Developer Tools1 month ago
Markora — Visual Bug Reporting for Modern Teams
Add a widget to your app that auto-captures screenshots, console logs, and network errors when users report bugs.
@markoradotme · X
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23 projects
Add a widget to your app that auto-captures screenshots, console logs, and network errors when users report bugs.
@markoradotme · X
Add a bug report widget in 30 seconds. It captures errors, user journeys and failed API calls — then AI analyzes your code and opens a pull request with the fix.
@syedarhamalii · X
Identify insects and bugs by uploading photos for instant AI analysis.
@buildwithahsan · X
Just launched an AI Bug Identifier 🐞 Upload a photo and identify bugs and insects instantly using AI. Building in public and learning every day 🚀 🌐 #AI #BuildInPublic #WebDevelopment
One script tag puts a review layer inside your running app. Your team clicks what is wrong; your developers get the source file and line, the route, and a repro that exports as a P
@_Zidl · X
Collect bug reports with full device context, plus feature requests, from inside your app. Publish release notes in the same place. Built for indie Expo and React Native developers
@henrypl_dev · X
Capture in-app recordings and screenshots, then triage feedback with AI to fix bugs faster.
@keshav__dev · X
Hi priyanka i am also building in customer feedback space but with some dev related features. Maybe you want to check
AI tool that discovers hidden bugs in your application using verification analysis.
@uriel_bitton · X
As you add more features to your vibe coded app, you increase the chances for bad behaviour. The worst part is you dont know they exist Every beta user i've had try out the app told me they found a few issues they had no idea existed Find yours using
AI-powered code review tool that executes code in microVMs to detect more bugs.
u/dumbfoundded · Reddit
Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db
PixelPush.in compares your design against your live site, pixel by pixel — then tells you which differences are bugs and which are just noise.
@j3rah_ · X
Built — a pixel-diff visual QA tool for devs and agencies shipping client sites. Compares your design mockups to your live site and gives you an AI verdict on what's off, so you catch visual drift before your clients do.
Tests LLM endpoints with adversarial cases and provides OWASP-mapped security audit reports.
@aryaan_sheth · X
- LLM security for small teams
Platform for security teams to create and deliver penetration test reports faster with reusable findings and client portals.
@TurvSec · X
Debug and monitor AI agents with real-time visualization and performance analytics.
@simranrambles