Minimalist, text only search engine with prefixes to play with

Hacker News

I made this classic looking text only search engine(sorry if that's overexaggerating) where you can get concise and straightforward results with sources. There are 5 prefixes right now. It is a prototype kinda thing right now. I am busy for my upcoming exams. So, I would appreciate if anyone is interested to contribute. I am currently using Gemini for this project. If anyone can implement better api's and improve code and the features it would be great help. It's generating fake source links right now, the search is slow, prefixes like weather and time needs to be fixed using api's related to weather and time cause gemini can't give the current time and weather of any place. Pdf resources its giving are fake and do not exist. Let me know what more I can add here. Or directly contribute by yourself.

Visit product View on Hacker NewsDec 24, 2024

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5points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: gemini, using, code · Missing: mac, agents, macos

85%85% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist · Missing: https docs, excited, just released

58%58% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, 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 · 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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