ScreenKite and PilotCut Developer Mike Chong Plans to Use AI Agents to Handle User Feedback
ScreenKite and PilotCut developer Mike Chong stated that they receive hundreds of user feedback daily, making it impossible for manual responses, scheduling investigations, and informing users in a timely manner. He is preparing to train Hermes agents or Grok bots to assist in handling this feedback. ScreenKite is a macOS native screen recording and AI video editing tool, positioned as a faster alternative to products like Screen Studio; PilotCut is an AI-native video editor that supports collaboration with models like Grok for editing. The developer has previously achieved a certain ARR scale through the product, with feedback volume significantly increasing as the user base grows. Independent developers face support bottlenecks during rapid product growth, shifting resources from manual customer service to AI agent automation, benefiting teams that can scale feedback handling while putting pressure on individual developers who have high outreach but low automation. This event-driven approach explores the implementation of AI customer service in early-stage products. Source: Public Information
ABAB AI Insight
Mike Chong previously built ScreenKite as an independent developer, positioning it against Screen Studio and emphasizing 3x export speed with Metal acceleration, primarily relying on his own time and minimal investment, later expanding to PilotCut to form a screen recording + editing combination. Resources are shifting from product feature iteration to supporting process automation, motivated by feedback volume exceeding the manual processing threshold, standardizing responses, scheduling, and closure notifications through agent training. Similar to the early automation support paths of independent products like Gumroad, current AI-native tools are transitioning from feature delivery to operational scaling, with the industry as a whole moving from reliance on manual customer service to a lightweight support system driven by agents. Essentially, this represents a technological replacement where the multi-step manual feedback loop is taken over by AI agents, with the mechanism allowing structured feedback to be classified, responded to, and updated by models, reducing the marginal support costs for solo developers. ABAB News · Cognitive Laws
- Feedback volume determines the necessity for automation
- Solo products die from support bottlenecks
- Agents take over repetitive loops.