101–110 of 283 posts
Nvidia is trying something similar: https://blogs.nvidia.com/blog/llm-semiconductors-chip-nemo/ I'd want to know about the results of these experiments before casting judgement either way. Generative modeling has actual applications in the 3D printing/mechanical industry.
The way I read that, I think they're saying hardware acceleration of specific algorithms can be 100 times faster and more efficient than the same algorithm in software on a general purpose processor, and since automated chip design has proven to be a difficult problem space, maybe we should try applying AI there so we can have a lower bar to specialized hardware accelerators for various tasks. I do not think they mea…
I also think OP is missing the point saying the target applications are too small of a market to be worth pursuing.
They’re too small to pursue any single one as the market cap for a company, but presumably the fictional AI chip startup could pursue many of these smaller markets at once. It would be a long tail play, wouldn’t it?
YC did well because they were good at picking ideas, not generating them.
Earlier quoted context omitted.
LLMs are pretty damn useful for generating tests, getting rid of a lot of tedium, but yeah, it's the same as human-written tests: if you don't check that your test doesn't work when it shouldn't (not the same thing as just writing a second test for that case - both those tests need to fail if you intentionally screw with their separate fixtures), then you shouldn't have too much confidence in your test.
If LLMs can generate a test for you, it's because it's a test that you shouldn't need to write. They can't test what is really important, at all. Some development stacks are extremely underpowered for code verification, so they do patch the design issue. Just like some stacks are underpowered for abstraction and need patching by code generation. Both of those solve an immediate problem, in a haphazard and error-prone…
https://github.com/williamcotton/search-input-query/blob/mai...
It is a good test suite and it saved me quite a bit of typing!
In fact, Claude did most of the typing for the entire project:
https://github.com/williamcotton/search-input-query
BTW, I obviously didn't just type "make a lexer and multi-pass parser that returns multiple errors and then make a single-line instance of a Monaco editor with error reporting, type checking, syntax highlighting and tab completion".
I put it together piece-by-piece and with detailed architectural guidance.
LLMs have a long way to go in the world of EDA. A few months ago I saw a post on LinkedIn where someone fed the leading LLMs a counter-intuitively drawn circuit with 3 capacitors in parallel and asked what the total capacitance was. Not a single one got it correct - not only did they say the caps were in series (they were not) it even got the series capacitance calculations wrong. I couldn’t believe they whiffed it a…
RC circuits man.
YC is technically incompetent and isn't about making the world better. Every single one of their words is a lie and hides the real intent: make money.
They want to throw LLMs at everything even if it does not make sense. Same is true for all the AI agent craze: https://medium.com/thoughts-on-machine-learning/langchains-s...
Oh yes.
I had a discussion with a manager at a client last week and was trying to run him through some (technical) issues relating to challenges an important project faces.
His immediate response was that maybe we should just let ChatGPT help us decide the best option. I had to bite my tongue.
OTOH, I'm more and more convinced that ChatGPT will replace managers long before it replaces technical staff.
The author here is missing a few important things about chip design. Most of the time spent and work done is not writing high performance Verilog. Designers spent a huge amount of time answering questions, writing documentation, copying around boiler plate, reading obscure manuals and diagrams, etc. LLMs can already help with all of those things.
I believe that LLMs in their current state could help design teams move at least twice as fast, and better tools could probably change that number to 4x or 10x even with no improvement in the intelligence of models. Most of the benefit would come from allowing designers to run more experiments and try more things, to get feedback on design choices faster, to spend less time documenting and communicating, and spend less time reading poorly written documentation.
I agree they're probably wrong but this article doesn't actually explain why they're wrong to bet on exponential progress in AI capabilities.
VCs are not investing in the current LLM-based systems to improve X, they're investing in a future where LLM based systems will be 100x more performant.
Writing is complex, LLMs once had subhuman performance, and yet. Digital art. Music (see suno.AI) There is a pattern here.