tmpo – CLI time tracker with Git integration and local-first storage

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I built tmpo because I was losing money tracking freelance hours in Google Forms. It's a CLI time tracker that auto-detects your project from Git and stores everything locally. Key technical decisions: - Pure Go implementation with modernc.org/sqlite (no CGO, easier cross-compilation) - Auto-detection via git rev-parse for project names - Local SQLite database in ~/.tmpo/ (nothing leaves your machine) - Single binary, zero dependencies New features since I started: - Milestone tracking - organize entries into sprints/releases with auto-tagging - Pause/resume workflow for context switching - Manual entry creation for forgotten time - Configurable date/time formats and currency (stored in global config) - Edit/delete entries Example workflow: ```bash cd ~/projects/client-website tmpo milestone start "Sprint 5" tmpo start "Fix auth bug" # ... work ... tmpo pause # lunch break tmpo resume tmpo stop tmpo stats --week tmpo export --milestone "Sprint 5" --format csv ``` It detects "client-website" from git and tags everything with the active milestone automatically. The codebase is ~6k LOC. Working on the Homebrew formula, but pre-built binaries are now available. GitHub: < https://github.com/DylanDevelops/tmpo > Questions for you all: 1. Are there any features that you want to see in tmpo? 2. Currently using a 3-tier config priority (.tmporc > git > directory). Better approaches? 3. Any concerns with the milestone auto-tagging approach?

Visit product View on Hacker NewsDec 30, 2025

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

82%82% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, new · Missing: agents, macos, agent

74%74% 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

44%44% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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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