You're creating a Machine Learning interface class that predicts monster rarity based on attributes like Level, Health, Energy, and Sanity. This class will train a model, make predictions, save/load the model, and integrate with your API.
Before you start, make sure you have:
Nice — fun project. I’ll plan this so the Pi Zero measures whether parts inside the washing machine are working (pump, motor, heater, valves, door switch) without asking you to directly wire high-voltage mains into the Pi. Safety first: do not connect Raspberry Pi GPIO directly to mains or to non-isolated circuits. For anything that touches mains, use galvanic isolation (opto-isolators, isolated sensors, or external AC→DC isolated modules), or test components removed from the appliance on a bench with safe low-voltage signals.
Below I give:
Course Goal: By the end of this course, you will build a fully functional, beautiful responsive image gallery using ONLY one HTML file, CSS, and pure JavaScript (no React, no frameworks).
Target student:
Final Project: A single-page image gallery that loads images from a JavaScript object/JSON, lets you click thumbnails to see a bigger version in a modal/lightbox, with next/prev buttons.
To "beat" your 6-node Mac Mini M4 Pro cluster (384GB total unified memory, ~4 tokens/second for 4-bit quantized DeepSeek-V3 671B inference, based on scaling from 8-node benchmarks), we're targeting a single GPU that delivers higher inference speed (e.g., >4 t/s for the full 671B model) while keeping costs reasonable for a local setup. DeepSeek-V3's MoE architecture (only 37B active params/token) helps, but the model's ~386GB 4-bit footprint means no consumer GPU can load it fully in VRAM alone—you'll need CPU offloading (e.g., via llama.cpp or vLLM with 128GB+ system RAM). This hybrid approach is common and works well for interactive use.
The NVIDIA RTX 4090 (24GB VRAM) is the clear winner as the best single GPU alternative. It outperforms the cluster in raw speed for DeepSeek-V3 (5-15 t/s with optimizations, vs. your 4 t/s), costs far less ($1,600 vs. $12-15K for the cluster), and draws less power (~450W peak vs. ~300-400W total for 6 Min
Goal: expose two hostnames via Cloudflare Tunnel that route to services running on a single EC2 instance:
<SUBDOMAIN 1>.<YOUR DOMAIN NAME>.com → http://localhost:80 (Python website)<SUBDOMAIN 2>.<YOUR DOMAIN NAME>.com → http://localhost:3000 (Express API)This guide is concise and written for Amazon Linux (YUM/RPM). Commands assume you run them as ec2-user with sudo where necessary.
AWS lets you issue a real ACM certificate for the exact string *.compute-1.amazonaws.com and for your specific instance DNS name. It’s public and trusted by all browsers.
Open ACM in us-east-1 (N. Virginia) — this is mandatory
https://us-east-1.console.aws.amazon.com/acm/home
Request a public certificate → click “Request”
Complete beginners who know what a browser and a text editor are.
2 hours (guided, hands-on).
By the end of this lesson, the student will have built a simple responsive image gallery laid out with CSS Grid and a tiny bit of JavaScript to open an image in a lightbox/modal.