Detail, a Bug Finder
Hi HN, tl;dr we built a bug finder that's working really well, especially for app backends. Try it out and send us your thoughts! Long story below. -------------------------- We originally set out to work on technical debt. We had all seen codebases with a lot of debt, so we had personal grudges about the problem, and AI seemed to be making it a lot worse. Tech debt also seemed like a great problem for AI because: 1) a small portion of the work is thinky and strategic, and then the bulk of the execution is pretty mechanical, and 2) when you're solving technical debt, you're usually trying to…
In plain words
Detail is a bug finder tool designed to identify issues in software codebases, with particular strength in app backends. It uses AI to detect bugs by analyzing code and leveraging test-based approaches to identify unintended behavior changes. The tool addresses technical debt problems where mechanical execution can be automated while preserving existing functionality. It is intended for developers and teams working on maintaining and improving codebases.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, tl;dr we built a bug finder that's working really well, especially for app backends. Try it out and send us your thoughts! Long story below. -------------------------- We originally set out to work on technical debt. We had all seen codebases with a lot of debt, so we had personal grudges about the problem, and AI seemed to be making it a lot worse. Tech debt also seemed like a great problem for AI because: 1) a small portion of the work is thinky and strategic, and then the bulk of the execution is pretty mechanical, and 2) when you're solving technical debt, you're usually trying to preserve existing behavior, just change the implementation. That means you can treat it as a closed-loop problem if you figure out good ways to detect unintended behavior changes due to a code change. And we know how to do that – that's what tests are for! So we started with writing tests. Tests create the guardrails that make future code changes safer. Our thinking was: if we can test well enough, we can automate a lot of other tech debt work at very high quality. We built an agent that could write thousands of new tests for a typical codebase, most "merge-quality". Some early users merged hundreds of PRs generated this way, but intuitively the tool always felt "good but not great". We used it sporadically ourselves, and it usually felt like a chore. Around this point we realized: while we had set out to write good tests, we had built a system that, with a few tweaks, might be very good at finding bugs. When we tested it out on some friends' codebases, we discovered that almost every repo has tons of bugs lurking in it that we were able to flag. Serious bugs, interesting enough that people dropped what they were doing to fix them. Sitting right there in peoples codebases, already merged, running in prod. We also found a lot of vulns, even in mature codebases, and sometimes even right after someone had gotten a pentest. Under the hood: - We check out a codebase and figure out how to build it for local dev and exercise it with tests. - We take snapshots of the built local dev state. (We use Runloop for this and are big fans.) - We spin up hundreds of copies of the local dev environment to exercise the codebase in thousands of ways and flag behaviors that seem wrong. - We pick the most salient, scary examples and deliver them as linear tickets, github issues, or emails. In practice, it's working pretty well. We've been able to find bugs in everything from compilers to trading platforms (even in rust code), but the sweet spot is app backends. Our approach trades compute for quality. Our codebase scans take hours, far beyond what would be practical for a code review bot. But the result is that we can make more judicious use of engineers’ attention, and we think that’s going to be the most important variable. Longer term, we think compute is cheap, engineer attention is expensive. Wielded properly, the newest models can execute complicated changes, even in large codebases. That means the limiting reagent in building software is human attention. It still takes time and focus for an engineer to ingest information, e.g. existing code, organizational context, and product requirements. These are all necessary before an engineer can articulate what they want in precise terms and do a competent job reviewing the resulting diff. For now we're finding bugs, but the techniques we're developing extend to a lot of other background, semi-proactive work to improve codebases. Try it out and tell us what you think. Free first scan, no credit card required: https://detail.dev/ We're also scanning on OSS repos, if you have any requests. The system is pretty high signal-to-noise, but we don't want to risk annoying maintainers by automatically opening issues, so if you request a scan for an OSS repo the results will go to you personally. https://detail.dev/oss
Does the same job
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Community, All the HN belong to you. This is an archive of hacker news that fits in your browser. When I made HN Made of Primes I realized I could probably do this offline sqlite/wasm thing with the whole GBs of archive. The whole dataset. So I tried it, and this is it. Have Hacker News on your device. Go to this repo (https://github.com/DOSAYGO-STUDIO/HackerBook): you can download it. Big Query -> ETL -> npx serve docs - that's it. 20 years of HN arguments and beauty, can be yours forever. So they'll never die. Ever. It's the unkillable static archive of HN and it's…
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