I do not think it is wise to brag that your solution to a problem is extremely painful but that you were impervious to all the pain. Others will still feel it. This code takes bandwidth to host and space on devices and for maintainers it permanently doubles the work associated with evolving the filesystem APIs. If someone else comes along with the same kind of thinking they might just double those doubled costs, and someone else might 8x them, all because nobody could feel the pain they were passing on to others
Note aside, OpenJS executive director mentioned it's ok to use AI assistance on Node.js contributions:
I checked with legal and the foundation is fine with the DCO on AI-assisted contributions. We’ll work on getting this documented.
It is great to have a legal perspective on compliance of LLM generated code with DCO terms, and I feel safer knowing that at least it doesn't expose Node.js to legal risk. However it doesn't address the well known unresolved ethical concerns over the sourcing of the code produced by LLM tooling.
Speed code all your SaaS apps, but slow iteration speeds are better for a runtime because once you add something, you can basically never remove it. You can't iterate. You get literally one shot, and if you add a awkward or trappy API, everyone is now stuck with it forever. And what if this "must have" feature turns out to be kind of a dud, because everyone converged on a much more elegant solution a few years later? Congratulations, we now have to maintain this legacy feature forever and everyone has to migrate their codebase to some new solution.
Much better to let dependencies and competing platforms like bun or deno do all the innovating. Once everyone has tried and refined all the different ways of solving this particular problem, and all the kinks have been worked out, and all the different ways to structure the API have been tried, you can take just the best of the best ideas and add it into the runtime. It was late, but because of that it will be stable and not a train wreck.
But I know what you're thinking. "You can't do that. Just look at what happens to platforms that iterate slowly, like C or C++ or Java. They're toast." Oh wait, never mind, they're among the most popular platforms out there.
Time is highly correlated with expertise. When you don’t have expertise, you may go fast at expense of stability because you lack the experience to make good decisions to really save speed. This doesn’t hold true for any projects where you rely on experts, good processes and tight timelines (aka: Apollo mission)
It's not an AI issue. Node.js itself is lots of legacy code and many projects depend on that code. When Deno and Bun were in early development, AI wasn't involved.
Yes, you can speed up the development a bit but it will never reach the quality of newer runtimes.
It's like comparing C to C++. Those languages are from different eras (relatively to each other).
Is it slop if it is carefully calculated? I tire of hearing people use slop to mean anything AI, even when it is carefully reviewed.
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.
Like, why on earth would I spent hours reviewing your PR that you/Claude took 5 minutes to write? I couldn't care less if it improves (best case scenario) my open source codebase, I simply don't enjoy the imbalance.
Not everyone has the same motivations. I’ve done open source for fun, I’ve done it to unblock something at work, I’ve done it to fix something that annoys me.
If your project is gaining useful functionality, that seems like a win.
Of course any chess bot is going to play better, but that's not the point
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.
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.
The fact is, it's useful as a tool, but you still should review what's going on/in. That isn't always easy though, and I get that. I've been working on a TS/JS driver for MS-SQL so I can use some features not in other libraries, mostly bridging a Rust driver (first Tiberious, then mssql-client), the clean abstraction made the switch pretty quick... a fairly thorough test suite for Deno/Node/Bun kapt the sanity in check. Rust C-style library with FFI access in TS/JS server environment.
My hardest part, is actually having to setup a Windows Server to test the passswordless auth path (basically a connection string with integrated windows auth). I've got about 80 hours of real time into this project so far. And I'll probably be doing 2 followups.. one with be a generic ODBC adapter with a similar set of interfaces. And a final third adapter that will privide the same methods, but using the native SQLite underneath but smothing over the differences.
I'm leveraging using/dispose (async) instead of explicit close/rollback patterns, similar to .Net as well as Dapper-like methods for "Typed" results, though no actual type validation... I'd considered trying to adapt Zod to check at least the first record or all records, and may still add the option.
All said though, I wouldn't have been able to do so much with so relatively little time without the use of AI. You don't have to sacrifice quality to gain efficiency with AI, but you do need to take the time to do it.
> Everyone talks about going faster and no one believes it is about higher quality.
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.
(a) The contribution was created in whole or in part by me and I have the right to submit it under the open source license indicated in the file; or
If there isn't, then (b) works fine, the code is taken from the LLM with no preexisting license. And it would be very strange if a mix of (a) and (b) is a problem; almost any (b) code will need some (a) code to adapt it.
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.
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.
Also the commercial version is limited to “…Customer and its personnel, successors, and assigns…”. I am very much not a lawyer and couldn’t find definitions of these in the agreement but I am not sure how transferable this indemnity would be to an open source project.
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.
Oh I'd use an llm to generate large amounts of feedback and request changes!