Alessio Fanelli: belief

Transcript context

…That's interesting. Alessio, you have a background in games? Yeah, in playing them, not in building them. So I will not fall into an enterprise company by doing that. Let's talk about R&D today and some of the ideas that you're working through, like some of the limitations that you run through. I think the most interesting thing about hackathons is you come with an idea and then you kind of hit a wall trying to build it. And then that takes you into another path. Like what are maybe funny things that you learn in terms of like the limitations of these models or like the missing infrastructure for using them? So we've got a couple of different frames for thinking about this. There's AI that we're putting into our products and then us knowing that our customers want to put AI into their products. So there's the, how do we support our customers in their product development using AI? But how do we do that ourselves? And this is a great opportunity for us to learn the challenges our customers are gonna see. And so the first thing there is let's just start from the beginning, assume we want to add AI to our product, which maybe isn't the best place to start, but let's just assume we want to. How do we start ideating opportunities to put stuff into our product? So we sort of came up with this framework where we look at our product and we think about what are the collaboration touch points? So where are the points that a human might hand off to another human? And then think where can we replace one of those humans with the machine? So instead of thinking of some AI, amorphous AI, LLM, whatever, we're thinking actually, what if we had a robot that we were collaborating, not just a human, not just some sort of thing that spits out numbers. So collaborating. Then there's thinking of these as tools. So this is like your auto-suggest, on your mobile keyboard or spell check or something. How do you integrate this stuff as deeply into your product? So what are the friction points that users go through? Maybe they check lots of boxes. Is there a way we can pre-check those boxes we can get? So that's the feature embedding really deeply into the tool you've already got, the product you've already got. And then you step back and think, okay, what's a tool? So a tool is like ChatGPT, where you go there, it's an AI powered tool. It's not necessarily connected to your product, but it's a supplementary tool that you add. So there's a sort of ideation process there that we went through. And we sort of landed on a couple. And one of the key things that Amplitude does is help our customers, one, collect data in like a standard and sort of queryable way. And then we help them query it and get insights out of that data. So we were thinking, what's the feature there? How do we embed that? But also what's the collaboration point? And you might be a product manager asking an analyst, hey, please help me. Let's have a conversation about this. I don't know what questions to ask, but you also might just be about to go click the big create button and fill in a bunch of fields. And can we fill in a bunch of the fields for you? So we went to what to us seemed like one of the most obvious places. And we built a text box. Surprise, surprise with LLMs. We've got a text box. You can type in a question, type in anything about your data that you want to know, and then it'll spit back a chart, which is kind of neat. And we hit a bunch of problems there with LLMs hallucinating, losing context, even within the context windows, not really sort of recalling everything within the context window.…

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