2025-06-25 06:01:05
Anthropic wins a major fair use victory for AI — but it’s still in trouble for stealing books
Major USA legal news for the AI industry today. Judge William Alsup released a "summary judgement" (a legal decision that results in some parts of a case skipping a trial) in a lawsuit between five authors and Anthropic concerning the use of their books in training data.The judgement itself is a very readable 32 page PDF, and contains all sorts of interesting behind-the-scenes details about how Anthropic trained their models.
The facts of the complaint go back to the very beginning of the company. Anthropic was founded by a group of ex-OpenAI researchers in February 2021. According to the judgement:
So, in January or February 2021, another Anthropic cofounder, Ben Mann, downloaded Books3, an online library of 196,640 books that he knew had been assembled from unauthorized copies of copyrighted books — that is, pirated. Anthropic's next pirated acquisitions involved downloading distributed, reshared copies of other pirate libraries. In June 2021, Mann downloaded in this way at least five million copies of books from Library Genesis, or LibGen, which he knew had been pirated. And, in July 2022, Anthropic likewise downloaded at least two million copies of books from the Pirate Library Mirror, or PiLiMi, which Anthropic knew had been pirated.
Books3 was also listed as part of the training data for Meta's LLaMA training data!
Anthropic apparently used these sources of data to help build an internal "research library" of content that they then filtered and annotated and used in training runs.
Books turned out to be a very valuable component of the "data mix" to train strong models. By 2024 Anthropic had a new approach to collecting them: purchase and scan millions of print books!
To find a new way to get books, in February 2024, Anthropic hired the former head of partnerships for Google's book-scanning project, Tom Turvey. He was tasked with obtaining "all the books in the world" while still avoiding as much "legal/practice/business slog" as possible (Opp. Exhs. 21, 27). [...] Turvey and his team emailed major book distributors and retailers about bulk-purchasing their print copies for the AI firm's "research library" (Opp. Exh. 22 at 145; Opp. Exh. 31 at -035589). Anthropic spent many millions of dollars to purchase millions of print books, often in used condition. Then, its service providers stripped the books from their bindings, cut their pages to size, and scanned the books into digital form — discarding the paper originals. Each print book resulted in a PDF copy containing images of the scanned pages with machine-readable text (including front and back cover scans for softcover books).
The summary judgement found that these scanned books did fall under fair use, since they were transformative versions of the works and were not shared outside of the company. The downloaded ebooks did not count as fair use, and it looks like those will be the subject of a forthcoming jury trial.
Here's that section of the decision:
Before buying books for its central library, Anthropic downloaded over seven million pirated copies of books, paid nothing, and kept these pirated copies in its library even after deciding it would not use them to train its AI (at all or ever again). Authors argue Anthropic should have paid for these pirated library copies (e.g, Tr. 24–25, 65; Opp. 7, 12–13). This order agrees.
The most important aspect of this case is the question of whether training an LLM on unlicensed data counts as "fair use". The judge found that it did. The argument for why takes up several pages of the document but this seems like a key point:
Everyone reads texts, too, then writes new texts. They may need to pay for getting their hands on a text in the first instance. But to make anyone pay specifically for the use of a book each time they read it, each time they recall it from memory, each time they later draw upon it when writing new things in new ways would be unthinkable. For centuries, we have read and re-read books. We have admired, memorized, and internalized their sweeping themes, their substantive points, and their stylistic solutions to recurring writing problems.
The judge who signed this summary judgement is an interesting character: William Haskell Alsup (yes, his middle name really is Haskell) presided over jury trials for Oracle America, Inc. v. Google, Inc in 2012 and 2016 where he famously used his hobbyist BASIC programming experience to challenge claims made by lawyers in the case.
Tags: law, ai, generative-ai, llms, anthropic, training-data, ai-ethics
2025-06-24 02:42:02
Here's a fascinating new entrant into the AI-assisted-programming / coding-agents space by Fly.io, introduced on their blog in Phoenix.new – The Remote AI Runtime for Phoenix: describe an app in a prompt, get a full Phoenix application, backed by SQLite and running on Fly's hosting platform. The official Phoenix.new YouTube launch video is a good way to get a sense for what this does.
First, some background. Phoenix is an open source web framework for Elixir, the Ruby-like language that compiles to Erlang's BEAM bytecode and runs on top of the highly concurrent Erlang runtime. The signature feature of the framework is Phoenix LiveView, a toolkit for building realtime interfaces through streaming diffs to server-side HTML over a WebSocket connection.
Phoenix was created by Chris McCord 11 years ago, and Chris joined Fly nearly four years ago. Phoenix.new is his latest project.
Phoenix LiveView is a really great fit for Fly's geographically distributed application serving infrastructure. Fly co-founder Kurt Mackey wrote about that in April 2021, before they had hired Chris, describing how LiveView benefits from low latency by "moving app processes close to users" - something Fly has been designed to help with from the start.
There's one major challenge though: Elixir is still a niche programming language, which means the number of people out there who are ready to spin up a new Phoenix app has always been artificially limited.
Fly's solution? Get LLMs to shave that learning curve down to almost nothing.
Phoenix.new is a prompt-driven application development platform. You describe what you want to build, then watch as an LLM-powered coding agent writes, tests and iterates on code to help achieve that goal. It's in the same broad category as Lovable, v0.dev and and Val Town's Townie.
One of the most important problems to solve with coding agents is to give them a robust sandbox where they can run code without breaking things outside of that space. Fly, at their heart, are a sandboxing company - their Fly Machines product makes it trivial to spin up a new sandboxed VM in just a few seconds. I'm building Datasette Cloud on Fly for exactly that reason.
I tried out Phoenix.new with the following starter prompt:
A notebook application. Notes are rich text, using a nice visual markdown editors. The first line of a note becomes the title. Notes have a URL that is a 6 character random text string - thise can be used to link notes to each other by dropping in an @ sign and that text string. A hash and then text becomes a tag, clicking a tag goes to a page with that tag on. Notes are all in SQLite FTS so they can be searched with a search tool.
Watching it work was fascinating. It provides a full IDE-style environment in the browser, running a dedicated container and with a forwarded port to allow you to interact with the running application while it is being built.
It spun up a plan.md, planned out the approach and started working through the code. Most interestingly, it constantly tested the application it was building using a testing tool that looked similar to my own shot-scraper. Here's an example of the output from that:
I can see the issue! The page is still showing the "Welcome to your Notebook" message instead of the form. Let me wait a moment for the JavaScript to execute and then check if the form appeared.
$ web http://localhost:4000 --js "setTimeout(() => { document.querySelector('button[phx-click=\"start_creating\"]').click(); }, 500); setTimeout(() => { console.log('Form visible?', !!document.querySelector('textarea')); console.log('Creating note state:', document.body.innerHTML.includes('Create New Note')); }, 2000);"
I'm used to coding agents that write and execute their own tests, but this is the most impressive example I've seen of a coding agent that actively interacts with and tests the live application while it is iterating on the code.
Most interestingly of all, since LiveView is a realtime framework, I could interact directly with the application at the same time as the machine was writing and testing it, seeing any changes it made instantly in my browser!
How did it do? It got there in the end, but with hindsight my initial prompt was a little too complex: I had to rescue it after it got stuck in an error loop trying to implement SQLite full-text search. Just like a project you build yourself it turns out to work much better if you start with the simplest possible version of the application and then iterate on it to add additional features.
One of my favorite details is how Git integration works. Phoenix.new commits constantly as it works, and a menu option for "Clone X to your local computer" then gives you a command that looks like this:
git clone "https://phoenix.new/git/WENQLj...big-random-string...VHFW/$RANDOM/notebook"
Run that locally to get a full copy of the repo! I ran the following to push it to a new repository in my GitHub account:
git remote add github https://github.com/simonw/phoenix-new-notebook.git
git push -u github mainYou can see the code (and the commit history) in my simonw/phoenix-new-notebook repo.
My initial experiments with Phoenix.new were very much vibe coding - I interacted with the resulting application but didn't pay a great deal of attention to the code that was being written, despite it being presented to me in an IDE that made it very easy to review what was going on.
As a result, I didn't learn much about the low-level syntax details of how Phoenix and Elixir work. I did however get a strong feel for the shape of Elixir and Phoenix at a high level as the code danced past my eye.
It turns out having an LLM write an application in front of you is a great way to start building understanding of what a framework can do.
It’s almost like watching a YouTube livestream of an experienced developer speed running building an application, except that app is exactly what you asked them to build and you can interject and send them in a new direction at any moment.
Chris's announcement included this note:
At this point you might be wondering – can I just ask it to build a Rails app? Or an Expo React Native app? Or Svelte? Or Go?
Yes, you can.
Our system prompt is tuned for Phoenix today, but all languages you care about are already installed. We’re still figuring out where to take this, but adding new languages and frameworks definitely ranks highly in my plans.
The browser-based IDE includes a terminal, and I checked and python3 and python3 -m pip install datasette work there already.
If Fly do evolve this into a framework-agnostic tool for building web apps they'll probably need to rebrand it from Phoenix.new to something a bit more generic!
Phoenix.new is currently priced as a $20/month subscription. Val Town recently switched the pricing for their similar Townie assistant from a subscription to to pay per use, presumably because for many users this kind of tool is something they would only use intermittently, although given the capabilities of Phoenix.new it's possible it could become a monthly driver for people, especially as it expands out to cover more frameworks.
Fly sponsor some of our work on Datasette Cloud (see disclosures), but this article is not sponsored content and Fly did not request or review this post.
Tags: erlang, sqlite, ai, fly, generative-ai, llms, ai-agents, vibe-coding, ai-assisted-search, coding-agents
2025-06-24 02:06:02
I've added a Disclosures section to my about page, listing my various sources of income and the companies that directly sponsor my work or have supported it in the recent past.
I do not receive any compensation writing about specific topics on this blog - no sponsored content! I plan to continue this policy. If I ever change this I will disclose that both here and in the post itself. [...]
I see my credibility as one of my most valuable assets, so it's important to be transparent about how financial interests may influence my writing here.
I took inspiration from Molly White's disclosures page.
Tags: blogging, molly-white
2025-06-22 23:28:46
So you can think really big thoughts and the leverage of having those big thoughts has just suddenly expanded enormously. I had this tweet two years ago where I said "90% of my skills just went to zero dollars and 10% of my skills just went up 1000x". And this is exactly what I'm talking about - having a vision, being able to set milestones towards that vision, keeping track of a design to maintain or control the levels of complexity as you go forward. Those are hugely leveraged skills now compared to knowing where to put the ampersands and the stars and the brackets in Rust.
— Kent Beck, interview with Gergely Orosz
Tags: gergely-orosz, ai-assisted-programming, ai, careers
2025-06-22 07:22:45
My First Open Source AI Generated Library
Armin Ronacher had Claude and Claude Code do almost all of the work in building, testing, packaging and publishing a new Python library based on his design:
- It wrote ~1100 lines of code for the parser
- It wrote ~1000 lines of tests
- It configured the entire Python package, CI, PyPI publishing
- Generated a README, drafted a changelog, designed a logo, made it theme-aware
- Did multiple refactorings to make me happier
The project? sloppy-xml-py, a lax XML parser (and violation of everything the XML Working Group hold sacred) which ironically is necessary because LLMs themselves frequently output "XML" that includes validation errors.
Claude's SVG logo design is actually pretty decent, turns out it can draw more than just bad pelicans!
I think experiments like this are a really valuable way to explore the capabilities of these models. Armin's conclusion:
This was an experiment to see how far I could get with minimal manual effort, and to unstick myself from an annoying blocker. The result is good enough for my immediate use case and I also felt good enough to publish it to PyPI in case someone else has the same problem.
Treat it as a curious side project which says more about what's possible today than what's necessarily advisable.
I'd like to present a slightly different conclusion here. The most interesting thing about this project is that the code is good.
My criteria for good code these days is the following:
sloppy-xml-py fits all of those criteria. It's useful, well defined, the code is readable with just about the right level of comments, everything is tested, the documentation explains everything I need to know, and it's been shipped to PyPI.
I'd be proud to have written this myself.
This example is not an argument for replacing programmers with LLMs. The code is good because Armin is an expert programmer who stayed in full control throughout the process. As I wrote the other day, a skilled individual with both deep domain understanding and deep understanding of the capabilities of the agent.
Via @mitsuhiko.at
Tags: armin-ronacher, open-source, python, xml, ai, generative-ai, llms, ai-assisted-programming, claude, claude-code
2025-06-22 02:31:56
Microsoft released a new text editor! Edit is a terminal editor - similar to Vim or nano - that's designed to ship with Windows 11 but is open source, written in Rust and supported across other platforms as well.
Edit is a small, lightweight text editor. It is less than 250kB, which allows it to keep a small footprint in the Windows 11 image.
The microsoft/edit GitHub releases page currently has pre-compiled binaries for Windows and Linux, but they didn't have one for macOS.
(They do have build instructions using Cargo if you want to compile from source.)
I decided to try and get their released binary working on my Mac using Docker. One thing lead to another, and I've now built and shipped a container to the GitHub Container Registry that anyone with Docker on Apple silicon can try out like this:
docker run --platform linux/arm64 \
-it --rm \
-v $(pwd):/workspace \
ghcr.io/simonw/alpine-edit
Running that command will download a 9.59MB container image and start Edit running against the files in your current directory. Hit Ctrl+Q or use File -> Exit (the mouse works too) to quit the editor and terminate the container.
Claude 4 has a training cut-off date of March 2025, so it was able to guide me through almost everything even down to which page I should go to in GitHub to create an access token with permission to publish to the registry!
I wrote up a new TIL on Publishing a Docker container for Microsoft Edit to the GitHub Container Registry with a revised and condensed version of everything I learned today.
Tags: github, microsoft, ai, docker, generative-ai, llms, ai-assisted-programming, anthropic, claude, claude-4
