Comment by yberreby
1 day ago
> I.e you can automate things like checking for memory freeing.
Or, if you don't need to use C (e.g. for FFI or platform compatibility reasons), you could use a language with a compiler that does it for you.
1 day ago
> I.e you can automate things like checking for memory freeing.
Or, if you don't need to use C (e.g. for FFI or platform compatibility reasons), you could use a language with a compiler that does it for you.
Right, a lot of the promise of AI can (and has) been achieved with better tool design. If we get the AI to start writing Assembly or Machine Code as some people want it to, we're going to have the same problems with AI writing in those languages as we did when humans had to use them raw. We invented new languages because we didn't find those old ones expressive enough, so I don't exactly understand the idea that LLMs will have a better time expressing themselves in those languages. The AI forgetting to free memory in C and having to go back and correct itself is a perfect example of this. We invented new tools so we wouldn't have to do that anymore, and they work. Now we are going backwards, and building giant AI datacenters that suck up all the RAM in the world just to make up for lost ground? Weak.
> We invented new languages because we didn't find those old ones expressive enough
Not quite. Its not about being expressive enough to define algorithms, its about simplification, organization and avoidance of repetition. We invented languages to automate a lot of the work that programmers had to do in a lower level language.
C abstracts away handling memory addresses and setting up frame stacks like you would in assembly.
Rust makes handling memory more restrictive so you don't run into issues.
Java abstracts away memory management completely, so you don't need to manage memory, freeing up you to design algorithm without worrying about memory leaks (although apparently you do have to worry if your log statements can execute arbitrary code).
Javascript and Python abstract type definition away through dynamic interpretation.
Likewise, OOP/Typing, functional programming, and other styles were included for better organization.
LLMs are right in line with this. There is no difference between you using a compiler to compile a program, vs a sufficiently advanced LLM writing said compiler and using it to compile your program, vs LLM compiling the program directly with agentic loops for accuracy.
Once we get past the hype of big LLMs, the next chapter is gonna be much smaller, specialized LLMs with architecture that is more deterministic than probabilistic that are gonna replace a lot of tools. The future of programming will be you defining code in a high level language like Python, then the LLM will be able to infer a lot of the information (for example, the task of finding how variables relate to each other is right in line with what transformers do) just from the code and do things like auto infer types, write template code, then adapt it to the specific needs.
In fact, CPUs already do this to a certain extent - modern branch predictors are basically miniature neural networks.