Your Product Is Now a Wrapper Around Go-To-Market | Hiten Shah
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In this episode
Hiten Shah has built Crazy Egg, KISSmetrics, and a long list of products, and right now he’s shipping a new batch of AI tools while stepping back into Crazy Egg after nearly 20 years. In this conversation we get into what’s actually real in AI right now versus what’s hype.
We start with Noats, Hiten’s local-first meeting recorder, and why “the tech wasn’t there” a year ago. That leads into local models: how much hardware you really need, and why even the frontier labs don’t fully know what a model will be good at until it’s done.
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Duration: 00:50:48
Episode's Transcript
[00:00:00] Speaker A: I want to start by talking about notes. So kind of granola replacement local recording for calls.
[00:00:19] Speaker B: Yeah. I mean we had initially thought of it as fully local and then and that was about a year ago and the product just wasn’t there.
Chapters
- 1 00:00:00 Notes app and local model parity with Granola
- 2 00:02:08 Local models, product motion, and go-to-market teaser
- 3 00:04:08 RAM requirements and practical limits of local compute
- 4 00:05:50 Why training is a black box and model 'flavor'
- 5 00:09:25 Chinese-trained models and the search for US open weights
- 6 00:11:14 Personal workflow for testing new models and Twitter as signal
- 7 00:15:42 Setting up systems to experiment like a factory
- 8 00:18:03 Muse vs startups: trust, security, and Meta's advantage
- 9 00:20:32 Notes go-to-market and distribution strategy
- 10 00:24:22 Product as a wrapper around go-to-market
- 11 00:28:29 Flexibility across enterprise vs consumer go-to-market
- 12 00:29:10 Returning to Crazy Egg and AI-driven efficiency
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