You write this.name = 'Dwayne' in a plain class with no decorators, no base class and no setState, and the page updates. There’s no digest loop running in the background and no virtual DOM being diffed. Aurelia found out about that assignment the moment it happened. It does that with a handful of mechanisms, and the same mechanisms explain every binding that refuses to update.
This post walks through each one, using the @aurelia/runtime source as the map, and finishes with the changes Aurelia can’t see and what to do about them.
Azure Inventory is a .NET 10 Blazor Server application (using Radzen components) that signs users in with Microsoft Entra ID , inventories the Azure resources they can see, analyzes Web App , Function App , and Container App configuration to detect connections, builds an in-memory knowledge graph , visualizes it with vis-network , and answers questions through an AI chat backed by graph tools and a self-hosted, read-only Azure MCP Server . In this article we will wal
Cassidy and Andrea are joined by Sameen Karim, Senior Product Manager at GitHub, to dig into stacked pull requests, GitHub's newly launched (and long-requested) way to break large changes into a linked chain of small, reviewable PRs. Sameen pulls back the curtain on just how deep this went: every time a PR updates, GitHub runs a live "ghost merge" simulation behind the scenes to check whether it's mergeable, and stacking meant multiplying that complexity across up to 100 linked PRs at once. The team had to rethink core assumptions about how a PR's base branch relates to its actual merge target, rewrite parts of the merge box that hadn't been touched in over a decade, build an entirely new asynchronous merge path, and close a code owners loophole that could otherwise let people sneak changes past review. Sameen also talks about "agent-driven testing," where GitHub ran swarms of agents around the clock trying to break the feature, how teams like Vercel, Nuxt, and the React team are already adopting stacks, and what's next: support for stacks across forks. The episode closes with three open source picks: Sarah Drasner's visual code explainers, the AGENTS.md standard, and the open source gh-stack CLI Sameen built (and rewrote in Go) to manage stacks locally.
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.
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
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.
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.
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
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.
Apple held its "Welcome Home" event, debuting Apple HomeView and Apple Home Collection, a lineup of new smart home accessories in partnership with LG. Apple also unveiled its Apple TV 4K. And will Apple finally debut its Touchscreen MacBook Pro later this month?
Meet Apple HomeView, a new way to enjoy entertainment and manage the home.
Introducing Apple Home Collection, a new lineup of smart home accessories.
Apple unveils the new Apple TV 4K, supercharged by Siri AI.
Apple set to debut touch-screen MacBook and new iPad Mini in late October.
You can now start the iPhone Duo pre-order process.
iPhone Duo has a book-like two-page Quick Look layout.
Apple discloses deal to hire team and license tech from personalized podcast startup Huxe.
Apple may have to officially support Apple Intelligence removal on Macs to protect sales.
Tim Cook, Elton John, King Charles launch Apple x King's Trust Creative Labs.
'Remnants' – Ti West directed the first immersive horror movie for the Apple Vision Pro.
After a 10-year redemption arc, No Man's Sky is coming to the iPhone and iPad.
Picks of the Week
Christina's Pick: Sprout Pi
Jason's Pick: HA Green + Claude
Leo's Pick deskflow/desktflow
Andy's Pick: It's the Great Pumpkin, Charlie Brown.
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Turning on every tool in M365 is rarely a successful strategy - so how do you choose? Richard chats with Joy Apple about her work as a consultant helping organizations use the huge array of products in the M365 universe. Joy talks about meeting people where they are - understanding how they currently work, and then finding tools that can best amplify their productivity. Collaboration tools are a huge part of that story - finding ways to work together is always a challenge. And that's one of the many reasons there are so many tools in M365... It's all about addressing what people want to do!
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