Apeing Efficiently

Web3 ecosystem moves fast, and investing in new projects is what most of us do without the correct knowledge, leading to bad decisions more often than good ones. Due diligence is sometimes complicated, especially if you are not an expert in the area of the particular gems. At the beginning of or during the bull run, everyone rushes to take entries in the most innovative, and promising projects with trending narratives. However, the smartest people have already aped in their bags during the bear market or before the hype of the project began during the bull market, and are waiting to follow the success of their entries. They are usually the most successful because they take time to analyse, deep-dive into the details of the project, and make all the necessary efforts to understand what they are getting involved in. This is what differentiates a pro from a beginner. Instead of waiting for the news to be published by the big ventures or KOLs, missing the correct timing for apeing, and losing your portfolio because of FOMO becoming someone else's exit liquidity, you need to act before and position yourself as a pioneer by correctly putting in the time and effort. Only some people can do it or have the necessary knowledge to do so, but fortunately, we do know someone who knows everything to help you: artificial intelligence. Understanding Web3 project data, looking into the details of token economics or the technical aspects like reading and understanding intelligent contracts, and knowing everything about hundreds of projects regularly is challenging and takes a lot of time. Keeping up with the latest trends and being able to follow them is rewarding for those who have the experience, even though it’s hard to get all the information from different sources. This is where Next Gem AI comes in places: we extract all information we can find on any existing or new projects that are being created in the crypto ecosystem by taking the best possible accurate data, and we feed the most intelligent AI to make that work for us, analyse and score them based on pre-existing rules (as shown in the figure below).

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