Agents learn from trial and error. The expensive part is the error.
A world model moves the error into simulation, which is the whole case for something like PAN 2.
This is a free, open "action-conditioned world model".
Takes an action you describe, and generates up to 20 seconds of video showing what would physically happen next. Its new trick is splitting the job in 2: a language model first works out the plot, then a 29B video model renders it.
You can now connect multiple local computers to the same dot and choose which one it uses for a task.
It can build and test your app on a dedicated machine while you keep coding on your laptop.
You can now connect multiple local computers to the same dot and choose which one it uses for a task.
It can build and test your app on a dedicated machine while you keep coding on your laptop.
@AIQuanting Benchmarking the search APIs makes sense, agents are only as good as what they retrieve. Curious whether latency or result quality moves the scores more.