The era of open voice assistants has arrived
Home Assistant Voice Preview Edition is the best way to get started with our open and privacy-focused voice assistant.
GitHub - exo-explore/exo: Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚ - exo-explore/exo
The 01 Project
The 01 Project is a voice interface for your home computer.
Thinking about the emerging landscape of AI hardware products
Posted on Friday 26 Jan 2024. 1,297 words, 12 links. By Matt Webb.
SettingUpML | Macs in Chemistry
Apple M2 Studio Learns Nuke Machine Learning - fxguide
Seeking Advice on Optimal Computer Configuration for Machine/Deep Learning : r/nvidia
The build-a-deep-learning-machine from vineetk1 - Giter Club
Prebuilt vs Building your own Deep Learning Machine vs GPU Cloud (AWS) | BIZON Custom Workstation Computers, Servers. Best Workstation PCs and GPU servers for AI/ML, deep learning, HPC, video editing, 3D rendering, CAD.
Build a super fast deep learning machine for under $1,000 – O’Reilly
How to build the perfect Deep Learning Computer and save thousands of dollars | by Jeff Chen | Mission.org | Medium
TitanXp vs GTX1080Ti for Machine Learning | Puget Systems
GPU Build - edegan.com
Building The Ultimate Machine Learning PC: A Step-by-Step Guide
Cloud GPU Guide
Firstly, which AI tools are worth running?
MinIO | High Performance, Kubernetes Native Object Storage
MinIO is a high-performance, S3 compatible object store. It is built for large scale AI/ML, data lake and database workloads. It runs on-prem and on any cloud (public or private) and from the data center to the edge. MinIO is software-defined and open source under GNU AGPL v3.
NVIDIA Jetson Orin
Next-level AI performance for next-gen robotics.
USB Accelerator | Coral
A USB accessory that brings accelerated ML inferencing to existing systems. Works with Windows, Mac, and Raspberry Pi or other Linux systems.
Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI
In this article from Sebastian Raschka, he reviews Apple's new M1 and M2 GPU and its support for PyTorch, along with some early benchmarks.