3/4/2023 0 Comments John schoenherr supermindWe collaborate on financial research because it brings a different lens to a decision without any individual necessarily committing capital upfront. The GameStop short saga started with a person posting on Reddit. It is a whole different story with collaborative research so far. If someone can genuinely trade on something and make a buck - they are likely better off trading it on their own. The lack of regulations and general incentive misalignment is part of the reason why. Very few social trading platforms took off meaningfully inspite of last year's euphoria. But I doubt it would stick at a time when most individuals are losing money. During a bull market, there is enough retail liquidity in the space to create a venture like that. ![]() There were multiple attempts at making social trading a thing in crypto throughout 2021. The network effects of multiple analysts discovering and collaborating on the platform are the product In my mind, I find the tool to be a hybrid of Kaggle, Tradeview and Twitter. The platform will have tools to visualise, collaborate and share analyses with others.Īnalysts can also open-source pre-defined templates on the platform for others to plug in new data sets and collaborate with them. The analysts do not need to go through the flow of downloading the data and cleaning it or syncing data sets constantly. The data and workflows are native primitives of the platform. Once an analyst has found the required information, they can query on-chain data on the platform and collaborate on the data set with other analysts. In this case, the product indexes the context around which a relevant person had the conversation on different platforms. At this point, it starts to get interesting and reflect how analysts actually search and discover. But what if you wanted to go deeper.Īn example she gave me was - searching across channels for people you follow or macro attributes of people such as " content shared today from people who run protocols, or are web3 founders ". The primary mode of discovery anchors on text search. If you look at how search currently is on Telegram or Reddit - it is clunky workflows that analysts get around with. When an analyst searches for information - about Avalanche or Near- Supermind first shows all the relevant conversations from relevant accounts on publicly available chats on the product. I mention all this because the product she is building uses search as the basis for redefining analyst workflows. But few of them focus on predictive capabilities or collaboration. Today’s data products allow you to look at what has already occurred. A common theme she found was a general lack of data on users and how a new tool could improve it. She stumbled on web3 apps and began spending four to five hours a day. ![]() ![]() Ekta was looking for a new opportunity that would allow a 10x improvement to users and grow for the next 20 years. ![]() Before this, she exited a venture in 2020 and spent nine years in varying data-science roles. She used to lead data platforms and feed personalisation at Sharechat. Ekta was more systematic with how she was doing her research, so I reached out, and we spoke. Generally, you find founders excited about something but their interest wanes with market conditions. Over the next few weeks, I observed her do countless user interviews through the Telegram community I run and was intrigued by what she was doing. She said she was building a new analytical tool and wanted to stay in touch. I had been trying to reduce calls and politely declined but asked for context. I met Ekta a few months back through LinkedIn - where she had reached out in a cold message.
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