MCU BLE Terminal vs Models Zoo

McuBleTerminal is a Bluetooth Low Energy terminal app designed for ESP32 development. It allows you to view serial output and send text commands wirelessly from your smartphone. No USB cable, no drivers, no WiFi and no cloud services required. The app works together with the McuBleTerminal ESP32 library and acts like a classic serial monitor, but over BLE. Typical use cases include debugging, viewing sensor data, testing firmware behavior and interacting with ESP32 devices while they are powered in real environments. Features include a clean terminal interface, monospace font, dark and light mode support, multilingual UI and a built in demo mode that works without any hardware. This app is intended as a development and debugging tool. ESP32 library and examples are available on GitHub: https://github.com/dominik-ewers-solutions/MCU_BLE_Terminal

Core ML Models is a curated, on-device AI playground. Download any model from the built-in catalog and try it out instantly — no server, no account, nothing leaves your device. Every model is an open-source release converted from PyTorch to Apple's Core ML format and tuned to run efficiently on the Neural Engine, GPU, or CPU. WHAT YOU CAN DO • Chat with small language models (Gemma, etc.) including multimodal vision+text • Generate images from text with Hyper-SD • Remove backgrounds, matte video subjects, and colorize old photos • Estimate depth and reconstruct 3D faces from a single photo • Detect and classify with YOLO, SigLIP, and zero-shot open-vocabulary models • Separate music stems with Demucs and transcribe audio to MIDI with Basic Pitch • Clone voices and synthesize speech with Kokoro and OpenVoice • Super-resolve images with SinSR and RealESRGAN. DEVELOPER TOOLS, NOT JUST A DEMO • Inspect — see every .mlpackage's input/output tensor shapes, data types, compute-unit setting, on-disk size, and Core ML metadata at a glance. • Benchmark — run warmup + timed iterations on your own device and get average / median / min / max latency plus memory delta. Switch compute units (All, CPU+GPU, CPU+ANE, CPU only) to find the fastest config for your hardware. • Swift Code — export a ready-to-paste Core ML loading + inference snippet wired to the model's actual input/output names, so integration into your own app is copy-paste fast. DESIGNED FOR THE NEURAL ENGINE Each model ships with compute-unit settings tuned per-architecture (Neural Engine, GPU, CPU) so you get the best inference speed Apple Silicon can deliver. OPEN AND TRANSPARENT Every model in the catalog links back to its original paper, repository, and license. Conversion scripts are public on GitHub so you can see exactly how the .mlpackage files were produced. PRIVACY FIRST All inference runs locally on your device. The app only fetches model files from public storage over HTTPS — there are no analytics, no ads, and no account to create. REQUIREMENTS • iPhone or iPad with Apple Silicon recommended for best performance • Some large models require several GB of free storage and ≥ 6 GB RAM