Alternatives
Products that do what Olympus AI does
Debug production issues before your users notice
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Trace, evaluate, and improve AI agents in production
Aug 2026 · telerik.com
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Hi HN, I'm the CEO at https://replay.io. We've been building a time travel debugger for web apps for several years now (previous HN post: https://news.ycombinator.com/item?id=28539247) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand…
2025 · nut.new
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Most feedback tools are built like people actually want to report bugs. They don’t. Unless you make it dead-simple, or better yet - a little fun. After shipping a few SaaS products, I noticed a pattern: Bugs? Yes. Bug reports? No. Not because users didn’t care but because reporting bugs is usually a terrible experience. Most tools want users to: * Fill out a long form * Enter their email * Describe a bug they barely understand * Maybe sign in or create an account * Then maybe submit it Let’s be real: no one’s doing that. Especially not someone just trying to use your product. So I built…
2025
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Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.com
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Hi Hacker News! We launched an autonomous agent that helps debug production issues, and we’re curious to get your feedback. Today's GenAI devtools, such as Copilot, are limited: they are great for writing code, but we all know that programming is only 20% coding, and 80% debugging. So how can GenAI be used for debugging? As opposed to code completion or test automation, production debugging is not about generating text. Debugging is mostly about root-cause analysis. We realized two things: 1) Generative AI is drastically changing the way we work with data, thanks to its ability to not only…
2023 · wildmoose.ai
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Hi HN, I'm the CEO at https://replay.io. We've been working on time travel debugging for web development for a while (https://news.ycombinator.com/item?id=28539247) and more recently an AI app builder that uses that debugger to get past problems instead of spinning in circles (https://news.ycombinator.com/item?id=43258585). We've gotten to where we can pretty easily build apps to replace business-critical SaaS tools, some of which we're now using internally: * We built our own issue tracker to keep track of all our development projects, tickets, bug…
Dec 2025
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Hello HN! I'm an Android OS engineer. I've worked with AOSP and Linux kernels all my career and always wondered about lack of sophisticated tools to debug and analyze system-level logs. Always had to resort to manually skimming through large log files to find something I needed to. With the rise of LLMs and the AI-age, I felt it was a great opportunity to build something for OS engineers, which is what led to logcat.ai! We are building the industry-first observability platform for system level intelligence. Think "Datadog for operating systems" instead of applications. Currently, we support…
2025 · logcat.ai
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We built HALO (Hierarchal Agent Loop Optimizer), an open-source tool for debugging and optimizing AI agents using their execution traces. It’s a loop. Run your agent, feed the traces to HALO, get the report, apply the fixes, then re-run your agent. HALO takes in OTEL compliant traces from AI agents using tracing frameworks such as Langfuse, Arize/OpenInference, or even just plain JSONL. It uses an RLM (Recursive Language Model) to more efficiently break trace analysis into smaller subproblems in order to find recurring patterns across large amounts of data and fix systemic issues that…
Jun 2026 · github.com
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