liquicity

Nice idea will watch though some - I made something similar but for food vids (filters over YouTube chefs - such as only vids with ingredients)

https://mealplannr.io/discover

Honestly it does somewhat match what I’ve felt playing a lot with all OpenAI / Claude / deepseek models

One thing I’m still surprised / unsure on is haiku 5.5 max besting latest deepseek - but also I think the last haiku was so incredibly bad I still hold some bias whilst using the new one.

Why is it all unpaid - shouldn’t some of these have applications of which it would be in companies with paid employees best interest to verify?

Also - this is no different to open source and code contributions now right

So now the average Joe has to make 3 vids instead of 1 every time - as everyone else is now min maxing their engagement?

Just sounds rubbish both for viewers (look how terrible titles and thumbnails are after a/b tests) and uploaders alike

Same token burn / cost though right?

I think OP's point remains, if you generate 140k pairs, your local model would need to run that many to offset having just used the generator (SOTA or not) model to begin with.

I wonder if another approach if latency is a concern is just to do a two shot pass with Jev (perhaps given small context you'd want one to match command, then one to match args of given command) would be an extremely fast, and cheap way to do it - rather than training your own.

Initial personal use case (on my side project https://mealplannr.io/lists) is lists have a "smart categorise" button to group items into 20 or so preset lists.

Previously this took up to ~30-60 seconds using deepseek v4 flash (even with a medium list size) - Jev is <1 second @ same cost with typesafe guarantee

As a bonus I can also instantly categorise new items - rather than sending them into an "unknown" category (and waiting for user to have to click "categorise" again)

They’re a believer, but they won’t be paying third parties for that once it becomes feasible

Why can't it make a judgement grounded in real data points - then the human can accept / deny that proof much quicker?

Surely there's a middle ground / this is faster than reading all papers front to back.

Openrouter and huggingface both sold to multi billion dollar entities in the same few weeks - perhaps this is great for the founders, but as a consumer I hate this acquisition loop that happens with every company of adequate size these days.

Downvotes are probably because it's obvious they are talking about the pricing, nothing to do with quality.

If that truly is the main selling point - it seems like a shallow moat versus skills + rest..

What's worse than an AI generated comment... a comment about having generated an AI comment that you can't read!

If it's useful to the conversation (and objective + with sources) post the key findings - IMO I think it's AI generated prose / subjective posts that will get blasted.

Was this directed at the voice technology in the article, or at AI in general - as they are two very different conversations?

If you're talking about ChatGPT in general (as I guess you are) I think the Jury's still out on whether this will have a net positive or negative on society.

Right now, it's leaning towards negative, but there are optimistic futures to be had at the same time.

As someone who likes to think that they would turn down a lot of tech jobs due to moral dilemmas - for me, currently working on AI wouldn't be one.

For me at-least it's a tool to learn quicker, and reduce friction on projects I otherwise may not have dived into. As with all new technology, we're still in this grace period / lack the bigger picture we now have on other, now known to be destructive technologies.

50k per head training and the largest takeaway was to clear context.. that is the "hello world" of using agents, insane.