simon84

The idea can work, but it will not prevent shadow AI use. Also, I am a bit curious about the legal implications regarding privacy and such.

EU GDPR normally applies to employees inbox emails, so their use of AI certainly will too. In the US, it could be interpreted as the Wiretap Act or similar...

There is probably a gap to fully address there before it can become a business model.

Except if this is targeting fully autonomous agents in which case it is not about human supervision of AI use.

From other comments that are focused on the code part of the subject, I think there are 2 big forgotten points here: time and hype.

Time: since platforms are big machines, their timeline and backlog is huge, and the self-taught geek does not consider (care) that. So when there is an obvious little hiccup somewhere, they create a bug report that asks for details, proof, reproduceability,... and finally get pushed back to eons because there are more pressing concerns to attend to at the platform level. Meanwhile, it takes about 1h (without AI) to code a workaround and feel like a hero because "I've fixed something that took them 3 years to triage".

Hype: yes documentation and code quality and all matter, but it matters even more when it comes from Google or Meta! We all feel little compared to giants and when they open their internal secret tooling framework (with big marketing budget) then we all feel like there is a secret to grasp and a key success factor to wield. So it is not so much whether a npm library some nobody created exists, but whether the main contributor works at a big brand name. It does not prevent smaller anonymous projects from emerging but lets face it, it is much more appealing when it is how Cloudflare handles it at 1e20 scale (because we all feel we face the same challenges of course).

The project would benefit from explaining the test setup. From what I can tell (from code), the machine is probably well under capacity, so I am unsure about how to interpret the results.

If you consider that a pixel is a container for RGB value, then the length and area collapse into simple storage requirement.

When an image is some px wide and some px high, it provides a shape (ratio between them), because a camera of 12mega px isn't 1px x 12M px in shape, but it does occupy 12M storage units nevertheless.

I agree though that like many IT related terms, their usage is twisted and simplified to amplify the buzz. Because it is more marketing friendly to put a bigger number than exact dimensions and proper terminology.

This is very interesting. I have not tested it (yet) but I believe this is what will come next when talking about "AI will be everywhere".

The self-contained disconnected mode is very convenient, though it could be useful to have an "online access" mode to allow it to access the Internet and perform lookup.

You can also guide the AI to organize the codebase according to some principles and attempt to untangle the mess.

But I totally agree on the fact that it will probably look like just any other frontend.

My own guess would be that what matters most for the frontend is UX (and accessibility).

So if you are able to properly evaluate the outcome, it is probably fine.

It will also depend on the type of frontend and its target complexity. From what I understand from your question, you would handle the backend and infrastructure so these do need higher rigour.

If you apply the principle of separation of concerns, the frontend shall be decoupled from the rest and may evolve on its own end. That means that you have some degree of freedom to test and fail the frontend without harming the rest of your project.

Your end users may not like it much, but from a dev perspective, there is no "flaw".

Also consider that LLM training dataset and available data is huge about frontends, so it is way better at doing these.

Did you consider that star growth stalling could mean product maturity? We always want more stars, but if your innovators user base (those that try at the early days and give stars) is now turning to average users that will use the product without rating.

Did you witness a download rate drop too?

While I agree on the fact, this is the typical case where you need a business analyst in between to 'translate' that requirement and confront the leadership if it is unclear.

If you mean that leadership IS the business analyst, then you are in trouble :D

From what I understand of the not-very-detailed text watermark, it comes up to masking the generated output with specific bias in the weights of the token generation (or something like this). Anthropic was explaining that it would survive edits (not full rewrite). So asking another AI to paraphrase will surely add its own, but since the bias is not disclosed, it will depend on how the 2nd AI is considering the existing tokens to steer its own.

Fictive example: imagine the bias is to inject notion of colorfullness in the text regardless or its content:

"I eat an apple" becomes "I eat a red apple"

The 2nd AI is about injecting size component, so the text becomes " I eat a big red apple"

There you get both watermarks. It is not that obvious obviously

I think you are very correct here! Now imagine how much worse it can get with AI in the way.

It is as always (think cybersecurity) the cat and mouse game, what is a weapon is also a defense. You, the simple fact that you are on this very site, means that you are probably more educated to AI than most, so it is not necessarily you that will really benefit from any constraining framework.

There are many people believing in many weird theories and those are more prone to be convinced by a nice narrative. AI did not introduce that, it just made it easier and available to anyone with any intent.

The goal of the AI act is not to determine if an "oh yeah!" comment was AI generated. The target is long papers that falsely claim human review and can have real significant consequences.

E.g. research paper, law makers, lawyers, state policies, notaries,...

These are much longer content and thus statistically they will disclose a better guess at AI generated content.

Asking another AI to paraphrase will not erase the mark (which they are unaware about) but rather cumulatively add their own mark and make it easier to detect.

The problem is not to use AI, but to endorse the responsibility of the content you (as a human) deliver and somehow make sure that fake-news, biased content or unverified output is detected as early as possible.

Real +1 for being transparent about the imported libraries and respective owner/license.

I think that part of the problem is that you try to apply this to low level variables.

If you consider a framework on top of any existing language that provides serialization mechanics that include metadata about the item, then you can approach your 2 goals.

I think that one important aspect (whether that is feasible or not is another question) is to have valid coercion. JS has some, but as your example shows, it is not complete.

Is the binary equal value a requirement or is it the semantic value which can take another form but can be interpreted in the same way?

Very interesting topic overall and I've been touching these questions a bit (until I had nightmares).

> What are its advantages? > There is only one rendering engine, cutting down complexity.

Actually there are 2 rendering, the server sending HTML and the browser drawing it on your screen.

So why not push the logic and send a jpg image of the rendered content itself. Lol

The whole concept of decoupling the frontend and the backend is that they can be agnostic of each other, they need to align but it is not the same skills/concerns.

Serve rendered html like in 2000 and you have recreated a smart way to do what industry spent decades running away from.