Search Results for "python user interface" - Page 17
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Showing 12085 open source projects for "python user interface"
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1
SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
SWE-agent turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI). -
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FuseSoC
Package manager and build abstraction tool for FPGA/ASIC development
FuseSoC is a package manager and build abstraction tool for hardware description language (HDL) code, aimed at simplifying the development and reuse of IP cores. It provides a standardized way to describe, manage, and build hardware projects, facilitating collaboration and reducing duplication of effort in FPGA and ASIC development. -
3
PettingZoo
An API standard for multi-agent reinforcement learning environments
PettingZoo is a standardized API and library for multi-agent reinforcement learning (MARL) environments. It provides a broad set of environments and tools to facilitate the development and evaluation of multi-agent algorithms. -
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Agent S
Agent S: an open agentic framework that uses computers like a human
Agent S is an open-source agentic framework designed to enable autonomous computer use through an Agent-Computer Interface (ACI). Built to operate graphical user interfaces like a human, it allows AI agents to perceive screens, reason about tasks, and execute actions across macOS, Windows, and Linux systems. The latest version, Agent S3, surpasses human-level performance on the OSWorld benchmark, demonstrating state-of-the-art results in complex multi-step computer tasks. ... -
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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic... -
6
Mem0
The Memory layer for AI Agents
Mem0 is a self-improving memory layer designed for Large Language Model (LLM) applications, enabling personalized AI experiences that save costs and delight users. It remembers user preferences, adapts to individual needs, and continuously improves over time. Key features include enhancing future conversations by building smarter AI that learns from every interaction, reducing LLM costs by up to 80% through intelligent data filtering, delivering more accurate and personalized AI outputs by... -
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OpenDAN
OpenDAN is an open source Personal AI OS
OpenDAN is an open-source Personal AI OS , that consolidates various AI modules in one place for your personal use. The goal of OpenDAN (Open and Do Anything Now with AI) is to create a Personal AI OS , which provides a runtime environment for various Al modules as well as protocols for interoperability between them. With OpenDAN, users can securely collaborate with various AI modules using their private data to create powerful personal AI agents, such as butlers, lawyers, doctors, teachers,... -
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IntelOwl
Centralized platform for automated threat intelligence analysis
IntelOwl is an open source platform designed to manage and enrich threat intelligence data at scale. It provides a centralized environment where security analysts can gather information about suspicious files and observables such as IP addresses, domains, URLs, or hashes using a single API request. The platform integrates numerous online intelligence sources and advanced malware analysis tools, enabling users to obtain comprehensive threat intelligence without manually querying multiple... -
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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time... -
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PDM
A modern Python package and dependency manager
PDM (Python Development Master) is a modern Python package and dependency manager that adheres to the latest PEP standards. It emphasizes a declarative approach to project configuration using pyproject.toml, facilitating reproducible builds and streamlined workflows. PDM's focus on simplicity and compliance with Python's evolving ecosystem makes it a valuable tool for developers seeking modern project management solutions. -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
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ACI.dev
Open platform connecting AI agents to tools via unified MCP server
ACI is an open source platform designed to enable AI agents to interact with external tools through a unified and structured interface. It focuses on simplifying tool integration by connecting hundreds of pre-built services into agentic environments, allowing developers to avoid building custom API clients and authentication flows for each service. ACI provides intent-aware tool access, meaning agents can dynamically discover and use tools based on context rather than rigid configurations.... -
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Mailu
Insular email distribution - mail server as Docker images
Mailu is a simple yet full-featured mail server as a set of Docker images. It is free software (both as in free beer and as in free speech), open to suggestions and external contributions. The project aims at providing people with an easily setup, easily maintained and full-featured mail server while not shipping proprietary software nor unrelated features often found in popular groupware. Security, enforced TLS, DANE, MTA-STS, Letsencrypt!, outgoing DKIM, anti-virus scanner, Snuffleupagus,... -
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WSA-Script
Integrate Magisk root and Google Apps into WSA
WSA-Script is a community-driven automation and scripting toolkit that helps Windows users extend the Windows Subsystem for Android (WSA) by integrating optional features like Magisk (for root access) and Google Apps into the otherwise vanilla WSA environment, leveraging GitHub Actions and scripted installers to do much of the heavy lifting. The project provides a way to download and unpack custom builds of the WSA package that bundle these enhancements and guide users through installation... -
15
Paul's AutoClicker
A fast auto clicker and macro tool made for windows
You can use this application as an auto clicker, a macro, or both. The program has many features built into it to be very versatile and fit everyone's needs. If you need an auto clicker that will click infinitely fast, this can click as fast as your CPU allows. If you need a clicker with some randomization, hold the mouse button down, and click on a certain spot for x times, it can do that too and more. If you want to be able to still use your PC while something is clicking in the background... -
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TorchDistill
A coding-free framework built on PyTorch
torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file. -
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sparkmagic
Jupyter magics and kernels for working with remote Spark clusters
...Sparkmagic interacts with remote Spark clusters through a REST server. Automatic visualization of SQL queries in the PySpark, Spark and SparkR kernels; use an easy visual interface to interactively construct visualizations, no code required. Ability to capture the output of SQL queries as Pandas dataframes to interact with other Python libraries (e.g. matplotlib). Send local files or dataframes to a remote cluster (e.g. sending pretrained local ML model straight to the Spark cluster) Authenticate to Livy via Basic Access authentication or via Kerberos. -
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Database-backed Periodic Tasks
Celery Periodic Tasks backed by the Django ORM
This extension enables you to store the periodic task schedule in the database. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run. Usage and installation instructions for this extension are available from the Celery documentation. If you change the Django TIME_ZONE setting your periodic task schedule will still be based on the old timezone. To create a periodic task executing at an interval... -
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screenshot-to-code
Drop in a screenshot and convert it to clean code
screenshot-to-code converts UI screenshots or design images into working front-end code, accelerating the path from concept to prototype. It uses modern vision-capable or code-generating models to infer layout structure, typography, and components, then outputs clean HTML/CSS (often Tailwind) or framework code. A web interface lets you upload images, tune options, and preview generated results, while a backend service orchestrates the model calls and post-processing. The tool focuses on... -
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Jittor
Jittor is a high-performance deep learning framework
...Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. ... -
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Daijishō
Daijishō is a retro launcher that let you manage your retro games
Daijishō is a retro launcher that lets you manage your retro games libraries. This repo is for assets and serves as a main page. Daijishō is a retro launcher that allows you to manage your retro game libraries. Daijishō cares about integrated experience, expandability, aesthetic, and pragmatic usability, allowing you to focus on the games themselves. It will be continually updated in the future based on user responses and my own retro gaming experience in my spare time. -
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. -
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MemMachine
Universal memory layer for AI Agents
...Unlike ephemeral LLM prompt state, MemMachine supports distinct memory types—short-term conversational context, long-term persistent knowledge, and profile memory for personalized facts—persisted in optimized stores (e.g., graph databases for episodic lines of reasoning and SQL for user facts) to support robust, context-aware intelligence in agents. It offers flexible APIs, a Python SDK, REST interfaces, and MCP (Model Context Protocol) connectivity to integrate seamlessly with agent frameworks receiving and storing memories over time, effectively boosting relevance, continuity, and tailored behavior. -
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Archon
The knowledge and task management backbone for AI coding assistants
Archon is an open-source “command center” designed to enhance AI coding assistant workflows by giving developers a centralized environment for knowledge management, context engineering, and task coordination across AI agents. It acts as a backend (including an MCP server) that allows different AI coding tools and assistants to share the same structured context, knowledge base, and task lists, improving consistency, productivity, and collaboration across multi-agent interactions. Users can... -
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notebooker
Productionise & schedule your Jupyter Notebooks
Productionise and schedule your Jupyter Notebooks, just as interactively as you wrote them. Notebooker is a webapp which can execute and parametrise Jupyter Notebooks as soon as they have been committed to git. The results are stored in MongoDB and searchable via the web interface, essentially turning your Jupyter Notebook into a production-style web-based report in a few clicks.
