Apple Neural Engine Explained: Optimizing Models for Apple Silicon

How the Apple Neural Engine works, which operations actually reach it, how to check compute unit assignment in Xcode, and how to design Core ML models it runs.

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.