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Google's Developer Expert community built an offline AI racing coach that processes live car telemetry at the edge, demonstrating real-time AI without cloud reliance.
Cactus Compute releases Needle 2, a 45M-parameter agentic LLM compressed to a 14MB binary for phones, wearables, smart home and robots, achieving 500+ tokens/sec on a Raspberry Pi 5 and running in 28MB RAM.
Cactus releases Needle 2, a 14MB agentic LLM for phones, wearables, smart home devices, and robots, achieving fast inference on low-end hardware and supporting structured extraction and fine-tuning.
Meta announces a new open-source model optimized for on-device deployment, aiming to bring efficient AI inference to edge devices.
VLX-Seek-1.5-10B is an open-source 10B vision-language model from omlab, designed for fine-grained visual grounding in embodied scenarios like drones, robots, and surveillance, using region-reference localization instead of coordinate generation.
MiCoPro presents an end-to-end hardware/software co-design framework for mixed-precision quantization, using a hardware-aware proxy model to search optimal layer-wise bitwidths under latency constraints and deploy directly to edge hardware, achieving up to 40% latency reduction with less than 3% accuracy drop.
A developer shows how to run a 28M parameter LLM locally on an Amazon Echo Dot 2 using llama.cpp, achieving around 4 tokens/s generation and ~2.3s latency with prompt caching, enabling simple offline voice commands.
TechCrunch Disrupt 2026 announces a new Real World AI Stage focusing on AI in the physical world, featuring robots, automated factories, and de-extinction, with speakers from Shield AI, Colossal Biosciences, FieldAI, and more, October 13-15 in San Francisco.
VisionPsy-Nano-460M-Flash is a new 460M vision-language model that uses only 64 visual tokens per image, cutting first-token latency to 0.3s on iPhone while retaining roughly 99% of the full model's benchmark score. The optimization trades off some OCR and fine-detail quality.
Qapla' is a project that demonstrates training a small transformer language model from scratch on an $8 ESP32-S3 microcontroller, showing that on-device training is feasible without a GPU or datacenter.
Discusses running Gemma 4 on a device with only 500MB of memory, likely through quantization or other optimization techniques.
Liquid AI released LFM2.5-2.6B, an on-device agentic model that plans, calls tools, and works through multi-step tasks on phones, laptops, PCs, and robots, with data never leaving the device.
The author used GPT-5.6 to complete a garage door recognition project on ESP32-CAM within 10 hours, covering data collection, labeling, training, quantization, and deployment, demonstrating AI's autonomous capabilities in hardware development, and sharing insights on human-machine collaboration.
NVIDIA showcases the Jetson platform for edge AI and robotics, highlighting the compact yet powerful Jetson Orin Nano Super developer kit that enables building AI agents and robots anywhere.
Join ipXchange's upcoming webinar to learn how to build an end-to-end AI application using the Arduino UNO Q and deploy it to a camera-mounted robotic arm for industrial inspection.
A developer demonstrates running a 28.9 million parameter language model on an $8 ESP32-S3 microcontroller using Google's Per-Layer Embeddings to store most parameters in flash, achieving around 9.5 tokens per second on-device text generation.
User reports successfully running a 27B parameter model quantized to 1-bit on a Jetson Orin NX 16GB edge device, expressing amazement at the feasibility.
OrangePi releases AI Studio Pro, a single-board computer optimized for running the Qwen3.5-122B-A10B large language model.
Arduino announces the ability to train and deploy custom AI models in Arduino App Lab via Edge Impulse integration, building on predefined edge AI models.
The paper presents BearingNAS, a hardware-aware neural architecture search framework that designs intelligent fault diagnosis systems for bearings, targeting microcontrollers and sensor processing units with extremely limited memory (4-8 KiB RAM, 16-32 KiB Flash) while running entirely on a laptop CPU and achieving 99.50% diagnostic accuracy.