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└── content └── develop ├── _index.md ├── api-reference ├── _index.md ├── caching-and-state │ ├── _index.md │ ├── cache-data.md │ ├── cache-resource.md │ ├── context.md

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Perceptron Pegboard Circuit Guide

You:

infromation on building a recreation of the orignal precpetron pegbaord ciruit

ChatGPT:

Building a recreation of Frank Rosenblatt’s original Perceptron pegboard circuit can be a fascinating blend of historical exploration and hands-on electronics. While many modern implementations of perceptrons are purely software-based, recreating the physical hardware can give you unique insight into how early neural networks were first conceived and tested. Below is a comprehensive overview of the original design, the theory behind it, and practical considerations for building your own pegboard Perceptron.

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Raspberry Pi Hotspot Setup

by default, the Raspberry Pi's network management system (wpa_supplicant or NetworkManager, depending on your setup) will automatically attempt to connect to known Wi-Fi networks during boot. If you’ve configured known networks in the wpa_supplicant.conf file or using NetworkManager or raspi-config, the Pi will try to connect to these networks without requiring additional scanning logic in a script.

The key idea here is to detect whether the Pi successfully connected to any network and, if not, start the hotspot. This simplifies the script, as you don't need to handle Wi-Fi scanning yourself—just check the connection status.

Simplified Script

This script assumes the Raspberry Pi will attempt to connect to known networks on its own. It checks if the Pi is connected to a network, and if not, starts the hotspot.

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import requests
import xml.etree.ElementTree as ET
from datetime import datetime
from googletrans import Translator
import re

def contains_hebrew(text):
    """Check if the text contains Hebrew characters"""
    hebrew_pattern = re.compile(r'[\u0590-\u05FF\uFB1D-\uFB4F]')
    return bool(hebrew_pattern.search(text))

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how_to_quary_a_repo

goal is to pass a git repo to the LLM so it can consume and we can quary it.
we will use a cli tool called llm
and an auxiliry tool called files-to-prompt from same auther

install

install llm

pip install llm 
llm install llm-openrouter 

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map remote port 8888 to localhost
ssh [email protected] -p 22408 -i ~/.ssh/id_ed25519 -L 8888:localhost:8888
grab the jupyter passwrod from runpod
JUPYTER_PASSWORD=qi14ld6tw7h4ino5fwh
set the local kernel to "exsiting one", then refer it to this url
http://localhost:8888/lab?token=qi14ld6g7h4ino56cwh

full thing here, but its a bit outdated

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install openDevin on windows wsl

all this inside your WSL ubuntu shell.

have python (3.11+)

check

python --version

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$condaenvs = (conda env list | Out-String) -split "`n"
foreach ($condaenv in $condaenvs) {
    $name = $condaenv.Split(' ')[0]

    if ($name -ne "" -and $name -ne "#") {
        Write-Output "================================================================"
        Write-Output "Environment: $name"

        Write-Output "`nChecking for Jupyter:"
        & cmd /c "call activate $name && jupyter --version"

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# an LLM attempt at an https://noisio.de/boards/levitation-oscillator

import streamlit as st
import numpy as np
from pydub import AudioSegment
from pydub.playback import play
import tempfile

def generate_sine_wave(frequency, amplitude, phase, duration, sample_rate):
    t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)