The node.js codebase and standard library has a very high standard of quality, hope that doesn't get washed out by sloppy AI-generated code.
OTOH, Matteo is an excellent engineer and the community owes a lot to him. So I guess the code is solid :).
From https://github.com/jupyterlite/jupyterlite/issues/949#issuec... :
> Ideally, the virtual filesystem of JupyterLite would be shared with the one from the virtual terminal.
emscripten-core/emscripten > "New File System Implementation": https://github.com/emscripten-core/emscripten/issues/15041#i... :
> [ BrowserFS, isomorphic-git/lightningfs, ]
pyodide/pyodide: "Native file system API" #738: https://github.com/pyodide/pyodide/issues/738 re: [Chrome,] Filesystem API :
> jupyterlab-git [should work with the same VFS as Jupyter kernels and Terminals]
pyodide/pyodide: "ENH Add API for mounting native file system" #2987: https://github.com/pyodide/pyodide/pull/2987
I like the idea of it mocking the file system for tests, but I feel like that should probably be part of the test suite, not Node.
The example towards the end that stores data in a sqlite provider and then saves it as a JSON file is mind-boggling to me. Especially for a system that's supposed to be about not saving to the disk. Perhaps it's just a bad example, but I'm really trying to figure out how this isn't just adding complexity.
I do see some original benefits to a VFS though, bad application decisions aside, but they are exceedingly minor.
As an aside I think JavaScript would benefit from an in-memory database. This would be more of language enhancement than a Node.js enhancement. Imagine the extended application capabilities of an object/array store native to the language that takes queries using JS logic to return one or more objects/records. No SQL language and no third party databases for stuff that you don't want to keep in offline storage on a disk.
You can’t import or require() a module
that only exists in memory.
You can convert it into a data url and import that, can't you?What I really want is a way of swapping FS with VFS in a Node.js program harness. Something like
node --use-vfs --vfs-cache=BIG_JSON_FILE
So basically Node never touches the disk and load everything from the memory- https://github.com/yarnpkg/berry/issues/7065
- https://github.com/nodejs/node/issues/62012
This is because yarn patches fs in order to introduce virtual file path resolution of modules in the yarn cache (which are zips), which is quite brittle and was broken by a seemingly unrelated change in 25.7.
The discussion in issue 62012 is notable - it was suggested yarn just wait for vfs to land. This is interesting to me in two ways: firstly, the node team seems quite happy for non-trivial amounts of the ecosystem to just be broken, and suggests relying on what I'm assuming will be an experimental API when it does land; secondly, it implies a lot of confidence that this feature will land before LTS.
node -e "new Function('console.log(\"hi\")')()"
or more to the point node -e "fetch('https://unpkg.com/cowsay/build/cowsay.umd.js').then((r) => r.text()).then(c => new Function(c + 'console.log(exports.say({ text: \"like this\"}))')())"
that one is particularly bad, because umd messes with the global object - so this works node -e "fetch('https://unpkg.com/cowsay/build/cowsay.umd.js').then((r) => r.text()).then(c => new Function(c)()).then(() => console.log(exports.say({ text: 'oh no'})))"By far the most critical issue is the over reliance on third party NPM packages for even fundamental needs like connecting to a database.
On a more serious note, I think that this will be thoroughly reviewed before it gets merged and Node has an entire security team that overviews these.
While the large code changes were maintained, they were often split up into a set of semantically meaningful commits for purposes of review and maintenance.
With AI blowing up the line counts on PRs, it's a skill set that more developers need to mature. It's good for their own review to take the mass changes, ask themselves how would they want to systematically review it in parts, then split the PR up into meaningful commits: e.g. interfaces, docs, subsets of changed implementations, etc.
See https://pnpm.io/motivation
Also, while popularity isn't necessarily a great indicator of quality, a quick comparison shows that the community has decided on pnpm:
https://web.archive.org/web/20161003115800/https://blog.mozi...
Databases are third party tech, I don’t think it’s unreasonable to use a third party NPM module to connect to them.
Node.js on the other hand is not owned or controlled by one entity. It is not beholden to the whims of investors or a large corporation. I have contributed to Node.js in the past and I was really impressed by its rock-solid governance model and processes. I think this an under-appreciated feature when evaluating tech options.
If submitter picks (a) they assert that they wrote the code themselves and have right to submit it under project's license. If (b) the code was taken from another place with clear license terms compatible with the project's license. If (c) contribution was written by someone else who asserted (a) or (b) and is submitted without changes.
Since LLM generated output is based on public code, but lacks attribution and the license of the original it is not possible to pick (b). (a) and (c) cannot be picked based on the submitter disclaimer in the PR body.
Well, the process you’re describing is mature and intentionally slows things down. The LLM push has almost the opposite philosophy. Everyone talks about going faster and no one believes it is about higher quality.
Not spamming, not affiliated, just trying to help others avoid so much needless suffering.
Open 80, closed 492.
If there is some bug that slips by review, having the PR broken down semantically allows quicker analysis and recovery later for one case. Even if you have AI reviewing new Node.js releases for if you want to take in the new version - the commit log will be more analyzable by the AI with semantic commits.
Treating the code as throwaway is valid in a few small contexts, but that is not the case for PRs going into maintained projects like Node.js.
Look, most of us realized around 2004 or so that if you had a choice between Norton and the virus you would pick the virus. In the Windows world we standardized around Defender because there is some bound on how much Defender degrades the performance of your machine which was not the case with competitive antivirus software.
I've done a few projects which involved getting container file formats like ZIP and PDF (e.g. you know it's a graph of resources in which some of those resources are containers that contain more resources, right?) and now that I think of it you ought to be able to virus scan ZIP files quickly and intelligently but the whole problem with the antivirus industry is that nobody ever considers the cost.
I'm not saying Node should support every db in existence but the ones I listed are critical infrastructure at this point.
When using Postgres in Node you either rely on the old pg which pulls 13 dependencies[1] or postgres[2] which is much better and has zero deps but mostly depends on a single guy.
Java also has a JIT compiling JS engine that can be sandboxed and given a VFS:
https://www.graalvm.org/latest/security-guide/sandboxing/
N.B. there's a NodeJS compatible mode, but you can't use VFS+sandboxing and NodeJS compatibility together because the NodeJS mode actually uses the real NodeJS codebase, just swapping out V8. For combining it all together you'd want something like https://elide.dev which reimplemented some of the Node APIs on top of the JVM, so it's sandboxable and virtualizable.
So it's an external dependency that is not part of Java. It doesn't really matter if the code comes from the vendor or not. Especially for OpenSource databases.
Whether AI output can fall under copyright at all is still up for debate - with some early rulings indicating that the fact that you prompted the AI does not automatically grant you authorship.
Even if it does, it hasn't been settled yet what the impact of your AI having been trained on copyrighted material is on its output. You can make a not-completely-unreasonable argument that AI inference output is a derivative work of AI training input.
Fact is, the matter isn't settled yet, which means any open-source project should assume the worst possible outcome - which in practice means a massive AI-generated PR like this should be treated like a nuke which could go off at any moment.
> The permission model implements a "seat belt" approach, which prevents trusted code from unintentionally changing files or using resources that access has not explicitly been granted to. It does not provide security guarantees in the presence of malicious code. Malicious code can bypass the permission model and execute arbitrary code without the restrictions imposed by the permission model.
Deno's permissions model is actually a very nice feature. But it is not very granular so I think you end up just allowing everything a lot of the time. I also think sandboxing is a responsibility of the OS. And lastly, a lot of use cases do not really benefit from it (e.g. server applications).
1. Copyright cannot be assigned to an AI agent.
2. Copyrighted works require human creativity to be applied in order to be copyrighted.
For point 2 this would apply to times were AI one shots a generic prompt. But for these large PRs where multiple prompts are used and a human has decided what the design should be and how the API should look you get the human creativity required for copyright.
In regards to being a derivative work I think it would be hard to argue that an LLM is copying or modifying an existing original work. Even if it came up with an exact duplicate of a piece of code it would be hard to prove that it was a copy and not an independent recreation from scratch.
>the worst possible outcome
The worst possible outcome is they get sued and Anthropic defends them from the copyright infringement claim due to Anthopic's indemnity clause when using Claude Code.