indutny

In my opinion, the benefits for end users are rather minimal since I doubt an average person would ever be checking C2PA provenance data, but there is a commercial incentive for Google and others to promote C2PA, since it makes preparing training material for Machine Learning significantly easier, and perhaps as a smaller benefit justifies hardware attestation that locks users down into proprietary OSes.

> The real risk with LLM-generated code is that it looks plausible but hasn't gone through the same level of scrutiny. It passes a quick review because it reads well, but edge cases get missed.

Precisely! Because the code is made to believable the risk of accepting it without understanding full implications is very high.

> If the answer is "the same review process we've always had" then the problem isn't AI, it's whether that process is rigorous enough for the stakes involved.

True, but there is also a reputational component to how changes are reviewed (whether we like it or not). The longer the tenure and the deeper the understanding of the changed code is - the chance of careless Pull Request gets lower.

I appreciate hearing your point of view on this. In my opinion the future of Open Source and AI assisted coding is a much bigger issue, and different people have different levels of confidence in both positive and negative outcomes of LLM impact on our industry.

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.

The submitted code must adhere to either of (a), (b), (c), and separately a (d) clause of: https://github.com/nodejs/node/blob/main/CONTRIBUTING.md#dev...

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.

As someone who was a part of the aforementioned security team I'm not sure I'd be interested in reviewing such volume of machine generated code, expecting trap at every corner. The implicit assumption that I observed at many OSS projects I've been involved with is that first time contributions are rarely accepted if they are too large in volume, and "core contributor" designation exists to signal "I put effort into this code, stand by it, and respect everyone's time in reviewing it". The PR in the post violates this social contract.

Taking the question of whether this would be a useful addition to Node.js core or aside, it must be noted that this 19k LoC PR was mostly generated by Claude Code and manually reviewed by the submitter which in my opinion is against the spirit of the project and directly violates the terms of Developer's Certificate of Origin set in the project's CONTRIBUTING.md

Here's a @property-based complex animation that I made recently https://indutny.github.io/on-call-rotation/#sindresorhus,jam...

Isn't it the opposite?

As a shameless plug I'd like to suggest the project that I have been using for my personal credentials for quite some years now: https://derivepass.com/ . The encrypted website metadata is stored only locally (with only optional remote sync, and even that has to be your own server), and the passwords are not stored at all (they are generated from your master password).

Nor can we tell why proton is a spin 1/2 particle because the gluon soup and particle-antiparticle pairs make this complicated.

Fantastic material! That being said I'd recommend to have several alternative textbooks for every subject at hand. Whenever stuck - one should switch to another and try a different take.

In addition to said here:

* https://www.susanjfowler.com/blog/2016/8/13/so-you-want-to-l...

There are plenty of textbooks and lecture notes available online and that article links to most of the popular choices. Make sure to choose correct order of topics to avoid getting stuck!

You can still sync encrypted website names and logins, and in fact you have to if you ever plan to change the passwords on the websites.

As a shameless plug: https://derivepass.com/

Not to mention that experiments suggest that atoms don't play any special role in the underlying physical structure of reality.

I think photon self-interaction might be thought of as a confirmation of photon's existence. Particle ontology is a tough question, but I don't think there is a reason to exclude photon from the list without excluding all other particles.

In this image: https://advances.sciencemag.org/content/advances/5/7/eaaw256... they describe their apparatus.

The light comes from bottom left, then gets into Beam Splitter (BS) and splits in two. The top-left detector has a pre-selected filter and a measurement device after a delay line. As far as I understand, this device gives binary output: either light received or not. The top-right detector captures whole image of the light ray, but does so only if the left detector has seen light.

The result is that the image from the top-right detector is highly correlated with the pre-selected filter of the top-left detector, even though they're placed in different arms of Beam Splitter! https://advances.sciencemag.org/content/advances/5/7/eaaw256...

Most of the collected data is published to github repo: https://github.com/indutny/gradtype/tree/master/datasets

It is easier than exporting model to TF.js . I wish the tooling was better, but there is way too much custom TF code in gradtype to export it easily.

Sorry to hear that! The web page sends each sentence to the server once you type it, but it looks like something has gone wrong.

I'll check server logs to identify the problem. Sorry again!

Oh gosh! This is very unexpected. I'm considering removing apostrophes, so this might help.

I'll investigate it. Thank you!

This is an ML model: https://github.com/indutny/gradtype/blob/master/src/model.js...

It is trained using (almost) a triplet-loss: http://openaccess.thecvf.com/content_cvpr_2016/papers/Cheng_...

I imagined that it would just ask to enter a pre-defined phrase along with the password.

I'm afraid that without registering there is not much to happen, sorry. The whole point is that given past samples it can identify new previously unseen samples. The identification is the crucial part of the demo.

Of course, but with masked identifiers.

This is fixed now.

Sorry, should be fixed now!

Will check it out, thank you!

Thank you for taking it to all 5 stars rating. That helps a lot!

Very interesting! I don't think that I ever had a sample from a non-standard keyboard layout in the dataset.

While it might sound like this should make your samples very different from others, it could actually act the other way and confuse the network. Hopefully, this will be improved in the next version of it, which I'll start training right after this data collection sprint.