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TimeRetain – A browser-based personal time tracker, no sign-up needed

Hacker News

TimeRetain – A browser-based personal time tracker, no sign-up needed

Hi HN, I built TimeRetain: a time tracker for individuals that runs 100% in your browser. No sign up, no personal data stored, and free while I’m working on the beta. Click the white demo banner to check it out quickly :) I’m using it to track my hours at work (e.g., overtime, and what I’ve done so I come prepared for stand up). In 2023 I launched it here [1], but with a back-end. After I became interested in local-first, I realized JS could handle everything Postgres did - so I ported it. Time data now never leaves your browser and the app will ping you to run an export from time to time. I also followed up on some of the feedback in the previous thread (it now has a pause button, a dedicated data export page, no longer relies on a working back-end, and I fixed the spacing in the name). It’s not a true local first app just yet (e.g., it's not a PWA, and backing up using the File System API is on my list), but I plan on making it one should it gain some traction. One of the other things I might add is encrypted at rest storage and sync of events, for which the foundation is done (it’s event-based and events are ordered using a HLC timestamp in IndexedDB). I wonder, though: is multi-device sync even necessary? I would much rather prefer not to store any personal data at all, even if it’s encrypted. Do you need sync? And if so, what would be your ideal solution? Would love to hear your thoughts in general. Thank you! [1] https://news.ycombinator.com/item?id=34457730

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos
65%65% 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 · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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 · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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
16%16% 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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