boxabirds

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

ivanfioravanti

# Required packages:
# pip install SpeechRecognition mlx-whisper pyaudio
# Note: This script requires Apple Silicon Mac for MLX Whisper

import speech_recognition as sr
import numpy as np
import mlx_whisper

r = sr.Recognizer()
mic = sr.Microphone(sample_rate=16000)

thomwolf

""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio
"""

from openai import OpenAI
import time

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

glyph

gg_replace() {
    if [[ "$#" -lt "2" ]]; then
        echo "
Usage:
  $0 term replacement file_mask

Example:
  $0 cappuchino cappuccino '*.html'
";
    else

rikonor

package main

import (
	"fmt"
	"log"
	"net/http"
	"sync"
	"time"
)

IshitaTakeshi

Calling LAPACK/BLAS from Kotlin Native

The original C file is cblas_example1.c

$cinterop -def liblapack.def -o build/c_interop/liblapack
$kotlinc-native lapack.kt -library build/c_interop/liblapack
$./program.kexe 
11.0
14.0

fitnr

-- adapted from https://medium.com/@goldrydigital/hex-grid-algorithm-for-postgis-4ac45f61d093

-- usage: select * FROM hexbin(numeric, geometry)
-- cover a table with something like:
-- select row_number() over () id, geom
-- from hexbin(5280, st_transform(st_setsrid(st_expand(ST_EstimatedExtent('table', 'geom'), 0.1), 4269), 2255));

CREATE OR REPLACE FUNCTION hexbin (height numeric, minx numeric, miny numeric, maxx numeric, maxy numeric, srid integer)
RETURNS TABLE (geom geometry(polygon))
AS $$

endolith

"""
Train a neural network to implement the discrete Fourier transform
"""
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential

N = 32
batch = 10000

ravnoor

function struct2csv(s,fn)
% STRUCT2CSV(s,fn)
%
% Output a structure to a comma delimited file with column headers
%
%       s : any structure composed of one or more matrices and cell arrays
%      fn : file name
%
%      Given s:
%