201–210 of 1001 posts
Better question is why use any code? Generate random functions and select based on measuring the distribution of output of these functions against metrics of interest. A pure black box of instruction that is more performant than any verbose code or algorithm we could come up with, because all we select for is performance above all. Directed evolution essentially of the codebase, generated through mutation and selecti…
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Just use Go. LLMs have seen a ton of it, they write it well, it compiles practically instantly, and it has all the advantages of a typed compiled language. I created a big Python codebase using AI, and the LLM constantly guesses arguments or dictionary formats wrong. Unit tests and stuff like pydantic help, but it's better to avoid that whole class of runtime errors altogether.
But what is the selling point for Go? I get that it is allegedly hailed to be a simple language with basically no batteries included, but why is that a selling point? Does Go excel at anything no other language does?
Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data. I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python. My follow up question: with AI now, why care about a lang until you need to?
Just use Go. LLMs have seen a ton of it, they write it well, it compiles practically instantly, and it has all the advantages of a typed compiled language. I created a big Python codebase using AI, and the LLM constantly guesses arguments or dictionary formats wrong. Unit tests and stuff like pydantic help, but it's better to avoid that whole class of runtime errors altogether.
AI/ML stuff: Python Personal: Rust/Go based on criticality of being able to glean code quickly, or memory usage, etc
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Go is fun, you should actually learn it
Oh man... I like go because it is compiled, performant, strong and statically typed. But "fun" is not something I would say about it. The ergonomics of error handling, lack of ternary operator and other stuff that compiled 30yo languages already had ...
Many here propose replacing Python with more performant, but less familiar languages - mostly Rust, Go. But I find the argument that the AI - HUMAN interface is the most important. A simple version of this is “no, stick with Python if that’s what you know”. A more interesting version is “use this new found AI leeway to move up the abstraction level”, “try something more expressive and human oriented”, “make a DSL and…
So yes, people can bless Go and Rust all they want. Nothing is wrong with the languages, but I agree that learning them for the sake of AI usage is probably not the best idea if one is competent in a language already.
Disclosure: Lattner is one of my programming heroes, so I might be biased.
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Just use Go. LLMs have seen a ton of it, they write it well, it compiles practically instantly, and it has all the advantages of a typed compiled language. I created a big Python codebase using AI, and the LLM constantly guesses arguments or dictionary formats wrong. Unit tests and stuff like pydantic help, but it's better to avoid that whole class of runtime errors altogether.
Why use Go when you can use Rust?
Earlier quoted context omitted.
But what is the selling point for Go? I get that it is allegedly hailed to be a simple language with basically no batteries included, but why is that a selling point? Does Go excel at anything no other language does?
For one thing it’s statically typed and has many fewer foot guns than Python, so the llm-produced code is more likely to do what you expect.
Go’s benefit are primarily around simplicity, readability, and concurrency.
Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data. I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python. My follow up question: with AI now, why care about a lang until you need to?
Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data. I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python. My follow up question: with AI now, why care about a lang until you need to?
Surprisingly, LLMs are actually much worse at reasoning in Python than other common programming languages for agentic coding tasks. Data here: https://gertlabs.com/rankings?mode=agentic_coding