Most AI dev tools just read your code and guess. Osloq actually runs it. Connect your GitHub, pick an issue, and an AI agent spins up a real sandbox, clones your repo, runs it, and tries to reproduce the bug the way a developer would. You get a report backed by real evidence. What happened, the steps it took, and whether the bug is real, not a hallucinated guess. No local setup, no "works on my machine." It handles the tedious reproduction step so you jump straight to fixing.
this came from a problem every developer knows too well. someone files a bug report, and before you can even think about fixing it, you drop what you're working on, dig through the repro steps, get your project into the exact state they described, and run it over and over just to confirm the bug is even real. half the time it ends in "works on my machine" and the issue sits there for weeks.
so i built osloq to do that part. you hand it a github issue, and it spins up a real sandbox, clones and runs your actual repo, and tries to reproduce the bug the way a developer would. then it hands you a report backed by real evidence. what it did, what happened, and whether the bug is actually real.
the hardest part while building it was trust. early on the agent would "reproduce" bugs that weren't real, basically confident hallucinations. so a lot of the work went into making it run real code and prove what it found, instead of guessing from reading the source. if it can't back a claim with evidence, it says so.
it's live today and i'd genuinely love your feedback. what would make this useful in your workflow? ask me anything, i'll be here all day.
About Osloq on Product Hunt
“An AI agent that reproduces GitHub issues for you”
Osloq launched on Product Hunt on July 3rd, 2026 and earned 180 upvotes and 50 comments, placing #4 on the daily leaderboard. Most AI dev tools just read your code and guess. Osloq actually runs it. Connect your GitHub, pick an issue, and an AI agent spins up a real sandbox, clones your repo, runs it, and tries to reproduce the bug the way a developer would. You get a report backed by real evidence. What happened, the steps it took, and whether the bug is real, not a hallucinated guess. No local setup, no "works on my machine." It handles the tedious reproduction step so you jump straight to fixing.
On the analytics side, Osloq competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Osloq performed against the three products that launched closest to it on the same day.
Who hunted Osloq?
Osloq was hunted by Enes. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Osloq including community comment highlights and product details, visit the product overview.
Enes
hey, product hunt! 👋
i'm enes, solo founder of osloq.
this came from a problem every developer knows too well. someone files a bug report, and before you can even think about fixing it, you drop what you're working on, dig through the repro steps, get your project into the exact state they described, and run it over and over just to confirm the bug is even real. half the time it ends in "works on my machine" and the issue sits there for weeks.
so i built osloq to do that part. you hand it a github issue, and it spins up a real sandbox, clones and runs your actual repo, and tries to reproduce the bug the way a developer would. then it hands you a report backed by real evidence. what it did, what happened, and whether the bug is actually real.
the hardest part while building it was trust. early on the agent would "reproduce" bugs that weren't real, basically confident hallucinations. so a lot of the work went into making it run real code and prove what it found, instead of guessing from reading the source. if it can't back a claim with evidence, it says so.
it's live today and i'd genuinely love your feedback. what would make this useful in your workflow? ask me anything, i'll be here all day.