After a week’s delay, the new version of Python, v3.15, has been released. With it comes a proper profiler, an experimental just-in-time compiler, and a tightening of dictionary structures. It also offers a time-saving Lazy Import option for programs to not load modules until needed, the implementation of which ironically halted this release until a few last-minute regressions were sorted. But of all these new delicacies spread before the Pythonistas, the data wranglers of the bunch may most appreciate default UTF-8 support across all platforms. And when we say “all platforms,” we’re really just throwing shade at Windows (as usual). Python has defaulted to macOS and Linux Python environments for some time now. It’s a small but important update. About half of all Python developers use the language for processing and analyzing data, and about 58 percent of Python users build on Windows, according to various Python Software Foundation surveys. Party crashers Earlier versions of Python defaulted to the host system's locale encoding. This led to much consternation for Windows developers whose scripts opened UTF-8-encoded data sets, only to have non-ASCII characters corrupt or crash the program. Culprits could be as innocuous as a smart quote or a fancy apostrophe. Managed by The Unicode Consortium, with the help of the IETF and ISO, UTF-8 is an 8-bit binary encoding of the Unicode catalog of all the world’s written symbols. ASCII offered only 128 characters to cover the world’s knowledge, but UTF-8 provides over 1.1 million slots, which is plenty of room for smart quotes, emojis, musical and math notations, all the Chinese dialects, and even a few extinct languages (for research purposes). There’s also UTF-16 and UTF-32, but UTF-8’s combination of efficiency and inclusiveness made it the default for the Web and most modern operating systems. By the time Python Enhancement Proposal 681 was proposed in 2022 to make UTF-8 the preferred encoding, the language was already behind the times. That year, Java settled into UTF-8 with JDK 18, and Ruby 3.0 defaulted to the encoding scheme in 2020. Today all the cool languages and runtimes pledge allegiance to UTF-8, including Rust, Go, and Node.js (JavaScript itself prefers UTF-16 for internal encoding). Most code editors, from Visual Studio Code to Windows Notepad, prefer UTF-8. Data sharing and configuration formats such as JSON, TOML, and YAML all require 8-bit encodings. Python had long understood UTF-8, but programmers had to put a flag in their code alerting the language to use it – if they knew they had to do so at all. Many didn’t and were left scratching their heads over why their data processing scripts worked just fine on MacOS or Linux but then would flame out on Windows (or some other obscure system that defaulted to a C/POSIX locale). Programmers love puzzles To acquaint developers with the new aspects of the language, longtime Python programmer Barry Warsaw (AKA “Uncle Barry”) created a terminal-based adventure game called “whatsnewt” that offers 18 puzzles, each one built on a new feature found in Python 3.15, including a puzzle for UTF-8 support, “The Encoding Tower of Babel.” Once you've downloaded Python 3.15, do a “pip install whatsnewt” and then run the game on the Python command line. It even works on Windows, we hear. ®

Anthropic's Claude models have been emitting so much code that the company's coding infrastructure hasn't been able to keep up. It's a symptom of the AI coding imperative and all-you-can-eat inference. Microsoft's GitHub in April published a blog post attributing recent availability problems to the exponential growth in pull requests and commits. The code flood has been noted by academic researchers, among others. For Anthropic, writing code is no longer a productivity bottleneck. Instead, the problem is managing the code after it's written, via source control, the build system, and continuous integration. "In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day," the biz said in a blog post on Tuesday. "We don’t expect this rate of growth to slow down anytime soon; the latent demand for merging code is practically infinite, and every bottleneck we remove immediately leads to a further increase in the amount of code we produce." After several months of model prompting and five months of migration, the result is a homemade version control system called Scion, a monorepo scheme that sounds a bit like Subversion, Apache's centralized version control system. Anthropic is not the first organization to look beyond Git's distributed architecture toward centralized source control. Google uses Piper, a monorepo system (along with Gerrit for Git-based code reviews). Microsoft has Virtual File System for Git, formerly GVFS, to help overcome some of the scaling issues with Git. Meta has Sapling. But as the AI biz notes, Claude's code writing ability both created the situation and offered a way out through the creation of scalable code infrastructure tools – in addition to Scion, Claude helped conjure Antique (a code review interface), a code search tool, and a source control IAM system. The decentralized approach embodied by Git is the right one for most code repos, according to Anthropic. But the company's main branch weighs in at about 100 GB, with a compressed working copy at more than a quarter of that. "We perform on the order of 25 million code checkouts per day for CI, before counting developer environments, agent sandboxes, and research jobs," Anthropic said. "Fetched naively, that would be over 600 PB of git traffic per day." Even with the tools Git provides for working with portions of a codebase, Anthropic says operations proved challenging. Scion has been designed to meet the needs of AI agents, so the client leaves much of the work to the server; the source control queries are exposed via API so internal tools can respond without needing a local repo, and there's less synchronization but more need for server speed. "In Scion, the unit of work is a 'changeset,' an entry in a write-ahead log," Anthropic explains. "A changeset stores most of what you’d expect from a commit in git. But where a git commit stores a pointer to the tree, a changeset stores a list of the files that changed relative to its first parent, along with content-addressed pointers to the compressed file contents in blob storage." Work on Scion started in January, and when Claude Mythos Preview became available internally, Anthropic's code coaxers told the model to rewrite an early Git-to-Scion ingestion pipeline. The much-hyped model produced a version that used 20 times less memory and ran four times as fast. When cost is no object thanks to ample venture funding, that sort of token spending just makes sense. Anthropic's Claude jockeys appear to be taking their obsolescence in stride. According to the AI biz, company developers who worked on Scion agree that Claude writes better code than they do. But they still possess a higher-level understanding that gives them a role as naysayers. "Very often, we found that the role of the engineers was to tell Claude 'no,'" Anthropic said. "Agents will sometimes violate core system constraints to satisfy a pattern that should never have been allowed in the first place." "Just say no." As a slogan, it's timeless. And it's never been more relevant. Anthropic says its bespoke system isn't right for general use. There will be no open source release. Nonetheless, the company contends that its own code scaling problems will become commonplace in other organizations over the next few months, so it wanted to share its approach – having ample funding to pay for writing custom infrastructure tools. ®