Figure of speech.

>Why not just generate svgs with any editing software?

How do you do that from the whiteboard/notebook?

Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.

> It's a small task whose result is an input to something else - ideal for a tool for LLM (or script)

Have you actually tried this? If it's a small task then why can't LLMs actually do it? Kind of like the pelican test. Every LLM can do a SVG pelican but most of the results are unusable in practice for production/productivity purposes.

Every LLm can extract some half-broken SVG hallucination of your jots, but I'm asking for an app that's polished and works accurately 99% of the time, which means a dedicated app/model tuned specifically for this task, and nobody makes this yet.

I don't feel like I'm a representative sample of general userbase, so I won't dispute this too hard. For me, it's rare but it does happen. Still, 90% of my mobile experience is "how is this so broken, why is it missing the obvious follow-up feature that completes a workflow, and why, again, am I not using a laptop for this?".

> If it's a small task then why can't LLMs actually do it?

What if they actually can?

This is exactly what Karpathy was talking about the other day[0]. If your only LLM experience is the default chat app in chat mode, then this doesn't make sense. If your experience includes using agentic harness like Claude Code/Cowork / Codex/ChatGPT Work, then it's obvious.

The LLM app in "chat mode" will just zero-shot you a pelican, or digitize your notes into SVG, and maybe improve on it a bit if you complain about the result. The same app[1] in "agentic mode" will digitize your notes, refine the SVG against the original image, reduce it to probable strokes based on high-frequency patterns in the photo and knowledge of how people write, OCR it, save both the text and SVG to your library, then extract parts you wanted and insert them into a document, typeset the rest of it, and print it on your printer - all in response to a single prompt.

LLMs in agentic mode are very good at stringing long and advanced workflows; today, if you give them shell access or computer use, they will use regular apps for you if that's the easiest way to accomplish the goal.

> Have you actually tried this?

Yes![2].

That's why I can confidently say: there is no limit to this pattern. LLMs can handle apps just fine; giving them tools just saves time and tokens.

Another example: I'm currently resurrecting a 30 year old game, and the other day I asked Claude to hack on a way to run it headlessly, so I can use my computer while it plays with it. It did, and then immediately leveraged it to just causally run several instances of the game in parallel, entirely in the background, whenever it needed to playtest something.

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[0] - https://news.ycombinator.com/item?id=50047422 - was on the frontpage two days ago.

[1] - Claude and ChatGPT now have both modes in their mobile, web and desktop apps. I personally use Claude Code a lot, but exclusively through these surfaces (I can't be arsed to deal with TUI at home anymore). IMO, these tools are already within reach of regular users - the users just didn't notice it yet.

[2] - Not with SVGs, but equivalent with voice recordings, and yes, Claude Code set up and is managing the whole local processing pipeline. Recordings go in, Markdown (or more advanced signal processing) comes out, and in itself, it's not a task, but merely a step.

One fictional example of what I mean: lets say I draw someone holding a purse. That would be a shape of their torso, with the purse overlaid. I could move those independently, and moving the purse would show the torso underneath.

However, using a png to svg, it would often just outline everything. So it's not a torso shape with a purse in front. It's a purse with a torso cut out around it, maybe even merged together as lots of individual shapes. If I were to delete the shapes making up the purse, it could be quite random what was left. Maybe some of the shapes did a double duty. Maybe it's a thousand small squares to make the purse. Maybe there is just a hole behind the purse etc.

So in the latter case, the svg isn't made up of logical elements, it's just made up of shape after shape after shape to mimic the pixels. But it's not usable as vector.

> How many pelican riding bicycle SVGs were there before this test existed? What if the training data is being polluted with all these wonky results...

But Simon has demonstrated before that asking for any animal doing something silly will still get similar results from the same model.

> That's the result of training on the same datasets and scraped web contaminated with other models' outputs

Maybe I'm misinterpreting though, or just semantics. I would consider contamination, or whatever the first example was that they all matched, as specific training data, along with all the duplications of. I doubt it's model collapse to the same pelican, without the same original example.

Expect Google models to be benchmaxxed as much as possible, but don't expect commensurate performance on real-world tasks.

This is not true. It is needless negative hyperbole that IMHO doesn't belong on HN.

If what you say is true, then the next set of benchmarks will be written to illuminate the true reflection of real-world tasks. Then those get benchmaxxed so managers can show "numbers go up."

Repeat enough times, and you get materially and genuinely improved products.

The burden is on the press, outside researchers, and general tech industry to both write and care about the metrics that matter.

In large companies (Fortune 1000 etc.), you don't have much of a choice, the culture was eroded long ago, and corporate governance by metrics is a lesser of evils. But thankfully, not all companies are there yet.

They look remarkably good, sure -- but not perfect.