SeaTicket – AI agent that resolve GitHub and Discord issues

Hacker News

https://seaticket.ai/ After maintaining Seafile, open-source file-sync software, since 2012. Somewhere across those fourteen years, "go check if someone already reported this" turned into one of the most common lines in our team chat. Because the same bug tended to show up multiple times. Nothing connected Github and Discord Issues until my team happened to remember seeing "that thing" somewhere else. SeaTicket is what we built to fix that for ourselves before opening it up. It connects GitHub Issues, and Discords with a handful of other sources like Notion, Confluence, Linear, Jira into one workspace. When something new comes in, the AI agent pulls together related issues, past resolutions, and anything relevant in your knowledge base, and proposes a next step. It doesn't send, reply, or close anything by itself because it waits for the approval of its suggestion first. We didn't want to ship something that talks to our own users unsupervised, so we're not asking anyone else to either. The genuinely hard part has been matching the same underlying issue across sources that describe it in almost no shared vocabulary like a two-line Discord comment and a ten-line Github Bug can be trace to the same bug and share zero keywords. Happy to go into how we approach that in the comments if HNPs want it; it's the part I'd most enjoy having picked apart. It's built for teams whose issues don't arrive through one queue like open-source maintainers especially, but also product and support teams managing multiple repos and Discord servers. It's deliberately not trying to be Zendesk or Fin: those assume a support team working a shared ticket queue, and we assume your team already lives in GitHub and wants issues handled there, not in a separate tool. Pricing has no per-seat cost on any plan, including a free one i think bug reports scattered across repos and Discord must have a one place that they can be resolved, I'd like to know whether this is actually useful to you or whether we're solving a problem you don't have. Happy to answer anything about how it works under the hood.

Visit product View on Hacker NewsJul 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, new · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released

44%44% 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: users · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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