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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. Language: en-us Genres: News, Tech News, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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#486 underscore-underscore-ghost-emoji
Episode 486
Tuesday, 30 June, 2026
Topics covered in this episode: Free-threaded Python: past, present, and future django-admin-site-search Qwen 3.6 27B is the sweet spot for local development A large batch of PEPs are finalized Extras Joke Watch on YouTube Show Intro Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Free-threaded Python: past, present, and future The GIL has prevented true multi-threaded parallelism in CPython since the beginning — multiple past attempts to remove it failed on performance grounds Sam Gross at Meta finally solved it; his work became PEP 703 and ships as free-threaded CPython today Python 3.13 was experimental with 20–40% single-threaded slowdown; 3.14 brought that to 0–10% Python 3.15 (October 2026) delivers a unified ABI — one extension binary works on both GIL and free-threaded builds Already >50% of the top PyPI binary wheels support free threading Wouters predicts free-threaded becomes the default between 3.16–3.20 (2027–2031), with the GIL eventually disappearing next decade Michael #2: django-admin-site-search via Adam Parkin A global/site search modal for the Django admin, by Ahmed Aljawahiry. Hit cmd+k anywhere in the admin and you get a command-palette-style search window, kind of like the one in VS Code. It doesn't just search one model's list page. It searches your entire site in one box: App labels Model labels and field attributes Actual model instances (your data) Two ways to search the instances: model_char_fields (the default): runs an __icontains across every CharField (and subclasses) on the model. Zero config, works out of the box. admin_search_fields: defers to each ModelAdmin's existing get_search_results(), so it respects the search_fields you've already set up. The part I like: it's permission-aware out of the box. Users only see results for the apps and models they actually have view permission on, so you're not leaking anything through search. Results appear as you type, with throttling/debouncing so you're not hammering the server on every keystroke, and it's full keyboard nav: cmd+k to open, up/down to move, enter to go. It's responsive, does dark and light mode, and it pulls Django's built-in admin CSS variables so it just matches whatever admin theme you're running. Under the hood it's Alpine.js, but bundled into static so there's no external CDN dependency. Setup is about what you'd expect: pip install django-admin-site-search, add it to INSTALLED_APPS, mix the AdminSiteSearchView into your AdminSite, and drop a few template includes into base_site.html. Supports Python 3.8 through 3.14 and Django 3.2 through 6.0, MIT licensed, and everything is overridable if you want to skip certain models, add TextField matching, etc. Calvin #3: Qwen 3.6 27B is the sweet spot for local development Qwen 3.6 27B is being called the first local model that genuinely competes as a general-purpose intelligence — benchmarks put it at roughly mid-2025 frontier level (comparable to GPT-5 / Claude Sonnet 4.5) Runs locally via llama.cpp; on an M5 MacBook Max with 8-bit quantization + multi-token prediction, it hits ~32 tokens/sec using ~42GB RAM 4-bit quantization gets it under 18GB, runnable on 32GB devices; Nvidia RTX cards run it even faster The dense 27B is recommended over the faster MoE 35B A3B — author prefers higher quality output over raw speed Privacy and reliability are the pitch: fine-tunable, can't be taken down, suitable for sensitive/proprietary data Author sees this as a stepping stone — frontier open-weight models like GLM 5.2 are now locally runnable with company-grade hardware, and smarter-still local models are coming Michael #4: A large batch of PEPs are finalized A bunch of PEPs went from accepted to final. 668, 687, 691, 699, 701, 703, 728, 770, 773, 829 But this wasn’t them making their way into CPython. It’s an admin sorta thing. (Thanks PyCoders) See the commit. Extras Calvin: More fun bling for your terminal this time - https://charm.land/ Michael: Follow up from pls, What the pls? Thanks Pito. Joke: BEMoji A production-grade utility and component framework built entirely on emoji class names via Jeff Triplett





