A New Google Model Is Nearly Perfect on Automated Handwriting Recognition

Wow I'm doing it way wrong. How do I get the good stuff?

I can vouch for the fact that LLMs are great at searching in the original language, summarizing key points to let you know whether a document might be of interest, then providing you with a translation where you need one.

The fun part has been build tools to turn Claude code and Codex CLI into capable research assistant for that type of projects.

Completely off topic, but out of curiosity, where are you reading these documents? As a Spaniard I’m kinda interested.

There are plenty of so called windows(or other) web 'os' clones.

There were a couple of these posted on HN actually this very year.

Here is one example I google dthat was also on HN : https://news.ycombinator.com/item?id=44088777

This is not an OS as in emulating a kernel in javascript or wasm, this is making a web app that looks like the desktop of an OS.

I have seen plenty such projects, some mimick windows UI entirely, you xan find them via google.

So this was definitely in the training data, and is not as impressive as the blog post or the twitter thread make it to be.

The scary thing is the replies in the twitter thread have no critical thinking at all and are impressed beyond belief, they think it coded a whole kernel, os, made an interpeter for it, ported games etc.

I think this is the reason why some people are so impressed by AI, when you can only judge an app visually or only how you intetcat with it and don't have the depth of knowledge to understand, for such people it works all the way.land AI seems magical beyond comprehension.

But all this is only superficial IMHO.

https://x.com/chetaslua/status/1977936585522847768

> I asked it for windows web os as everyone asked me for it and the result is mind blowing , it even has python in terminal and we can play games and run code in it

And of course

> 3D design software, Nintendo emulators

No clue what these refer to but to be honest it sounds like they've incrementally improved one-shotting capabilities mostly. I wouldn't be surprised if Gemini 2.5 Pro could get a Gameboy or NES emulator working to boot Tetris or Mario, while it is a decent chunk of code to get things going, there's an absolute boatload of code on the Internet, and the complexity is lower than you might imagine. (I have written a couple of toy Gameboy emulators from scratch myself.)

Don't get me wrong, it is pretty cool that a machine can do this. A lot of work people do today just isn't that novel and if we can find a way to tame AI models to make them trustworthy enough for some tasks it's going to be an easy sell to just throw AI models at certain problems they excel at. I'm sure it's already happening though I think it still mostly isn't happening for code at least in part due to the inherent difficulty of making AI work effectively in existing large codebases.

But I will say that people are a little crazy sometimes. Yes it is very fascinating that an LLM, which is essentially an extremely fancy token predictor, can one-shot a web app that is mostly correct, apparently without any feedback, like being able to actually run the application or even see editor errors, at least as far as we know. This is genuinely really impressive and interesting, and not the aspect that I think anyone seeks to downplay. However, consider this: even as relatively simple as an NES is compared to even moderately newer machines, to make an NES emulator you have to know how an NES works and even have strategies for how to emulate it, which don't necessarily follow from just reading specifications or even NES program disassembly. The existence of many toy NES emulators and a very large amount of documentation for the NES hardware and inner workings on the Internet, as well as the 6502, means that LLMs have a lot of training data to help them out.

I think that these tasks which extremely well-covered in the training data gives people unrealistic expectations. You could probably pick a simpler machine that an LLM would do significantly worse at, even though a human who knows how to write emulation software could definitely do it. Not sure what to pick, but let's say SEGA's VMU units for the Dreamcast - very small, simple device, and I reckon there should be information about it online, but it's going to be somewhat limited. You might think, "But that's not fair. It's unlikely to be able to one-shot something like that without mistakes with so much less training data on the subject." Exactly. In the real world, that comes up. Not always, but often. If it didn't, programming would be an incredibly boring job. (For some people, it is, and these LLMs will probably be disrupting that...) That's not to say that AI models can never do things like debug an emulator or even do reverse engineering on its own, but it's increasingly clear that this won't emerge from strapping agents on top of transformers predicting tokens. But since there is a very large portion of work that is not very novel in the world, I can totally understand why everyone is trying to squeeze this model as far as it goes. Gemini and Claude are shockingly competent.

I believe many of the reasons people scoff at AI are fairly valid even if they don't always come from a rational mindset, and I try to keep my usage of AI to be relatively tasteful. I don't like AI art, and I personally don't like AI code. I find the push to put AI in everything incredibly annoying, and I worry about the clearly circular AI market, overhyped expectations. I dislike the way AI training has ripped up the Internet, violated people's trust, and lead to a more closed Internet. I dislike that sites like Reddit are capitalizing on all of the user-generated content that users submitted which made them rich in the first place, just to crap on them in the process.

But I think that LLMs are useful, and useful LLMs could definitely be created ethically, it's just that the current AI race has everyone freaking the fuck out. I continue to explore use cases. I find that LLMs have gotten increasingly good at analyzing disassembly, though it varies depending on how well-covered the machine is in its training data. I've also found that LLMs can one-shot useful utilities and do a decent job. I had an LLM one-shot a utility to dump the structure of a simple common file format so I could debug something... It probably only saved me about 15-30 minutes, but still, in that case I truly believe it did save me time, as I didn't spend any time tweaking the result; it did compile, and it did work correctly.

It's going to be troublesome to truly measure how good AI is. If you knew nothing about writing emulators, being able to synthesize an NES emulator that can at least boot a game may seem unbelievable, and to be sure it is obviously a stunning accomplishment from a PoV of scaling up LLMs. But what we're seeing is probably more a reflection of very good knowledge rather than very good intelligence. If we didn't have much written online about the NES or emulators at all, then it would be truly world-bending to have an AI model figure out everything it needs to know to write one on-the-fly. Humans can actually do stuff like that, which we know because humans had to do stuff like that. Today, I reckon most people rarely get the chance to show off that they are capable of novel thought because there are so many other humans that had to do novel thinking before them. Being able to do novel thinking effectively when needed is currently still a big gap between humans and AI, among others.

Basically we all know that AI is just a stochastic parrot autocomplete. That's all it is. Anyone who doesn't agree with me is of lesser intelligence and I feel the need to inform them of things that are obvious: AI is not a human, it does not have emotions. It just a search engine. Those people who are using AI to code and do things that are indistinguishable from human reasoning are liars. I choose to focus on what AI gets wrong, like hallucinations, while ignoring the things it gets right.

Thanks for this, I was almost convinced and about to re-think my entire perspective and experience with LLMs.

Those clones are all HTML/CSS, same for game clones made by Gemini.

"users have reported some truly wild things" "the results were shocking" "the most amazing thing I have seen an LLM do" "exciting and frightening all at once" "the most astounding result I have ever seen" "made the hair stand up on the back of my neck"

Some time ago, I'd been working on a framework that involved a series of servers (not the only one I've talked to claude about) that had to pass messages around in a particular fashion. Mostly technical implementation details and occasional questions about architecture.

Fast forward a ways, and on a lark I decided to ask in the abstract about the best way to structure such an interaction. Mark that this was not in the same chat or project and didn't have any identifying information about the original, save for the structure of the abstraction (in this case, a message bus server and some translation and processing services, all accessed via client.)

so:

- we were far enough removed that the whole conversation pertaining to the original was for sure not in the context window

- we only referred to the abstraction (with like a A=>B=>C=>B=>A kind of notation and a very brief question)

- most of the work on the original was in claude code

and it knew. In the answer it gave, it mentioned the project by name. I can think of only two ways this could have happened:

- they are doing some real fancy tricks to cram your entire corpus of chat history into the current context somehow

- the model has access to some kind of fact database where it was keeping an effective enough abstraction to make the connection

I find either one mindblowing for different reasons.

  >I really have a hard time making (or believing) the argument that they are just predicting the next word.

(It's a pretty constraining interface though - the model outputs an entire distribution and then we instantly lose it by only choosing one token from it.)

It's incredibly frustrating to have a model start to hallucinate sources and be incapable of revisiting its behavior.

Couldn't even understand that it was making up non-sensical RFC references.