Your comment about programming historically being 1% judgement and 99% effort is interesting. I'm not sure I agree with those exact percentages, but nonetheless, I think the more consequential part of your comment is that the 99% is being reduced by a couple of orders of magnitude. I think that's what ought to trouble us as software engineers. Expending effort is often how we learn and grow. This is true in the context of physical activity (e.g., going to the gym to strengthen muscles) as well as in the context of intellectual activity (e.g., struggling through a problem set). If I go to the gym with my forklift, I can lift things, but I'm not likely to get stronger. Similarly, if I have Claude write all my code, I'm probably not learning much.

I'm not sure I agree with that. Many (or most) of the software engineers I know find the heavy reliance on AI coding agents/assistants pretty soul-sucking and uninteresting. I feel the same, and I'm looking for some kind of middle ground. For example, I will only use agents when doing so would not deprive me of learning and discovery.

I'm very bullish on the long-term prospects of Rust in computational biology—as well as research computing more generally.

[1] https://github.com/stjude-rust-labs

(1.) The overall level of complexity. For example, I think (today) you would need to do an extreme amount of hand-holding to get an AI to write a browser, an OS, or anything with similarly very high level of complexity.

(2.) The representation in the AI's training set. Anything that is common in the training set (e.g., to-do app in JavaScript) will be trivial for an AI to one-shot. Anything that is rare, or absent in the training set is going to be much, much harder for the AI. If I try to ask an AI tool to develop a automated, ultra-low-latency high-frequency trading system, it's probably going to struggle because those kinds of applications aren't in the open-source domain. The same is true for completely novel algorithms. So maybe things that are essentially new science/engineering.

I'm very curious to hear others' thoughts on this, as I've been wondering about this, too.

Comments URL: https://news.ycombinator.com/item?id=47394939

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Comments URL: https://news.ycombinator.com/item?id=47341073

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Comments URL: https://news.ycombinator.com/item?id=47270214

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curl cheat.sh/grep # fetches brief grep cheat sheet

Comments URL: https://news.ycombinator.com/item?id=46231295

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Public clouds have enabled thousands of companies to put their products into the world without having to build and maintain hardware. They've completely changed the landscape of computing.

[1] https://docs.astral.sh/uv/

But then again, I'm probably guilty of anthropomorphizing the lawnmower. [1]

[1] https://www.youtube.com/watch?v=-zRN7XLCRhc&t=2308s

What is changing?

[1] https://www.nvidia.com/en-us/data-center/gb200-nvl72/

[1] https://eusprig.org/

When a researcher at a university gets a grant, that's the federal government hiring that researcher and their team to complete a specific research project. The research team has a particular research question that the federal government has deemed important enough that U.S. tax payers would benefit from getting an answer.

[1] https://www.chronicle.com/article/the-white-house-sent-its-c...