Detail, a Bug Finder
Details
- External ID
- 46207852
- Source
- HN
- Company
- —
- Product
- Detail, a Bug Finder
- Website domain
- detail.dev
- Launched
- Dec. 9, 2025
- Cohort
- —
- Upvotes
- 67
- Upvotes percentile
- 0.8501908396946565
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- —
- Project type
- Hobby / open-source project
- Normalized one-liner
- bug finder
- Manually corrected
- False
Could you build this?
No Building an effective automated bug finder for app backends requires sophisticated program analysis, abstract interpretation, AST traversal, or symbolic execution to avoid drowning users in false positives.
What it would actually take: Requires static analysis frameworks (e.g., Tree-sitter, Semgrep engine, or LLVM/custom compiler passes) paired with fine-tuned models or symbolic execution engines. The hard part is building precise call-graph analyzers, taint analysis, and semantic verification systems that accurately detect complex state or concurrency bugs with high precision.
Discussion
20 comments analyzed.
Competitors mentioned: Dependabot, Renovate, GitLab Premium, bug bounty services
Concerns raised: Pricing too steep at $30/committer/month for enterprise adoption, Requires broad GitHub OAuth permissions ("act on your behalf") causing trust concerns, No public security vulnerability results published, Unclear company information on website (location, legal entity, data access details), Difficulty getting teams to merge automated PRs from similar tools
Feature requests: Support for monorepo projects, Alternative OAuth providers (Google, Okta, password auth), GitLab integration, Self-hosted/on-premise deployment option, Pricing model based on bugs found (per-bug or bug-bounty style) with severity-based scoring
Competitors
Other products that read as similar to this one — 324 launches clear the similarity bar, closest 8 shown.
Attention rank: #54 of 325 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 40 days after the earliest competitor.
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- CodeLeash: framework for quality agent development, NOT an orchestrator · hn · 2026-02-27 · 12 upvotes · similarity 0.48
- We vibe coded our team's issue tracker, knowledge base, telemetry board · hn · 2025-12-09 · 8 upvotes · similarity 0.47
- I nerfed our coding agents on purpose · hn · 2026-06-05 · 27 upvotes · similarity 0.47
- Toad. A unified terminal UI for coding agents · hn · 2025-12-18 · 14 upvotes · similarity 0.46
- Autofix Bot · hn · 2025-12-11 · 37 upvotes · similarity 0.45
Other launches for this product
- No other launches for this product.
Same idea, different domain
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