Sq

Squads – Social Trading for Web3

Hacker News

Squads – Social Trading for Web3

hey hey! I'm the co-founder of squads -- a product that makes it easy to see what your friends and top investors are doing in web3. This product was developed out my own frustration trying to get more involved in web3. One of the greatest challenges in the crypto space is figuring out where to start. For some of us, we're lucky to have a friend who can serve as a guide, and we wanted to build a product that would make introducing your friends to crypto a more inviting experience. There is so much alpha and information living on Twitter + Discord, but the problem is finding the signal from the noise. With Squads, you don't have to sift through all of the noise -- you can see what the best players in the space are doing the moment they do it. In fact, we have pre-seeded the product with a squad of a16z investors, BAYC holders, and a group of well known twitter profiles so you can get a taste of what the squad structure is like. With this product, you don't have to sit in hundreds of discords and become inundated with notifications. Instead, you can follow who you think is interesting, and see a feed of their activity all in one place. Some quick product highlights: - Social layer for web3 -- create a squad and share the invite link with your friends or anyone else. We took inspiration from discord for the invitation flow, try it out! - Web3 Feed: see what activity is taking place in any of your squads, or from individual wallets that you follow - Profile Page: Showcase all of your digital assets, and your performance, all in one place - Social Graph: Follow your friends or favorite people on the blockchain - Search: Search for any wallet address or ens domain - Returns: See how profitable an individual is in flipping tokens or NFTs

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Actual performance

5points
8comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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: activity · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · 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: trading, profitable · Missing: mobile apps, ios, personal
42%42% 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, profitable · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: web3, crypto · Missing: chat, cryptocurrency, 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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