IME waiting for an agent to work through a problem is a detriment to attention span; your mind drifts to other things while you wait. Maybe you can steer several agents in a round robin instead, but then there's a cognitive tax from context switching. Faster models mean fewer gaps in focus.
Imagine speeding up current agents 10x, you switch from directing agents to pair-vibing on the fly.
Speed them up 10x more, and you get a SOTA model capable of analyzing and rethinking your entire file in between your key strokes. That would make for one hell of an autocomplete.
Pivot over application, going from coding to anything else, and this can easily give computers features previously impossible to make. In video games, fully general characters reacting realistically to arbitrary dynamic situations. In "serious" apps, interactive work with a system that understands your goals and adapts to you on the fly. Hell, even an OS that can tell you "hey, the data you're obviously looking for is in the tab over there, now highlighted".
And that's just tip of the iceberg. I'd personally love to explore the possibilities.
It also spent almost 800k tokens on these lines…
Basically, the code is a function which takes in an expression where you can use generic data structures as variables and then some specific data structures, and it plugs them in for the variables. It then computes the structure of the resulting data type.
So, admittedly not a trivial task – hence the choice of Fable as the model. Also, this would have taken me few days to do by hand! So, we are living in the future! But one could always wish for more speed and more intelligence.
well, I am.
look at what the market thinks of CPU manufacturers and general computation now that agentic workflows have taken up, all went to the moon after being picked over in favor of GPUs and RAM for years
most computers have been idling, waiting for human input, for decades, and if there was a computationally intensive process it was offloaded to GPUs a long time ago, over the last decade, so CPUs and general processors have remained idle, relegated to just defined conditional statements to switch between tasks with no reasoning capability to occupy compute
now, there are reasoning capabilities to tell a CPU what to do (as a byproduct of the varied processes). Cerebras is not a CPU, it is a special purpose chip for inference, but is hosting LLMs that tell CPUs of all its clients what to do faster than a human can. Outside of Cerebras, LLMs are not doing much to optimize compute of the system they're affecting, as they're reading or compiling code when being used for coding, very few processes are intensive and the CPU is just waiting as if a human was using it because the LLM can't digest and output information fast enough. The CPU ecosystem is very mature for general and varied tasks, but is underutilized.
To the what: any kind of compositing or configurations that humans do, agents can do. AutoCAD, video editing, sequencing in music, all forms of media, all forms of configuration done digitally. right now they rely on snapshots to see and react, and this increases the 'framerate' per say, and rapid and relentless iteration they can do.