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TODAY AI BRIEF
3 things that happened while you were busy
1 GitHub is rebuilding the plumbing under every repository you use.
Developers and agents made 7.38 billion commits on GitHub in September, more than five times as many as a year earlier, while pushes rose from 0.69 billion a month to 3.35 billion. GitHub is now separating storage from compute, moving the authoritative copy of your code into Azure Blob Storage and serving reads from cheap workers that cache it. Internal benchmarks show up to 35 times higher write throughput, and the company says it is doing all of this without taking anything offline. Read GitHub's engineering post.
2 Anthropic is handing enterprises up to $50,000 to let AI review their code.
Eligible Claude Enterprise organizations can claim between $10,000 and $50,000 in promotional credits for Code Review, with the amount set by how many Claude Code seats they run. The claim window opened on October 15 and closes at the end of January 15, and credits expire 30 days after you claim them. This is Enterprise only: Team, Pro, Max and Free plans are excluded, as are organizations billed through a reseller or running GitHub Enterprise Server. Read Anthropic's terms.
3 Linus Torvalds refuses to ban AI from the Linux kernel.
Kernel developer Sasha Levin proposed dropping the Assisted-by tag that flags AI-assisted patches, after finding only about 10% of the 18,533 commits in a recent release carried it. Torvalds went further than the proposal, rejecting tool restrictions outright with the line that he does not understand the whole concept of telling people not to use AI for something, and joking that developers should be banned from compilers and made to write assembly. His position is that you are responsible for understanding your code, whatever wrote it. Read the mailing list roundup.
Bonus reads: Anthropic's engineering post on replacing Git entirely, which is the other half of today's lead story, plus GitHub's merge queue documentation if your own pushes have started queueing.
AI FORWORD
Git is being rebuilt twice, by two companies, in opposite directions
The load. Git was designed for humans typing commits. It is now absorbing machine output: 7.38 billion commits in a single month on GitHub, with Actions running 3.26 billion times, more than four times last year. One repository alone took roughly a billion requests in August.
The real bottleneck. Reading code is easy to scale because you can copy it anywhere and cache it. Writing is not. A change has to be stored and visible before the next agent builds on top of it, so everything funnels into the same narrow point, and the queue forms there.
Bet one: keep Git. GitHub is splitting storage from compute, holding the authoritative data in cloud storage and treating read capacity as disposable workers it can add during a rush. Only the branch update still needs coordination, and the rest of a push now runs in parallel. Its internal benchmarks show up to 35 times the write throughput.
Bet two: leave Git. Anthropic went the other way and replaced Git for its main codebase with Scion, moving commits, merges and history lookups onto the server. It reports running on 200 times less processor and memory than the Git mirrors it retired. Anthropic is explicit that this is not a general Git replacement.
The reframe. Most teams will never choose between these two bets, because both arrive as someone else's upgrade. What you can do is notice the symptom before it bites: pushes that take longer than they used to, and a merge queue that stops draining. That is the same signal both companies acted on, and it shows up in small repositories months before anybody calls it an infrastructure problem.
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SOCIAL SNAPSHOT
What people are actually watching and sharing
An agent takes the pager. Cognition launched Devin Oncall, which picks up production alerts, traces the likely cause and can open a tested fix for a human to approve. The company says Devin was already handling thousands of alerts a day before launch, which is its own figure.
Your flat as a menu bar. A developer built his apartment as a live 3D model that sits in the Mac menu bar and switches real lights and air conditioning, then open sourced the whole thing. It has no account and no backend of its own.
The tag that nobody used. Only about 10% of 18,533 commits in a recent kernel release carried the tag meant to flag AI assistance, which is the data behind the proposal to scrap it. Some contributors did not know it existed and others left it off deliberately.
Apple's quiet AI toolkit. MLX, Apple's machine learning framework, keeps turning up in local AI setups, including the one Meta used to show its new open model running on desk hardware. It is MIT licensed and installs with a single pip command.
Two hundred times less hardware. The detail most people missed in Anthropic's version control post: the replacement runs on 200 times less processor and memory than the Git mirrors it retired, which is a cost story as much as a speed one.
PROMPT STATION
Audit any tool before you let it near your business
Today's issue is full of free tools, and free is not the same as safe. This prompt works in Claude, ChatGPT or Gemini and turns a product page, a repository or a model card into a plain answer about where your data goes and what it would cost to leave. Run it before you connect anything to your accounts.
I am considering using [TOOL NAME] for [WHAT YOU WOULD USE IT FOR]. Here is its page or documentation: [PASTE THE TEXT OR LINK].
Answer six questions in plain language, and say clearly when the page does not tell you. Where does my data physically go, and does it leave my machine? Who can read it, and is it used for training? What does it actually cost, including the parts that are free now and priced later? What access does it want to my accounts, and what is the worst thing it could do with that? If it disappeared tomorrow, how do I get my work out? Who maintains it, and when was it last updated?
Finish with a one-line verdict in this form: safe for [THE WORK], safe only for non-sensitive work, or not worth the risk, and name the single fact that decided it.Tip: Swap [WHAT YOU WOULD USE IT FOR] with something specific, such as client invoices, customer emails or draft contracts, because the same tool can be fine for one and wrong for another. For a sharper answer, paste the licence or terms as well as the marketing page, and ask the AI to flag anywhere the two disagree.