Since the Sahara AI testnet started in December last year, I have been tracking this project and have written a few articles introducing the basic situation and important progress of the project. Recently, Sahara AI is about to have its TGE, opening up 1.4167% of its share to the community. With a FDV of 600 million, it's actually not cheap (the last round of financing was valued at 400 million USD), but I still decided to participate.

Mainly, I am optimistic about @SaharaLabsAI as a public chain designed for native AI, which has the opportunity to become a leading representative in this new track. Additionally, Sahara AI's data performance during several rounds of the testnet phase has been very good, with high community enthusiasm and positive ecological feedback.

The platform for this TGE is Buidlpad, whose founder also came from Binance and was previously responsible for Binance Launchpad and CoinMarketCap.

There is a detail worth noting: the assets users need to invest are BNB or USD1, rather than stablecoins like USDT or USDC. As we all know, USD1 is a stablecoin issued by the Trump family, and previously, when the Abu Dhabi sovereign fund invested in Binance, there were 2 billion funds based on USD1. Under such a structure and background, it also indicates that the Sahara AI project will likely receive significant support from ecosystems like Binance and BNB Chain in the future.

Looking back at the key milestones and data of Sahara AI over the past six months:

◦ Sahara AI started with high financing from star institutions (https://t.co/4SbqyFDDFA), co-led by Polychain, YZi Labs, and Pantera Capital, quickly becoming a representative of AI public chains.

◦ During the testnet phases S1 and S2, over a hundred thousand users participated in data labeling, with millions of data points, making it highly competitive (https://t.co/ZMWnF36rvb).

◦ The data labeling capabilities are fed back to other AI applications (https://t.co/2uA3mtYm39), with the first collaboration being with MyShell, opening up a new business model.

◦ Before the launch of the SIWA public testnet (https://t.co/VFyuvdgse4), there were over 3.2 million wallet addresses, over 1.4 million daily active wallets, and more than 200,000 users contributed data to DSP.