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Ollaya on Hacker News
The launch thread for Ollaya, with commenters asking when a hosted decision model loses to a classifier trained on your own labels.
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- Ardakilic
- Last verified
- Sep 2026
Works forJev for Classification
Used inJev for Software & DevTools
Highlights
- 608 points and 145 comments on 25 September 2026.
- One comment says that if you have an eval set, training a classifier can be the simpler move.
- Another points at a Hugging Face space of similar models and a tweet claiming a $3,000 training run.
Watch out
The $3,000 figure is a comment citing a tweet, not Ollaya's own measurement. The product page is a separate entry.
Questions about this entry
Short answers drawn from the linked project or page.
The thread raises this as the burning question and does not resolve it. The suggested workarounds are prompt-level: describe the domain and what your proper nouns imply, add examples, feed back the ones it got wrong, and use its scores to escalate.
Several commenters say close to that, including one who expects someone to replace the whole thing with logistic regression. The counter-argument is timing: earlier fine-tuning attempts were judged not worth it, and only now does the cost tradeoff work.
One commenter says yes, that the 2023 to 2024 fine-tuning wave tested this in ads and recommendations and judged results not worth it against chain-of-thought. Another replies that small models got far more capable since, so the idea now clears the bar.
The entry copy notes a comment pointing at a Hugging Face space of similar models and a tweet claiming a $3,000 training run. Treat that figure as a comment citing someone else's post, not as Ollaya's own measurement.
It is described as Ollama for open-source, Jev-style decision models, posted to Hacker News at ollaya.dev. The thread drew 618 points and 145 comments.
dotancohen has 80,000 voice recordings to classify, few in English and with non-English class names including proper nouns, and asked how a model not trained on that data could work. atroche suggested domain clues in the prompt and providing examples, prioritising previously wrong ones, with input caching to control cost.
Some called it basically trivial, like the old Dropbox FTP comment, and said anyone with basic ML knowledge could have built it in a few hours; the interesting question to them was why nobody had productized it.
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