timothygold
Joined 1 year ago
Joined 1 year ago
https://github.com/hibernatus-hacker/ai-hedgehog
This is a simple code assistant that doesn't get in your way and makes sure you are coding (not losing your ability to program).
You configure a replicate API token from replicate... install the tool and point it at your code base.
When you save a file it asks the LLM for advise and feedback on the file as a "senior developer".
Run this along side your favorite editor to get feedback from an LLM as your working on (open source code nothing you don't want third parties to see).
You are still programming and using your brain but you have some feedback when you save files.
The feedback is less computationally expensive or fraught with difficulty than actually getting code from LLM's so it should work with much less powerful models.
It would be nice if there was a search built in so it could search for useful documentation for you.
You configure a replicate API token from replicate... install the tool and point it at your code base.
When you save a file it asks the LLM for advise and feedback on the file as a "senior developer".
Run this along side your favorite editor to get feedback from an LLM as your working on (open source code nothing you don't want third parties to see).
You are still programming and using your brain but you have some feedback when you save files.
The feedback is less computationally expensive or fraught with difficulty than actually getting code from LLM's so it should work with much less powerful models.
It would be nice if there was a search built in so it could search for useful documentation for you.
Hedgehogs always recommend programming yourself to stay sharp.. so this is A.I assistance in a much simpler offering letting you do all of the work as the developer (how it should be).
Lets say a cluster of raspberry pi's / low powered devices producing results as good as claude 3.7-sonnet. Would it be completely infeasible to create a custom model that is trained on your own code base and might not be a fully fledged LLM but provides similar features to cursor?
Have we all gone bonkers sending our code to third parties? The code is the thing you want to keep secret unless your working on an open source project.
* It has terrible support for Elixir (my fav language) because the models are only really trained on python.
* Terrible clunky interface... it would be nice if you didn't have to click around, do modifier ctrl + Y stuff ALL the time.
* The code generated is still riddled with errors or naff (apart from boiler plate)... so I am still * prompt engineering * the crap out of it.. which I'm good at but I can prompt engineer using phind.com...
* The fact that the code is largely broken first time and they still haven't really fixed the context window problem means you have to copy paste error codes back into it.. defeating the purpose of an in integrated IDE imo.
* The free demo mode stops working after generating one function... if I had been given more time to evaluate it fully I would never have signed up. I signed up to see if it was any good.. which it isn't.
I hope your not relying on big tech / cloud providers who will suspend your account without warning and have a self hosted alternative.
Why will a financial institution accept a drivers licence, letter addressed to me and bank statements but AWS wont?
I'm so annoyed that I don't care if they don't let me in. It shows you exactly what AWS is really like and that they only serve huge international corporations.
That's called a monopoly.
Would it be so hard for some of the smartest people in the world to run their own infrastructure and be more self reliant?
At least they aren't wasting tax payers money on all this total crap.. oh wait.. they are.
I'm pretty sure that's exactly how they work.
Depending on the quality of the LLM and the complexity of the thing your asking about good luck fact checking it's output. It is about the same effort as finding direct sources and verified documentation or resources written by humans.
LLMs generate human like answers by using statistics and other techniques on a huge corpus. They do hallucinate but what is less obvious is that a "correct" LLM output is still a hallucination. It just happens to be a slightly useful hallucination that isn't full of BS.
As the LLM takes in inconsistent input and always outputs inconsistent output you * will * have to fact check everything it says. Making it useless for automated reasoning or explanations and a shiny turd in most respects.
The useful things LLMs are reported to do where an emergent effect found by accident by natural language engineers trying to build chat bots. LLM's are not sentient and have no idea if the output is good or bad.