An app that finds your perfect laptop in seconds (no affiliate agenda)

Hacker News

I hate laptop shopping. Need to keep 20 Chrome tabs open, cross-reference specs, check pricing. "Best laptop 2026" lists are commission-optimized, not helpful. They go stale when prices shift. 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

Visit product View on Hacker NewsJan 27, 2026

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, coding · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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