Curious Kitty

Because Tapfree can use on-screen context, trust is a big hurdle: what exactly is read from the screen, what’s processed on-device vs sent to a server, and what controls/guarantees did you add so a cautious user can feel safe enabling the needed permissions?

How do you think about personalization versus privacy: what do you store or remember to make the experience feel continuous, what do you deliberately avoid retaining, and how can users control or delete that history?

A lot of teams get burned by false positives and stale installs; what are the main failure modes you’ve seen in technographics, and what concrete mechanisms did you build to reduce them (recrawl cadence, decay rules, customer feedback loops, suppression lists, etc.)?

What exactly does it mean to “buy a track outright” on claw.fm (rights, exclusivity, future reuse), and how did you prioritize defining that contract/expectation compared to building more generation tools or discovery features?

When someone enters an ICP and gets a company count, how should they interpret that number versus their “true” TAM—how do you account for database coverage gaps, misclassified industries, or missing offline/stealth segments?

How do you handle the “where does the video live?” decision in real customer setups—do you expect people to upload to VForms, use YouTube/Vimeo, or self-host—and what tradeoffs did you make around privacy, access control, and performance?

The optional token layer adds powerful incentives but also risk (speculation, misalignment, trust). In what situations do you actively recommend *not* launching a token, and what guardrails did you design to keep the product’s utility—not the token—at the center?

Complex documents fail in very specific ways (tables, footnotes, scanned PDFs, inconsistent terminology). Which failure modes are you optimizing for first, and how do you measure quality—coverage, citation correctness, numeric fidelity, or something else?

People join self-hosting/local-AI communities for opposite reasons—some want deep privacy and control, others want speed and convenience. Which type ends up getting the most value, and how do you design the club so both don’t churn?

A lot of “no-code agent” tools break down when you need real integrations, multi-step interactions, and production requirements—what did you deliberately *not* build first (channels, auth/multi-tenancy, exportability, approvals), and how did you decide those tradeoffs?

When someone evaluates you against modern BI (Looker/Tableau/Power BI) plus “AI add-ons,” what’s the strongest reason to switch? This is a pretty crowded space, what makes you stand out?

Molthunt bakes in token mechanics (coins/leaderboards). How do you prevent tokenization from hijacking the core goal of discovering useful agent-built work—i.e., what’s your principle for when economics should amplify quality vs when it creates perverse incentives?

How do you balance “creative exploration” with production constraints (scale, collisions, instancing, LODs/performance budgets, naming/scene hierarchy)? Which constraints did you choose to enforce first, and which did you deliberately postpone because they slow iteration?

For an existing Stripe customer, what does adoption look like end-to-end (API changes, webhooks, customer portal/checkout, finance workflows), and what’s the smallest viable rollout path that avoids double-charging or breaking renewals?

A lot of prompt tools either add generic structure or change intent. How do you measure that Pretty Prompt improves outcomes without “polishing the wrong meaning,” and what does success look like (fewer iterations, higher satisfaction, better task completion)?

ChatGPT’s UI changes often and extensions can be fragile—what did you optimize for to keep the experience reliable over time, and what tradeoffs did you make between “deep integration” and “breakage risk”?

WhatsApp operations often get blocked by onboarding, verification, and template approval/delivery quirks. What parts of that process does Kipps actively de-risk or simplify, and how do you help customers keep costs predictable as WhatsApp pricing/models change (especially around templates and outbound messaging)?

Many people can already get “a resume draft” from ChatGPT or an AI builder—what is the specific switching trigger you’re betting on (e.g., better questions, better bullets, fewer hallucinations, faster tailoring), and where do you draw the line between “writer” vs “optimizer”?

What’s your differentiation against (1) enterprise AI visibility platforms and (2) doing this manually with prompts + spreadsheets + content briefs—specifically, what do you do that others don’t, and where do you intentionally not compete?

Open-source and self-hosting are powerful but raise trust and operational questions: how do you think about supportability and reliability (e.g., “who do I call at 2am”), and what’s your long-term model for balancing community-led roadmap with business-critical, time-sensitive needs?