Finbud.ai – The AI-Powered Stock Research Platform

Hacker News

The key value propositions: 1. Dive deep into any company’s performance with AI-driven insights and 10+ years of historical market data. 2. Quickly slice and dice key metrics, unlock advanced analysis, and discover actionable investment opportunities. 3. Technical analysis on any companies or crypto - For crypto, you'll need to append USD to the ticker (e.g. BTCUSD, PEPEUSD, etc) The interesting prompts (that are not in the suggested prompts): What's a good entry and exit point for NVDA for day trade ---- As an engineer, I’ve always been fascinated by the blend of art and science in investing. This inspired me to create FinBud.ai, a tool that leverages AI to make stock research simpler and more accessible. I know FinBud.ai could be a lot better with access to higher-quality datasets, but those can get pretty pricey! Still, I wanted to get it out there early so I can build and improve it alongside our users. Discord is open.

Visit product View on Hacker NewsJan 15, 2025

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, 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

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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