Lazygotest – Terminal UI for running Go tests interactively

Hacker News

Hi HN! I'm the creator of lazygotest. I built this because I love lazygit's UX and wanted the same smooth experience for Go testing. Instead of switching between terminal and editor, or scrolling through verbose test output, you can now: Browse packages and tests with vim-style navigation (j/k, gg/G) Run tests with Enter and watch real-time results in split panes Track test history and quickly rerun failed tests with 'r' Filter tests with '/' to focus on what matters Technical highlights: Built with tview ( https://github.com/rivo/tview ) for the TUI framework Catppuccin Mocha color scheme, WCAG 2.1 AA compliant for accessibility Auto-detects go.mod by walking up parent directories Supports Docker for isolated testing environments The interface has 4 panes: Packages (left), Tests (top-right), History (middle-right), and Log (bottom). The status bar shows context-sensitive keyboard shortcuts. Looking for contributors! Here are some areas where I'd love help: CI/CD integration: GitHub Actions workflow for running tests in lazygotest Benchmark mode: Support for go test -bench with visual graphs Coverage visualization: Show code coverage % per package/test Test generation: Auto-generate table-driven tests from function signatures Plugin system: Allow custom commands and key bindings If you're interested in contributing, the codebase follows Clean Architecture with clear separation between domain/application/infrastructure layers. PRs are welcome – I aim for small, focused changes (≤20 lines when possible) with tests included. I'd love to hear your feedback, especially from fellow gophers who spend a lot of time running tests! Install: go install -tags tview github.com/YuminosukeSato/lazygotest/cmd/lazygotest@latest

Visit product View on Hacker NewsNov 20, 2025

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

90%90% 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: dock, context, visual · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

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

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

33%33% predicted probability of success on TrustMRR, 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

31%31% 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 · Strong signals: active · 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 · 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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