开发者工具1 month ago
Markora — Visual Bug Reporting for Modern Teams
向应用添加小部件，自动捕获用户报告错误时的截图、控制台日志和网络错误。
@markoradotme · X
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26 projects
向应用添加小部件，自动捕获用户报告错误时的截图、控制台日志和网络错误。
@markoradotme · X
Loopers是一个为AI代理提供故障闭合保护的反向代理和断路器。
varad-khoriya · HN
Loopers – Fail-closed reverse proxy and circuit breaker for AI agents
粘贴你的 URL，查看你的应用暴露了哪些客户数据。
@artem_vysotskyi · X
I'm building exactly what u need mates: 1. to find security leaks in ur vibe-coded apps and fix them 2. to track ur links in X replies => like the link above
审计QuickBooks或Xero，自动查找和恢复重复付款及供应商超额收费。
@AdiSanghaviLeak · X
Building Leaki ( Leaki is an AI platform that detects revenue leakage, fraud, billing errors, and financial anomalies before they become costly. Always happy to connect with founders, AI builders, and SaaS teams!
监控 AI agent 对话，找出失败原因，测试更优提示词。
sailrock · HN
Greenflash – we read every conversation your AI agent has with users
扫描AI应用发现安全和代码质量问题，自动生成GitHub修复。
@LaunchGuardHQ · X
I'm an HVAC installer, not a developer. Built LaunchGuard nights & weekends with AI coding tools. It scans AI-built apps for leaked API keys, wide-open databases, and vulnerable dependencies — then opens a real GitHub PR with the fix.
OutageDeck 监控 160+ 云服务商故障并通过邮件、Slack、Discord 或 Webhook 接收告警
@outagedeck · X
OutageDeck, outage alerts for 168 cloud vendors (AWS, Cloudflare, OpenAI, Slack), built only on their official status feeds. No crowd reports, no scraping, no false positives. Free email alerts for 5 providers + a free JSON API, no key.
Who gets the call? Your logo. Your link. Your country—until another company successfully challenges it.
@SolvoOps1 · X
We just launched Make it easier for companies to find what they are looking for in the IT industry.
上传客户数据预测流失风险，获得留存策略。
u/ajaysurya_ · Reddit
Looking for feedback Built Bayeso after realizing most churn prediction tools ignore small businesses. Looking for feedback. hey everyone, i've been working on bayeso ( https://bayeso.tech ), an ai platform built for businesses that don't have massive datasets. while building it, i kept running into the same problem. almost every churn prediction solution is designed for companies with thousands or even millions of customer records. if you're a small business with a few hundred customer
为订阅业务提供AI驱动的留存智能。
@cobraisystem · X
Manual retention can’t keep up with your acquisition - sees the slip, forecasts the churn, and triggers the save that keeps your growth compounding.
用多个模型实时审计AI回应以判断其可靠性。
u/inc_23 · Reddit
Hey, I created a tool that catches when your LLM is confidently wrong, in production, in real time — looking for beta testers. Your bot sounds sure of itself even when it's wrong, and you usually only find out when a customer complains. Auscope audits every LLM response in the background: 3 models from 3 different providers independently check it, a 4th "chairman" model resolves disagreements, and you get one verdict — verified, uncertain, or unreliable. Runs async, doesn't slow your respon
