61–70 of 111 posts
I feel much more confident that I can take on a project in a domain that im not very familiar with. Ive been digging into llvm ir and I had not prior experience with it. ChatGPT is a much better guide to getting started than the documentation, which is very low quality.
I feel much more confident that I can take on a project in a domain that im not very familiar with. Ive been digging into llvm ir and I had not prior experience with it. ChatGPT is a much better guide to getting started than the documentation, which is very low quality.
Careful - if you’re not familiar with the domain how are you going to spot when the LLM gives you suboptimal or even outright wrong answers?
I have gotten much more value out of AI tools by focusing on the process and not the product. By this I mean that I treat it as a loosely-defined brainstorming tool that expands my “zone of knowledge”, and not as a way to create some particular thing. In this way, I am infinitely more tolerant of minor problems in the output, because I’m not using the tool to create a specific output, I’m using it to enhance the thin…
Your role is between the two: deciding on the architecture, writing the top-level types, deciding on the concrete system design.
And then AI tools help you zoom in and glue things together in an easily verifiable way.
I suspect that people who still haven't figured out how to make use of LLMs, assuming it's not just resentful performative complaining which it probably is, are expecting it to do it all. Which never seemed very engineer-minded.
I feel much more confident that I can take on a project in a domain that im not very familiar with. Ive been digging into llvm ir and I had not prior experience with it. ChatGPT is a much better guide to getting started than the documentation, which is very low quality.
I have been exploring local AI tools for coding (ollama + aider) with a small stock market simulator (~200 lines of python).
First I tried making the AI extract the dataclasses representing events to a separated file. It decided to extract some extra classes, leave behind some others, and delete parts of the code.
Then I tried to make it explain one of the actors called LongVol_player_v1, around 15 lines of code. It successfully concluded it does options delta hedging, but it jumped to the conclusion that it calculates the implied volatility. I set it as a constant, because I'm simulating specific interactions between volatility players and option dealers. It hasn't caught yet the bug where the vol player buys 3000 options but accounts only for 2000.
When asking for improvements, it is obsessed with splitting the initialization and the execution.
So far I wasted half of Saturday trying to make the machine do simple refactors. Refactors I could do myself in half of an hour.
I'm yet to see the wonders of AI.
I have gotten much more value out of AI tools by focusing on the process and not the product. By this I mean that I treat it as a loosely-defined brainstorming tool that expands my “zone of knowledge”, and not as a way to create some particular thing. In this way, I am infinitely more tolerant of minor problems in the output, because I’m not using the tool to create a specific output, I’m using it to enhance the thin…
I think it's a U-shaped utility curve where abstract planning is on one side (your comment) and the chore implementation is on the other. Your role is between the two: deciding on the architecture, writing the top-level types, deciding on the concrete system design. And then AI tools help you zoom in and glue things together in an easily verifiable way. I suspect that people who still haven't figured out how to make…
I have found it fascinating how AI has forced me to reflect on what I actually do at work and whether it has value or not.
Deciding what to build and how to build it is often harder than building.
What LLMs of today do is basically super-autocomplete. It's a continuation of the history of programming automation: compilers, more advanced compilers, IDEs, code generators, LINTers, autocomplete, codeinsight, etc.
I feel much more confident that I can take on a project in a domain that im not very familiar with. Ive been digging into llvm ir and I had not prior experience with it. ChatGPT is a much better guide to getting started than the documentation, which is very low quality.
Good luck with that. I have been exploring local AI tools for coding (ollama + aider) with a small stock market simulator (~200 lines of python). First I tried making the AI extract the dataclasses representing events to a separated file. It decided to extract some extra classes, leave behind some others, and delete parts of the code. Then I tried to make it explain one of the actors called LongVol_player_v1, around…