Thanks for your comments, @chaboud! I definitely agree that LLMs _can_ be an amazing tool for learning, but as you note, one must be intentional about using them that way. I feel like the messages being pushed down from leadership are NOT of the form "Use AI to learn topics deeply and discover new things." The leadership's perspective is more like "Use all the AI you can to ship as fast as you can."
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.
> The work that’s left is more interesting and more valuable than the work that’s leaving.
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.
This is interesting; thanks for sharing! I have been curious about the adoption of Rust in computational biology. I know that the folks at Saint Jude's [1] are also using Rust for their 'omics research.
I'm very curious to hear folks' thoughts on this. My intuition is that there are at least two key factors that make tasks difficult for AI tools.
(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.
This might not be exactly what you mean by a CLI app called "whatisthis", but I have been using cheat.sh and the pattern below for a few years. It works really well!
I'm sorry if you've had a bad experience working with cloud technologies, but this comparison strikes me as pretty unfair.
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.
If you haven't written off Python completely, I would suggest giving it a second chance using uv [1] for managing environments and Python versions. In my opinion, uv is the best thing to happen to Python in 10 years.
I agree with this, and I'd go even a bit further and describe them as predatory. They seem to have absolute contempt for their customers, and look for every possible opportunity to bleed them dry.
But then again, I'm probably guilty of anthropomorphizing the lawnmower. [1]
I had the same reaction. Haven't they been selling DGX boxes for almost 10 years now? And they've been selling the rack-scale NVL72 beast for probably a few years.[1]
I wish this was better understood more broadly. Grants aren't no-strings-attached gifts—far from it; they are contracts.
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.
Yeah, I am very curious to see the responses from other institutions. The University of Texas (Austin) said they were "honored" to have received the compact.[1] That is obviously very concerning.
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.