Chris Matthieu chrismatthieu

National Coding Week (https://codingweek.org/) RealSense Developer Challenge — Rules

Sponsor: RealSense, Inc. (the “Sponsor”).

Eligibility: Open to individuals 18 years or older. Void where prohibited. No purchase necessary.

How to Enter:

During National Coding Week, a new RealSense challenge will be posted daily on Reddit.

	Corebrum will run on that ThinkPad as the existing Linux binary. Omarchy is Arch, so skip the APT instructions in the README and install the release tarball plus a matching Zenoh router.

	Checked on this machine: Omarchy 4.0.5, x86_64, glibc 2.44. uname -m printed x86_64. If it prints aarch64, use the aarch64 assets in the same steps. The binaries need only libc, libgcc, libm, and (for corebrum) libz. There is no Zenoh router package in the Arch repos. AUR has zenoh-c and zenoh-cpp, which are not this router. Corebrum 0.3.9 talks to Zenoh 1.10.1.

	1. Install the two programs
	The Corebrum git repo is private. The same 0.3.9 Linux archive is public, so GitHub login is not required:

	https://corebrum-releases.s3.amazonaws.com/releases/v0.3.9/corebrum-x86_64-unknown-linux-gnu.tar.xz

	sha256: 044a3fa539a081768e4fc04ba2a43f4ca75f9d7747ee2b2fde11ce9b89bd2399

	# AgenticROS / OpenClaw Startup Optimization

	## Purpose

	This document describes a startup-performance problem discovered when loading AgenticROS as an OpenClaw plugin, the diagnostic changes made on a deployed NVIDIA Jetson installation, what we learned, and what still needs to be implemented cleanly in the AgenticROS source repository.

	Important: The deployed copy under `~/.agenticros/plugin-deploy` contains experimental diagnostic modifications. Do not blindly copy that directory over the source repository.

	The goal is to translate the proven optimizations into maintainable source changes while preserving all AgenticROS transports and functionality.

	import rclpy
	from rclpy.node import Node
	from geometry_msgs.msg import Twist

	class TwistSubscriber(Node):
	def __init__(self):
	super().__init__('twist_subscriber')
	self.subscription = self.create_subscription(
	Twist,
	'cmd_vel',

	import time
	import json

	def fibonacci_recursive(n):
	"""Generate Fibonacci sequence using recursion (inefficient for large n)."""
	if n <= 0:
	return []
	elif n == 1:
	return [0]
	elif n == 2:

	"I was thinking about building a new RealSense stereo camera demo project, but wanted to run it by you to see if it's practical. It might be interesting to use the RGB camera or sensor on the RealSense camera to film like a short movie, and then use the stereo depth sensors to set like the depth of the movie. Like, say, film everything one meter and closer, and then see if we can put a green screen on everything behind the one meter depth range or whatever variable we set, so that it's like filming on a green screen studio but in about in the real world. Do you think one, this is doable, and then do you think two, this is useful? I would call this a RealSense GreenScreen app"

	Yes — this is very doable, and I think it could make a surprisingly compelling RealSense demo. In computer-vision terms, what you’re describing is essentially real-time depth keying: instead of chroma-keying pixels based on “is this pixel green?”, you key them based on “is this pixel farther than X meters away?”

	With a RealSense stereo

	import time
	import json

	def factorial(n):
	"""Compute factorial of n recursively."""
	if n < 0:
	raise ValueError("Factorial is not defined for negative numbers")
	if n == 0 or n == 1:
	return 1
	return n * factorial(n - 1)

	#!/bin/bash

	set -e # Exit immediately if a command exits with a non-zero status

	# --- Helper Functions ---
	# Function for timed confirmation, takes message and function name to run
	timed_confirm() {
	local message=$1
	local function_to_run=$2
	local timeout=5 # seconds