Test Viewer for GitHub

Hacker News

Recently I've started writing CI pipelines in GitHub Actions, and found out there was no easy way to browse test results. So I wrote a site to display them right out of your artifacts. I chose an implementation where the client fetches and parses the data by itself, so I don't have to manage user's data (or have a backend really). I didn't want to spend too much time on this side-project, so I used AI tooling, and was delightfully surprised with their effectiveness. I used Vercel's v0 for the initial design (that uses mock data), and used Cursor to introduce API calls and add features. I then manually reviewed all code and touched up the design and UX, all in a span of 2 days. v0 generated most of the logic in the main page TSX file (it seems like AI works better when the code is centralized) and the most impressive part was that Cursor managed to (all in one prompt per change with minimal or no bugs): - Convert it from next.js to react-router - Split it into nicely decoupled logical components - Switch to a state-management framework (zustand). Here's the code: https://github.com/Wazzaps/test-viewer Pardon my english, I am not a native speaker :)

Visit product View on Hacker NewsJun 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, user, code · Missing: mac, agents, macos

91%91% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

29%29% 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 · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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