[0] intentional human use of an em-dash

> "Hasn't Anthropic's new auto-caching feature solved this?"

> Largely, yes — Anthropic's automatic caching (passing "cache_control": {"type": "ephemeral"} at the top level) handles breakpoint placement automatically now. This plugin predates that feature and originally filled that gap.

[0] https://dottxt.ai/

1. Use native PDF parsing if the model supports it

2. Use this Mistral OCR model (we updated to this version yesterday)

3. UNLESS you override the "engine" param to use an alternate. We support a JS-based (non-LLM) parser as well [0]

So yes, in practice a lot of OCR jobs go to Mistral, but not all of them.

Would love to hear requests for other parsers if folks have them!

[0] https://openrouter.ai/docs/guides/overview/multimodal/pdfs#p...

Comments URL: https://news.ycombinator.com/item?id=46314684

Points: 51

# Comments: 47

> Why, then, are the reconstructions so ugly?

> ...may be that they are hampered by conservation doctrines that forbid including any feature in a reconstruction for which there is no direct archaeological evidence. Since underlayers are generally the only element of which traces survive, such doctrines lead to all-underlayer reconstructions, with the overlayers that were obviously originally present excluded for lack of evidence.

That seems plausible -- and somewhat reasonable! To the credit of academics, they seems aware of this (according to the article):

> ‘reconstructions can be difficult to explain to the public – that these are not exact copies, that we can never know exactly how they looked’.

But there's a lot more functionality that becomes relevant when building in production. We do automatic fallbacks, route between providers based on data policies, syndicate your data to agent observability tools / your logging platform of choice, user-level and api-key-level budget management and model allow/block lists, programmatic API key management, etc, etc. More good stuff shipping all the time!

Comments URL: https://news.ycombinator.com/item?id=45721332

Points: 6

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=45696596

Points: 3

# Comments: 0

We are heavily incentivized to prioritize/make transparent high-quality inference and have no incentive to offer quantized/poorly-performing alternatives. We certainly hear plenty of anecdotal reports like this, but when we dig in we generally don't see it.

An exception is when a model is first released -- for example this terrific work by artificial analysis: https://x.com/ArtificialAnlys/status/1955102409044398415

It does take providers time to learn how to run the models in a high quality way; my expectation is that the difference in quality will be (or already is) minimal over time. The large variance in that case was because GPT OSS had only been out for a couple of weeks.

For well-established models, our (admittedly limited) testing has not revealed much variance between providers in terms of quality. There is some but it's not like we see a couple of providers 'cheating' by secretly quantizing and clearly serving less intelligence versions of the model. We're going to get more systematic about it though and perhaps will uncover some surprises.

> As a quick review, embeddings are compressed numerical representations of a variety of features (text, images, audio) that we can use for machine learning tasks like search, recommendations, RAG, and classification.

Current standalone embedding models are not intrinsically connected to SotA LLM architectures (e.g. the Qwen reference) -- right? The article seems to mix the two ideas together.

“With the constant upward pressure on embedding sizes not limited by having to train models in-house, it’s not clear where we’ll slow down: Qwen-3, along with many others is already at 4096”

But aren’t embedding models separate from the LLMs? The size of attention heads in LLMs etc isn’t inherently connected to how a lab might train and release an embedding model. I don’t really understand why growth in LLM size fundamentally puts upward pressure on embedding size as they are not intrinsically connected.

We have a simple model comparison tool that is not-at-all-obvious to find on the website, but hopefully can help somewhat. E.g.

https://openrouter.ai/compare/qwen/qwen3-coder/moonshotai/ki...