LBan

Love the shift from quarterly brand reports to a continuous signal model. One execution question: when awareness goes up but conversion stays flat in the same period, what do you usually recommend teams diagnose first—creative, channel mix, or audience segmentation? Curious if you have a practical triage sequence.

In this category, “explainability” may matter as much as detection accuracy for retention. Would you consider adding a decision card for each cleanup action (why it was marked duplicate, why keep A/delete B, and rollback window) so users feel safer doing bulk actions?

If a team wants to migrate from browser-based reviews to Pulldog, which 3 adoption metrics would you track first to decide whether to roll it out broadly? (e.g., first-review response time, PR aging backlog, cross-repo context switches)

I’d love to see a reusable efficiency benchmark: for one short-form asset pack (cover + 3 chat visuals), how many minutes does GetMimic typically save vs. a traditional design workflow? Do you have a recommended production flow (brief → generate → QA → export)?

For buyers, the key question is often not “does it run?” but “can it be maintained long-term?” Which trust signals will you prioritize first to improve conversion—update cadence, dependency health, reusability score, changelog quality, or support SLA?

In your lending workflow, which stage did you automate first (doc intake, pre-underwriting, risk checks, or pre-disbursement review)? And what explainability fields do you surface for human reviewers at each stage?

What are the top 3 GitHub workflows where users save the most time (e.g., cross-PR diffing, tracing change rationale, finding key review threads)? Any average time-saved metrics would be super compelling.

How are you defining merge-blocking thresholds in real teams today—strict all-tests-pass, or risk-tiered gates (core journeys must pass, edge cases warn)? Would love to see a practical policy template that teams can copy.

Great concept for creator marketing teams. Curious: what are the best practices you recommend to keep AI influencer content consistent across multi-post story arcs?

Really interesting “proactive AI” angle. How does Enia learn team coding standards over time without introducing style drift across repos?

Love the value prop — super clear. Which website types are currently the hardest to normalize into stable API outputs (e.g., dynamic pages, auth walls, anti-bot pages)?

Love seeing what people are publishing with AI tools lately. One thing we noticed internally is that a lot of these “vibe projects” are surprisingly fun even though they’re tiny — things like little generators, interactive toys, mini games, or niche utilities you build in one sitting. Curious what others here have built with Claude / Cursor / Bolt recently — what’s the most random or fun thing...