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Better and Faster Search / Discovery

Hacker News

Better and Faster Search / Discovery

Sometimes the best results come from the questions you didn’t think to ask. Most searches start generic, e.g. “Find a toy for a 10-year-old boy.” Foundational AI models give you the usual suspects: Legos, STEM kits, gadgets. That's a great start! But… what if that’s not exactly what you want? Guiver helps you refine your goal before recommending anything. You start with a simple query and then answer smart, AI-generated questions and then you finally get more specific recommendations. Example: “Find a toy for a 10-year-old boy” Guiver refines with questions: What kind of toys? (board games), Budget? (< $20), Collaborative or solo? (collaborative), Activity length? (<30 minutes). Now Guiver's tailored recommendations are: 'Ticket to Ride', 'Catan Junior', and 'Boggle'. Very different from the initial suggestions by the foundational AI models; More personalized and more of what you actually want. Same goal. Better results. Guiver is collaborative: a) Recall — Saves every detail of your search, i.e. choices made, URLs shown (no more 37 open tabs), summary table. b) Resume — Pick up exactly where you left off, anytime. Every choice. Every URL. c) Refine — Change your choices later, get updated results instantly. d) Share your search so others can adjust and send it back. E.g. choosing renovation materials with your spouse, or finding the right appliances for your home, or planning trips with friends. See for yourself, here's the search for “Find a toy for a 10-year-old boy” → https://www.guiver.ai/?r=dadaf84bc46230ab Cognitively faster. Less stressful. More precise.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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: model, models, activity · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
51%51% predicted probability of success on TrustMRR, 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
44%44% 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 · Missing: plus, platform, intuitive
36%36% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
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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