alt Hacker NewsIn the age of agentic programming and the ever increasing pressure to ship faster, I'm afraid this kind of knowledge will become more and more fringe, even moreso than it is today. Who has the time to think through the intricacies of parallel data structures? Clearly we'll just throw more hardware at problems, write yet another service/api/http endpoint and move on to the next hype. The LLM figures out the algorithms and we soon lose the skills to develop new ones. And tell each other the scifi BS myth that "AI" will invent new data structures in the future so we don't even beed humans in the loop.
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I think the opposite is the case. We increasingly need to care more about performance and know how to leverage hardware better.
The market is telling us that through increased hardware prices.
LLMs being very powerful means that we need to start being smarter about allocating resources. Should chat apps really eat up gigabytes of RAM and be entitled to cores, when we could use that for inference?
People underestimate the effect of knowledge accumulation that happens when learning from high quality sources imo.
LLMs aren't even close to the level of knowledge distillation capacity a human has yet.
Or..? A golden era for people who want to think of new things and test out their ideas quickly by having AI code it up.
AI is like a genie: be careful what you wish for or you'll get what you asked for.
Lately at work I've done C++ optimization tricks like inplace_map, inplace_string, placement new to inline map-like iterators inside a view adapter's iterators and putting that byte buffer as the first member of the class to not incur std::max_align_t padding with the other members. At a higher architecture level, I wrote a data model binding library that can serialize JSON, YAML and CBOR documents to an output iterator one byte at a time without incurring heap allocation in most cases.
This is because I work on an embedded system with 640 KiB of SRAM and given the sheer amount of run-time data it will have to handle and produce, I'm wary not only about heap usage, but also heap fragmentation.
AI will readily identify such tricks, it can even help implement them, but unless constrained otherwise AI will pick the most expedient solution that answers the question (note that I didn't say answers the problem).