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Decipher – An AI powered error monitoring tool

Hacker News

Decipher – An AI powered error monitoring tool

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 building an AI coding assistant that traced code execution to teach it how a specific codebase behaves (see a demo here: https://youtu.be/2LjVH2sH2Q8 ). In doing so, we realized that if you capture the right code-level runtime data (local variables, request, arguments, etc.) LLMs become quite effective at figuring out the impact of errors and debugging them. From that insight, we began working on Decipher which monitors for errors and collects all the relevant context in one place. This includes logs, endpoint, request bodies, the stack trace, local variables, and relevant code. With this centralized context, we use LLMs to summarize the issue and investigate a solution. What’s cool is that by summarizing this stuff, you can get a gist of the bug directly in a Slack alert (rolling out soon). For example, here’s an issue on a shopping app with a Flask backend, there was a `List Index out of Range error`: https://imgur.com/a/uEfoS9k ) You can see all the relevant details are brought to the forefront and the AI was able to summarize the issue and hint at what the solution might be. We’re exploring capturing additional context like the function call graph and argument values and analyzing session replays to figure out the user impact of errors. Would love for folks to try out our free beta and would really appreciate any feedback! We currently support Express, Next.js, and Flask with just a few minute setup (install the library here: prod.getdecipher.com/docs) Library code: https://github.com/decipherai/decipher-client-js , https://github.com/decipherai/decipher-client-py

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos
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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
7%7% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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