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WhatToBuy – Describe your situation, get AI-curated shopping carts

Hacker News

WhatToBuy – Describe your situation, get AI-curated shopping carts

Before reading text please try the app https://www.whattobuy.app (to get great UX feedback) Shopping research is one of the most challenging tasks and people spend 30-60 min before buying an item. We developed a platform called “WhatToBuy” to save people time. In some cases shoppers are not super aware of what to really order for a trip or occasion. Our app helps them to get a range of products needed for each use-cases hence saving time and money. App workflow: Describe your situation in plain English. In Fast mode, you get three ready-to-shop carts: Budget, Balanced, and Premium with real products, real prices, and direct buy links. In Deep mode, AI assistant has a conversation with you first and builds a single cart tailored specifically to your answers. How it works: * You type something like "camping weekend with two young kids" or "setting up a home office on a tight budget" * AI assistant (powered by Claude) parses the scenario and generates a list of specific product search queries. For example, in the above query for camping, product search will be "tent 4-person easy setup" instead of simply "tent". * Those queries hit Shopping API and return real-time results. * A scoring layer ranks by price, rating, and review count to pick one winner per product category per tier. Two modes: * Deep (default): AI assistant asks a few follow up questions before building a single personalized cart. We default to this because more context means dramatically better picks. Sign in is required for this mode, but you can always drop back to Fast mode with one click. * Fast: Instant three-tier carts, no sign in needed, works right away. Please take a look: https://www.whattobuy.app No account needed to try the fast mode. Would love feedback on where the recommendations miss or where the UX feels off.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, context, single · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, answers, way · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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