Klynda, a portfolio tracker focused on true performance (TWR/XIRR)

Hacker News

Hi HN, I’m Daniel and I built Klynda, a portfolio tracker I started after my spreadsheet became too complex and error-prone, especially around cashflows and price updates. Klynda focuses on true performance by using a unitized accounting approach so deposits and withdrawals don’t distort returns. It also pulls real market data for stocks, ETFs, and crypto. Link: https://klynda.com I’d love feedback on the core model and UX: what feels confusing, what’s missing, and what you’d want to see before trusting the numbers. Happy to answer technical questions about how the performance calculations work.

Visit product View on Hacker NewsJan 28, 2026

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Actual performance

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Did not reach leaderboard

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

76%76% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Product Hunt, 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

24%24% 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

20%20% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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