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Bird Search(Twitter Search 2.0) – The Search Tool You've Always Wanted

Hacker News

Bird Search(Twitter Search 2.0) – The Search Tool You've Always Wanted

The majority of micro-influencers and influencers know how to game the system (the Twitter algorithm) to increase the reach of their content by doing many tricks such as having their inner circle start creating immediate engagement, using popular hashtags, talking about the popular trend of the day, and so on. One thing these twitterratis have in common is that they seek out the best stuff, game it, and remix it in their own unique style. Started digging into finding answers, how do they find out the best content - Follow few people(mostly prolific users), learn from their tweets - Follow lists, learn from their tweets and topics - Find any content, conversations, trends via Twitter search While the first two options seems to be straightforward, Twitter search appears to be more intriguing to me. The more I look into it, the more I realize that Twitter search is a gold mine for finding the greatest content from certain individuals, conversations between two users, users that continuously tweeting around a hashtag, and many other things. As an example: I wanted to find out what Elon musk was tweeting about dogecoin in 2020, here is the query dogecoin (#dogecoin) from:elonmusk since_date: 2020/01/01 I wanted to find out what conversations were happened between Jack and Elon Musk around crypto currency last year, here is the query (crypto OR cryptocurrency OR bitcoin) from:elonmusk to:jack since_date: 2020/01/01 until_date:2020/12/31 My initial impression was that it is a search, and one can benefit from it only if they know what search parameters to use. It appears that Twitter does not expose all the search parameters to users unless you know what all parameters are available and how to use them (which requires some learning), and it is not even available on the mobile app. To scratch my itch, I created Bird search for mobile and desktop. My goal was for everyone to find content on Twitter easily without the need to learn search operators at all. I reverse engineered how Twitter search works and tried to expose all the search parameters(they are more parameters yet to be done). For instance, here is what all search operators being used on Twitter App page: https://photos.google.com/share/AF1QipOIGQkHpqWMtQU1KvwydBdn... Search operators available on Twitter https://photos.google.com/share/AF1QipNHTkWjxlcMzpCd7m9m6hBF... You can find tweets based on - Content - words - Conversations - discussion between two users - etc You can narrow down your search to - Verified users, lists, people you follow - Specific date range - Filter: media only, links only ,photos only & videos only. - etc There is no other social platform that allows you to access such content from the archive, which is why Twitter's search functionality is so unique. It's amazing to watch how people utilize Twitter for so many different things. To inspire others what type of content one can search for, I introduced “Discover” section https://photos.app.goo.gl/9dJvtu1K7XfFynzD7 https://photos.app.goo.gl/MvvwikcK2Y37ynLz8 https://photos.app.goo.gl/GBzndG9NszDLSfgGA Search phrases are available everywhere - Spotlight search - Siri Supports Siri https://photos.app.goo.gl/ufRJF3vXiUJZKsKz7 Supports Shortcuts https://photos.app.goo.gl/kgHiTwwUFrC8o3zs5 Supports as side by side app https://photos.app.goo.gl/DYaeg798k3Gbm2429 Support spotlight search https://photos.app.goo.gl/5gJUKDcN455f3DcU7

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, started · Missing: reddit linkedin, podcasting, latex
92%92% 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.
TrustMRRFits verified-revenue profile · Strong signals: video, google, answers · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
53%53% 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
41%41% 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 HuntUnlikely to reach the leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto, cryptocurrency, introduce · Missing: web3, chat, make money
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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