jaggzh

User (NON-deep):
comparison of characteristics of texts captured by different common small vector embeddings. What types of things are captured/represented in each of them? We should include the most common 20 to 40 depending on variations/information spread, and include model memory consumption and speed along with the details on what's captured or what they excel in and what they miss. An estimated popularity rank should be included but it doesn't need to be reliable. Only models runnable locally/foss. Before we begin, help me resume this request for a deep research prompt; I'm not actually sure if I'm asking the right questions. I want a reference I can turn to for making decisions on model use when working on different projects. We should include less popular models as well if they have special characteristics making them more suitable for some tasks.

cgpt:
Great target. Here’s a tight research brief + “ready-to-run” master prompt that locks scope, outputs, and criteria so we can build a dependable, deci

jaggzh

#!/usr/bin/env python3

# embed-search
# Takes query text (utext) on the commandline,
# and many lines of text from stdin or a file (lines).
# Outputs the highest semantically-matching line(s)

# gist-paste -u https://gist.github.com/jaggzh/d57ef8757c10e945d99c77f91449e33a embed-search-wrapped

# Dependencies:

jaggzh

#!/usr/bin/env python3
# gist-paste -u https://gist.github.com/jaggzh/47b1c58617601b5aaab9da6233aaeae1 mapper-linear
import json
import datetime
import folium
from folium.plugins import TimestampedGeoJson

# Load the fire perimeter data
with open("query-to-2025-01-09_23-14pm.json") as F:
    data = json.load(F)

jaggzh

#!/bin/bash
# Source at: https://gist.github.com/jaggzh/4f4159b1b137cbc9553efb7d8364f0c9

. ansi.sh # ansi.sh from: https://gist.github.com/jaggzh/6b3746475bbde5a077dfaeae793b7d1f
bgblu=$'\e[44m'
bmag=$'\e[35m'
whi=$'\e[37m'
rst=$'\e[0m'

# blank to disable

jaggzh

#!/usr/bin/env python3
# Copyleft 2024 jaggz.h {over yonder at} gmail.com
# jaggzh @ reddit

# Usage:
# python plotter.py -i defocus.ods -o defocus.png

# Gist url for this script:
# https://gist.github.com/jaggzh/f4aea3e8c09363bf4924b8bd2f80895d

jaggzh

#!/bin/bash
# gist-paste -u https://gist.github.com/jaggzh/f687be9a23d267e641dc7ff7a1967b9a spect
# Dependencies:
#   ffmpeg (we extract audiosecs=60 for the spectrogram)
#   sox (for spectrogram image creation)
#   chafa (for conversion of spectrogram to text)
tmpdel_s=$((60)) # 5 minutes
def_maxh=5
def_maxh_1=16  # Height for 1 file
def_maxh_2=10  # Height for each of 2 files

jaggzh

readcountdown1 () {
	if [[ "$#" -lt 3 ]]; then
		cat <<-EOT
			Accepts a single char of user input
			Usage: readcountdown1 seconds variable message
			Ex: readcountdown1 5 inp '(d)elete (anything else exits)'
			gives 5 seconds and sets inp if anything (even enter) entered
			returns 0 on success, 1 for timeout
		EOT
		return 1

jaggzh

# gist-paste -u https://gist.github.com/jaggzh/6b3746475bbde5a077dfaeae793b7d1f
bgbla=''; bgred=''; bggre=''; bgbro='';
bgblu=''; bgmag=''; bgcya=''; bggra='';
bla=''; red=''; gre=''; bro='';
blu=''; mag=''; cya=''; gra='';
bbla=''; bred=''; bgre=''; yel='';
bblu=''; bmag=''; bcya=''; whi='';
rst=''; inv=''; cll=''; cllr='';
cls=''; clsb=''; 

jaggzh

package Proc::Knife;

use strict;
use warnings;
use v5.36;
use Exporter 'import';
use POSIX ":sys_wait_h";

our @EXPORT_OK = qw(knife);

jaggzh

import numpy as np
import simpleaudio as sa
import threading
import time

def play_tone(freq, dur, delay):
    # Wait for the specified delay before playing the tone
    time.sleep(delay)
    
    # Sample rate (samples per second)