Search Results for "no code" - Page 19
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Showing 2677 open source projects for "no code"
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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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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
...The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. Many of the algorithms are written in a simplified style that prioritizes clarity and educational value over production-level optimization. Because the code is compact and easy to follow, it is often used as a learning resource by developers who want to understand how machine learning algorithms are constructed. -
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Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
...In addition to academic references, the project provides practical code implementations of many transfer learning algorithms so that researchers can reproduce experiments or build their own applications. The repository also catalogs well-known scholars, research laboratories, and datasets relevant to transfer learning studies. -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
...Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. ... -
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vim-ai
AI-powered code assistant for Vim. OpenAI and ChatGPT plugin for Vim
vim-ai is an AI-powered assistant plugin for Vim and Neovim that brings language-model features directly into the editor. It allows users to generate code or text, edit selections in place, and carry on interactive chat-style conversations without leaving the terminal editing environment. The plugin is built around OpenAI-compatible APIs, which means it can work not only with OpenAI itself but also with compatible proxies and alternative providers. Its command set covers text completion, editing, chat continuation, image generation, and debugging utilities, making it more versatile than a narrow autocomplete add-on. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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AWS Agent Skills
AWS Skills for Agents
...The skills cover critical AWS services such as IAM, Lambda, DynamoDB, S3, API Gateway, EKS, and many more, letting agents offer actionable advice on infrastructure as code, debugging, security configurations, and architectural workflows. Skills are kept up to date with weekly documentation checks, ensuring they reflect current AWS patterns and service changes. -
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AutoCoder
A long-running autonomous coding agent powered by the Claude Agent
...The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable. -
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Atheris
A Coverage-Guided, Native Python Fuzzer
Atheris is a coverage-guided fuzzer for CPython that treats Python as a first-class fuzzing target, enabling rapid discovery of crashes and logic errors in pure-Python code and native extensions. It hooks into Python’s interpreter to collect fine-grained coverage and uses that signal to evolve inputs, pushing programs into previously unexplored code paths. Because many Python libraries are thin wrappers over C/C++ code, Atheris is equally adept at surfacing memory safety issues in extension modules compiled with sanitizers. ... -
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Mozc Devices
Circuit diagrams and firmware source code for Gboard DIY keyboards
mozc-devices is an open source collection of circuit diagrams, firmware, and technical documentation for a series of experimental and often humorous Gboard and Google Japanese Input hardware keyboards, many of which were originally released as April Fools’ projects by Google Japan. Each subproject in the repository corresponds to a unique input device prototype, including versions such as the Drum Set, Morse Code, Patapata, Magic Hand, Piropiro, Physical Flick, Puchi Puchi, Nazoru, Mageru, Yunomi, Bar, Caps, Double Sided, and Dial editions. These devices creatively reinterpret how users can interact with Japanese text input, blending humor, engineering, and physical computing. The repository serves as an archive of the schematics, firmware, and PCB designs for these inventive input mechanisms, with many projects including promotional videos and technical references. -
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Think Python 2
LaTeX source and supporting code for Think Python, 2nd edition
ThinkPython2 is the repository for the second edition of Allen Downey’s Think Python textbook, which teaches programming fundamentals in Python to beginners. The code includes all of the example programs, exercises, and supplementary files referenced in the book, allowing learners to run the examples, experiment, and extend them. The repository contains clean, well-commented Python scripts that are easy to follow and map directly to chapters of the text, covering topics like variables, control flow, functions, recursion, data structures (lists, dictionaries), classes and objects, file I/O, and algorithmic thinking. ... -
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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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
...Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As shown below, it results in readable code that can always be visualized. Hamilton loads that definition and automatically builds the DAG for you. Hamilton brings modularity and structure to any Python application moving data: ETL pipelines, ML workflows, LLM applications, RAG systems, BI dashboards, and the Hamilton UI allows you to automatically visualize, catalog, and monitor execution. -
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MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
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amrlib
A python library that makes AMR parsing, generation and visualization
...A QT-based GUI to facilitate the conversion of sentences to graphs and back to sentences. Methods to plot AMR graphs in both the GUI and as library functions. Training and test code for both the StoG and GtoS models. A SpaCy extension that allows direct conversion of SpaCy Docs and Spans to AMR graphs. Sentence to Graph alignment routines FAA_Aligner (Fast_Align Algorithm), based on the ISI aligner code detailed in this paper. RBW_Aligner (Rule Based Word) for a simple, single token to single node alignment. -
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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. -
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AI Runner
Offline inference engine for art, real-time voice conversations
...It is implemented as a desktop-oriented Python application and emphasizes privacy and self-hosting, allowing users to work with text-to-speech, speech-to-text, text-to-image and multimodal models without sending data to external services. At the core of its LLM stack is a mode-based architecture with specialized “modes” such as Author, Code, Research, QA and General, and a workflow manager that automatically routes user requests to the right agent based on the task. The project has a strong focus on developer ergonomics, with thorough development guidelines, environment configuration using .env variables, and a clear structure for tests, tools and agents. -
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Qwen3
Qwen3 is the large language model series developed by Qwen team
Qwen3 is a cutting-edge large language model (LLM) series developed by the Qwen team at Alibaba Cloud. The latest updated version, Qwen3-235B-A22B-Instruct-2507, features significant improvements in instruction-following, reasoning, knowledge coverage, and long-context understanding up to 256K tokens. It delivers higher quality and more helpful text generation across multiple languages and domains, including mathematics, coding, science, and tool usage. Various quantized versions,... -
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StatsForecast
Fast forecasting with statistical and econometric models
...The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery of benchmarking and baseline methods, giving users flexibility in selecting forecasting approaches depending on data characteristics (trend, seasonality, intermittent demand, etc.). Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts. -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. -
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Diffusers
State-of-the-art diffusion models for image and audio generation
...Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code. Interchangeable noise schedulers for different diffusion speeds and output quality. Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. -
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NiceGUI
Create web-based user interfaces with Python
NiceGUI is a Python-based UI framework that enables developers to create interactive web applications using only Python code. It abstracts away the complexities of HTML, CSS, and JavaScript, allowing for rapid development of web interfaces directly from Python scripts. NiceGUI is suitable for building dashboards, control panels, and other web-based tools, especially in contexts like robotics and data visualization. -
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Taipy
Turns Data and AI algorithms into production-ready web applications
...Struggle with sluggish performance and excessive memory usage, as every data point demands processing. Large datasets become cumbersome, complicating the user experience and data analysis. Scenarios are made easy with Taipy Studio. A powerful VS Code extension that unlocks a convenient graphical editor. Get your methods invoked at a certain time or intervals. Enjoy a variety of predefined themes or build your own. -
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Covalent workflow
Pythonic tool for running machine-learning/high performance workflows
...Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent in AI/ML experimentation. -
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VisiData
A terminal spreadsheet multitool for discovering and arranging data
...VisiData supports tsv, CSV, SQLite, JSON, xlsx (Excel), hdf5, and many other formats. Requires Linux, OS/X, or Windows (with WSL). Hundreds of other commands and options are also available; see the documentation. Code in the stable branch of this repository, including the main vd application, loaders, and plugins, is available for use and redistribution under GPLv3. VisiData is a free, open-source tool that lets you quickly open, explore, summarize, and analyze datasets in your computer’s terminal. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources. -
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Python for OSINT
In this repository you will find sample code files for each day
Python for OSINT: 21-Day Course is a beginner-oriented training repository focused on automating repetitive open-source intelligence tasks with Python. It accompanies a free course PDF and provides sample code for lessons organized across three weeks. Topics progress from basic syntax and command-line tools to HTTP requests, APIs, JSON, CSV, databases, scraping, regular expressions, proxies, and file handling. Later lessons cover domain research, document generation, charts, maps, web archives, dates, and simple web applications. The course is designed for investigators and researchers rather than people seeking comprehensive software-engineering training. ... -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
...The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. ... -
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Hello-Agents
Building an Intelligent Agent from Scratch
...It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems.
