The old Tiemen @ recallnet progress bar has just been raised to 30%, and the TGE time window has basically been locked to the end of the month. The community has suddenly turned into a "qualifying race" scene these days, and everyone is wondering if their scores can make it into the top 500. I have always thought that Recall's approach is quite aggressive: without empty words, it directly pulls AI onto the chain to fight in the arena. If the model wants a position, it can run around the trading floor with real skills, leaving all traces of results, relying on data to speak for itself. This round of challenge is underway, from 9/8 to 9/12, with a prize pool of $10000. There are already around twenty agents running on the field, and the voting is still double the points. Want to watch the live broadcast or cast a vote casually. After the end, there's no need to wait too long. From 9/15 to 9/19, the next game follows closely. From an ecological perspective, @ recallnet's goal is actually to "implement" - to make useful models visible and validated in a public environment, and then gradually grow into a self-sustaining ecosystem. Yesterday, the official also released an update stating that the "signal" and "noise" on the chain will be separated in real-time, and the performance of each agent will be directly displayed in Arena. Now there are several people on the list who have achieved double-digit growth, indicating that this set of evaluations is not just a decoration and can run. The enterprise side is also implementing it, and early adopters like Fellow can directly use the Recall API to complete a task that originally took three months in a week - this kind of integration efficiency is visually friendly to team costs. Looking further ahead, the project has received considerable financial support (in the range of over 40 million US dollars), and the route also states that it will be connected to Coinbase's gas free verification. Once this is completed, the user threshold will be lowered even further. If you have already scored on the list, don't miss this round of double votes; If you have just arrived, start doing interaction, voting, and simple on chain actions now, even if the score is not high, don't miss the window - the subsequent benefits of such projects usually depend on the early footprints. In short, Recall is not just a "technical project", but more like an experimental field that pulls everyone in: the community sets questions, the model takes the stage, the results are uploaded to the chain, and users use them according to their resumes. Wait until the TGE lands at the end of the month and see how much sparks it can ignite. @cookiedotfun SNAPS RecallNet