Varun Mohan: prediction

Transcript context

…was there. We used the alpha, I think prem on our discord came to us and say, you guys should try this out. So it was really fast. Even then, prompt optimization is another big thing, and model outputs and UX kind of how you bring them together. Which ones of these things are maybe like the one or two that new founders should really think about first? Yeah, I think, I think my feeling on this is unless you are ex, you probably should always bootstrap on top of an existing a. Because like even if you were to, the only reason why we didn't is because we knew that this product was actually buildable. Probably if we worked hard enough to train a model, we would actually be able to build a great product already. But if you're actually going out and trying to build something from scratch, unless you genuinely believe, I need to fine tune on top of, you know, terabytes of data terabyte is a very large amount of data, but like tens of gigabytes of data. Probably go out and build on top of an API and spend most of your time to make it so that you can hit that quality latency trade off properly. And if I were to go out and think about like the three categories of like an LM product, it's probably like latency, quality, and correct ability. The reality is, you know, if I were to take a product like co-pilot or Coum, the latency is very low. The quality I think, is good enough for the task, but the correct ability is, is very easy. Credibility. What, what is correct ability? Correct ability means, let's say the quality is not there. Like you consider the the case where, The answer is wrong. How easy is it for your user to actually go and leverage parts of the generation? Maybe a, a concrete example. There's a lot of things people are excited about right now where I write a comment and it generates a PR for me, and that's like, that's like really awesome in theory. I think that's like a really cool thing and I'm sure at some point we will be able to get there. That will probably require an entirely new model for what it's worth that's trained on diffs and commits and all these other things that looks at like improvements and code and stuff. It's probably not gonna be just trained on generic code. But the problem with those, those sort of, I would say, applications are that, let's suppose something does change many files, makes large amounts of changes. First of all, it's guaranteed not gonna be. Because even the idea of like reviewing the change takes a long time. So if the quality and the correct ability is just not there, let's say you had 10 file, a 10 file change and you modified like, you know, file two and four, and those two modifications were consistent, but the other eight files were not consistent. Then suddenly the correct ability is like really hard. It's hard to correct the output of the model. And so the user interface is 100% really important. But maybe until you get the latency down or the correct ability, like correct ability, like a lot better, it's probably not gonna be shippable. And I think that's what you gotta spend your time focusing on. Can you deliver a product that is actually something users want to use? And I think this is why I was talking about like demo. shippable. And I think that's what you gotta spend your time focusing on. Can you deliver a product that is actually something users want to use? And I think this is why I was talking about like demo. It's like very easy to hand to handpick something that like works, that works for a demo, exceedingly hard for something that has large scope, like a PR to work consistently. It will take a lot of engineering effort to make it work on small enough chunks so that a user is like, wow, this is value generative to me. Because eroding user trust or consumer trust is very easy. Like that is, it is is much, much, it's very easy to erode user trust versus enterprise. So just be mindful of that, and I think that's probably like the mantra that most of these companies need to operate under. Have you done any…

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