Python Bytes Podcast Por Michael Kennedy and Calvin Hendryx-Parker capa

Python Bytes

Python Bytes

De: Michael Kennedy and Calvin Hendryx-Parker
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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 space.Copyright 2016-2026 Política e Governo
Episódios
  • #503 It’s not 85% HTML

    #503 It’s not 85% HTML
    Nov 3 2026
    Topics covered in this episode: Your GitHub language bar is lying (how to fix it)Python Typing Survey 2026: developers mix type checkers, and AI is writing the hintsFlask Vibe - Build Apps You Can UnderstandPython on my wrist: MicroPython on a PineTimeExtrasJokeWatch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk PythonConsulting from Six Feet Up Connect with the hostsMichael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: 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: Your GitHub language bar is lying (how to fix it) GitHub's language bar comes from Linguist, which counts every tracked byte in the repo. Michael's chameleon-rs project read as 75.5% HTML because committed docs-site output and test suites outweighed the Rust engine. Three .gitattributes attributes changed the story: linguist-generated for build output, linguist-vendored for an upstream test suite, and linguist-detectable=false for tests and benchmarks on both sides. The bar now shows the real mix, roughly 62% Rust and 38% Python. That bar is one of the first things people see on your repo; if generated files skew it, a few lines of .gitattributes tell the truth.The bar is byte-proportional, so a 9 MB committed docs build can make a Rust project read as mostly HTMLlinguist-generated hides build output and collapses those diffs in pull requests toolinguist-vendored covers copied-in code; linguist-detectable=false drops anything from the statschameleon-rs went from 75.5% HTML to roughly 62% Rust and 38% Python after a few lines of configTests and benchmarks were excluded symmetrically, on both the Rust and Python sides, so neither skews the ratioCheck patterns before pushing with git check-attr; GitHub recalculates a few minutes after the push Calvin #2: Python Typing Survey 2026: developers mix type checkers, and AI is writing the hints Meta's 2026 Python Typing Survey results: 1,794 responses, Aug 5 - Sep 2. Pyrefly (Meta), JetBrains, Microsoft and Astral's ty team helped write it.Typing is stable: 85.5% use hints often or always (88.0% in 2024). Which checker people use is not stable.49.9% run two or more checkers (46.0% in 2025), and 83.3% of Pyrefly users also run another one. Speed is the top reason for picking a checker (56.5%).Checkers disagree: 33.3% report inconsistencies between them, rising to 42.7% among multi-checker users.AI is writing the types: 42.5% had AI write hints, and 38.4% say types make AI code more reliable (18.5% last year).29.8% are "not sure" whether types help AI-assisted coding, about 4x the uncertainty on any other benefit.Help sources flipped: Stack Overflow 46.4% to 22.9% and LLM chat 26.0% to 41.7%, 2024 to 2026.Top pain is still missing library types (65.1%; Django, SQLAlchemy, pandas, NumPy). The most-wanted feature is better Callable syntax (45.7%). Raw data zip is linked. Michael #3: Flask Vibe - Build Apps You Can Understand Flask Vibe is a new tutorial site from Frederick Tubiermont, a developer who started coding at 47 and builds with Flask, PostgreSQL, raw SQL, and vanilla JS. The pitch: AI assistants generate more reliable, easier-to-verify code when the stack has no build step, no ORM, and no hydration. It ships a manifesto, tutorials on modular vanilla JS and AI-friendly coding patterns, and a Flask plus psycopg2 CRUD walkthrough. For vibe coders burned by Next.js defaults on Lovable or Replit, this is a concrete, opinionated case that simpler Python stacks pair better with AI-generated code.Claims a structural 1.8x LCP win over Next.js on the same dashboard app: 905ms vs 1.66s, both scoring Lighthouse 100Tutorials cover modular vanilla JS, AI-friendly coding patterns, and Flask CRUD with raw psycopg2 SQL and no ORMCreator started Python at 47 in 2022 and runs the site on the stack it teaches; the homepage is 42 KB with Lighthouse 100Solo London-based project (adsyme Ltd), launched via an r/flask self-post in February 2026; early, opinionated, and the benchmark leans on one self-run comparison with its methodology published on site Calvin #4: Python on my wrist: MicroPython on a PineTime Michiel (Python Leiden user group) runs MicroPython on a PineTime, Pine64's ~$26 open smartwatch: 64 MHz Cortex-M4, 64 KB RAM, 512 KB flash.He revived wasp-os, a dormant MicroPython watch OS stuck on a 2019 MicroPython fork whose maintainer is asking for new maintainers (issue #536).The build is now one command with uv. Bluetooth flashing uses bleak instead of the deprecated gatttool, cutting update time from over 8 minutes to about 3.5.Free RAM at boot went from 7.1 KB to 12.4 KB by using the flash chip's real 4 KB blocks. He also ...
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    30 minutos
  • #502 Introducing chameleon-rs

    #502 Introducing chameleon-rs
    Oct 27 2026
    Topics covered in this episode: PHP wasn’t the only problem last weekchameleon-rsBringing uv and ty togetherStlite runs Streamlit apps entirely in the browserExtrasJokeWatch on YouTube About the show Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: 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: PHP wasn’t the only problem last week Three security incidents in one week, each at a different layer: CA trust (eMudhra), the release pipeline (LiteLLM) and a C decoder (Brotli). All three have fixes released.eMudhra CA: the keys of a compromised eMudhra intermediate CA are now distrusted by CPython. 3.15.1, 3.14.9 and 3.13.17 shipped Oct 21. 3.15.2, 3.14.10, 3.13.18, 3.12.16 and 3.11.18 followed Oct 26 (CVE-2026-115557) and also fix a tarfile filter bypass. Python's post says pip operations are safe even before pip is upgraded.eMudhra, tooling: pip 26.2.2 bundles certifi 2026.10.19, which removes the root. uv 0.13.4 (GHSA-hq85-5cpj-mr62) and 0.13.5 (rustls-webpki 0.103.16) are the uv fixes. After upgrading uv, run uv python upgrade. Existing venvs need pip upgraded by hand, and the 3.9 and 3.10 builds still bundle a pip that trusts the root. Anyone holding those intermediate keys can mint certificates for any domain that an unpatched client will accept as valid.That enables man-in-the-middle attacks: reading or altering HTTPS traffic without a certificate warning.The risk is highest where the attacker can sit on the network path, such as hostile Wi-Fi, a compromised router or ISP, or a cloud network position.For Python specifically, the exposed traffic would be package downloads from PyPI, API calls with credentials or tokens, and webhooks. A tampered package download is the worst case.Containers and old venvs that nobody rebuilds are where this lingers. uv says its Python 3.9 and 3.10 builds still bundle a pip that trusts the root, and existing venvs don't pick up the fix automatically.LiteLLM 1.107.0: malicious wheels hit PyPI on Oct 21. All seven wheels were live from about 11:40 to 13:23 UTC, with roughly 49k downloads. The Docker images were also infected. It's the second LiteLLM compromise this year, after 1.82.7 and 1.82.8 in March.LiteLLM payload: cook.pth runs on every Python start, so nothing has to import litellm. It steals SSH, cloud, browser, CI and AI-tool credentials, and an hourly loader runs remote code. The clean pin is litellm==1.106.4. Kill the loader first, rebuild rather than repair, then rotate everything the host could reach.LiteLLM root cause: a master key committed to a public repo on May 27 was never revoked. On Oct 10 it yielded an org owner's GitHub CLI token. The attacker then changed the trusted release workflow to build from a lookalike Berri-AI/litellm repo. BerriAI's first explanation (tokens in logs) was wrong, and the Oct 23 report supersedes it.Broccoli2Shell: CVE-2026-111497 is an out-of-bounds write in Google's Brotli C decoder. It affects the Python Brotli and brotlicffi packages 1.0.3 through 1.2.0(.x). Upgrade to Brotli 1.2.1 or brotlicffi 1.2.1.0. The CVE was published Oct 22 and rated 9.2 Critical by GitHub.Broccoli2Shell workaround: if you can't upgrade, uninstall both packages. urllib3, requests and aiohttp then fall back to gzip and deflate. Also reject Content-Encoding: br request bodies at the proxy or WAF. Sourceshttps://blog.python.org/2026/10/python-3152-31410-31318-31216-31118/https://www.stepsecurity.io/blog/litellm-1-107-0-compromised-pypi-backdoored-wheels-credential-stealerhttps://broccoli2shell.com/ Michael #2: chameleon-rs Chameleon templates, much faster than before (up to 70x faster on start)Modernized: A Rust engine that runs your Chameleon templates unchanged with async rendering, partials (think HTMX), MarkdownBuilt-in framework support: Flask, Quart, FastAPI, Starlette, Pyramid, Robyn and Litestar integrations.A compatibility shim for testing out quickly: chameleon_rs.compat.install()AI agent support via docs (every doc page has a .md extension variant, eg FastAPI docs) and the skill is installable or downloadable.Byte-for-byte identical on every page for talkpython.fm and training.talkpython.fmPasses 100% of the unit tests of the original chameleon project. See compatibility page.Natively supports my chameleon-flask, fastapi-chameleon, and chameleon-partials libraries.Swapping Talk Python Training to chameleon-rs made it 20% fast (end-to-end, not just template rendering)! Calvin #3: Bringing uv and ty together Status: Astral (Sara Sklenka, Oct 13) announced "an experimental integration between uv and ty, available in preview." They ask for feedback, and say "in parallel, we're ...
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    34 minutos
  • #501 A Nod to Sir-Mix-Alot

    #501 A Nod to Sir-Mix-Alot
    Oct 20 2026
    Topics covered in this episode: Nod: pause your Python program and open a notebook at any linePolars 2.0 ships: streaming engine by default, spill-to-disk, and a row-order gotchadjango-cachalot caches your ORM queries and invalidates them for youTowards async transactions in DjangoExtrasJokeWatch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk PythonConsulting from Six Feet Up Connect with the hostsMichael: Mastodon / BlueSky / X / LinkedInCalvin: Mastodon / BlueSky / X / LinkedInShow: Mastodon / BlueSky / XJoin 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 itInstall is pip install nodpy plus nod --install-kernel, then run with nod python -m myfileThe round trip back to source is the differentiator; markdown cells become comments, cells become newlines, and notebooks are disposable since the source file recreates themnodLog() stashes variable values across calls to explore later; the article also pitches notebook() for stepping through AI-generated code line by lineEarly 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 ...
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    38 minutos
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