Predicting "America" in "The United States of ..." Is a different task from predicting the whole sentence.
So the small model is laying the blocks, and the bigger model would be cementing them in place or kicking them down. The bigger model's course correction is what keeps the smaller models predictions relatively on track
"Write a function that takes a string parameter (Todo) and saves it into a text file with the name <current date time (as a Unix epoch)>.txt, and if already present, append to it to the file instead"
The probability distribution for the potential output is different, and it's more limited in the second case perhaps.
Besides, even the "deterministic" systems the author is referring to, are not fully deterministic. They are "deterministic" if we ignore a certain threshold of randomness that could afflict the system. Yes perhaps this threshold is higher when using LLMs, but even when using LLMs, not all inputs share the same level of indeterministic output
"In the matter of reforming things, as distinct from deforming them, there is one plain and simple principle; a principle which will probably be called a paradox. There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, "I don't see the use of this; let us clear it away." To which the more intelligent type of reformer will do well to answer: "If you don't see the use of it, I certainly won't let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.""
Also what people forget, even read access alone can be used to communicate with an attacker.
Assume locally i know a read only agent (running on account A) is reading a specific file from user B. Assume it has access to a secret that user B cannot observe. By prompt injection, you can have the read only agent encode the secret as "read" pattern that user B can decode by looking at file access times.
(You can think of fetch requests and the likes for more involved cases)
So read only, while helpful, does not innately prevent communication with an attacker
I've been trying to work on a new LLM code editor that does just that. When you instruct it to do something, it will evaluate your request, try to analyze the action part of it, the object, subject, etc, and map them to existing symbols in your codebase or, to expected to be created symbols. If all maps, it proceeds. If the map is incomplete, it errors out stating that your statement contained unresolvable ambiguity
I think there is a real benefit here, and it might be the actual next beneficial grounded AI sustainable use in programming. Since I the current "Claude code and friends" are but a state of drunkenness we fell into after the advent of this new technology, but it will prove, with time, that this is not a sustainable approach
My main gripe with tmux is the nested use case (tmux session on my local machine, in which I ssh to another machine, only to tmux attach within the remote machine too). Is there a terminal multiplexer/session daemon that supports nested sessions out of the box with ease?
I wrote quite a bit of configuration to support an "outer" tmux process and "inner" tmux processes on all the remote hosts I have various and different tasks to accomplish. I am not sure how some software would manage these, but in the very least configuring my outer session to use Ctrl+a while the inner one uses Ctrl+b is working well. I have aliases that specify a socket so I can refer to the sessions easily and not get them confused.
Predicting "America" in "The United States of ..." Is a different task from predicting the whole sentence.
So the small model is laying the blocks, and the bigger model would be cementing them in place or kicking them down. The bigger model's course correction is what keeps the smaller models predictions relatively on track
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