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The code was written mostly by doing cut & paste on ChatGPT…
I am constantly shocked by how many people put up with such a painful workflow. OP is clearly an experienced engineer, not a novice using GPT to code advice their knowledge. I assume they usually care about ergonomics and efficiency in their coding workflow. But so many folks put up with cutting and pasting code back and forth between GPT and their local files.
This frustrating workflow was what initially led me to create aider. It lets you share your local git repo with GPT, so that edits are applied directly to your files. Aider also shares related code context with GPT, so that it can write code that is integrated with your project, not just stand alone pure helper functions (that are easy to copy & paste).
https://github.com/paul-gauthier/aider
If you use VS Code or a JetBrains IDE, Continue works well with Ollama and it’s really easy to get going.
[0] https://continue.dev/
[1] https://ollama.ai/
Shameless plug: https://github.com/codespin-ai/codespin-cli
It's similar to aider (which is a great tool btw) in goals, but with a different recipe.
I'm not OP, but I just ask GPT to turn code or process or whatever else into a mermaid diagram. Most of the time I don't even need to few-shot prompt it with examples. Then you dump the resulting text into something like https://mermaid.live/ and voilà.
I see a lot of recommendations for kagi, but no mention of brave search - specifically the (beta) feature called “goggles”. Afaiu it’s a blend of kagi’s “lenses” and the site ranking in search results.
https://search.brave.com/help/goggles There is a list (search) of public goggles: https://search.brave.com/goggles
The goggles itself are just text files with basic syntax and can be hosted on e.g. github gist. (though you have to publish it to brave)
https://github.com/brave/goggles-quickstart/blob/main/goggle...
Tbh, I can’t really compare brave search to kagi, since I never used kagi (though I’m using Orion - webkit based browser from the same dev and love it). Afaik, brave search is using its own index, thus making the results somehow limited and inferior to kagis. Just wanted to throw some (free) alternative here that works for me. :)
* Note that Brave search, despite privacy oriented, is still ad funded and there was few controversies about brave’s (browser) privacy in the past. (if that’s relevant for you)
To rephrase it a little bit.
Much of programming, coding and developing is done by a person who is a knowledge worker and writes code. A good proportion of code to be written, will be written just once and never again. The one-off code snippet will stay in a file collecting dust forever. There is no point in trying to remember it in the first place, because without constant repetition of using it, it will be forgotten.
LLMs can help us focus our knowledge where it really matters, and discard a lot of the ephemeral stuff. That means that we can be more of knowledge workers and less of coders. I will push it even further and state that we will become more of knowledge workers and less of coders until we will be, eventually and gradually, just knowledge workers. We will need to know about algorithms, algorithmic complexity, abstractions and stuff like that.
We will need to know subjects like that Rust book [1] writes about.
[1]https://github.com/QMHTMY/RustBook/tree/main/books
After looking at https://github.com/antirez/simple-language-model/blob/main/p... that does seem likely.