> All that to say, this might be known to people who’ve been around training for years, even decades, but it’s new to a lot of us. And we’re excited about it.

I've done some training and fine-tuning for a few years, and the ideas here were new to me. I'm also having a breakthrough on a problem and am very excited about it.

There's a very narrow classification task which I've been building around Jev. The cost and latency of Jev are great, the accuracy is pretty good - definitely usable - but I disliked sending the data in this particular task off-box. Not a criticism of Typesafe, it's the nature of the task which is privacy oriented. I decided to see if a model whose weights were one-shotted by an LLM (the model architecture was designed thoughtfully ahead of time) could be competitive with a similarly architected but trained model, and both of those against Jev.

Results are early in my experiment, but it appears that a "vibe tuned" classifier consisting of weights small enough to run on a laptop GPU are competitive in accuracy and latency with Jev. The process hasn't even proceeded to the point of the trained models having meaningful results, and I already have something usable for an MVP.

Whatever may have been possible previously or was obvious to other people, the absurd efficacy of this process was not obvious to me. Emphasizing that this particular task was very narrowly defined, I still believe that this effortless model-building capability is a profound and exciting discovery.