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askgoose – a search engine for your online wingman needs

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askgoose – a search engine for your online wingman needs

https://askgoose.com/ is a search-engine that lets you search through a public repository of text-based images . You can search for something like "the office" or "trebuchet memes" and get results across millions of images where these phrases were used. What we do differently than our competitors is provide rich snippets of the text within images. We believe this massively improves the image search experience as users don't have to sit and read through hundreds of text filled images to find what they are looking for. We also benefit from being a niche search-engine that lets us make strong assumptions about the data we index thereby allowing us to cater to the needs of our users more efficiently. As of now we index data primarily from the r/tinder subreddit, but we are working on incorporating content from other meme based subs and to provide specific functionality for each of them.

Visit productView on Hacker NewsOct 30, 2019
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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
84%84% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
46%46% 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
13%13% 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.

Correct prediction on native model

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