The gap between tracking time and getting paid is frustratingly manual

Hacker News

I've freelanced for 15 years. Every month it's the same routine: check my time tracker, open Xero, manually create an invoice, try to remember what "Client X - 2.5hrs" actually meant, type up line items, double-check rates, hope I didn't miss anything. The time tracker knows what I did. The accounting software knows how to invoice. Why am I the glue between them? I built Hour Cap to close that gap. Track time with invoice-ready descriptions, then push to Xero as a draft invoice. The descriptions you write while working become the line items your client sees. No reconstruction, and no rewriting. The part that took the most engineering: retainer billing. If you have a client on 40 hours/month, you need budget tracking that auto-resets each period, handles different period types (not everything is monthly), alerts when you're approaching the cap, and lets you invoice per-period. Most time trackers treat this as a static project estimate when it's not. Other things I found surprisingly complex to get right: - Rate cascading (project → member → org default) so different team members can bill different rates on different projects - Timer midnight crossing (if someone forgets to stop a timer, it should split across days, not show 18 hours on Tuesday) - Invoice line item grouping (some clients want one line per task, others want one line per project - same data, different views) Built with Laravel 12 and Livewire 4. Solo dev, bootstrapped. Free tier available, no credit card required. If you use Xero and want to try it, I'm giving 12 months free to beta testers who connect their Xero account. Genuinely looking for feedback on the invoicing workflow. https://hourcap.com

Visit product View on Hacker NewsFeb 25, 2026

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84%84% 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.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

42%42% 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 · Missing: plus, platform, intuitive

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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