Alternatives
Products that do what VybeSec does
Monitor browser & backend failures, get AI root causes fast!
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Hey HN! We’re open-sourcing highlight.io (https://github.com/highlight/highlight), a session replay and error monitoring tool. Highlight.io gives you a high-precision video-like replay of what users are doing when an error or exception occurs in your web app, along with a full-fledged error monitoring experience (similar to bugsnag, rollbar, etc..). The main value prop of highlight.io is that we help you understand the full context surrounding an error and allow you to drill down to the code path that a user invoked (i.e user clicked button X, sent network request Y, and…
2023 · github.com
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Hey HN commmunity, Karthik here! Super stoked to announce the launch of Zipy today. Launching the product that you've been so dearly working on for months is like sending your newborn to school for the first time. Excitement to nervousness, anxiety to thrill, all sorts of emotions hit you at the same time. But the entire team of Zipy is confidently looking forward to the feedback you guys have in store for the beautiful product we’ve built. In community we trust! Over the past decade, from being a Web Development Intern to a UX Designer and a Product Manager, I have seen the struggles that…
2022 · zipy.ai
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Hey HN. We’re Jay and Vadim from Highlight.io (https://highlight.io). We’re building an open source [1] monitoring platform for web applications. Today we’re excited to be sharing a performance tool we’ve been working on, which helps you inspect the latency of code execution from the client to the server. As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had…
2024 · github.com
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Trace, evaluate, and improve AI agents in production
Aug 2026 · telerik.com
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Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…
2023 · github.com
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This project is inspired by 37signals’ ONCE idea. I replicated the whole process and have already sold a few copies (the testimonials are real).
Oct 2025 · telebugs.com
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Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…
2025 · infinitcode.ai
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I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail. AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions. Kelet automates that investigation. Here's how it works: 1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.) 2. Kelet processes those signals and extracts facts about each session 3. It forms hypotheses…
Apr 2026 · kelet.ai
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Hi HN, we’re Rohan and Michael from Decipher (https://prod.getdecipher.com/docs) and we’re working on an AI-powered error monitoring tool that helps engineers quickly understand what went wrong, the user impact, and how to fix it. It’s an alternative to tools like Sentry and BugSnag. We noticed that when there’s an application error it often takes too much time to figure out which issues are important and how to fix them. Because the overhead is too high, people (including us…) sometimes just pay attention to bugs that come directly from a user complaint. We were initially…
2024 · decipherai.mintlify.app
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