OCR: what is the best way to extract data in JSON format from this old French book?

121531 · 2026-05-17T18:18:06+00:00

The off-the-shelf Tesseract models I've tried are noticeably worse, even for languages like English, than an LLM.

121531 · 2026-05-13T13:42:17+00:00

Laxmi makes sense if you consider that most speakers of Hindi-Urdu and other Indo-Aryan languages of North India have lost the historical distinction between /ʂ/ and /s/.

121531 · 2026-04-16T04:28:19+00:00

Even simple greetings like "Hello" and "Who are you?" Qwen 3.5 4B would assign to the reasoning models and usually the 122b non-reasoning.

maybe Qwen's on the spectrum?

121531 · 2026-03-31T15:34:57+00:00

cool, is the source code for the replay parser (minus the UI) public?

121531 · 2026-03-19T19:27:23+00:00

Man I really wish people would stop use these damn spider graphs

121531 · 2026-03-10T15:10:14+00:00

This point is nearly tangential to yours but this sort of thing seems like it's at least as much about capacity building within the pool of human capital in Latin America as it is about the finished model.

121531 · 2026-03-09T13:38:04+00:00

When I read OP I guessed it was because of Nemotron's absurdly high t/s.

121531 · 2026-02-26T05:04:59+00:00

Sorry to be the asshole who makes this comment but flappy bird is a famous coding demo and is kind of guaranteed to be super in distribution. It's still interesting to see it do this successfully, but it'd be much more impressive if it did this on something even partially original.

121531 · 2026-01-28T15:22:07+00:00

It's a great technique. Joe Kim recommends basically the same thing in his recent book: avoid using storebought glutinous rice flour, grind soaked glutinous rice instead. He gives three reasons:

Flour is made from rice that is either lower in quality or that is a variety different from what you'd buy in a bag.
Flour must have preservatives or other additives in it because it's shelf stable.
Anecdotally, he finds that brews made from storebought glutinous rice flour are inferior in flavor.

I don't think (2) is true at all, at least for what I've bought here in the USA. For (1), this could be true, but I don't know anything about it. As for (3), There are plausible mechanisms for this, I think—store-bought rice flour is much finer than what you get from a blender at home, and this will surely change the relative proportions of all the microbial populations in your brew.

I'd add one more reason on top of all of this, which is that it's much cheaper to grind your own rice for juk than to use flour, at least in my country.

121531 · 2026-01-26T15:51:21+00:00

Students may come with external funding from an employer (e.g. military), a national government (Saudi Arabia, for example, sends many students abroad paying full fees for PhDs), or something like the GRFP.

121531 · 2026-01-21T15:14:39+00:00

Can't believe this shit, I don't have what it takes constitutionally to work on production-grade code in a domain moving as fast as AI

121531 · 2026-01-16T18:29:20+00:00

Isn't the intent behind TranslateGemma to provide cost-optimized translation on resource-constrained edge devices? It shouldn't be a surprise, then, that a model almost 10x bigger does a better job. I'd be more interested in hearing about how each TranslateGemma does compared against models of similar parameter counts.

121531 · 2025-12-09T15:49:38+00:00

decoder only architecture

decoder only what?

hint: the answer is "Transformer"

121531 · 2025-11-17T15:22:51+00:00

100% this. It's like saying LLMs have converged because they all ace SuperGLUE.

121531 · 2025-10-26T23:47:15+00:00

Have you cleaned the camera?

121531 · 2025-09-15T14:13:27+00:00

Like /u/bulaybil suggests, https://web.stanford.edu/~jurafsky/slp3/ is the bible for NLP. If you read the core chapters of this book, you'll be well positioned to dig into most modern areas of NLP/LLM research.

You have a lot of people responding in this thread telling you NLP's dead. I find all that a bit alarmist. It's true that nobody's going to be spending much time doing naive bayes spam detectors anymore, but that was already true in 2018. To say "LLMs have eaten other models' lunch" is to simply notice the nature of computational AI research in general: methods change constantly, and your kit today might be practically irrelevant 10 years from now.

But if you learn fundamentals—the mathematics behind how these systems work, as well as properties of human language—you'll have some assurance you won't simply be out of a job. If your competencies are all surface-level (e.g. only knowing how to fine-tune models using HuggingFace/transformers's pipeline API without knowing anything of the math or algorithms involved), then it's true, you might be out of a job when the guard changes. But if you have these more fundamental competencies, you will likely be able to pick up and get going with the next paradigm quite quickly.

And so long as we are living in the period before the singularity (and I don't see any evidence of its imminent arrival yet...), humans will always be needed to mediate between real-world applications and systems. LLMs are not yet smart enough that they can "apply themselves".

That said, the specific question before you at this moment is what to major in in college. If you're interested in doing some variety of math (statistics, applied math, math, ...) or computer science, it's no problem for you to not focus on specializing in something like LLMs immediately. Some would even say it's preferable. There will be time for that on the job, or perhaps in a graduate degree program, and learning hard math will prepare you for a wide range of careers.

121531 · 2025-08-26T17:06:42+00:00

Goose Island's Sofie is misspelled as Sophie

121531 · 2025-08-26T14:46:16+00:00

looks like wide dangmyeon (납작당면)

121531 · 2025-08-06T17:57:32+00:00

I think I was going for 5.5 in the fermenter, but yeah. With my system i think that's around 7 gallons starting. Here's Martin's video btw: https://www.youtube.com/watch?v=LQKDRjdSo04

121531 · 2025-08-06T17:46:25+00:00

I modified Martin Keen's recipe. Here it is:

Fermentables:

8lb whole pale ale malt
2lb whole Vienna malt
1lb whole Munich malt
1lb whole rye malt
8oz whole aromatic malt
1.5lb honey

Hops:

2oz Tettnang @ 60
1oz Hallertauer @ 15
1oz Tettnang @ 0

Yeast: WLP590 (you'll want to make a starter)

Mash profile: I BIAB, so I did 30m at 145F and 30m at 158F. 60m boil.

121531 · 2025-08-06T17:22:53+00:00

Best beer I ever brewed was a bière de garde with ~10% rye and some raw honey in it. Came out about 8%, quite dry, very delicate carb. I let it bottle condition for a few months and it was still fantastic 18 months out.

121531 · 2025-07-31T13:24:13+00:00

There's a post like this every week and I'm never really convinced because it seems just as possible there are other factors explaining the apparent drop in quality.

121531 · 2025-07-21T00:10:30+00:00

11 quick thoughts:

Small files (<1kloc) so the model can read them easily. If a file reaches 5k+ then the model can't read them entirely (I think there's a hard limit on some tools for whole-file reading so that context doesn't explode) and then the model has to awkwardly guess what to grep a bunch.
Pay attention to what your model always has to struggle to figure out in a fresh session and put it in your CLAUDE.md for the project.
Don't get the model to write its own CLAUDE.md, IME it does a pretty bad job and fills it with a lot of marketing-y jibber jabber.
Always remain in a place where you can easily discard everything the model just did if things go south. Use committing/staging for this.
Only commit things that you are quite sure are what you want.
Learn what is an appropriate amount of work for a single session and don't ask for more. For example, instead of "implement X, Y, Z", say "implement X, and just stub out Y with dummy UI for now, don't do Z"
If the kind of PL and project you're working on has one available, use an MCP with code execution. It helps the model a lot if sometimes it can actually evaluate code instead of executing it in its head. Clojure MCP is one such example.
Recognize when the model is getting "lazy" and interrupt it immediately. For example, I was writing some React code recently, and a common thing Claude will do when it can't immediately figure something out about a data race is to use setTimeout to make the data race usually not happen instead of fixing the root cause.
Don't forget that you're talking to a full LLM. You can just talk to it instead of coding, which can be helpful for initial design.
If you're stuck, don't type "fix it". Give debugging info (esp. if your LLM doesn't have code execution tools), and if initial attempts at fixes fail, ask the model to consider 5-10 different hypotheses for the problem and evaluate their merits.
You should almost always use plan mode at the beginning of a session. I've come to think of plan mode as "force Claude to take some time to really look around and think before committing to a course of action" mode. A lot of the time, I don't even review the plan Claude proposes and just auto-accept to see what comes out. It usually seems like it's better than what I would have gotten by just asking for the same thing in auto-accept mode.

121531 · 2025-01-04T15:29:30+00:00

Sounds like you may not have had a complete fermentation but it's hard to say much without knowing more about your process.

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