Search Results for "no coding" - Page 7
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Showing 321 open source projects for "no coding"
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GPTImage2Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
GPTImage2Skill is a curated prompt gallery, agent skill, and command-line workflow for working with GPT Image 2 generation and editing. It provides reusable image prompts across creative, technical, academic, interface, design, photography, typography, gaming, anime, map, tattoo, and reference-editing use cases. The project is designed to help agents and users produce stronger visual outputs without starting from a blank prompt every time. Its gallery is organized into category files so an... -
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Claude Code Skills & Plugins
232+ Claude Code skills & agent plugins for Claude Code, Codex
Claude Skills is a repository that provides a collection of structured skill definitions designed to enhance the capabilities of Claude-based AI systems. Each skill encapsulates a specific capability, such as coding, analysis, or workflow execution, allowing the model to perform tasks more effectively. The project emphasizes modularity, enabling skills to be combined and reused across different contexts. It is designed to integrate seamlessly into AI workflows, providing a plug-and-play approach to extending functionality. The repository also includes examples and templates, making it easier for developers to create their own custom skills. ... -
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pg-aiguide
MCP server and Claude plugin for Postgres skills and documentation
pg-aiguide is a tool designed to enhance AI-assisted development with PostgreSQL by providing structured knowledge and skills directly to coding agents. It acts as a bridge between database documentation and AI tools, enabling more accurate generation of SQL queries and database interactions. The system integrates with Claude Code through an MCP server, allowing agents to access curated PostgreSQL knowledge in real time. It focuses on improving developer productivity by reducing errors and providing context-aware suggestions. ... -
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GenericAgent
Self-evolving autonomous agent framework
...It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language models to provide planning, execution, and iterative reasoning capabilities, making it suitable for complex workflows. The project also focuses on extensibility, allowing developers to plug in custom tools or APIs and tailor agent behavior to specific use cases. By abstracting common agent patterns, it reduces the overhead of building agent systems from scratch. ... -
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Reflexion
Reflexion: Language Agents with Verbal Reinforcement Learning
...The framework introduces a mechanism where agents maintain a memory of past attempts and use that memory to guide future decisions, effectively simulating a learning process without requiring traditional model retraining. This approach is particularly useful for complex reasoning tasks, coding challenges, and decision-making scenarios where initial outputs may be incomplete or incorrect. Reflexion also emphasizes transparency by making intermediate reasoning steps explicit, allowing developers to inspect how conclusions are reached and where improvements occur. -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
...The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer architecture, pre-training paradigms, and model scaling strategies while also providing hands-on coding examples so readers can implement and experiment with their own models. The tutorial emphasizes practical understanding by walking users through building and training small language models, including tokenizer construction, pre-training workflows, and fine-tuning methods. -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
...To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
...The repository integrates numerous popular machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. It aims to strike a balance between theoretical explanation and practical coding by demonstrating algorithms both from scratch and using established libraries. The content is organized into multiple sections covering topics such as clustering, regression, dimensionality reduction, recommender systems, and model evaluation. -
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PyTorch-Tutorial-2nd
CV, NLP, LLM project applications, and advanced engineering deployment
...The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization strategies. It also introduces practical machine learning techniques such as convolutional neural networks, recurrent networks, and other architectures commonly used in modern AI applications. ... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Unstract
No-code LLM Platform to launch APIs and ETL Pipelines
Unstract is a powerful open-source, no-code platform built to automate the extraction and structuring of unstructured documents using large language models and flexible workflows, enabling developers and data teams to turn messy files into organized JSON content without complex coding. It integrates a visual Prompt Studio environment where users can iteratively design extraction schemas, compare outputs from different models, and monitor costs and accuracy side by side, making it easier to refine prompts and extraction logic before deploying at scale. Unstract supports deploying structured extraction as REST API endpoints or embedding it into data engineering ETL pipelines, which allows it to plug directly into data warehouses, cloud storage, or downstream analytics systems. ... -
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Ling-V2
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI
...Trained on more than 20 trillion tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-V2’s models demonstrate strong general reasoning, mathematical problem-solving, coding understanding, and knowledge-intensive task performance. -
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claude-code-transcripts
Tools for publishing transcripts for Claude Code sessions
claude-code-transcripts is a command-line utility that takes session files exported from Claude Code (in JSON or JSONL format) and turns them into clean, navigable HTML transcripts that can be viewed in any modern web browser. It is designed to make the often dense and verbose outputs from AI coding sessions easier to read, share, and archive by breaking conversations into paginated, annotated pages with navigable timelines of prompts and responses. Users can run this tool locally or fetch sessions from the Claude API, giving flexibility for individual workflows or team documentation practices. The generated HTML includes interactive navigation and can optionally be published to GitHub Gists for sharing with collaborators or embedding in other documentation. ... -
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AI Agents Masterclass
Follow along with my AI Agents Masterclass videos
...It breaks down how autonomous agents interact with external systems, handle iterative reasoning, and integrate with third-party services or APIs to perform real tasks — for example, web search, browsing, scheduling, or coding assistance. Students of the masterclass can follow written modules or Jupyter notebooks that illustrate concepts step by step and progressively build more capable agents. The content is suitable for both beginners and intermediate developers because it starts with basic principles and escalates to advanced architectures like multi-agent coordination. -
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PythonPark
Python open source project "The Road to Self-Study Programming"
...Because of this breadth, PythonPark serves both as a reference library (for quick lookup) and as a structured learning path for beginners and intermediate learners in Python. For someone self-teaching Python (or transitioning into coding/data science), the repository presents a one-stop “home base” of content, saving them from hunting scattered tutorials across the internet. -
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screenshot-to-code
Drop in a screenshot and convert it to clean code
...It also supports multi-model backends and local-first options to balance cost, speed, and privacy. Teams use it for rapid prototyping, migrating static mockups to codebases, and exploring design alternatives without hand-coding every pixel from scratch. -
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E2B Cookbook
Examples of using E2B
E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code... -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
AutoAgent is a fully automated, zero-code LLM agent framework that lets users create agents and workflows using natural language instead of manual coding and configuration. It is structured around modes that cover both “use” and “build” scenarios: a user mode for running a ready-made multi-agent research assistant, plus editors for creating individual agents or multi-agent workflows from conversational requirements. The framework emphasizes self-managing workflow generation, where it can infer steps, refine them, and adapt plans even when users cannot fully specify implementation details up front. ... -
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InfiAgent
Build your own Cowork, AI Scientist and other SoTA Agents
...Designed as a “Multi-Level Agent” (MLA) system, it externalizes persistent state to the file system so that agents can operate over unlimited runtime without the need for token-intensive context compression, enabling workflows such as research paper drafting, experiments, coding, and document generation to run reliably. The framework uses a serial multi-agent hierarchy where specialized agents coordinate in tree-structured paths for clear task delegation and minimal tool conflicts, while batch file operations and persistent workspaces ensure reproducibility and traceability. It aims to solve real-world challenges in long-horizon reasoning and execution, offering configuration-driven customization so that users can define domain-specific agents like research assistants. -
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Continuous Claude v3
Context management for Claude Code. Hooks maintain state via ledgers
...Rather than relying on a single session’s context, Continuous Claude uses mechanisms like ledgers, YAML handoffs, and a memory system to preserve and recall state across multiple sessions, ensuring that learned insights and plans are not lost when context compaction occurs. The project orchestrates many specialized agents and skills—109 skills and 32 agents—so that complex coding tasks can be broken down, analyzed, and executed collaboratively by different components. It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually. -
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
...They support native long contexts up to 128K tokens, enabling them to reason across large codebases and multi-file interactions without context fragmentation, and include “Thinking” variants optimized for complex reasoning and “Loop” variants with recurrent mechanisms to improve inference efficiency. IQuest-Coder-V1 delivers state-of-the-art performance on multiple coding benchmarks, demonstrating strong results in competitive programming, tool use, and agentic code generation. -
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rLLM
Democratizing Reinforcement Learning for LLMs
rLLM is an open-source framework for building and training post-training language agents via reinforcement learning — that is, using reinforcement signals to fine-tune or adapt language models (LLMs) into customizable agents for real-world tasks. With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to... -
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Positron
Positron, a next-generation data science IDE
...It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding experience along with specialized panes and tooling for variable inspection, data-frame viewing, plotting previews, and interactive consoles designed for analytical work. The IDE supports notebook and script workflows, integration of data-app frameworks (such as Shiny, Streamlit, Dash), database and cloud connections, and built-in AI-assisted capabilities to help write code, explore data, and build models. -
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Professional Programming
A collection of learning resources for curious software engineers
...It goes far beyond basic “learn to code” material and covers topics like system design, debugging, testing, performance, security, architecture, and software craftsmanship. The list is organized by themes such as coding, design, operations, communication, and career, making it easy to dive into specific aspects of engineering practice. Each resource is hand-picked by the maintainer, focusing on timeless, high-signal articles, talks, and books rather than trendy or shallow content. Because it has been maintained for many years, it also acts as a kind of “canon” of articles that many engineers reference throughout their careers. ... -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments... -
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
Simple Pytorch implementation of Stylegan2 that can be completely trained from the command-line, no coding needed. You will need a machine with a GPU and CUDA installed. You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file. ...
