CSV Convertor – Handle US/UK Dates and Bank Balances

Hacker News

My wife and I are trying to get a better handle on our personal finances and having settled on a tool to use I found that my bank statements couldn't be uploaded as my bank uses d MMM yyyy format when dd/MM/yyyy format was required. There was also no option to support the very standard credits in one column and debits in another column that exists widely. While editing the file in Excel was on option I ultimately plan to delegate the task and so wanted something simple, clean and easy to use. As there is no state, and no cookies, hosting on GitHub pages felt like an easy solution. The solution is vibe coded with Kiro and the source code is available at https://github.com/cactus-bm/csvprocessor/ and pull requests are welcome. The project is hosted on my company's Github and licensed under MIT

Visit product View on Hacker NewsJul 24, 2025

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

85%85% 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 NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released

53%53% 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: host · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: finances · 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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