Visual 'what if' financial simulations and AI agent (human in the loop)

Hacker News

We’re trying to create a Financial Jarvis. Think Excel meets decision trees - but in the Multiverse. Here’s the thesis. No one in their right mind would trust an LLM to drive a bunch of time-series financial calculations, years out into the future, of any real complexity. Simply too many variables, dependencies, nested calculations. Plus their job would be on the line if it was incorrect. Spreadsheets work - sort of - but they lack context. Each cell is just a number - no true metadata. And you can only really generate one scenario at a time. You have outputs - but limited insights into the when, the what and the why of those outputs. At its core, spreadsheets are not designed with TIME as their primary axis. To make this work - to provide operational context to a model - you need a different, repeatable data structure for defining time series financial calculations and the business logic. You need to be able to see each calculation and validate the inputs to trust the outputs. whatifi is a visual, financial and strategic modeling framework that uses GPT and a node-based architecture to help you build (and audit) complex “what-if” scenarios without drowning in spreadsheets.

Visit product View on Hacker NewsJan 16, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, context · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

78%78% predicted probability of success on Indie Hackers, 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.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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

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