Search Results for "concept" - Page 3
Sort By:
Showing 120 open source projects for "concept"
View related business solutions-
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
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.
-
1
Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During training, the system performs a forward execution where the agent completes a task and records the trajectory of prompts, outputs, and tool usage. ... -
2
Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. ... -
3
JamAI Base
The collaborative spreadsheet for AI
...JamAI Base exposes its functionality through a simple REST API and a spreadsheet-style interface that allows users to manage AI workflows visually. One of the key ideas behind the platform is the concept of generative tables, which allow database columns to automatically populate with AI-generated content. The system also supports action tables and chat tables that simplify the creation of interactive AI features such as conversational interfaces and dynamic workflows. -
4
LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
...The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. These operators allow tasks such as semantic filtering, ranking, clustering, and summarization to be expressed directly within data processing pipelines. The LOTUS engine automatically optimizes how language models are used during execution, which can significantly improve performance and reduce computational cost. -
Earn up to 16% annual interest with Nexo.Put idle assets to work with competitive interest rates, borrow without selling, and trade with precision. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
5
Acontext
Context data platform for building observable, self-learning AI agents
Acontext is a cloud-native context data platform designed to support the development and operation of advanced AI agents. It provides a unified system to store and manage contexts, multimodal messages, artifacts, and task workflows, enabling developers to engineer context effectively for their agent products. The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also... -
6
GLM-4.1V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
...Given its open-source availability under the same project repository, it provides an accessible entry point for testing multimodal reasoning and building proof-of-concept applications. -
7
Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
This project is a comprehensive, code-first deep learning curriculum built around TensorFlow and Keras, designed to guide learners from foundational concepts to practical model deployment through hands-on experimentation. It is structured as a series of progressively complex Jupyter notebooks that emphasize writing and understanding code before diving into theory, reinforcing learning through repetition and application. The material covers core machine learning workflows including... -
8
Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning points would easily apply to Imagen), make a few minor modifications for attention across time and other ways to skimp on the compute cost, do frame interpolation correctly, get a great video model out. ... -
9
Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions rather than memorize trivia. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
Adeus
An open source AI wearable device that captures what you say
Adeus is an experimental open-source framework focused on building autonomous AI agents capable of reasoning, planning, and executing complex tasks with minimal human intervention. The project explores the concept of agent-based AI systems that can break down objectives into smaller steps, execute them sequentially, and adapt based on intermediate results. It provides a structured environment for orchestrating workflows that involve multiple actions, such as retrieving data, generating content, or interacting with external tools. The framework is designed to be modular, allowing developers to customize agent behavior, integrate APIs, and extend capabilities depending on the use case. ... -
11
VideoCrafter2
Overcoming Data Limitations for High-Quality Video Diffusion Models
...The system is optimized for generating videos from textual descriptions or still images, leveraging advanced diffusion models. VideoCrafter2, an upgraded version, improves on its predecessor by enhancing motion dynamics and concept combinations, especially in low-data scenarios. Users can explore a wide range of creative possibilities, producing cinematic videos that combine artistic styles and real-world scenes. -
12
OneFlow
OneFlow is a deep learning framework designed to be user-friendly
...Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous distributed expansion. It adheres to the core concept and architecture of static compilation and streaming parallelism and solves the memory wall challenge at the cluster level. world-leading level. Provides a variety of services from primary AI talent training to enterprise-level machine learning lifecycle integrated management (MLOps), including AI training and AI development, and supports three deployment modes of public cloud, private cloud and hybrid cloud. -
13
Weak-to-Strong
Implements weak-to-strong learning for training stronger ML models
Weak-to-Strong is an OpenAI research codebase that implements the concept of weak-to-strong generalization, as described in the accompanying paper. The project provides tools for training larger “strong” models using labels or guidance generated by smaller “weak” models. Its core functionality focuses on binary classification tasks, with support for fine-tuning pretrained language models and experimenting with different loss functions, including confidence-based auxiliary losses. ... -
14
Sonnet
TensorFlow-based neural network library
Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already ship with Sonnet, making it quite powerful and yet simple at the same time. Users are also encouraged to build their own modules. Sonnet is designed to be extremely unopinionated about your use of modules. ... -
15
Faum
Fast Autonomous Unsupervised Multidimiensional Classification
This is the proof-of-concept implementation of the FAUM Clustering method. This implementation was used to perform the published results and is now released in the hope that it will be useful. -
16
Prompt-to-Prompt
Latent Diffusion and Stable Diffusion Implementation
...The repository includes reference notebooks and scripts that plug into popular latent diffusion backbones, making it practical to try the technique on your own prompts and seeds. It’s especially useful for workflows that need consistent framing, product shots, illustrations, and concept art, etc. -
17
CSAw - NLP for low-resource languages
CSAw is an NLP framework for low-resource languages
...The primary goal is to build language models automatically from bilingual text (e.g., front and back translations) using a deep transfer rule-based approach. The core of this strategy is the Concept Specification and Abstraction semantic representation which is specially designed with machine translation in mind. See the preprint article here: https://arxiv.org/abs/1807.02226 The current framework includes transduction algorithms (i.e., from text to semantic representation and back again) and some components needed for automatic language model building (lexical alignment and grammar rule generation). ... -
18
RoomGPT
Upload a photo of your room to generate your dream room with AI
...The project is often described as an open-source clone or alternative to tools like InteriorAI, making AI-driven interior design experimentation accessible to developers. Because it is open source, developers can fork it, plug in different models, change the UI, or adapt the concept to other domains like garden layouts or product staging. -
19
TurboPilot
Open source large-language-model based code completion engine
TurboPilot is a self-hosted copilot clone that uses the library behind llama.cpp to run the 6 Billion Parameter Salesforce Codegen model in 4GiB of RAM. It is heavily based and inspired by on the fauxpilot project. This is a proof of concept right now rather than a stable tool. Autocompletion is quite slow in this version of the project. Feel free to play with it, but your mileage may vary. -
20
TaskMatrix
Enable sending and receiving images during chatting
...The project expands beyond traditional chatbot behavior by enabling AI systems to process, generate, edit, and reason about images while coordinating multiple specialized models simultaneously. Originally introduced alongside the Visual ChatGPT concept, TaskMatrix acts as an orchestration framework where a central language model delegates subtasks to domain-specific AI systems such as image generators, segmentation tools, or recognition models. The architecture focuses on modularity, allowing new APIs and foundation models to be integrated as interchangeable task-solving components. ... -
21
AICommand
ChatGPT integration with Unity Editor
AICommand is a proof-of-concept integration that lets you control the Unity Editor using natural language via ChatGPT. Instead of manually hunting through menus or writing editor scripts, you can prompt the editor to perform tasks, generate snippets, and automate actions. The project showcases an emerging workflow where LLMs augment game and tooling development by understanding intent and producing editor-side outcomes. -
22
LifeAI is an artificial intelligence system that can be applied to robotics, games, or business. It simulates key processes of our minds, such as organizing data into concepts and categories, planning actions based on their predicted outcome, and communication. LifeAI was designed to be simple, but powerful and flexible enough to have many applications.
-
23
minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported.... -
24
2020 Machine Learning Roadmap
A roadmap connecting many of the most important concepts
machine-learning-roadmap is an open-source educational project that provides a visual and conceptual guide to the most important ideas and tools in machine learning. The repository organizes machine learning knowledge into a structured roadmap that helps learners understand how different concepts connect within the field. It outlines the typical workflow of solving machine learning problems, starting from problem formulation and data preparation to model training and evaluation. The roadmap... -
25
Hello AI World
Guide to deploying deep-learning inference networks
...We’ll provide you with all the tools you need, including easy to follow guides, software samples such as TensorRT code, and even pre-trained network models including ImageNet and DetectNet examples. Follow these directions to integrate deep learning into your platform of choice and quickly develop a proof-of-concept design.
