Best AI tools for< Hardware Engineers >

Bench is an AI tool designed to automate hardware documentation for Hardware Engineers. It helps users document less and create more by utilizing AI for documentation writing, management, and discoverability. The tool offers features such as adapting to specific use cases, AI documentation writing, single source of truth, data-rich asset pages, highlighting compliance gaps, automated reports, and physical asset logging. Bench is advantageous for increasing productivity, improving documentation accuracy, streamlining workflows, enhancing compliance, and enabling seamless integrations. However, it may have limitations in customization options, initial learning curve, and potential dependency on AI accuracy. The tool is suitable for Hardware Engineers, Technical Writers, Documentation Specialists, Compliance Officers, and Quality Assurance Engineers. Users can find Bench using keywords like AI documentation, hardware documentation automation, AI writing tool, documentation management tool, and asset logging AI. Tasks users can perform with Bench include automate documentation, manage assets, write AI documentation, generate reports, and log physical assets.

Edge Impulse is a leading edge AI platform that enables users to build datasets, train models, and optimize libraries to run directly on any edge device. It offers sensor datasets, feature engineering, model optimization, algorithms, and NVIDIA integrations. The platform is designed for product leaders, AI practitioners, embedded engineers, and OEMs across various industries and applications. Edge Impulse helps users unlock sensor data value, build high-quality sensor datasets, advance algorithm development, optimize edge AI models, and achieve measurable results. It allows for future-proofing workflows by generating models and algorithms that perform efficiently on any edge hardware.

Quilter is an end-to-end PCB design platform that uses generative AI to automate the layout process. It can place components, route traces, define board stackups, and generate pours. Quilter is compatible with existing design tools and libraries, and it returns completed designs in the same file format they were submitted in. Quilter is currently in open beta and is free to use.

Luxonis is a platform that offers robotic vision solutions through high-resolution cameras with depth vision and on-chip machine learning capabilities. Their products include OAK Cameras and Modules, providing features like Stereo Depth Sensing, Computer Vision, Artificial Intelligence, and Cloud Management. Luxonis enables the development of computer vision products and companies by offering performant and affordable hardware solutions. The platform caters to enterprises and hobbyists, empowering them to easily build embedded vision systems.

AIM is an intelligent machine application that transforms existing heavy equipment into fully autonomous machines. It automates various heavy machinery to make jobs faster and safer, with a focus on enabling equipment to run autonomously without operators. AIM ensures full utilization of equipment every day, in any weather condition, with 360-degree safety measures to prevent accidents. The application also provides insurance for autonomous operation by a global insurance company. AIM's team consists of experts in autonomy, robotics, hardware, and advanced machine learning deployed at scale.

PC Builder AI is an artificial intelligence tool developed by AE Studio to assist users in building a customized PC based on their budget and preferences. The tool allows users to input their desired budget and PC specifications, and it provides real-time price updates to help users get the best value for their money. With a user-friendly interface, PC Builder AI simplifies the process of selecting components and building a PC tailored to individual needs. Whether you are a tech enthusiast or a novice, PC Builder AI can help you create a high-performance PC without the hassle of researching and comparing components.

The Code Generator for Arduino is a web application that assists users in generating code for Arduino projects. Users can input project details such as Arduino board type, sensors, motors, and components to receive customized code snippets. The tool provides guidance on creating code for various Arduino applications, from simple LED blinking to complex automated systems. It emphasizes specificity, breaking down prompts, and testing to refine the output. The website also includes information on best practices and examples to help users effectively utilize the code generator.

Please Don't Code is an AI code generator designed for Arduino and ESP32 developers to streamline and accelerate their coding process for embedded systems. It offers a user-friendly interface that guides users through a 6-step process to generate error-free Arduino code quickly and efficiently. By automating code generation, it eliminates the need for manual coding and error checking, making programming simple, fast, and stress-free. The service aims to empower freelance programmers and small/medium businesses to bring their prototype projects to life in less time compared to traditional manual coding approaches.

Wallaroo.AI is an AI inference platform that offers production-grade AI inference microservices optimized on OpenVINO for cloud and Edge AI application deployments on CPUs and GPUs. It provides hassle-free AI inferencing for any model, any hardware, anywhere, with ultrafast turnkey inference microservices. The platform enables users to deploy, manage, observe, and scale AI models effortlessly, reducing deployment costs and time-to-value significantly.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

SoundHound is a leading innovator of conversational intelligence and voice AI technologies. Our independent voice AI platform is built for more natural conversation, enabling businesses to create customized and scalable voice AI solutions for their specific industries and use cases. With SoundHound, you can build voice assistants, enhance smart devices, improve customer experiences, and drive business value.

Luminar is a leading developer of automotive lidar technology. The company's mission is to make roads safer by eliminating vehicle accidents. Luminar's lidar sensors provide cars with a detailed view of their surroundings, enabling them to make better decisions and avoid collisions. Luminar's technology is being used by a number of automakers, including Volvo, SAIC Motor, and Polestar.

OpenCV is the world's largest computer vision library. It's open source, contains over 2500 algorithms and is operated by the non-profit Open Source Vision Foundation.

VisionLabs is a leading provider of facial recognition technology that enhances digital identity experiences. Their Artificial Intelligence and Machine Learning technology, based on neural network algorithms, ensures a safer and more secure world, enabling seamless navigation in the digital realm. With applications in over 60 countries across various industries, VisionLabs aims to facilitate better and safer interactions through facial recognition technology.

Google Quantum AI is a leading platform dedicated to advancing the field of quantum computing. The platform offers educational resources, research publications, open-source tools like Cirq, and career opportunities. Google Quantum AI's mission is to build best-in-class quantum computing systems with error correction capabilities, enabling the solution of complex problems that are currently intractable for classical computers. The platform also hosts the XPRIZE Quantum Applications competition, collaborates with industry and academic partners to explore future applications of quantum computing, and shares research and blog posts on quantum algorithms and advancements.

ThirdAI is a production-ready AI platform designed for enterprise use, offering a comprehensive solution for building AI applications without the need for extensive proof of concept cycles or manual fine-tuning. The platform provides enterprise-grade security, scalability, and performance from day one, enabling business leaders to quickly address critical needs. With features like Enterprise SSO, LLM Guardrails, built-in models, no-code interface, and turnkey deployment, ThirdAI empowers users to create and deploy AI applications efficiently. The platform also allows for customization and regular updates without the need for AI expertise, making it accessible to non-specialists.

Windows Central is a website that provides news, reviews, and help on Windows 10, Windows 11, Xbox, and more. It covers a wide range of topics, including hardware, software, gaming, and accessories. The website also has a forum where users can ask questions and discuss topics related to Windows and Xbox.

Tensoic AI is an AI tool designed for custom Large Language Models (LLMs) fine-tuning and inference. It offers ultra-fast fine-tuning and inference capabilities for enterprise-grade LLMs, with a focus on use case-specific tasks. The tool is efficient, cost-effective, and easy to use, enabling users to outperform general-purpose LLMs using synthetic data. Tensoic AI generates small, powerful models that can run on consumer-grade hardware, making it ideal for a wide range of applications.

NVIDIA is a world leader in artificial intelligence computing, providing hardware and software solutions for gaming, entertainment, data centers, edge computing, and more. Their platforms like Jetson and Isaac enable the development and deployment of AI-powered autonomous machines. NVIDIA's AI applications span various industries, from healthcare to manufacturing, and their technology is transforming the world's largest industries and impacting society profoundly.

Helsing is an AI tool designed to serve and protect democracies by leveraging artificial intelligence technology. The company focuses on developing software-first solutions that enhance defense capabilities and safeguard democratic values. Helsing collaborates with industry and governments to integrate advanced AI with hardware platforms, aiming to achieve technological leadership in the defense sector. The team at Helsing comprises experts with diverse backgrounds in software, defense, intelligence, and artificial intelligence, united by a shared commitment to upholding democratic principles.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

MaixPy is a Python SDK that enables users to easily create AI vision projects on edge devices. It provides a user-friendly API for accessing NPU, making it suitable for AI Algorithm Engineers, STEM teachers, Makers, Engineers, Students, Enterprises, and Contestants. The tool supports Python programming, MaixVision Workstation, AI vision, video streaming, voice recognition, and peripheral usage. It also offers an online AI training platform called MaixHub. MaixPy is designed for new hardware platforms like MaixCAM, offering improved performance and features compared to older versions. The ecosystem includes hardware, software, tools, documentation, and a cloud platform.

The 'jobs' repository by comma.ai focuses on solving self-driving cars by building a robotics stack that includes state-of-the-art machine learning models, operating system design, hardware development, and manufacturing. The company aims to deliver constant incremental progress in self-driving technology to users, with a focus on practical solutions rather than hype. Job opportunities at comma.ai include technical challenges, phone screenings, and paid micro-internships, with perks such as chef-prepared meals, on-site gym access, and health insurance. The teams at comma.ai are organized into web, systems, infrastructure, product, design, and electrical engineering, with specific challenges for each team. The repository also offers opportunities for non-job seekers to participate in challenges and win prizes.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

SwanLab is an open-source, lightweight AI experiment tracking tool that provides a platform for tracking, comparing, and collaborating on experiments, aiming to accelerate the research and development efficiency of AI teams by 100 times. It offers a friendly API and a beautiful interface, combining hyperparameter tracking, metric recording, online collaboration, experiment link sharing, real-time message notifications, and more. With SwanLab, researchers can document their training experiences, seamlessly communicate and collaborate with collaborators, and machine learning engineers can develop models for production faster.

MARS5 is a novel English speech model (TTS) developed by CAMB.AI, featuring a two-stage AR-NAR pipeline with a unique NAR component. The model can generate speech for various scenarios like sports commentary and anime with just 5 seconds of audio and a text snippet. It allows steering prosody using punctuation and capitalization in the transcript. Speaker identity is specified using an audio reference file, enabling 'deep clone' for improved quality. The model can be used via torch.hub or HuggingFace, supporting both shallow and deep cloning for inference. Checkpoints are provided for AR and NAR models, with hardware requirements of 750M+450M params on GPU. Contributions to improve model stability, performance, and reference audio selection are welcome.

Notes on AI state of the art, with a focus on generative and large language models. These are the "raw materials" for the https://lspace.swyx.io/ newsletter. This repo used to be called https://github.com/sw-yx/prompt-eng, but was renamed because Prompt Engineering is Overhyped. This is now an AI Engineering notes repo.

LitServe is a high-throughput serving engine for deploying AI models at scale. It generates an API endpoint for a model, handles batching, streaming, autoscaling across CPU/GPUs, and more. Built for enterprise scale, it supports every framework like PyTorch, JAX, Tensorflow, and more. LitServe is designed to let users focus on model performance, not the serving boilerplate. It is like PyTorch Lightning for model serving but with broader framework support and scalability.

The Universal Numbers Library is a header-only C++ template library designed for universal number arithmetic, offering alternatives to native integer and floating-point for mixed-precision algorithm development and optimization. It tailors arithmetic types to the application's precision and dynamic range, enabling improved application performance and energy efficiency. The library provides fast implementations of special IEEE-754 formats like quarter precision, half-precision, and quad precision, as well as vendor-specific extensions. It supports static and elastic integers, decimals, fixed-points, rationals, linear floats, tapered floats, logarithmic, interval, and adaptive-precision integers, rationals, and floats. The library is suitable for AI, DSP, HPC, and HFT algorithms.

AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving. It offers high performance close to roofline fp16 TensorCore (NVIDIA GPU) / MatrixCore (AMD GPU) performance on major models. AITemplate is unified, open, and flexible, supporting a comprehensive range of fusions for both GPU platforms. It provides excellent backward capability, horizontal fusion, vertical fusion, memory fusion, and works with or without PyTorch. FX2AIT is a tool that converts PyTorch models into AIT for fast inference serving, offering easy conversion and expanded support for models with unsupported operators.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

The 'awesome-mobile-robotics' repository is a curated list of important content related to Mobile Robotics and AI. It includes resources such as courses, books, datasets, software and libraries, podcasts, conferences, journals, companies and jobs, laboratories and research groups, and miscellaneous resources. The repository covers a wide range of topics in the field of Mobile Robotics and AI, providing valuable information for enthusiasts, researchers, and professionals in the domain.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

ZX Spectrum expert in programming, game dev, and hardware troubleshooting.

AI Hardware Engineer

Valuating custom computer hardware. Copyright (C) 2023, Sourceduty - All Rights Reserved.

An advanced virtual assistant specializing in RaspBerry Pi Pico's and Micropython. Designed to offer expert advice, troubleshoot code, and provide detailed guidance.

Magically transform your design sketches and images into software, HDL code, and more!

AI Expert on Electronics and Embedded Systems.

Expert in Raspberry Pi and related tech

Checks through Embedded System Datasheet and Suggest Source Codes

专注于单片机设计和代码问题解答

This GPT will build app & answer questions for flipper zero.

Expert in MicroPython, C, and C++ for Raspberry Pi Pico and RP2040 and other microcontroller oriented applications.

Encouraging STM32 coding expert

Discover the versatile capabilities of the ESP32, the go-to board for IoT innovations. Easily create IoT applications leveraging its Wi-Fi and BLE functionalities.

Your digital design & verification guide. The conversation data will not be used for training.

Assists with Azure Quantum computing jobs, simulates API requests.

A Beginners Guide to Quantum Magic

Aid for quantum information experts.

Finds optimal PC parts based on user criteria

Leading Quantum Computing Expert and Assistant, powered by OpenAI.

A research assistant for neutral atom quantum computing.