
Markora — Visual Bug Reporting for Modern Teams
向应用添加小部件,在错误报告时自动捕获屏幕截图、日志和网络错误。
@markoradotme · X
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向应用添加小部件,在错误报告时自动捕获屏幕截图、日志和网络错误。
@markoradotme · X

上传照片,用AI快速识别昆虫。
@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

由AI驱动的代码审查工具,在microVM中运行代码来捕获更多错误。
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

CodeFlovio 将代码转换为交互式流程图,便于学习、调试和文档编制。
@Sparsh209 · X
Hi isha! Check it out here at: Would love to hear your feedbacks

Auto-fix production bugs,获得 AI code reviews,每个 PR 都有严重级别评分
@akshay_nocode · X
Not exactly — Sentry captures errors, BugOps acts on them. It reads the Sentry alert, checks recent commits, traces root cause, and opens a fix PR. Think Sentry as the sensor, BugOps as the on-call engineer that never sleeps.

两个AI引擎独立审查代码并相互验证bug发现。
@BotariaDotBot · X
This Tuesday I'm launching Botbugger on @ProductHunt 🚀 One AI reviewer gives you 10 "critical" bugs — half hallucinated. So I built two: Claude Code + Codex review your code independently, then cross-check each other. Real bugs with fixes, not noise.

AI 图像编辑与生成工具,在浏览器和移动设备上编辑和扩展图像。
visuali · HN
Like Claude Code for Images

在应用中录制屏幕和截图,用 AI 分类反馈并快速解决问题。
@keshav__dev · X
Hi priyanka i am also building in customer feedback space but with some dev related features. Maybe you want to check

捕获UI元素及其代码上下文,与AI代理分享以调试视觉缺陷。
Loerei · HN
I found that describing where a broken UI is and taking screenshots for AI agents really sucks. I’m too lazy to explain an indescribable visual bug or capture a millisecond-long flash. I also don’t want to remember which file defines an element, whether it's right in the .tsx or a problem with the Parent Styles in .css. And even if you can point out the exact file, your agent still has to dig through thousands of lines of code to know what on earth you're yapping about. In a 7700-file monorepo like Cal.com, simply giving the right file in the prompt for your agent saves about 68.9% of execution time and 94.2% of the tokens your agent needs to digest. HoverSource gives all the needed information and further pushes it to -88.5% time and -94.5% tokens, basically skipping the entire digging session and jumping straight to reasoning and executing. The save is linear to how big your codebase is. I want to save tokens, but don’t want to spend my time and energy digging manually, and a junio

查看生产 agent traces 以识别和修复重复故障。
@guru3s · X
IMF team for when your AI agent ( Ethan Hunt ) is about to fail v0 at

用对抗测试检查LLM端点安全,获取OWASP审计报告。
@aryaan_sheth · X
- LLM security for small teams

Vulnsy是为安全团队提供的渗透测试报告平台,可10倍快速交付客户报告。
@TurvSec · X