I

I built an AI Agent to do Visual QA and Front end bug fixing

Hacker News

I built an AI Agent to do Visual QA and Front end bug fixing

Hi HN! I am excited to share this new tool, How it works: 1. Detects visual inconsistencies (misalignments, colors, spacing, typography, missing assets) between design mockups and developed UI. 2. Auto-generates Jira/Linear tickets, eliminating manual bug tracking. 3. AI fixes frontend bugs, creating GitHub PRs with clean code. 4. Browser Agent monitors live UI changes, preventing visual regressions in production. Discord: https://discord.gg/xGsWnujG

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, visual · Missing: mac, agents, macos
98%98% 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
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
43%43% 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
41%41% 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
23%23% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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