Theodore M mamonu

## normcore-llm.md

      
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                Normcore LLM Reads
              
          
    Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
Foundational Concepts


Pre-Transformer Models


## morphagene_onset3.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Usage:
    morphagene_onset3.py -w <inputwavfile> -o <outputfile> -s <splicecount>

Use the Superflux onset detection algorithm with backtracking to generate
    splice locations.
Use these splice locations with a converted WAV (to 32-bit float / 48000Hz)
    to make Morphagene reels.

## python-django-postgres-ci.yml
name: CI

on: [push]

jobs:

  test:
    runs-on: ubuntu-latest

    services:

## black_doctest.py
import argparse
import sys


import black
from blib2to3.pgen2.tokenize import TokenError

TEST_DATA = """
Normal

## .BASH-PROMPT.png

      
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                Clean informative bash prompt with git info
              
          
## nord_modular_osx.md

      
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                arirusso
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                Use the original Nord Modular Editor with OSX
              
          
    Use the original Nord Modular Editor with OSX

Required


Homebrew

Compatibility

Confirmed working with
	#!/usr/bin/env python3
	# -- coding: utf-8 --
	"""
	Usage:
	morphagene_onset3.py -w <inputwavfile> -o <outputfile> -s <splicecount>

	Use the Superflux onset detection algorithm with backtracking to generate
	splice locations.
	Use these splice locations with a converted WAV (to 32-bit float / 48000Hz)
	to make Morphagene reels.
	import argparse
	import sys


	import black
	from blib2to3.pgen2.tokenize import TokenError

	TEST_DATA = """
	Normal