FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
Our open-source project has 12,000 open issues. Can this help dig out of that hole?
Yes, via a backlog cleanup phase. AI CTO runs a one-time pass on the existing backlog: identifies true duplicates (typically 15-25% of old issues), stale issues with no activity in >12 months (auto-closes with a 'please reopen if still relevant' comment), and issues that are actually resolved (feature shipped, bug fixed but issue never closed). Typical cleanup on a 12K-issue repo: 3-4K issues auto-closed, 1-2K auto-merged to dupes, leaving 6-8K that truly need triage. Takes 1-2 weeks of AI running in low-priority mode; then ongoing triage keeps it clean.
We're a private repo, not open-source. Does this still apply?
Yes, adapted. Private repos usually have lower volume (internal bug reports, stakeholder feature requests) but the triage problems are similar: classification, deduplication, routing. For internal: AI CTO triages against your internal priorities + roadmap, assigns to engineers on the owning team, and routes stakeholder feature requests to product managers for review. First-response SLAs are different (24-48 hours instead of 15 minutes), but the workflow shape is the same.
What about spam / low-quality issues from random GitHub accounts?
Spam filtering is the easy part: AI CTO flags issues from accounts with no profile, no repos, first-time contribution to your project, and body patterns that match known spam templates (crypto promotions, SEO link building). Flagged issues get auto-closed with a spam label. False positives are rare (<1%) because the signals are strong. For the borderline cases (real but low-quality, e.g., 'please add feature X' with no context), the 'request repro' workflow applies — if they don't engage, it closes naturally.
Can it distinguish a security-relevant bug from a regular bug?
Yes, and this is where automation matters most. AI CTO looks for security signals: mentions of auth, session, token, XSS, CSRF, SQL injection, unauthorized access, data leakage, credential exposure. Suspected security issues get: (1) immediate private conversion to GitHub Security Advisory, (2) original issue closed with a redirect note, (3) urgent Slack ping to security-on-call. False positive rate is low because most security reports use recognizable language. The rare false positive is better than a public security bug sitting in the open queue.
How does it handle feature requests — does it auto-close low-priority ones?
No auto-closing for feature requests (that frustrates users fast). Instead, AI CTO routes low-priority requests to a 'future consideration' label and posts a friendly comment: 'Thanks for this idea. We're not prioritizing it right now but would love community upvotes to gauge interest.' Upvotes get tracked automatically. Requests with >25 upvotes escalate to the active backlog. Low-engagement requests age out naturally without you needing to say no. Community feels heard; backlog stays actionable.