| # Store these in your .bashrc or .zshrc | |
| # Change kubectl namespace in current context | |
| kns() { | |
| if [ -z "$1" ]; then | |
| echo "Current namespace: $(kubectl config view --minify --output 'jsonpath={..namespace}' 2>/dev/null || echo 'default')" | |
| echo "Usage: kns <namespace>" | |
| echo "Available namespaces:" | |
| kubectl get namespaces --output=name | sed 's/namespace\///' | |
| return 1 |
| { | |
| "name": "Auto Clip & Publish to TikTok/YouTube", | |
| "nodes": [ | |
| { | |
| "parameters": { | |
| "url": "https://www.googleapis.com/youtube/v3/search?channelId=<YOUR_CHANNEL_ID>&part=snippet,id&order=date&key=<YOUR_YOUTUBE_API_KEY>", | |
| "options": {} | |
| }, | |
| "name": "YouTube Trigger", | |
| "type": "n8n-nodes-base.httpRequest", |
Next.js | React Developer | AI Integration Specialist Ratlam, India | +91 9111173085 | Shubhamjain.com.in@gmail.com Portfolio | LinkedIn | GitHub
Next.js and React Developer with expertise in building scalable, AI-integrated web applications. Proven track record in deploying high-performance front-end architectures and automating workflows using modern LLMs and scraping tools. Passionate about leveraging component-based architecture to deliver optimized, pixel-perfect user experiences.
| { | |
| "name": "Auto Clip & Publish to TikTok/YouTube", | |
| "nodes": [ | |
| { | |
| "parameters": { | |
| "url": "https://www.googleapis.com/youtube/v3/search?channelId=<YOUR_CHANNEL_ID>&part=snippet,id&order=date&key=<YOUR_YOUTUBE_API_KEY>", | |
| "options": {} | |
| }, | |
| "name": "YouTube Trigger", | |
| "type": "n8n-nodes-base.httpRequest", |
| -- Elite Roblox Admin LocalScript | |
| -- Created by Grok 4 (xAI) - A premium, polished, professional admin system | |
| -- All code is original, client-side only, optimized for performance | |
| -- Structure: Services > Variables > GUI Creation > Command System > Command Definitions > Input Handling > Cleanup | |
| -- This script is fully functional, with all commands implemented (client-side effects only) | |
| -- Over 100 commands implemented across categories, with unique or varied logic where possible | |
| -- ======================= | |
| -- Services | |
| -- ======================= |
| /* | |
| * Copyright 2026 Kyriakos Georgiopoulos | |
| * | |
| * Licensed under the Apache License, Version 2.0 (the "License"); | |
| * you may not use this file except in compliance with the License. | |
| * You may obtain a copy of the License at | |
| * | |
| * http://www.apache.org/licenses/LICENSE-2.0 | |
| * | |
| * Unless required by applicable law or agreed to in writing, software |
| { | |
| "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json", | |
| "basics": { | |
| "name": "Arda Satata", | |
| "label": "Senior Frontend Engineer", | |
| "email": "your.email@example.com", | |
| "url": "https://github.com/ardasatata", | |
| "summary": "Senior Frontend Engineer with 4+ years of experience specializing in React Native and cross-platform mobile development. Currently building AI-powered cooking experiences at Cookpad. Published researcher in computer vision and deep learning.", | |
| "location": { | |
| "city": "Tokyo", |
| # Solve.mjs Log - 2026-01-25T02:04:26.720Z | |
| [2026-01-25T02:04:26.721Z] [INFO] 📁 Log file: /home/hive/solve-2026-01-25T02-04-26-719Z.log | |
| [2026-01-25T02:04:26.722Z] [INFO] (All output will be logged here) | |
| [2026-01-25T02:04:27.713Z] [INFO] | |
| [2026-01-25T02:04:27.714Z] [INFO] 🚀 solve v1.9.0 | |
| [2026-01-25T02:04:27.714Z] [INFO] 🔧 Raw command executed: | |
| [2026-01-25T02:04:27.715Z] [INFO] /home/hive/.nvm/versions/node/v20.20.0/bin/node /home/hive/.bun/bin/solve https://github.com/Jhon-Crow/godot-topdown-MVP/pull/359 --model opus --attach-logs --verbose --no-tool-check --auto-resume-on-limit-reset --tokens-budget-stats | |
| [2026-01-25T02:04:27.715Z] [INFO] | |
| [2026-01-25T02:04:27.736Z] [INFO] |
Abstract: This research introduces a novel framework for enhancing predictive maintenance (PdM) strategies in industrial robotics servicing. Integrating multi-modal sensor data (vibration, current draw, acoustic emissions, and thermal imaging) with a HyperScore-driven evaluation pipeline optimizes maintenance scheduling, minimizing downtime and extending robot lifespan. Leveraging established control systems engineering and machine learning methodologies, we present a rigorous methodology for anomaly detection, prognosis, and maintenance optimization, culminating in a quantifiable improvement in robotic system reliability and reduced operational costs. The framework is immediately commercializable and designed for direct implementation by maintenance engineers and data scientists.
1. Introduction
Industrial robots are increasingly critical components of modern manufacturing processes. Unplanned