Building an Investment Management Powerhouse

Hacker News

The idea for Stockle came from simply having no usable tools to track, manage and explore investments. It felt like every single one existing platforms lacked even the most basic functionalities. In addition the UIs were not focusing on user experience. The lack of detail in the existing platforms led us to try out a different approach to investment management. Focus points for Stockle have been Modern UI, Detailed coverage on investments and flexible portfolio creation. For future we are focusing on even more personalised features. Feel free to put Stockle-beta to test whether you are an investor or not! Many features still under development but core features are there! Maybe it can even spark up your inner Warren Buffet. Feedback is also appreciated!

Visit product View on Hacker NewsNov 25, 2023

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Did not reach leaderboard

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

65%65% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · 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.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, single, using · Missing: mac, agents, macos

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

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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 · Missing: mrr, revenue, profit

9%9% 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.

Correct prediction on native model

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