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Amsflow Global Stock Screener – 550 metrics and AI-driven queries

Hacker News

Amsflow Global Stock Screener – 550 metrics and AI-driven queries

Hi HN, I’m Ajay Thakur, founder & CEO of Amsflow. Why I built this. Most stock screeners I have used are over complicated, full of nested dropdowns and endless options and force you to remember arcane syntax. I never really got into any screener, so I built one that just works. What this is. Amsflow Global Stock Screener unifies 550 plus financial metrics and approximately 8,000 filters across all major markets in one seamless interface. You can perform natural language AI queries via our Lisa agent (for example, “Find fintech firms with more than 5 B revenue, 20 percent year on year growth, sorted by P E TTM”). You can use our world class inline query builder that auto suggests metrics and filters as you type. What is different. Truly global: one screener for the US, Europe, Asia and beyond. Hybrid query: switch between natural language and precise filter chains. Jump right in here: [ https://amsflow.com/stock-finder ]. Zero signup barrier. Try up to five Lisa queries and twenty inline filters immediately. No email required. I would love your feedback on the UI flow, query accuracy and any edge cases you uncover. Thanks!

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Actual performance

4points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% 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: agent, email · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
50%50% 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 · Missing: mobile apps, ios, personal
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, interface, builder · Missing: platform, intuitive, reviews
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, revenue, ttm · Missing: mrr, profit, saas
26%26% 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.

Incorrect prediction on native model

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