Search Results for "no code" - Page 14
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Showing 2582 open source projects for "no code"
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Ungoogled Chromium
A lightweight approach to removing Google web service dependency
In descending order of significance (i.e. most important objective first), ungoogled-chromium is Google Chromium, sans dependency on Google web services, ungoogled-chromium retains the default Chromium experience as closely as possible. Unlike other Chromium forks that have their own visions of a web browser, ungoogled-chromium is essentially a drop-in replacement for Chromium. ungoogled-chromium features tweaks to enhance privacy, control, and transparency. However, almost all of these... -
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Knowledge Work Plugins
Open source repository of plugins intended for knowledge workers
Knowledge Work Plugins is Anthropic’s open-source repository of plugin-style Markdown packs for knowledge-work use cases in Claude Cowork and related Claude Code workflows. It is designed to give AI assistants structured domain instructions rather than relying on generic chat behavior. The repository includes plugins for practical office, research, legal, and business workflows, with each plugin stored as editable Markdown. This makes the system easy to inspect, fork, customize, and adapt to an organization’s own process. ... -
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PipesHub
Workplace AI platform for enterprise search and workflow automation
...The platform uses knowledge graphs and ranking algorithms to provide context-rich answers along with traceable sources, improving transparency and trust in AI-generated insights. PipesHub also enables the creation of custom AI agents and applications through a no-code interface, allowing teams to automate workflows and build intelligent tools without deep technical expertise. It supports flexible deployment options, including on-premise and cloud environments, ensuring compatibility with different security and infrastructure requirements. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. ... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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pyttsx3
Offline Text To Speech synthesis for python
...On Windows it uses SAPI5, on Linux it typically uses eSpeak or eSpeak-NG, and on macOS it can use NSSpeechSynthesizer or AVSpeechSynthesizer, giving it broad cross-platform compatibility. The library exposes a simple but flexible API for controlling voice selection, speaking rate, volume, and other synthesis parameters from Python code. It supports both a high-level speak convenience function and a lower-level engine object with event hooks, queuing, and saving output to audio files. The repository includes examples and documentation that show how to adjust properties dynamically, persist synthesized output, and integrate pyttsx3 into GUIs or background services. -
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ContextGem
ContextGem: Effortless LLM extraction from documents
ContextGem is an open-source framework designed to simplify the extraction of structured data and insights from documents using large language models (LLMs). It provides a flexible, intuitive API that minimizes boilerplate code, enabling developers to build complex extraction workflows efficiently. ContextGem supports various document formats and integrates with multiple LLM providers, making it a versatile tool for tasks like contract analysis, anomaly detection, and information retrieval. -
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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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Django Rules
Awesome Django authorization, without the database
...Seamless integration with Django views, templates and the Admin for testing for object-level permissions. Efficient and smart. No need to mess around with a database to figure out whether John really wrote that book. Simple. Dive in the code. You'll need 10 minutes to figure out how it works. Powerful. rules comes complete with advanced features, such as invocation context and storage for arbitrary data, skipping evaluation of predicates. -
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PyTorch Ignite
Library to help with training and evaluating neural networks
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Less code than pure PyTorch while ensuring maximum control and simplicity. Library approach and no program's control inversion. Use ignite where and when you need. Extensible API for metrics, experiment managers, and other components. The cool thing with handlers is that they offer unparalleled flexibility (compared to, for example, callbacks). -
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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MLC LLM
Universal LLM Deployment Engine with ML Compilation
...The system supports deployment on environments including Linux, macOS, Windows, iOS, Android, and web browsers while utilizing different acceleration technologies such as CUDA, Vulkan, Metal, and WebGPU. It also provides OpenAI-compatible APIs that allow developers to integrate locally deployed models into existing AI applications without major code changes. -
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Eigent
The Open Source Cowork Desktop to Unlock Your Exceptional Productivity
Eigent is an open-source cowork desktop application designed to help you build, manage, and deploy a custom AI workforce. It enables multiple specialized AI agents to collaborate in parallel, turning complex workflows into automated, end-to-end tasks. Built on the CAMEL-AI multi-agent framework, Eigent emphasizes productivity, flexibility, and transparent system design. You can run Eigent fully locally for maximum privacy and data control, or choose a cloud-connected experience for quick... -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
...TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. ... -
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AgenticSeek
Fully Local Manus AI. No APIs, No $200 monthly bills
AgenticSeek is a fully local autonomous AI assistant designed as a privacy-focused alternative to cloud-based agent platforms. It runs entirely on the user’s hardware and can autonomously browse the web, write code, and plan multi-step tasks without sending data to external services. The system is optimized for local reasoning models and emphasizes zero cloud dependency to maintain full user control. AgenticSeek includes intelligent agent selection, allowing it to determine the best internal agent to handle a given request. It also supports hands-free workflows such as automated web form interaction and information extraction. ... -
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Apache Airflow
Programmatically author, schedule, and monitor workflows
Apache Airflow is a community-created platform for programmatically authoring, scheduling, and monitoring workflows. It’s much easier to do all these things when workloads are defined as code. They become more versionable, testable, maintainable and collaborative. With Airflow you can author workflows as directed acyclic graphs (DAGs) of tasks. This means that your tasks are executed on an array of workers while following the specified dependencies. And with rich command line utilities in place, performing complex surgeries on DAGs is simple and straightforward. ... -
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claude-video
Give Claude the ability to watch any video
...The skill is useful for summarizing videos, reviewing screen recordings, analyzing content structure, diagnosing visual bugs, and turning course material into notes. It can be installed through Claude Code, agent skill hosts, claude.ai, or manual setup. -
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Agent Reach
Give your AI agent eyes to see the entire internet
...It is designed for agent workflows that need to read and search sources like Twitter, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu without relying on paid platform APIs. The project focuses on giving AI tools broader visibility into social, video, code, and community data through one interface. It can be useful for research agents, automation workflows, competitive monitoring, content analysis, and social listening. Agent-Reach also supports modern agent environments by exposing capabilities that can be connected into larger AI workflows. Its main value is making scattered web platforms easier for agents to query, inspect, and summarize from the command line. -
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MyPy
Optional static typing for Python
mypy is a static type checker for Python that analyzes source code without executing it, catching type errors early in development. It implements PEP 484-style type hints and supports gradual typing, allowing untyped or partially typed modules to coexist with statically typed code. The tool integrates with common editors and CI systems, and offers a daemon mode for fast, incremental checks across large codebases. -
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Rapid LaTeX OCR
Formula recognition based on LaTeX-OCR and ONNXRuntime
Formula recognition based on LaTeX-OCR and ONNXRuntime. rapid_latex_ocr is a tool to convert formula images to latex format. The reasoning code in the repo is modified from LaTeX-OCR, the model has all been converted to ONNX format, and the reasoning code has been simplified, Inference is faster and easier to deploy. The repo only has codes based on ONNXRuntime or OpenVINO inference in onnx format and does not contain training model codes. If you want to train your own model, please move to LaTeX-OCR. ... -
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ChatGPT Academic
ChatGPT extension for scientific research work
ChatGPT extension for scientific research work, specially optimized academic paper polishing experience, supports custom shortcut buttons, supports custom function plug-ins, supports markdown table display, double display of Tex formulas, complete code display function, new local Python/C++/Go project tree Analysis function/Project source code self-translation ability, newly added PDF and Word document batch summary function/PDF paper full-text translation function. All buttons are dynamically generated by reading functional.py, you can add custom functions at will, and liberate the pasteboard. ... -
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pre-commit
Framework for managing and maintaining multi-language pre-commit hooks
Git hook scripts are useful for identifying simple issues before submission to code review. We run our hooks on every commit to automatically point out issues in code such as missing semicolons, trailing whitespace, and debug statements. By pointing these issues out before code review, this allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks. As we created more libraries and projects we recognized that sharing our pre-commit hooks across projects is painful. ... -
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Agent S
Agent S: an open agentic framework that uses computers like a human
...It supports flexible deployment via CLI, SDK, or cloud, and integrates with multiple model providers including OpenAI, Anthropic, Gemini, Azure, and Hugging Face endpoints. With optional local code execution, reflection mechanisms, and compositional planning, Agent S provides a scalable and research-driven framework for building advanced computer-use agents. -
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Hypothesis
The property-based testing library for Python
...Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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Python-Dotenv
Load environment variables from .env files into Python apps
python-dotenv is a Python utility that reads key-value pairs from a .env file and sets them as environment variables. This is especially useful for managing configuration values, secrets, or settings outside of source code, in line with the 12-factor app principles. Commonly used in Flask, Django, and other frameworks, it helps maintain clean and secure codebases across environments like development, staging, and production. -
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django-pgtrigger
Write Postgres triggers for your Django models
django-pgtrigger is a Django library for defining and managing PostgreSQL triggers directly in Python code. It allows developers to create database-level logic like automatic field updates, auditing, or validation without writing raw SQL. It’s ideal for teams that want stronger data integrity while keeping logic version-controlled. -
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Axolotl
Go ahead and axolotl questions
Axolotl is a powerful and flexible framework for fine-tuning large language models on custom datasets. Built for researchers and developers, Axolotl simplifies the process of adapting LLMs for specific tasks, including chat, code generation, and instruction following. It supports a wide variety of model architectures and offers out-of-the-box optimization strategies for efficient training.
