Bastiaan Quast bquast

## self-learning-adaptive-grapling-system.py
import time
import numpy as np
from lerobot.common.robot_devices.robots.so101 import SO101Follower
from lerobot.common.robot_devices.cameras import get_cameras


# initialize robot and cameras
robot = SO101Follower(port="/dev/ttyACM0", id="my_so101")  # Adjust port
robot.connect()

## SmolLM-135M-quantize-int8-mlx.py
import mlx.core as mx
from mlx.utils import tree_flatten
from mlx_lm import load

model, _ = load("HuggingFaceTB/SmolLM2-135M")

def absmax_int8(w):
    scale = mx.max(mx.abs(w)) / 127.0
    w_q   = mx.round(w / scale).astype(mx.int8)
    w_hat = w_q.astype(w.dtype) * scale

## big-files-list.sh
#!/bin/zsh

# cleanup.sh
# Usage: ./cleanup.sh [directory]
# Default to HOME if no directory is provided
TARGET="${1:-$HOME}"

echo "### macOS SSD Analysis for: $TARGET"
echo "### Date: $(date)"
echo "---"

## llama.go
package main

import (
	"encoding/binary"
	"fmt"
	"math"
	"math/rand"
	"regexp"
	"strings"
	"syscall/js"

## rsa_homomorphic_demo.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>RSA — Multiplicative Homomorphism</title>
<link href="https://fonts.googleapis.com/css2?family=Archivo+Black&family=Work+Sans:wght@400;600&family=Space+Mono&display=swap" rel="stylesheet">
<style>
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }

## webcamqr.swift
import Foundation
import AVFoundation
import Vision
import CoreImage

class CameraCapture: NSObject, AVCaptureVideoDataOutputSampleBufferDelegate {
    var foundQR: String?
    let qrQueue = DispatchQueue(label: "qrqueue")

    func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {

## CKKS-encode-encrypt.py
import numpy as np
from numpy.polynomial import Polynomial

# Set the parameters
M = 8
N = M // 2
scale = 64
xi = np.exp(2 * np.pi * 1j / M)

def vandermonde(xi: np.complex128, M: int) -> np.array:

## CKKS-encode-crypt.R
## ----setup----
library(polynom)
library(HomomorphicEncryption)

## ----seed-----
set.seed(123)

## ----params-----
M     <-   8
N     <-   M / 2

## microgpt.R
input_file <- "input.txt"
if (!file.exists(input_file)) {
  names_url <- "https://raw.githubusercontent.com/karpathy/makemore/refs/heads/master/names.txt"
  download.file(names_url, input_file)
}

lines <- readLines(input_file, warn = FALSE)
docs <- lines[nchar(trimws(lines)) > 0]
set.seed(42)
docs <- sample(docs)

## rnn.R
# rnn.R
# Bastiaan Quast
# bquast@gmail.com

# Set the seed to obtain identical random values
set.seed(0)

# compute sigmoid nonlinearity
sigmoid = function(x)
  1 / (1+exp(-x))
	import time
	import numpy as np
	from lerobot.common.robot_devices.robots.so101 import SO101Follower
	from lerobot.common.robot_devices.cameras import get_cameras


	# initialize robot and cameras
	robot = SO101Follower(port="/dev/ttyACM0", id="my_so101") # Adjust port
	robot.connect()
	import mlx.core as mx
	from mlx.utils import tree_flatten
	from mlx_lm import load

	model, _ = load("HuggingFaceTB/SmolLM2-135M")

	def absmax_int8(w):
	scale = mx.max(mx.abs(w)) / 127.0
	w_q = mx.round(w / scale).astype(mx.int8)
	w_hat = w_q.astype(w.dtype) * scale
	#!/bin/zsh

	# cleanup.sh
	# Usage: ./cleanup.sh [directory]
	# Default to HOME if no directory is provided
	TARGET="${1:-$HOME}"

	echo "### macOS SSD Analysis for: $TARGET"
	echo "### Date: $(date)"
	echo "---"
	package main

	import (
	"encoding/binary"
	"fmt"
	"math"
	"math/rand"
	"regexp"
	"strings"
	"syscall/js"
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>RSA — Multiplicative Homomorphism</title>
	<link href="https://fonts.googleapis.com/css2?family=Archivo+Black&family=Work+Sans:wght@400;600&family=Space+Mono&display=swap" rel="stylesheet">
	<style>
	, ::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
	import Foundation
	import AVFoundation
	import Vision
	import CoreImage

	class CameraCapture: NSObject, AVCaptureVideoDataOutputSampleBufferDelegate {
	var foundQR: String?
	let qrQueue = DispatchQueue(label: "qrqueue")

	func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
	import numpy as np
	from numpy.polynomial import Polynomial

	# Set the parameters
	M = 8
	N = M // 2
	scale = 64
	xi = np.exp(2 * np.pi * 1j / M)

	def vandermonde(xi: np.complex128, M: int) -> np.array:
	## ----setup----
	library(polynom)
	library(HomomorphicEncryption)

	## ----seed-----
	set.seed(123)

	## ----params-----
	M <- 8
	N <- M / 2
	input_file <- "input.txt"
	if (!file.exists(input_file)) {
	names_url <- "https://raw.githubusercontent.com/karpathy/makemore/refs/heads/master/names.txt"
	download.file(names_url, input_file)
	}

	lines <- readLines(input_file, warn = FALSE)
	docs <- lines[nchar(trimws(lines)) > 0]
	set.seed(42)
	docs <- sample(docs)
	# rnn.R
	# Bastiaan Quast
	# bquast@gmail.com

	# Set the seed to obtain identical random values
	set.seed(0)

	# compute sigmoid nonlinearity
	sigmoid = function(x)
	1 / (1+exp(-x))