Slice and Dice – analyze and explore User Prompts at scale

Hacker News

Hi HN, we’ve recently sold our previous product and are currently building on new ideas. This one may be the most exciting and overlooked growth opportunity for AI products we’ve found: TLDR: We’ve built a tool for analyzing and exploring user prompts — so you can actually understand how users are interacting with your AI product, and compare behavior across different segments (languages, paid vs free, etc). If you’re used to Mixpanel / Amplitude / PostHog to analyze user behavior, you could notice how irrelevant they become when your product is just a chat box (or voice interface). That's because in the age of AI you don’t need button events — you need to analyze a large corpus of text. To solve this, we’ve built what we call a Mixpanel for GenAI apps — an NLP tool to analyze and explore your user chats at scale. We can already do: 1/ Multi-layer semantic clustering (see a big picture of all the topics and drill down) 2/ Filters and groups (compare usage between languages, demography, free/paid, etc) 3/ Latent space exploration 4/ Semantic search of prompts 5/ Topics and token usage breakdown 6/ (coming) Trends and audience drift over time So you can answer questions such as: - What’s the main use case of my app? - What do users who pay the most do? - What do users who spend the most time do? - Which quiet audiences and use cases am I missing? - How do the user patterns differ between languages? - What are the new audiences we can appeal to? Please check the link for the screenshots and instructions on how to start! Any feedback is appreciated (I don't say I won't cry if it's negative)

Visit product View on Hacker NewsJul 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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