Detailed reference companion to the RuView tutorial "Pi 5 + Hailo Cluster: Building a Cognitive RF Observer with rvcsi". This is the unabridged recipe — every command, every config, every unit file. The tutorial is the narrative; this is the manual.
Target cluster: 4× Raspberry Pi 5 (8GB), 2× Hailo-8 M.2 HAT, 1× workstation (Linux, ≥16GB RAM), all on Tailscale.
Date: 2026-03-06 Analyzed by: AQE Queen Swarm (6 specialist agents) Project: RuView -- WiFi-based Human Pose Estimation & Vital Sign Monitoring Repository: https://github.com/ruvnet/RuView Scope: Rust codebase only (15 workspace crates + 65 WASM edge modules + ESP32 firmware)
Note: Python v1 (
v1/) is a legacy codebase and excluded from this analysis. All findings, recommendations, and test plans target the Rust port only.
This document provides a comprehensive overview of five advanced algorithms, detailing their technical implementations using Python and Pydantic for data validation, as well as asynchronous programming for efficiency. Each algorithm is also explored in terms of practical applications across various domains. The algorithms covered include:
NEUMANN: Differentiable Logic Programs for Abstract Visual Reasoning - This algorithm integrates differentiable logic programming with neural networks, enabling advanced visual reasoning and logical deduction. It is particularly useful in computer vision, robotics, and medical imaging.
Scheduled Policy Optimization for Natural Language Communication - This algorithm optimizes policies for natural language communication, enhancing dialogue systems, customer support automation, and machine translation. It leverages policy gradient methods and scheduled learning to improve interaction quality and efficiency.
**LEFT: Logic-Enhanced Foundatio
A deep technical audit of the ruflo (@claude-flow/cli) ecosystem — what it claims vs what it actually does.
Investigated 2026-04-04 by spawning 8 research agents across two analysis phases, reading source code from the ruflo GitHub repo, tracing local process execution, and testing every major tool category hands-on.
A deep technical audit of MemPalace (github.com/MemPalace/mempalace) — what it claims vs what it actually is.
Every source file read. Every benchmark traced. Every MCP tool verified. Compared against Mem0, Zep/Graphiti, and Letta/MemGPT. GitHub stargazer timestamps analyzed for bot patterns.
| // Call arrayToObj() passing your array and function. | |
| function arrayToObj(array, keyValueMap) { | |
| var obj = {}; | |
| var len = array.length; | |
| // Your function will be called for each item in the array. | |
| for (var i = 0; i < len; i++) { | |
| var curVal = array[i]; | |
| // Just like with [].forEach(), your function will be passed the | |
| // curent item of the array, its index, and the array it's in. | |
| var keyValuePair = keyValueMap(curVal, i, array); |
| // === Arrays | |
| var [a, b] = [1, 2]; | |
| console.log(a, b); | |
| //=> 1 2 | |
| // Use from functions, only select from pattern | |
| var foo = () => [1, 2, 3]; |
| import cv2 | |
| import time | |
| import math | |
| import numpy as np | |
| capture = cv2.VideoCapture(0) | |
| print capture.get(cv2.CAP_PROP_FPS) | |
| t = 100 | |
| w = 640.0 |
| import React, {DeviceEventEmitter} from 'react-native'; | |
| import {Observable} from 'rx-lite' | |
| /** | |
| * Creates an Observable to listen to any event of DeviceEventEmitter | |
| * @param type {string} Event type | |
| */ | |
| export default createObservableFromDeviceEventEmitter$ = type => { | |
| let subscription; | |
| return Observable.fromEventPattern( |
