Show HN: TabPFN-2.5 – SOTA foundation model for tabular data
I am excited to announce the release of TabPFN-2.5, our tabular foundation model that now scales to datasets of up to 50,000 samples and 2,000 features - a 5x increase from TabPFN v2, published in the Nature journal earlier this year. TabPFN-2.5 delivers state-of-the-art predictions in one forward pass without hyperparameter tuning across classification and regression tasks.
What’s new in 2.5:
TabPFN-2.5 maintains the core approach of v2 - a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for the test data. It natively supports missing values, cateogrical features, text and numerical features is robust to outliers and uninformative features.
The major improvements:
- 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2)
- SOTA performance: TabPFN-2.5 outperforms tuned tree-based methods and matches the performance of a complex ensemble (AutoGluon 1.4), that itself includes TabPFN v2, tuned for 4 hours. Tuning the model improves performance, outperforming AutoGluon 1.4 for regression tasks.
- Rebuilt API: New REST interface along with Python SDK with dedicated fit & predict endpoints, making deployment and integration more developer-friendly
- A distillation engine that converts TabPFN-2.5 into a compact MLP or tree ensemble while preserving accuracy and offer low latency inference.
There are still some limitations. The model is designed for datasets up to 50K samples. It can handle larger datasets but that hasn’t been our focus with TabPFN-2.5. The distillation engine is not yet available through the API but only through licenses (though we do show the performance in the model report).
We’re actively working on removing these limitations and intend to release newer models focused on context reasoning, causal inference, graph networks, larger data and time-series.
TabPFN-2.5 is available via API and a package on Hugging Face. Would love for you to try it and give us your feedback!
Model report: https://priorlabs.ai/technical-reports/tabpfn-2-5-model-repo...
Package: https://github.com/PriorLabs/TabPFN
Client: https://github.com/PriorLabs/tabpfn-client
Docs: https://docs.priorlabs.ai/quickstart
Comments URL: https://news.ycombinator.com/item?id=45838540
Points: 65
# Comments: 11
Thu, 6 Nov 2025, 6:26 pm
Show HN: qqqa – A fast, stateless LLM-powered assistant for your shell
I built qqqa as an open-source project, because I was tired of bouncing between shell, ChatGPT / the browser for rather simple commands. It comes with two binaries: qq and qa.
qq means "quick question" - it is read-only, perfect for the commands I always forget.
qa means "quick agent" - it is qq's sibling that can run things, but only after showing its plan and getting an approval by the user.
It is built entirely around the Unix philosophy of focused tools, stateless by default - pretty much the opposite of what most coding agent are focusing on.
Personally I've had the best experience using Groq + gpt-oss-20b, as it feels almost instant (up to 1k tokens/s according to Groq) - but any OpenAI-compatible API will do.
Curious if the HN crowd finds it useful - and of course, AMA.
Comments URL: https://news.ycombinator.com/item?id=45833811
Points: 132
# Comments: 80
Thu, 6 Nov 2025, 10:59 am
Show HN: See chords as flags – Visual harmony of top composers on musescore
I designed a relative piano-roll-based music notation. I used 12 colored arranged in a specific way to make visible the main effects and oppositions of Western tonal harmony. The tonic is always white, so a manual annotation/interpretation is required for each MIDI file.
All chords are flags of three to four colors. Minor mode is darker, major mode is lighter. Colors are arranged in thirds.
I sorted the pieces from simple complex harmony. I also wrote a bit of text to explain what you may see. There's also a corpus of structures: hyperlinks of tags that allow you to find similar patterns throughout my corpus of 3000+ popular pieces.
My method makes chord progressions memorizable and instantly visible in the scores. No preparation of Roman numeral analysis / chord symbols analysis is required. After a bit of training the chords will stare right in your eyes.
It's not synesthesia, it's a missing script for tonal music which makes harmonically identical things look the same (or similar).
I've also recorded lectures on my method in Russian (https://www.youtube.com/playlist?list=PLzQrZe3EemP5pVPYMwBJG...). I'm sorry I haven't yet found time to re-record in English.
I've also sketched a friendlier intro: https://vpavlenko.github.io/d/
Sorry, but this thing won't make any sense if you're color-blind.
It's open-source: https://github.com/vpavlenko/rawl
Earlier context: https://news.ycombinator.com/item?id=39165596
(Back then colors were less logical, and there was no corpus of 3000+ piece annotated yet)
Comments URL: https://news.ycombinator.com/item?id=45826472
Points: 107
# Comments: 27
Wed, 5 Nov 2025, 6:57 pm