Comment by brainless
9 hours ago
More and more such experiments. I felt sad for a couple months when I realized that writing code will not be the same since. Now I am on the other side.
LLMs are interesting in their own ways but as an engineer, this is a way to unlock a new way of building software.
I recently build a Claude-assisted Excel/CSV parser for a US based property management system (tax compliance). Uses Haiku and has a lot of deterministic code to extract column/row combinations to check known formats and finally handing out the headers to Haiku to give us a translation plan to our support columns.
These would eventually become part of the software, in a tiny LLM. The gap between training (such tiny LLMs) and inference will shrink. We can consult Claude for edge cases, create sample dataset and train a the tiny LLM on demand so we go to Claude less.
The tooling that a project needs is really important. Something I have been feeling as well. Not just in LLM building projects, but regular software projects that are LLM generated.
I think I am on your arc as well. My learning on different topics is growing every day, but there’s a limit to how much I can absorb. With the LLMs the experiments stay just beyond that horizon and I keep chasing.
Stated too strongly, but I think this could be the model for education (some subjects anyway). Everything personalized to your learning goals, grounded in experiments that give a tight feedback loop and with a model that never gets tired of re-explaining something for the 10th time.
Yes they are great for learning at own pace, trying out new things.
I have accepted two things that make me a happy engineer now: AGI is not here no matter what they say and LLMs are still very useful if one knows how to use them.
They are another layer of abstraction and like you said they do not tire. There is a lot of optimization needed so we can reduce wastage (running 1T+ LLMs for most work is wastage).