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LaminarFlow – AI-native, open-source finance platform for startups

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LaminarFlow – AI-native, open-source finance platform for startups

I'm Yash, and I'm building LaminarFlow — an AI-native, open-source platform to help startups, founders, SMBs, manage their fincial ops more efficiently. It brings together financial insights, banking, invoicing, payment tracking, time tracking, and banking-style reconciliation — all powered by an AI agent that automates the boring stuff. We’re building this as an open startup, sharing everything publicly, and keeping it fully open-source (MIT). Would love your feedback and thoughts!

Visit productView on Hacker NewsMay 31, 2025
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Actual performance

7points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, open · Missing: mac, agents, macos
87%87% 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 · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% 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: platform, efficient · Missing: plus, 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 · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: time tracking · Missing: web3, chat, crypto
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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