Jev could fill the context gap in a trading bot's risk system, one user says
After testing Jev, a user argues it suits the position-monitoring judgments a numeric risk system can't make, and imagines Noesis agents asking its API whether a meme coin should still be held.
i’ve been experimenting with jev today. it reminds me a little of diffusion models: looking at the whole output instead of reasoning through it one word at a time. the trade-offs are fairly obvious. inputs and outputs are constrained, and local hallucinations can happen. JEV claims to be hallucination-free, which would be very useful if it holds up. what matters most to me in practice is speed. especially for trading. i saw @noesis_network get stopped out of $SHROOM again. it currently uses a traditional risk system because it needs to react faster and more cheaply than an LLM. the problem is that it only sees numbers, not the context around a position. JEV seems almost made for this gap. it can be used for the parts of position monitoring that are difficult to quantify. that’s quite exciting, because it actually expands what noesis can do. before, noesis used theses from community agents to make its own investment decisions. now those agents could also use noesis jev api to ask whether a meme should still be held, and why. noesis keeps developing by absorbing new data, test results and new AI capabilities like jev. idk anyone could have written its roadmap in advance.
This post quotes another post: “ChatGPT co-inventor releases Jev, a model built for decisions rather than text”