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by skydhash 385 days ago
That rely heavily on your mental model of ALSA to write a prompt like that. For example, I believe macOS audio stack is node based like pipewire. For someone who is knowledgeable about the domain, it's easy enough to get some base output to review and iterate upon. Especially if there was enough training data or you constrain the output with the context. So there's no actual time saving because you have to take in account the time you spent learning about the domain.

That is why some people don't find AI that essential, if you have the knowledge, you already know how to find a specific part in the documentation to refresh your semantics and the time saved is minuscule.
1 comments
rerdavies 385 days ago
Fer goodness sake. Eyeroll.

   Write an audio processing loop for pipewire. Wrap the code up in a 
   C++ class. Read audio data, process it and output through an output 
   port. Skip the explanations. Use CamelCase names for methods.
   Bundle all the configuration options up into a single
   structure.
Run it through grok. I'd actually use VSCode Copilot Claude Sonnet 4. Grok is being used so that people who do not have access to a coding AI can see what they would get if they did.

I'd use that code as a starting point despite having zero knowledge of pipewire. And probably fill in other bits using AI as the need arises. "Read the audio data, process it, output it" is hardly deep domain knowledge.

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skydhash 385 days ago
Results with gemini

https://pastebin.com/6b4yhfYw

A 5 second search on DDG ("easyeffects") and a 10 second navigation on github.

https://github.com/wwmm/easyeffects/blob/master/src/plugin_b...

But that is GPL 3.0 and a lot of people want to use the license laundering LLM machine.

N.B. I already know about easyeffects from when I was seeking for a software equalizer

EDIT

Another 30 seconds exploration ("pipewire" on DDG, finding the main site, then goes on the documentation page, and the tutorial section).

https://docs.pipewire.org/audio-dsp-filter_8c-example.html

There's a lot of way to find truthful information without playing Russian roulette with an LLM.

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