Ben Hylak

Building AI Products That Actually Work — Ben Hylak (Raindrop), Sid Bendre (Oleve)

Jul 24, 2025 · 18:42

Ben Hylak (Raindrop) and Sid Bendre (Oleve) argue that building reliable AI products requires iterative real-world signals over traditional evals. Ben debunks eval myths—evals don't measure product quality, LLM-as-judge fails, and production evals are costly—and stresses tracking explicit signals (thumbs up/down, copy rate) and implicit signals (refusals, frustration) to identify issues. Sid introduces Trellis, a framework for scaling viral AI apps that uses discretization—breaking infinite output into intent buckets—prioritization by volume times negative sentiment times achievable delta, and recursive refinement. Starting with an MVP, teams classify user intents, convert them into semi-deterministic workflows, then repeatedly drill into sub-intents to engineer repeatable, attributable magic. Oleve's approach, powered by Raindrop, has scaled six viral products to $6M ARR profitably with four people.

The era of unbounded products: Designing for Multimodal IO: Ben Hylak

Sep 25, 2024 · 20:32

Ben Hylak, founder of Dawn and former Apple Vision Pro designer, argues that the key to building intuitive AI products in the era of unbounded interfaces is adding structure—highlighting what matters, establishing hierarchy, and leveraging familiarity—lessons from designing VisionOS. He shows how successful AI apps like Dot, Perplexity, and Claude use structure (e.g., Claude pulling code into artifacts) while the Vercel chatbot's inline dynamic UI is an anti-pattern because it disrupts conversation flow. For agents, spreadsheets (like Clay) make unfamiliar multi-step tasks familiar. Looking ahead, Hylak predicts less prompt engineering via sparse autoencoders for millions of ranked, personalized presets, shifting product evaluation from evals to user analytics as apps become increasingly personalized.