in WASM/Pyodide mode per chapter, and per-chapter launch buttons. If you've wanted to publish a marimo notebook as a real book or course site without hosting a kernel, marimo-book gives you the static, searchable, fast-loading output you'd expect from Jupyter Book. Alpha (0.1.x), but in production: pin marimo-book>=0.1.5,<0.2; the book.yml schema is stable for v0.1, and dartbrains.org is a real-world user. Two-stage build by design: a marimo-aware preprocessor emits plain Markdown + inline HTML, then mkdocs (Material today, zensical tomorrow) renders it. Not a mkdocs plugin, so the shell stays swappable. Interactive widgets without a kernel: anywidget Canvas/Three.js/Plotly mounts render statically, and mo.ui.slider with explicit steps gets pre-computed as a static lookup table. WASM escape hatch per chapter: set mode: wasm and the chapter routes through marimo's MarimoIslandGenerator, shipping the marimo runtime + Pyodide bundle for full reactivity where you need it. Per-chapter launch buttons and extras: readers can jump to molab, GitHub, or a downloaded .py; optional [social], [linkcheck], and [pdf] extras cover OG cards, htmlproofer, and WeasyPrint PDF export. Sandboxed notebooks: the sandbox mode reads PEP 723 inline metadata and provisions per-notebook envs via uv for portable builds, at the cost of slower first runs. Extras Calvin: Great overview of a new feature in Python 3.15 - frozendict Michael: Microsoft Plugs Nearly 1,000 Security Holes in Windows I’ll be speaking at PyBay 2026 PyCon NL on October 15 in Utrecht Heading to Europe in October? PyCon NL is October 15th in Utrecht. One day, three tracks, about 350 people. It's an hour by train from Schiphol. There's also a session just for community organizers from groups like PyLadies, PyData, and Django. And the location fits. The Netherlands is where Python itself was born. Joke: You have homework (no really ;) ) Watch Interview with Big Data engineer in 2026 by Kai Lentit
#496 A lake house in Seattle
Topics covered in this episode: Pandas Should Go Extinct Pydantic-pint puts real-world units in your Pydantic models How Libraries Run Rust Inside Python (With PyO3) AWS acquires DuckLabs Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal 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: Pandas Should Go Extinct Pandas' slowness pushes teams toward "Big Data" tools (Spark, Databricks) they don't actually need — most workloads never hit true Big Data scale Amazon Redshift telemetry: ~95% of tables are under 100GB, ~87% of queries touch 80GB or less — that's "Medium Data," not Big Data Polars and DuckDB fill that gap: single-machine, fast, no cluster required 1 Billion Row Challenge benchmark: Pandas took 4m28s vs. Polars 5.04s and DuckDB 5.19s — DuckDB also used 19x less memory On a real-world NYC taxi dataset (3GB parquet), pure DuckDB ran 2x faster than pure Pandas while using a fraction of the RAM Bonus: Apache Arrow lets you pass data between Pandas/Polars/DuckDB with zero copying, so trying them out doesn't mean a full rewrite Michael #2: Pydantic-pint puts real-world units in your Pydantic models Pydantic-pint bridges Pydantic and Pint so models can validate physical quantities like 4m or 12 meters instead of bare floats. Fields annotated with PydanticPintQuantity parse user input, convert between compatible units, and serialize quantities back out as strings. That closes a real gap for anything consuming API payloads, config files, or sensor data with measurements, letting you enforce units at the validation boundary instead of hoping every caller remembered them. via PyCoder's Weekly newsletter Unit mix-ups have literally crashed spacecraft; now your Pydantic models can refuse them at the door. Annotate a field as Annotated[Quantity, PydanticPintQuantity('km')] and inputs like 12 meters arrive auto-converted to kilometers Validation covers string, numeric, and quantity inputs, and model_dump_json serializes quantities as readable unit strings Installable from PyPI as pydantic-pint, MIT licensed, with docs at pydantic-pint.readthedocs.io Early-stage solo project at version 0.4, so API stability and maintenance are open questions worth discussing Calvin #3: How Libraries Run Rust Inside Python (With PyO3) Pydantic v2's validation core (pydantic-core) is Rust under the hood, built with PyO3 — this post shows how that bridge actually works via a small hand-built JSON parser Four steps to get Rust into Python: write a normal Rust module, annotate with PyO3 macros (#[pyfunction], #[pymodule]), compile/install with maturin, then just import it The parser builds a Rust tree first — Python never touches it until the boundary crossing Key insight: converting the Rust result into Python objects (.into_pyobject) is often the expensive part, not the parsing — 100,000 JSON values means ~100,000 Python objects built after parsing's already done Errors cross the boundary too: Rust's typed errors convert into real Python exceptions (ValueError, FileNotFoundError) via From/?, so callers get clean Python semantics Takeaway for anyone porting Rust in: if you're returning a scalar, don't sweat it; if you're returning a big structure, profile the boundary — that's the real cost, not the algorithm Michael #4: AWS acquires DuckLabs Thank you Dylan McConnell. What does this mean for the DuckDB ecosystem? DuckDB is the open-source in-process analytical SQL engine. MIT licensed. The IP is not owned by any company - it's held by the nonprofit DuckDB Foundation, which was created when the team spun out of CWI Amsterdam. Peter Boncz, the CWI representative on the Foundation board, describes it as the entity that holds all IP of open-source DuckDB. DuckLabs (ducklabs.com) is the company, formerly branded DuckDB Labs. Founded a little over five years ago by Hannes Mühleisen and Mark Raasveldt to give the DuckDB team a stable long-term home, bootstrapped deliberately instead of taking VC, grown to 30+ people in Amsterdam, funded by support and feature-prioritization contracts. It employs the core devs. It does not own DuckDB. DuckLake is one of three projects DuckLabs builds, what they call the Duck Stack: DuckDB, DuckLake, and Quack. DuckLake is the lakehouse format that puts catalog metadata in a SQL database instead of in files on object storage. Quack is newer - an RPC-style protocol that turns DuckDB into a client-server system where both ends are DuckDB instances, slated to stabilize in DuckDB v2.0 in September 2026. MotherDuck is a separate Seattle company, Jordan Tigani's, selling serverless hosted DuckDB. It was started in partnership with DuckDB Labs and has worked closely with Hannes and Mark for four years. It contracted DuckLabs for engineering work and contributes heavily upstream - three of its engineers are among the top 10 outside contributors to DuckDB. It also sells its own DuckLake offering. Customer and collaborator, never owner. What the AWS post changes. Amazon bought the company, not the project. DuckLabs joined AWS effective September 1, with the process concluding August 31, 2026. Hannes and Mark keep leading the team and the project's technical direction, the team stays in Amsterdam, and DuckDB stays MIT under the Foundation. AWS gets the people and a direct line to the roadmap. The license protects your code, not your priorities. Three second-order effects worth tracking: The Foundation board is the real question. It has three directors: Mühleisen, Raasveldt, and Boncz. Two now work for AWS. Commentary on the deal has focused on exactly this - the license protects the code, not the roadmap. The announced counterweight is governance: a technical advisory board on the Foundation, and opening the extension stack so extensions signed by other developers can run in DuckDB. MotherDuck immediately moved into the business DuckLabs vacated. It now sells DuckDB enterprise support, which it had avoided because it didn't want to compete with DuckLabs' business model, and says it has explicit blessing from Hannes and Mark now that they're joining Amazon. It also bought Tower.dev the day before the AWS announcement. Everyone expects an AWS DuckDB service. Tigani says Amazon will likely release one eventually, and welcomes the competition, citing Redshift's failure to slow Snowflake on AWS. The groundwork is already visible: Amazon Quick uses DuckDB to query S3 Tables and has processed over 2.5B queries with it since launching in October 2025. The DuckLake angle is the one to watch. AWS is heavily committed to Iceberg through S3 Tables, and it just acquired the team behind a competing lakehouse format. The stated plan is to use DuckDB, DuckLake, and Quack together to power a new generation of data services, but which format wins internal priority is unannounced. Extras Calvin: astral-sh/uv 0.12.12: code-signed release binaries 🥳 Michael: My MacBook power supply rebooted to install updates (?!?) The Story of VS Code | Official Documentary Amazon/AWS acquires DuckLabs (see recent episode on DuckLake) Joke: We’re agentic now
#495 Banned
Topics covered in this episode: EuroPython 2026 videos are online The State of Django 2026: Boring is so back htmx 4.0.0 has been released 🐍 Functionally Zen Extras Joke Watch on YouTube 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 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: EuroPython 2026 videos are online The EuroPython Society has published all 117 recordings from EuroPython 2026 on the official EuroPython Conference YouTube channel. The conference ran July 13-19 in Krakow, Poland and celebrated the conference series' 25th anniversary. The playlist covers keynotes, panels, lightning talks, and full talk recordings across Python core, web, DevOps, data/ML, embedded, and other tracks. If you missed EuroPython 2026 in Krakow, this is the complete free on-demand archive of one of the year's biggest European Python events. 117 videos now live on the EuroPython Conference YouTube channel, last updated Aug 17, 2026. Michael’s personal watch list. Calvin #2: The State of Django 2026: Boring is so back State of Django 2026 (JetBrains/DSF survey, ~3,500 devs, 40+ countries) - "boring is so back": Django's core stays reliable while everything around it moves fast Core is stable: Postgres 76–79% for 5 years running, templates ~80%, 43% already on Django 6.0 AI is routine now (only 10% use none) but workflow's unsettled - Claude Code leads at 35%, and 56% still just use it for chat, not autonomous edits Tooling is consolidating: uv and Ruff both at 43% adoption, each replacing several older single-purpose tools Type hints are winning (57% use them) but the checker is up for grabs - IDE-built-in leads at 40%, Mypy 32%, with ty/Pyrefly emerging Two Django communities coexist happily: 72% server-rendered templates vs. 53% API-only - and htmx adoption jumped from 5% to 34% in five years Michael #3: htmx 4.0.0 has been released After 8 months of work, the htmx team shipped 4.0.0, a rewrite that moves internals from XMLHttpRequest to fetch() while keeping the API almost identical to htmx 2. Three changes may need action: attribute inheritance is now explicit via an :inherited suffix, event names follow a htmx:phase:action pattern, and history no longer caches pages in localStorage. Additions include built-in morph swaps, the new hx-partial tag, and many core extensions. htmx 2 stays supported and remains latest on npm until early 2027. htmx is the go-to frontend layer for Python server-rendered apps (Flask, Django, FastAPI), and 4.0 is deliberately low-drama: nearly behavior-compatible, so teams can upgrade on their own schedule and pick up morph swaps and streaming extensions. Explicit inheritance is the biggest migration item: hx-confirm, hx-headers, hx-target and friends no longer cascade to children unless you append :inherited; hx-disinherit and hx-inherit are gone A CLI upgrade checker (npx [email protected] upgrade-check) flags spots needing :inherited, renames like hx-disable to hx-ignore, removed attrs like hx-vars, and old event names in templates and JS Events follow htmx:phase:action (htmx:beforeRequest becomes htmx:before:request); most error events collapse into htmx:error and htmx:xhr:* events are removed with XMLHttpRequest History no longer snapshots pages in localStorage; back navigation re-fetches and swaps into the body, fixing a chronic support headache, with a new hx-history-cache extension for sessionStorage caching New features: out-of-the-box morphing swaps, the [HTML_REMOVED] tag for multi-element updates, streaming over SSE/WebSockets/multipart, and hx-live, their Alpine-inspired DOM scripting solution No forced upgrade: 2.x stays latest on npm until early 2027 (4.0 remains next) and is supported indefinitely; the team even ships official LLM skill files for guidance and upgrading Calvin #4: 🐍 Functionally Zen Functionally Zen (Kyle Adams, Test Double) - riffs on "simple is better than complex" with 7 extra tenets for Python simplicity Core claims: idiomatic > non-idiomatic, data > functions, pure functions > impure functions > classes Favorite example: a medical-dosage calculator replaced with a plain lookup dict - no logic, no tests needed Big idea: keep a thin "impure shell" around a "pure core" (Gary Bernhardt's functional core / imperative shell) - push side effects (API calls, DB, files) to the edges Side note: constructors that do I/O are "poison pills" - the side effect infects every class that depends on them Payoff: pure functions and no-mock tests are just easier to read and reason about than the alternative Extras Calvin: uv ships trusted-publisher token revocation and Python 3.15 support Making a Python interpreter in 1024 bytes Michael: Steering council voting is now open Joke: Makes you look like this?
#494 Python Wrapture
Topics covered in this episode: OpenAI's Python SDK has migrated to HTTPX2 TMOG - Native Task Manager for macOS, Windows, and Linux wrapture - one wrapper for mocking, tracing, and observability linkedin2md: turn your LinkedIn export into 40+ Markdown files 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 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: OpenAI's Python SDK has migrated to HTTPX2 The OpenAI Python SDK has migrated to HTTPX2, the Pydantic-stewarded fork of httpx. Pydantic picked it up citing "limited activity recently" in the original project, promising "a reliably maintained path forward." If you just use the default client, nothing to do. No code changes. The catch is TLS. Quoting the guide: HTTPX "previously verified certificates against the CA bundle provided by certifi. HTTPX2 instead uses the operating-system trust store, and the SDK no longer installs certifi." That "can break certificate verification in minimal container images without system CA certificates, environments using corporate TLS-inspecting proxies, and deployments that relied on a custom or modified certifi bundle." The fix is SSL_CERT_FILE or SSL_CERT_DIR, or pass your own ssl.SSLContext via verify. Deeper integrations need real edits: custom clients, auth handlers, hooks, and request mocking all take HTTPX2 objects now, and plain httpx is no longer pulled in transitively. So import httpx in your own code means declaring it yourself or moving over. Temporary escape hatch: a legacy HTTPX client Michael #2: TMOG - Native Task Manager for macOS, Windows, and Linux A native, deeply instrumented system monitor for macOS, Windows, and Linux, now in public beta - from Plummers' Software, i.e. Dave Plummer, who wrote the original Windows Task Manager and donated it to Microsoft in 1995. Wikipedia Three real native apps: Swift/AppKit on macOS, Win32 on Windows, C++/Qt 6 on Linux, with a shared C++ core keeping metric semantics aligned - no browser shell anywhere. One dense summary: CPU, clocks, thermals, GPU, memory, storage, network, energy, and the processes responsible for the load, all click-through. Per-core honesty: logical processor and NUMA views, P and E cores color-coded, optional kernel time, 60 FPS live meters. Memory with context: pressure, wired, compressed, cached, committed, available, and swap, plus configurable scrolling history. Processes that act like processes: tree view, filtering, sorting, follow mode, and native verbs including service and launchd control. Phosphor themes: light, dark, green, amber, blue, or mono, with color and saturation you tune yourself. Calvin #3: wrapture - one wrapper for mocking, tracing, and observability Graham Dumpleton, author of wrapt and the original New Relic Python agent, has released wrapture. The name is wrapt plus capture. The core idea: wrap real code instead of replacing it, so the real code still runs while you watch every call. Name a method with wrapture.binding(Class, "method"), open a timeline(), and you get a tape of what actually happened. Real return values, real nesting, arguments normalised against real signatures. tape.tree() prints the call graph as it ran. One mechanism, three jobs: monkey patching with a real lifecycle (apply, remove, suspend, plus returns, raises, transforms_args), unit testing that asserts on real call flow instead of a flat MagicMock call list, and ad-hoc tracing of a running app. The testing pitch is error paths. Inject TimeoutError at the payment gateway, then assert the ledger was never written. Stubs and mocks are strict and spec-required, and there is deliberately no bare Mock(). Tracing needs no code at all. A wrapture.toml naming targets and a sink, run with python -m wrapture main.py, and you get a live call tree with timings. It captures ordinary logging calls as nested events, and with the otel extra it exports spans, metrics and correlated logs with W3C trace ids that join across services. Every line of code and docs was AI-written under their direction, and they say so up front. Two weeks from first commit, eleventh alpha, over 1000 tests, 150+ pages of docs. Alpha on PyPI, needs Python 3.12+ and wrapt 2.4.0+. Michael #4: linkedin2md: turn your LinkedIn export into 40+ Markdown files Via Juan Manuel Daza - a Python CLI that unpacks LinkedIn's data-export ZIP into clean, per-category Markdown you can drop straight into an LLM. One command: linkedin2md Complete_LinkedInDataExport.zip, plus o for output dir, -lang en|es, and -pdf. 40+ output files: profile, experience, education, skills, connections, posts, comments, reactions, recommendations, endorsements, job applications, even ad targeting and LinkedIn's inferences about you. Built for LLM analysis: the README pitches NotebookLM, Claude Projects, Obsidian, and Ollama, with example prompts like "what patterns do you see in my career transitions?" PDF resume mode: -pdf renders an A4 CV via weasyprint, and degrades gracefully to Markdown-only if it isn't installed. Dependency note: "pure Python / zero-dep" holds for the Markdown path only - the PDF path needs weasyprint and markdown installed. Install: pipx install linkedin2md recommended, pip in a venv otherwise - 86% Python, 10 releases, v0.3.1 in May. Agentic dev angle: repo ships opencode config and an N3RV subagent pipeline, including a "judgment day" dual-model adversarial PR review. Extras Calvin: EVE Online Migrates to Python 3 Michael: Dinkus by Will McGugan Joke: Tao of Programming: Book 5 Maintenance
#493 CalVer and LTS
Topics covered in this episode: Web UIs for your reverse proxy Wagtail 8.0 is hot off the presses RISC-V is now officially supported by CPython Django’s annual releases make every version an LTS Extras Joke Watch on YouTube About the show Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Web UIs for your reverse proxy Traefik, nginx, and Caddy all sit in front of a lot of self-hosted infrastructure, and all three are configured by hand-editing files. Three active projects put a control plane on top: Traefik Manager (Python + Flask), Nginx UI (Go + Vue), and caddy/ui (React + Node). All three are additive rather than replacements - none of them take ownership of your config away from you - which is the part that matters when the thing has write access to production routing. Traefik Manager is the Python one: Flask 3.1 and Gunicorn for the control plane, a lightweight Go agent for remote instances, currently v1.10.0 with an Android companion app. Nginx UI is a single Go binary at 11.3k stars, with a block-style config editor, an Ace editor doing LLM completion on nginx syntax, and an MCP server so agents can drive it. caddy/ui runs as two containers next to your existing Caddy, reads and writes your Caddyfile directly, and uses Caddy's /adapt API to validate before reload - no Docker socket required. Each one edits the config the underlying server already reads, so your files stay the source of truth and you can drop the UI without unwinding anything. Undo is a first-class feature across all three - timestamped backups with optional Git history, config version compare and restore, Caddyfile snapshots with one-click rollback. Observability is where they diverge: Traefik Manager does CrowdSec and a visual route map, Nginx UI does server metrics, caddy/ui streams access logs over SSE and pulls p50/p95/p99 off Caddy's Prometheus endpoint. Maturity spread is wide - Nginx UI has 11.3k stars, caddy/ui has 4 and was built in a single Claude session - and caddy/ui ships with auth off by default, so set CADDY_UI_USER and JWT_SECRET before it goes anywhere near a public interface. Calvin #2: Wagtail 8.0 is hot off the presses Link: https://github.com/wagtail/wagtail/releases/tag/v8.0 Custom base page models are now supported, so projects aren't locked into subclassing Wagtail's Page as shipped (Matt Westcott). New v3 REST API handles both read and write CMS operations, a first for Wagtail's API. A global registry for permission policies, plus full customizability for the remaining page views via PageViewSet. AVIF and WebP images are no longer auto-converted to PNG by default, a real behavior change to watch on upgrade. Five security fixes: page admin API restrictions, document identification by SHA1 hash, descendant collections in the Documents/Images API, snippet copy permissions, and the page translation endpoint. Formalized Django 6.1 support, and CI now runs on uv with a lockfile. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: RISC-V is now officially supported by CPython Link: https://blog.python.org/2026/08/riscv-now-officially-supported/ CPython added RISC-V as a tier 3 platform under PEP 11, specifically the 64-bit Linux target riscv64-unknown-linux-gnu. RISC-V is an open ISA anyone can implement, unlike x86 and ARM, and its market is projected to quadruple by 2032. The RISE Project donated real RISC-V machines for buildbots; the author's work was funded by a Sovereign Tech Agency fellowship. What changes: the port is now a maintained compatibility target, so CPython changes are less likely to quietly break it. What doesn't: no python.org installers, no binary wheel parity for native extensions. Next up: RISC-V runners in CPython CI for pre-merge feedback, then a push toward tier 2, plus architecture-specific optimizations. The ask is testing. If you have RISC-V hardware, build CPython, run your test suite, file what breaks. Tier 3 is the weakest support tier. PEP 11 tier 3 requires a core developer contact and a buildbot, but failures on tier 3 platforms explicitly do not block a release. Saying "ongoing CI/testing expectations" oversells it. The honest bit is "someone is now on the hook for it, and breakage gets noticed," not "it's guaranteed working." Worth the caveat that this is Linux SBCs, not microcontrollers. A VisionFive 2 counts, an ESP32-C6 or Pico 2 does not. Those are 32-bit non-Linux parts where MicroPython is still the answer. Michael #4: Django’s annual releases make every version an LTS Starting with Django 2028, Django will move to one January feature release per year, adopt calendar-based version numbers, and support every release for three years. The old distinction between standard and LTS releases disappears, giving teams a predictable annual upgrade path that aligns more closely with Python’s own release and support cadence. Every Django release becomes the safe, long-supported choice, so teams no longer need to wait for a specially designated LTS version or absorb two years of changes at once. Each release gets one year of mainstream bug fixes followed by two years of security and data-loss fixes. New releases support the three latest Python versions and add the next Python release during their first year. Calendar versioning begins with Django 2028, followed by Django 2029 and so on. Three Django versions will be supported at any time, giving third-party packages a clearer rolling target. Nothing changes before 2028, and existing commitments for Django 5.2 LTS and 6.2 LTS remain in place. Extras Calvin: The Python docs now document the time complexity of built-in types https://docs.python.org/3.16/library/time-complexity.html Thinking in Python - Bruce Eckel's free book https://thinkinginpython.com/ Michael: prune_uv_pythons.py - Prune uv-managed Python installs, keeping only the newest patch per minor version Runs automatically in my system “upgrade” script: upgrade-output-2026.png Started using Ollama cloud models for my Hermes assistant. Thanks to Jeff Triplett I learned they are not just local models. Joke: The Tao of Programming - Book Seven: Corporate Wisdom
#492 Codeberg Puts Head in Sand
Topics covered in this episode: Python 3.12.14, 3.11.16, 3.10.21 - security releases Codeberg’s AI-code ban tests its role as a GitHub alternative Brett Cannon: what's missing for reproducible builds on PyPI nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. Extra extra extra, hear all about it Extras Joke Watch on YouTube Sponsored by Logfire from Pydantic pythonbytes.fm/logfire This episode is brought to you by Pydantic Logfire. It's observability for AI apps from the team behind Pydantic - agents, LLMs, APIs, database, and infrastructure in a single trace, queried with Postgres-compatible SQL. Your coding agent can query it too, through their MCP server. I'll tell you more later. Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Python 3.12.14, 3.11.16, 3.10.21 - security releases https://blog.python.org/2026/08/python-31214-31116-31021/ Source-only security releases for the three branches now in security-fix-only mode; release team blamed the European solar eclipse for the timing. tarfile hardening. Multiple path-traversal bypasses of the data filter closed, including a symlink escape that bypassed the CVE-2025-4330 fix; extract() now applies the filter to link targets too. Four fresh CVEs: CVE-2026-2297 (SourcelessFileLoader not using io.open_code() for .pyc), CVE-2026-4224 (expat crash on deeply nested content models), CVE-2026-3644 (control chars in http.cookies.Morsel), plus the completed CVE-2021-4189 fix in ftplib.ftpcp. Quadratic-complexity DoS cleanup across the stdlib: HTMLParser, configparser regexes, unicodedata.normalize(), csv.Sniffer.sniff(), and ElementTree XPath index predicates. Header/injection fixes: CR/LF rejected in HTTPConnection.set_tunnel(), control chars blocked in wsgiref.handlers status, and webbrowser now rejects leading dashes (plus a %action prefix bypass). http.client now caps chunked trailer lines and 1xx interim responses at 100 each - a hostile server could previously hang the client forever despite a socket timeout. Memory-safety odds and ends: stale pointers in lzma/bz2/zlib decompressors after MemoryError, a bz2 stack overflow on reuse-after-error, and bundled libexpat bumped to 2.8.3. If you're still on 3.10, 3.11, or 3.12 - and you extract tarballs from anywhere you don't fully control - this one's not optional. Michael #2: Codeberg’s AI-code ban tests its role as a GitHub alternative Armin’s article “Codeberg Divides” Armin Ronacher argues that Codeberg’s new terms, which prohibit projects mostly written with generative AI, create a vague and difficult-to-enforce boundary. His larger concern is that a democratically governed host can still be unpredictable or ideologically narrow, weakening Codeberg’s potential as a broad European alternative to GitHub. The strongest question for Python developers is whether repository hosting should judge legal open source by how code was produced, or focus on behavior and resource abuse. “Mostly generated” is hard to measure in modern codebases where developers mix handwritten code, completions, agents, and generated refactors. Ronacher suggests clearer alternatives: ban all LLM involvement, or target autonomous repository spam, abusive resource use, and low-quality generated contributions directly. Codeberg is free to choose a values-driven community, but that may conflict with being predictable, neutral infrastructure and a serious GitHub competitor. Worth discussing: can open-source communities set meaningful AI boundaries without driving maintainers and projects into opposing camps? Very first search for these terms lands on this page. Codeberg looked like a viable alternative. … Unfortunately, the latest update to its terms of service seems to mark a first step in changing one part I moved there for, namely the “freedom” part. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: Brett Cannon: what's missing for reproducible builds on PyPI Framing came out of his 2026 Python Packaging Council nomination - the secure-supply-chain gap he found is that Python has no defined way to do reproducible builds at all. Design goal is zero friction: producers uploading to PyPI shouldn't have to do anything. The work lands on build backends and installers. Gap #1: nothing records the source code a distribution came from. direct_url.json captures it when you install from a repo or archive, so the fix is putting the same info in sdist/wheel metadata. Gap #2: recording the build tools. Wheels can already do this via PEP 770 SBOMs in .dist-info/sboms/ - sdists can't, since they're a tarball plus a precalculated PKG-INFO with nowhere to hang extra metadata. Either "don't use sdists" or an sdist v2. The replay mechanism already exists: [build-system] in pyproject.toml is a defined entry point, so if backends recorded their own environment, you could reinstall and re-run the build. Payoff idea: trusted third parties report successful reproductions back to PyPI, which displays "independently reproduced by X" - surfaced in the index API so installers could prefer reproduced files. Explicitly framed as a perk, not a requirement - roughly SLSA build level 1, no shaming projects that don't opt in. Verbal kicker option: "And don't think pure-Python wheels are off the hook. Something built that wheel, and if that something was compromised, so is your wheel. SolarWinds was a build-process attack." Michael #4: Extra extra extra, hear all about it Python 3.14.7 Upgraded the MCP servers to 2026-07-28 v2 protocols (talk python, python bytes) Got agentsview running synced via postgres Talk Python courses, teams trial offering Talk Python courses, government procurement offering Lean TDD audio book is out Extras Calvin: uv now prefers post-quantum key exchange - https://github.com/astral-sh/uv/releases/tag/0.12.4 Joke: Beware of dog
#491 Feeling Judged
Topics covered in this episode: Claude Code /insights Post-quantum crypto lands in Python MCP goes stateless — and FastMCP gets renamed inshellisense - IDE style command line auto complete Extras Joke Watch on YouTube About the show Sponsored by Xweather Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Michael will tell you more about them later in the show. Get started for free at pythonbytes.fm/xweather Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Claude Code /insights Michael’s Insights: michael-kennedy-claude-code-insights-2026-08-09.html Be careful sharing these outputs, they include details references to your projects, errors, security findings, etc. ;) /insights reads your last 30 days of local session transcripts and hands back an interactive HTML report on how you actually work. One command, zero setup: type /insights in a session, or run claude -p "/insights" from the shell for a non-interactive version that just prints the path Reads what's already on disk: pulls session logs from ~/.claude/projects/, skipping agent sub-sessions and anything under 2 messages or 1 minute Project areas: clusters your sessions into themes like "CLI Tooling" or "Documentation" with session counts Friction analysis: categorizes where things went wrong by root cause - and quotes your own prompts back at you Interaction style: tells you whether you're a delegator or a micromanager, plus which workflows are worth doubling down on Actually actionable: suggests concrete CLAUDE.md additions and Claude Code features you're not using The catch: Haiku does the per-session classification, so the first run takes several minutes; results cache to ~/.claude/usage-data/facets/ and the report lands at ~/.claude/usage-data/report.html Calvin #2: Post-quantum crypto lands in Python pyca/cryptography 48 ships ML-KEM (key establishment) and ML-DSA (signatures) — NIST's post-quantum standards, now one pip install away. Big deal because it's the 11th most-downloaded package on PyPI (~1.2B downloads/month) and sits under Ansible, Certbot, Airflow, and paramiko. No PQ there, no PQ anywhere in Python. Trail of Bits did the work (Rust bindings, cross-backend API, tests, AWS-LC backend support), funded by the Sovereign Tech Agency. Timing tracks a June 22 White House order setting federal deadlines: PQ key establishment by end of 2030, PQ signatures by end of 2031. Not a drop-in swap — the wire sizes explode. ML-DSA-65 signatures are 3,309 bytes vs Ed25519's 64; ML-KEM-768 public keys are 1,184 bytes vs X25519's 32. Hardcoded field sizes and length prefixes will bite. API looks like the existing asymmetric primitives, except ML-KEM is encapsulate/decapsulate rather than a Diffie-Hellman exchange. SLH-DSA (the hash-based conservative backstop) is still in progress. The primitives are here, but protocols haven't caught up — so you won't be running post-quantum Certbot this week. Sponsor: Xweather You're using agents that can write code, summarize documents, and automate workflows. But they're missing one thing: awareness of the world around them. This is where today's sponsor, Xweather comes in. Xweather combines enterprise-grade weather intelligence with agent-ready APIs, natural language capabilities, and an MCP server built for tools like Claude, Codex, Copilot, and modern IDEs – so your agents can adapt workflows, automate responses, and make better decisions based on real-world conditions. Backed by Vaisala, whose instruments fly on NASA missions to Mars, Xweather delivers trusted data and unique insights that go beyond conditions to actual impact – from real-time lightning strikes to road surface forecasts. Start with 15,000 free API calls each month and pay only for what you use as you grow. Xweather is your full weather stack, for developers by developers. Start building for free today at pythonbytes.fm/xweather. The link is in your podcast player's show notes and on the episode page. Thanks so much to Xweather for supporting Python Bytes. Calvin #3: MCP goes stateless — and FastMCP gets renamed From Philipp Acsany over at Real Python The 2026-07-28 spec landed July 28 and the Python SDK shipped 2.0.0 the same day. Biggest rewrite since MCP launched, and it's breaking on purpose. Context for scale: the Tier 1 SDKs are pulling close to half a billion downloads a month, with TypeScript and Python each past a billion total. The headline is the stateless core. The initialize/initialized handshake and the Mcp-Session-Id header are both retired — protocol version, client identity, and capabilities now ride in _meta on every request, with an optional server/discover RPC if a client wants capabilities up front. Any request can land on any instance behind plain round-robin, no shared storage. Server-initiated calls are the hard part of the migration. Sampling, elicitation, and roots/list no longer call back to the client; instead the server returns resultType: "input_required" and the client retries with inputResponses attached. Multi Round-Trip Requests, MRTR. Also: Mcp-Method and Mcp-Name are now required headers so gateways route on headers instead of cracking JSON bodies, and missing-resource errors move to standard 32602. Deprecation sweep with an actual policy behind it — Roots, Sampling, Logging, and the legacy HTTP+SSE transport all deprecated with a twelve-month minimum offramp. Tasks graduated out of the experimental core into a real extension, which is what the formalized extensions framework was for. MCP Apps is now an official extension too, so a tool call can return sandboxed interactive HTML. Auth picked up RFC 9207 issuer validation, issuer-bound credentials, and a shift from DCR toward CIMD. Python SDK 2.0 is where it gets personal: FastMCP is now MCPServer, no alias, no shim. McpError → MCPError. Wire types went snake_case (is_error, input_schema) and moved to a standalone mcp_types package, with mcp.types kept as a permanent alias. One Client object replaces the old transport + ClientSession + initialize() stack. httpx became httpx2. Sync handlers run on worker threads now, so asyncio.get_running_loop() raises inside them. The good news: one MCPServer serves both protocol eras, so 2025-era clients keep working with nothing to configure, and a Resolve(fn) parameter lets one tool body cover MRTR and the old path. 1.x is maintenance-and-security-fixes only — pin mcp>=1.28,<2 if this week is already full. The Tasks extension isn't in 2.0.0 yet, so Tasks has left the core spec but hasn't landed in the SDK. If you only call MCP servers, you mostly just get the benefits for free. If you ship one, you already know what your week looks like. And if you use the standalone fastmcp package instead of the official SDK — different project, 3.x line, none of this touches you. The rename is partly to stop the two from being confused. Michael #4: inshellisense - IDE style command line auto complete via Doug Nichols inshellisense provides IDE style autocomplete for shells. It's a terminal native runtime for autocomplete which has support for 600+ command line tools. inshellisense supports Windows, Linux, & macOS. If you are using a NerdFont patched font, you can enable the NerdFonts support in your config file Extras Calvin: Django 6.1 Released — https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/ DjangoCon US is quickly arriving, grab your tickets now! — https://2026.djangocon.us/ Michael: AI integration: Python Bytes for AI Up and Running with Rust Course is out! Joke: But they already know
#490 It’s a vibe coding party
Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O’Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from [email protected]. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We’re Safe
#489 Or JSON?
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 See the full show notes for this episode on the website at pythonbytes.fm/489
#488 tau - it's 2pi and it writes code
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 See the full show notes for this episode on the website at pythonbytes.fm/488
#487 Minimum requirements
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 See the full show notes for this episode on the website at pythonbytes.fm/487
#486 underscore-underscore-ghost-emoji
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 See the full show notes for this episode on the website at pythonbytes.fm/486
#485 Creating memories
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 See the full show notes for this episode on the website at pythonbytes.fm/485
#484 All our tools
Topics covered in this episode: pi + superpowers Terminal: Warp.dev + OhMyZSH {Blink,kitty} + mosh + tmux Claude code MacWhisper or Handy Tailscale Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/484
#483 Thanks Brian
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 See the full show notes for this episode on the website at pythonbytes.fm/483
#482 Mr. Beast's episode
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 See the full show notes for this episode on the website at pythonbytes.fm/482
#481 Ways to die
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 See the full show notes for this episode on the website at pythonbytes.fm/481
#480 Proud Parents
Topics covered in this episode: Using Django Tasks in production Co-authored with Claude? PyPI packages are increasing rapidly httpx2 Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/480
#479 Talking About Types
Topics covered in this episode: httpxyz one month in Learn concurrency - a deep dive into multithreading with Python pip 26.1 - lockfiles and dependency cooldowns Python 3.15 sentinal values from PEP 661 Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/479
#478 Iodine tablets and potable water
Topics covered in this episode: profiling-explorer Reverting the incremental GC in Python 3.14 and 3.15 VSCode AI Co-author defaults to on, then off django freeze Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/478
#477 Lazy, Frozen, and 31% Lighter
Topics covered in this episode: Django Modern Rest Already playing with Python 3.15 Cutting Python Web App Memory Over 31% tryke - A Rust-based Ptyhon test runner with a Jest-style API Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/477
#476 Common themes
Topics covered in this episode: Migrating from mypy to ty: Lessons from FastAPI Oxyde ORM Typeshedded CPython docs Raw+DC Database Pattern: A Retrospective Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/476
#475 Haunted warehouses
Topics covered in this episode: Lock the Ghost Fence for Sandboxing MALUS: Liberate Open Source Harden your GitHub Actions Workflows with zizmor, dependency pinning, and dependency cooldowns Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/475
#474 Astral to join OpenAI
Topics covered in this episode: Starlette 1.0.0 Astral to join OpenAI uv audit Fire and forget (or never) with Python’s asyncio Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/474
#473 A clean room rewrite?
Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/473
#472 Monorepos
Topics covered in this episode: Setting up a Python monorepo with uv workspaces cattrs: Flexible Object Serialization and Validation Learning to program in the AI age VS Code extension for FastAPI and friends Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/472
#471 The ORM pattern of 2026?
Topics covered in this episode: Raw+DC: The ORM pattern of 2026? pytest-check releases Dataclass Wizard SQLiteo - “native macOS SQLite browser built for normal people” Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/471
#470 A Jolting Episode
Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/470
#469 Commands, out of the terminal
Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/469
#468 A bolt of Django
Topics covered in this episode: django-bolt: Faster than FastAPI, but with Django ORM, Django Admin, and Django packages pyleak More Django (three articles) Datastar Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/468
#467 Toads in my AI
Topics covered in this episode: GreyNoise IP Check tprof: a targeting profiler TOAD is out Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/467
#466 PSF Lands $1.5 million
Topics covered in this episode: Better Django management commands with django-click and django-typer PSF Lands a $1.5 million sponsorship from Anthropic How uv got so fast PyView Web Framework Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/466
#465 Stack Overflow is Cooked
Topics covered in this episode: port-killer How we made Python's packaging library 3x faster CodSpeed Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/465
#464 Malicious Package? No Build For You!
Topics covered in this episode: ty: An extremely fast Python type checker and LSP Python Supply Chain Security Made Easy typing_extensions MI6 chief: We'll be as fluent in Python as we are in Russian Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/464
#463 2025 is @wrapped
Topics covered in this episode: Has the cost of building software just dropped 90%? More on Deprecation Warnings How FOSS Won and Why It Matters Should I be looking for a GitHub alternative? Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/463
#462 LinkedIn Cringe
Topics covered in this episode: Deprecations via warnings docs PyAtlas: interactive map of the top 10,000 Python packages on PyPI. Buckaroo Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/462
#461 This episdoe has a typo
Topics covered in this episode: PEP 798: Unpacking in Comprehensions Pandas 3.0.0rc0 typos A couple testing topics Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/461
#460 Overlooked Python Typing
Topics covered in this episode: Advent of Code starts today Django 6 is coming Advanced, Overlooked Python Typing codespell Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/460
#459 Inverted dependency trees
Topics covered in this episode: PEP 814 – Add frozendict built-in type From Material for MkDocs to Zensical Tach Some Python Speedups in 3.15 and 3.16 Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/459
#458 I will install Linux on your computer
Topics covered in this episode: Possibility of a new website for Django aiosqlitepool deptry browsr Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/458
#457 Tapping into HTTP
Topics covered in this episode: httptap 10 Smart Performance Hacks For Faster Python Code FastRTC Explore Python dependencies with pipdeptree and uv pip tree Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/457
#456 You're so wrong
Topics covered in this episode: The PSF has withdrawn a $1.5 million proposal to US government grant program A Binary Serializer for Pydantic Models T-strings: Python's Fifth String Formatting Technique? Cronboard Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/456
#455 Gilded Python and Beyond
Topics covered in this episode: Cyclopts: A CLI library The future of Python web services looks GIL-free Free-threaded GC Polite lazy imports for Python package maintainers Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/455
#454 It's some form of Elvish
Topics covered in this episode: djrest2 - A small and simple REST library for Django based on class-based views. Github CLI caniscrape - Know before you scrape. Analyze any website's anti-bot protections in seconds. 🐴 GittyUp Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/454
#453 Python++
Topics covered in this episode: PyPI+ uv-ship - a CLI-tool for shipping with uv How fast is 3.14? air - a new web framework built with FastAPI, Starlette, and Pydantic. Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/453
#452 pi py-day (or is it py pi-day?)
Topics covered in this episode: Python 3.14 Free-threaded Python Library Compatibility Checker Claude Sonnet 4.5 Python 3.15 will get Explicit lazy imports Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/452
#451 Databases are a Fad
Topics covered in this episode: PostgreSQL 18 Released Testing is better than DSA (Data Structures and Algorithms) Pyrefly in Cursor/PyCharm/VSCode/etc Playwright & pytest techniques that bring me joy Extras Joke See the full show notes for this episode on the website at pythonbytes.fm/451