Over time (~15 years in law and public policy in US and EU) I've come to view that kind of project as something worthwhile, and interesting, but not something that will meaningfully change how laws are created and understood. In the US, as a mixed but substantially common law system, the text of the US Code, which in the LawVM model is the tree, can remain fixed, but the interpretation of that text will still vary over time. (Same syntax, different semantics, which feels like an AI-ism as I write it.)

A couple examples:

I have worked in competition policy. Section 2 of the Sherman Antitrust Act has read the same since 1890 but it's operated across structuralist and consumer-welfare regimes, and the outcomes in Standard Oil, AT&T, MSFT, and more recent cases have all been shaped by those regimes.

US constitutional law may be an even better example of the issue: Article I, sec. 8, cl. 3 (the commerce clause) has not been amended since 1789, but it has expanded and contracted and been reinterpreted over time (most recently with NFIB v Sebelius).

Another example of the problem with a LawVM like system making "the law in force [...] knowable" is US overturning of Chevron deference on 28 June 2024.

Basically, the day before, courts were expected to defer to expert agencies, constrained by the Administrative Procedures Act, in their interpretation of legislation. The day after, courts were expected to make an independent assessment. All sorts of legislative texts were unchanged, but their meaning shifted dramatically: Clean Air Act, Communications Act, etc.

There are a bunch of other issues in figuring out "what the law is", like agency rulemakings in the CFR, and then instruments which sit below those, including sub-regulatory guidance, prosecutorial discretion, etc. Things which fall into this bucket:

- DOJ pot enforcement

- SEC no-action letters (letters which say in effect "we won't try to enforce if you do X")

Altogether, my thinking of this shifted to believing that the real thing that needs to be modeled is not the US Code as a tree serialization format, but the interpretative algorithm (the things lawyers and courts do) that translates that serialized tree into decisions. That interpretive algorithm has a lot of inputs, one which is the serialized tree, but also a bunch of other stuff.

As a final note, I will add that the ambiguity in legislative text is often a feature, not a bug. I have worked on legislative text that was intentionally crafted to provide different plausible interpretations to different coalition members both in the immediate context and in the long term.

In summary: projects like this are good and admirable; they're likely to have more direct utility in jurisdictions on one extreme of the civil law spectrum (i.e. not the US); and in all jurisdictions there likely needs to be an interpretive mechanism that sits above the representation of legal text which has more inputs if the goal is to model what the law is at any given time.

- amber.dev

- quarry.sh

You’ll probably need to play with gTLDs to find something that works.

Can also echo “scm” from fossil’s domain:

- amberscm.dev

Along with useX.com, Xhq.com, etc., patterns.

Of the two you have listed, I’d choose fossilforge, but would vote for an alternative TLD since .io has an expected meaning coming from GitHub.

I read the original chapters online but appreciate this format.

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> That's not an incremental improvement. That's a category change in how Claude Code navigates your code.

I don’t know anything about the human(s) behind this project, assuming there are any, and intend no malice towards them, but when I encounter language like this, it just kills my enthusiasm for a project.

I wonder if that reaction is now, or will become, a majority one, and that AI flavoured language and products will face some audience headwinds if there aren’t indications of some level of human authorship / editing.

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It describes copying and pasting actual code from one project into a prompt so a language model can reproduce it in another project.

It’s a mechanical transformation of someone else’s copyrighted expression (their code) laundered through a statistical model instead of a human copyist.

> One trick I use constantly: for well-contained features where I’ve seen a good implementation in an open source repo, I’ll share that code as a reference alongside the plan request. If I want to add sortable IDs, I paste the ID generation code from a project that does it well and say “this is how they do sortable IDs, write a plan.md explaining how we can adopt a similar approach.” Claude works dramatically better when it has a concrete reference implementation to work from rather than designing from scratch.

Licensing apparently means nothing.

Ripped off in the training data, ripped off in the prompt.

https://www.instagram.com/reels/DUlye8NETR3/

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When working in a linguistics lab as an undergraduate long ago, we looked at spectrograms to identify sounds (specifically places of articulation) as much as listened to recordings.

So it makes some sense to build a model on them rather than some other representation of the sound.

I hadn’t heard of this result, and my exposure to Diophantine equations is limited to precisely one seminar from undergrad, but this feels like taking von Neumann’s famous quip to its most fantastical extreme:

> With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.

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