Usare Jev per risparmiare tantissimi token nel tuo agente AI mantenendo alte le prestazioni.

The transcript discusses a novel approach to optimizing token usage in AI agents. JEV is introduced as a specialized model with a particular architecture designed for speed, with the specific capability of making decisions and returning Boolean values, scores, or choices. A common problem in agent systems is that as conversations progress, the context window fills up, requiring a cleanup function called slash compact. However, slash compact is slow because it creates summaries of previous context. Tamara's innovation addresses this inefficiency by leveraging JEV's speed to make simple binary decisions about whether function outputs should be retained or discarded. This approach frees up space much more quickly than traditional summarization methods. The solution has been implemented as a Cloud Code Plugin called Fast Jeev Comp, which has achieved significant adoption with over 6,800 GitHub stars. The transcript also notes that the same approach can be replicated locally using open-source models with similar functionality to JEV, such as Laya or Rizzo Flow models, making the solution more accessible and flexible for different deployment scenarios.