Search Results for "energy"
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Showing 75 open source projects for "energy"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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OpenJarvis
Personal AI, On Personal Devices
...Developed as part of the Intelligence Per Watt research initiative, it focuses on improving the efficiency and practicality of on-device AI systems. The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and accuracy. OpenJarvis integrates with local inference engines like Ollama, vLLM, SGLang, and llama.cpp to run language models directly on personal hardware. It also includes a learning loop that allows models to improve over time using locally generated interaction traces. By prioritizing local execution and efficiency, OpenJarvis aims to provide a foundation for privacy-preserving personal AI assistants. -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
...The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the dataset to include over 520,000 materials within 1 meV/atom of the convex hull as of August 2024. The repository provides datasets, model definitions, and interactive Colabs for exploring these materials, computing decomposition energies, and visualizing chemical families. ... -
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bitnet.cpp
Official inference framework for 1-bit LLMs
...At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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AutoClip
AI-powered video clipping and highlight generation
...The system supports downloading videos from major platforms (e.g. YouTube, Bilibili), or accepting local uploads, and then applies AI analysis to identify segments worth clipping based on content (e.g. high energy moments, speech, or other heuristics). Once highlights are identified, AutoClip can automatically cut those segments and optionally assemble them into a compilation, thus greatly reducing manual video editing effort. It uses a modern web application stack with a front end (React + TypeScript) for user interaction and a back end that handles downloading, processing, clipping, and queue management, allowing real-time progress feedback and easy deployment, e.g. via Docker. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Bear Stone Smart Home
Custom Home Assistant configuration with automations and scripts setup
...It demonstrates how to structure Home Assistant YAML files for scalability and maintainability in a real-world deployment. Bear Stone Smart Home also showcases custom automations and scripts designed to improve convenience, energy efficiency, and overall smart home behavior. Additionally, it may include examples of dashboards and user interface customization to enhance usability and visualization of home data. Overall, it serves as a practical reference for building and refining a tailored Home Assistant setup. -
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Black
The uncompromising Python code formatter
...Black delivers speed and certainty, while freeing you from constant pycodestyle nagging. Your code review becomes a lot faster and easier, so you can spend more time and mental energy on more important things. Blackened code makes the smallest diffs possible and looks the same no matter the project. Its formatting eventually becomes transparent, so you can simply forget about it and focus on your task at hand. Black has been successfully used in many projects, and has gained stellar user reviews as an exceptional, uncompromising PEP 8 compliant opinionated formatter. -
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SpeechRecognition
Speech recognition module for Python
...Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using pip install SpeechRecognition. The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library. ... -
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Duino-Coin
A coin that can be mined with almost everything
...There are no dependencies required. Duino was founded in 2019 and is a for-fun, fully original crypto coin project developed by a team of young developers that focuses on energy-efficient mining. It's mostly, but not only, dedicated to people who are just starting out in the crypto world as it doesn't require any expensive equipment. To make mining with underpowered devices possible Duino uses the so-called Kolka system that consists of a few stages that test the device performance, assign a difficulty tier and distribute the rewards. -
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wger
Self hosted FLOSS fitness/workout, nutrition and weight tracker
...Create your personal diet plan by creating as many meals with as many different ingredients as you need. The application will calculate the nutritional values (total energy, proteins, carbohydrates, etc.) of the entire plan and of each of the meals. Enter the weights and reps you've done for each exercise to generate diagrams that let you see at a glance how well you're doing. Of course, the raw numbers are still accessible. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Cactus
Low-latency AI inference engine optimized for mobile devices
Cactus is a low-latency, energy-efficient AI inference framework designed specifically for mobile devices and wearables, enabling advanced machine learning capabilities directly on-device. It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. ... -
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GT New Horizons Mod Pack
New Modpack with Gregtech, Thaumcraft and Witchery
You are looking at a big progressive kitchensink pack for Minecraft 1.7.10 balanced around the mod GregTech. Over 9 years of development (and still going) have formed a balance and refinement that only a handful of packs can keep up with. We are talking about thousands of recipe tweaks, a massive questbook with over 3000 quests, unique world generation, custom mods coded for the pack, custom Thaumonomicon pages, and many more. The main intentions of the pack are a long-lasting experience and... -
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SpikingJelly
SpikingJelly is an open-source deep learning framework
...The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. This makes it especially relevant for researchers interested in biologically inspired computing, event-driven processing, and energy-efficient AI systems. The framework includes neuron models, surrogate gradient training methods, encoding strategies, network components, and utilities for simulation and experimentation, allowing users to develop a wide variety of spiking architectures. It also supports integration with familiar PyTorch workflows, which lowers the barrier for machine learning practitioners who want to explore spiking approaches without abandoning mainstream tooling. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
...Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. ... -
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hls4ml
Machine learning on FPGAs using HLS
...This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. The framework was originally developed for high-energy physics experiments where real-time decision systems must process large volumes of data with strict latency constraints. Over time, it has expanded to support a variety of scientific and industrial applications including signal processing, embedded systems, and biomedical monitoring. -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
...Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU. -
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IVY
The Unified Machine Learning Framework
...Choose the most appropriate device or combination of devices for your needs. DeepMind releases an awesome model on GitHub, written in JAX. We'll use PerceiverIO as an example. Implement the model in PyTorch yourself, spending time and energy ensuring every detail is correct. Otherwise, wait for a PyTorch version to appear on GitHub, among the many re-implementation attempts that appear (a, b, c, d, e, f). Instantly transpile the JAX model to PyTorch. This creates an identical PyTorch equivalent of the original model. -
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DimCE
Tool for evaluating an energy sharing project in Wallonia.
DIMCE was created out of a desire to give everyone a tool for assessing the profitability of a shared energy project. DIMCE enables a detailed analysis of a collective energy project, making it possible to evaluate its relevance. It can simulate photovoltaic, wind, and cogeneration/anaerobic digestion production. -
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ERmod
Approximate solvation free energy calculator
ERmod (Energy Representation Module) is a program to calculate the solvation free energy based on the energy representation method (J. Chem. Phys. 113, 6070 (2000)). The program allows users to calculate the solvation free energy to arbitrary solvents, including inhomogeneous systems, and can run in cooperation with state-of-art molecular simulation software, such as LAMMPS, GROMACS and/or AMBER. -
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SIREN
Renewable Energy Network tool
SIREN demonstrates how electricity demand may be met by a mix of renewable energy sources. Users explore potential location and scale of renewable energy sources (stations, storage, transmission) to meet electricity demand. SIREN allows you to build scenarios for the energy mix for your area of interest. It is based around the map of your chosen area and allows you to visualise the layout of the existing electricity network plus the additional stations you place on the map to build your scenario(s). ... -
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Pixoo
A library to help you make the most out of your Pixoo 64
Pixoo is a Python-based library for controlling Divoom Pixoo LED displays using Bluetooth Low Energy (BLE). It allows users to send images, animations, or text to Pixoo devices, enabling creative integrations like desktop widgets, real-time data displays, or custom artwork. -
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Magnetic_Power_Controller
Magnetic Power Controller for Free Energy extraction.
This project is a hardware and software project to build up a Magnetic Power Controller running on a ESP32-S3-RLCD-4.2 produced by Waveshare. The firmware presents multiple pages on the display for different types of data and processing methods to be selected. More to be defined on the go. -
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KherveFitting
Fit X-ray Photoelectron Spectroscopy (XPS) and XAS data
⚠️ KherveFitting has moved. New releases will no longer be published here. Download the latest version from https://khervetools.com or directly from GitHub: https://github.com/KherveFitting/KherveFitting/releases/latest. This SourceForge page is kept for archival/older versions only. YouTube Channel: https://www.youtube.com/@xpsexamples-imperialcolleg6571 -
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Eva AI
Eva is an A.I. assistant that helps users multi-task.
...Eva is also privacy focussed, because now the STT and TTS engines are running locally. For more instructions, check the instruction manual included in the application. 🆕 Improved latency 🆕 Added Energy-based RMS signal-processing algorithm for speech detection 🆕 Added a new on-device speech recognition engine: 🪨 Granite-Speech 🆕 Added a new on-device speech synthesis engine: Piper 🪈 🐞 Fixed audio clipping issues -
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PYroMat
Thermodynamic properties in python
PYroMat brings the thermodynamic properties of nearly 1,000 species to Python users with only a few keystrokes. Entropy, enthalpy, specific heats, inverse functions, and more are available from ideal gases, their mixtures, and multi-phase steam. Accurate, fast, and tested; PYroMat is the open-source solution for students, researchers, design engineers, and tinkerers everywhere who need thermodynamic properties. -
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Fosite - advection problem solver
numerical simulation code for solving transport equations in 1D/2D/3D
Fosite is a generic framework for the numerical solution of hyperbolic conservation laws in generalized orthogonal coordinates. Its main purpose is the simulation of compressible flows in accretion disks. The underlying numerical solution method belongs to the family of unsplit conservative finite volume TVD schemes. The method is 2nd order accurate in space and uses high order Runge-Kutta and multistep schemes for time evolution. In addition to the pure advection code several source terms...
