Edge Impulse Tutorial: No-Code TinyML for Embedded Devices

Train and deploy TinyML models without code using Edge Impulse: data collection, impulse design, the EON Tuner, validation, and deploying to Arduino or a browser.

MediaPipe Tutorial: Real-Time On-Device ML for Vision and Audio

Run real-time vision and audio ML on-device with MediaPipe: setup for Python, Android, and web, gesture and audio pipelines, Model Maker, and pitfalls.

Core ML Tutorial: Deploy Machine Learning Models on iPhone

Deploy machine learning models on iPhone with Core ML: convert PyTorch with coremltools, integrate in Xcode, run Vision predictions, and cut size.

Fine-Tuning a Local LLM with LoRA on Consumer Hardware

Fine-tune a local LLM with LoRA and QLoRA on a consumer GPU: hardware sizing, Unsloth setup, dataset prep, training, evaluation, and running it locally.

LM Studio Tutorial: Run LLMs Locally with a GUI

Install LM Studio and run LLMs locally: download quantized GGUF or MLX models, chat with documents offline, and serve an OpenAI-compatible API on your machine.

Best Books on Deep Reinforcement Learning for Robotics

Four deep RL books compared for robotics work: Sutton & Barto, Lapan, Morales, and Kober & Peters — plus the reading order that actually works.

Best Robotics Simulation Software Compared (MuJoCo, PyBullet, Isaac Gym)

Compare MuJoCo vs PyBullet vs Isaac Gym for robotics simulation: contact physics accuracy, learning curve, GPU-parallel RL training, and the Isaac Lab successor.

Training AI Agents in Minecraft with Reinforcement Learning

Train AI agents in Minecraft with reinforcement learning: MineRL setup, VPT video pretraining, hierarchical RL, and Voyager LLM-driven planning.

Path Planning with Reinforcement Learning for Mobile Robots: Mapless Navigation and Hybrid Architectures

Deep RL path planning for mobile robots: mapless navigation, costmap observations, reward shaping, dynamic obstacles, and hybrid RRT-plus-RL architecture.

Training a Quadruped Robot to Walk with RL

Train a quadruped to walk with RL in Isaac Lab: 4,096 environments, terrain curriculum, domain randomization, teacher-student distillation, sim-to-real.