Built an app that takes "lightweight laptop for coding under $1200" and returns curated picks with fresh pricing and reasoning. Currently laptops and phones. Design: consumer journey = contraction/expansion of opportunities. Explore → AI curates → shortlist → compare. Each interaction updates recommendations via LLM. Avoided the chatbot trap. LLMs as foundation, but keep good mobile UX (cards, scrolling, navigation).
Not sure if this beats manual research. Honest feedback wanted. iOS app: https://apps.apple.com/us/app/great-recs/id6756940360
Most people buy what the sales person is selling, they don't care.
The tech enthusiast minority watch and read reviews, compare specs.
The only realistic audience for this product is somewhere inbetween those two. Imagine that venn diagram. People who care enough to research but not enough to "research".
My thesis is that group is bigger than the enthusiast community assumes. Most people I know (friends, family, coworkers) fall into it: they don't want to get ripped off, but they also don't want to spend a weekend on r/laptops and YouTube reviews.
The sales floor person optimizes for commission. The enthusiast optimizes for specs. This tries to optimize for "what actually fits your life" — which requires understanding context (budget, use case, priorities) more than raw benchmarks.
Whether it actually beats manual research for that middle group — perhaps not yet but I do feel there’s a path somewhere