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lorey

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Without benchmarking LLMs, you're likely overpaying

karllorey.com
197 分·作者 lorey·6个月前·93 评论

Show HN: Free Logo API – logos for any company or domain

logos.apistemic.com
9 分·作者 lorey·7个月前·6 评论

评论

lorey
·17天前·讨论
Their response:

> The team that made dataroom has stated that they did not use any of papermark’s code and that dataroom was made from scratch with inspiration from existing document sharing softwares, and that this post’s allegations of us stealing code are false. [...]

The screenshots clearly show they copied whole pages verbatim, both design and texts. The founder, Nico Laqua, basically responding with "we didn't copy _code_" and not taking any responsibility says a lot about his and his company's moral code. It might not be enough to get sued. That doesn't make it right.

https://x.com/nico_laqua/status/2070158170937581951
lorey
·6个月前·讨论
That is not what the article argues.
lorey
·6个月前·讨论
Haha, very true. Exactly as described in the article.
lorey
·6个月前·讨论
This is true with one caveat.

In most cases, e.g. with regular ML, evals are easy and not doing them results in inferior performance. With LLMs, especially frontier LLMs, this has flipped. Not doing them will likely give you alight performance and at the same time proper benchmarks are tricky to implement.
lorey
·6个月前·讨论
This is a very good point. When I came in, the founder did a lot of evaluation based on a few prompts and with manual evaluation, exactly as described. Showing the results helped me underline the fact that "works for me" (tm) does not match the actual data in many cases.
lorey
·6个月前·讨论
Doesn't this depend a lot on private vs company usage? There's no way I could spend more than a few hundreds alone, but when you run prompts on 1M entities in some corporate use case, this will incur costs, no matter how cheap the model usage.
lorey
·6个月前·讨论
It's not you, it's the HN hug of death. There's so much load on the server, I'm barely able to download the redis image I need for caching...
lorey
·6个月前·讨论
Thanks. Will take a look.
lorey
·6个月前·讨论
Depends on your remaining budget ;)
lorey
·6个月前·讨论
I've skipped that in the article, but absolutely!
lorey
·6个月前·讨论
Fixed, thanks. Not a native speaker.
lorey
·6个月前·讨论
That's interesting. Similarly, we found out that for very simple tasks the older Haiku models are interesting as they're cheaper than the latest Haiku models and often perform equally well.
lorey
·6个月前·讨论
Pushed a fix. Could you check, please?

Any resources you can recommend to properly tackle this going forward?
lorey
·6个月前·讨论
Will fix, thanks :)
lorey
·6个月前·讨论
Totally agree with your point. While I can't say specifically, it's a traditional (German) business he's doing vertically integrated with AI. Customer support is really bad in this traditional niche and by leveraging AI on top of doing the support himself 24/7, he was able to make it his competitive edge.
lorey
·6个月前·讨论
This went straight to prod, even earlier than I'd opted for. What do you mean?
lorey
·6个月前·讨论
Appreciate the feedback, will work on that.
lorey
·6个月前·讨论
You're right. We did a few use cases and I have to admit that while customer service is easiest to explain, its where I'd also not choose the cheapest model for said reasons.
lorey
·6个月前·讨论
Yes, absolutely. This aligns with what we found. It seems to be necessary to be very clear on scoring (at least for Opus 4.5).
lorey
·6个月前·讨论
Very interesting points. Would you mind sharing a few examples of when cherry-picking is necessary and why atomic changes are a lie?

I'm using a monorepo for my company across 3+ products and so far we're deploying from stable release to stable release without any issues.