Python Bytes

By Michael Kennedy and Calvin Hendryx-ParkerNews Technology Tech News

Python Bytes episodes

October 20#501 A Nod to Sir-Mix-Alot
Topics covered in this episode:
Nod: pause your Python program and open a notebook at any line
Polars 2.0 ships: streaming engine by default, spill-to-disk, and a row-order gotcha
django-cachalot caches your ORM queries and invalidates them for you
Towards async transactions in Django
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: Nod: pause your Python program and open a notebook at any line
Nod (Notebooks-on-Demand) is a JupyterLab extension by Eric Rawn that drops a notebook into a running Python program. Add a notebook() call anywhere, run the program with nod, and execution pauses there with a notebook containing all variables at that point in the call stack. Edits go back to the source file with a button, with cells converted to code and markdown to comments via Jupytext.
It answers the classic 'this would be easier in a notebook' debugging moment without copying snippets out to files.
Like a breakpoint with a notebook inside: the left panel walks the call stack and notebook state switches with it
Install is pip install nodpy plus nod --install-kernel, then run with nod python -m myfile
The round trip back to source is the differentiator; markdown cells become comments, cells become newlines, and notebooks are disposable since the source file recreates them
nodLog() stashes variable values across calls to explore later; the article also pitches notebook() for stepping through AI-generated code line by line
Early stage: repo from December 2025 with 16 stars and essentially one contributor, so expect rough edges
Calvin #2: Polars 2.0 ships: streaming engine by default, spill-to-disk, and a row-order gotcha
Polars 2.0 released Oct 6, 2026 (Ritchie Vink). The post says it's "not intended to be a big feature release", but it bundles a few big changes.
LazyFrame.collect now defaults to the streaming engine, with large memory and speed gains on most queries.
Breaking: streaming doesn't guarantee row order for join, group_by and unpivot. Use maintain_order=True if order matters. This is why it's a major version.
Out-of-core (spill-to-disk) is on by default. It spills at about 80% of RAM ("may need tuning") with a 64 GB default disk budget. It covers sort, window functions and many expressions. Joins and group-bys are planned, not shipped.
The new native Map dtype (Arrow MapType) replaces the old List(Struct(key, value)) reading. It comes with map.get, map.contains_key, map.len, map.keys and map.values.
Stricter by design: errors should surface up front and implicit handling of data mismatches is opt-in. collect_schema() catches schema mismatches without reading data.
SQL is now a first-class interface, with join reordering, better common-subplan elimination, and dynamic predicates/bloom filters.
Benchmarks are self-run TPC-H/TPC-DS derived numbers against DuckDB 1.5.6 and 2.0 alpha, and DataFusion 54. They are not official TPC results.
Polars was fastest on all but one benchmark. At 192 threads its constant overhead hurts small data (SF10 TPC-DS is 1.8x slower), and a fix is hoped for in the next release.
Roadmap, not released: out-of-core joins and group-bys, better scaling at high core counts, Polars Cloud, and early GeoPolars work.
Upgrade guide: https://docs.pola.rs/releases/upgrade/2/
Michael #3: django-cachalot caches your ORM queries and invalidates them for you
django-cachalot is a Django package that caches ORM queries and invalidates them automatically when the underlying data changes. Install it, add one line to INSTALLED_APPS, and read queries run through your cache backend with no code changes. The trick is that it caches whole tables, so any write flushes that table's cached queries, making it a strong fit for read-heavy sites and a poor one for frequently updated tables. It supports Django 4.2 through 6.1 and Python 3.8 to 3.14.
ORM caching that normally takes careful engineering drops in with pip install and one settings line, invalidation included.
Near-100x on joins: the README reports nearly 100x speedups for initial deployments heavy on select_related and prefetch_related.
It caches the entire table, so a hot table with frequent writes invalidates constantly; docs suggest staying under 50 modifications per minute and mixing in cache-machine for hot paths.
Setup is two lines, plus caveats: sync clocks across servers sharing one cache, and run a manage.py command after restoring a database outside Django.
Calvin #4: Towards async transactions in Django
Carlton Gibson, The Stack Report, Oct 9, 2026. It's about getting async with transaction.atomic() into Django, hoped for 6.2 and not a commitment. Status check: both PRs are still open as of Oct 19-20, with no approvals and no milestone.
The problem: asyncio tasks share one thread and so one DB connection. A slow transaction that rolls back also discards a sibling task's quick write. Today transactions go through sync_to_async, so everything runs serially on one thread.
Design: async with transaction.atomic() opens a nested ThreadSensitiveContext, which gets its own worker thread and connection. On exit the connection is cleaned up and control returns to the request's connection. Siblings keep using the request's connection in autocommit, so their writes survive a rollback.
Only the task that opened the block may nest further atomic() blocks. Any other task raises TransactionManagementError, and this is enforced.
Subtasks inside a transaction are discouraged, since the connection is serial and parallel queries gain little. If you need them, use structured concurrency (TaskGroup or an awaited gather()) so they finish before the block exits. That rule is not enforced yet, and Gibson wants Django to catch late queries before 6.2.
Caveats: each async transaction uses an extra connection for its duration, so pools may need tuning. The advice to disable CONN_MAX_AGE for async still applies.
Origin: the Django on the Med sprint in Pescara (hosted by Paolo Melchiorre). An expert panel on Day 1 included Lily Acorn, Simon Charette, Jacob Walls, and Charlie Denton and Sam Searles-Bryant of Kraken Tech (authors of django-subatomic, which is being integrated into Django). Caleb Hattingh then investigated the design and opened both PRs.
PRs: Django #22074 ("Fixed #33882 -- async with transaction.atomic") has review comments from four people, with the latest commit on Oct 19 adding a test for a late child-task query. asgiref #596 (ThreadSensitiveContext changes) had Gibson's review on Oct 19 and follow-up edits on Oct 20.
Extras
Calvin:
Jazzband is Shutting Down (announced in March), but there are alternatives like https://django-commons.org/
Treemd — nice TUI for viewing Markdown https://github.com/Epistates/treemd
Michael:
Talk Python Membership
Talk Python Visual topic guides
Quick Follow Up to Episode #499 where we shared PEP 824 None coalescing operator, we also have PEP 823 (None Aware access operators) the successor to PEP 505. - via Nick Muoh and Rafael
Free Threaded Polars, but staled on PyPI?
Joke: LGTM (Looks Good to Me) - Claude Opus 5.5 music video
38 min
October 14#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.
1 hr 9 min
October 06#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.
33 min
September 29#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…
34 min
September 23#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.
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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
27 min
September 15#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
33 min
September 08#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?
28 min
September 01#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
29 min
August 26#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
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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
42 min
August 18#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
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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.
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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
40 min

About Python Bytes

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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…