Wonder how it will age.

I'm a bit shocked they don't care much about the strangle contract this introduces by being so dependant on external models.

I do agree with you

The performance as a general model is indeed really impressive and i think they might actually win compared to fine tuned models.

Their feedback loop of training on user data is incredibly strong. I've learned that lots of accuracy results depends on threshold configs, which llms should be able to dynamically set.

Or the future will develop in llms using fine-tuned models as tools? Inference cost and speed does still seem to be below user expectations.

But for being able to one shot with this accuracy... IMPRESSIVE

https://countrx.app/ is something I vibed in a month. Can people here tell? Sure the typical gradiënt page is something to spot, but native apps i think are harder. I would love to see app store and Google Play Store stats to see how many new apps are onboarded.

Looking at distribution channels like Google Play, they added significant harder thresholds to be able to publish an app to reduce low quality new apps. Presumably due to gen ai?

Edit: Jesus guys, the point I'm trying to make is that there are probably a lot more out there that are not visible... Im not claiming i developed the holy grail vibe coding.

https://kauwenofspauwen.be/en Belgian food hygiene rating from official gov reports

All out of pocket. No monetisation. No analytics