Search Results for "learning"
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Showing 23 open source projects for "learning"
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Tabby
Self-hosted AI coding assistant
Tabby is an open-source, self-hosted AI coding assistant that provides code completion and suggestions within your preferred development environment. It enhances developer productivity by leveraging machine learning models to predict and generate code snippets. -
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SocratiCode
Enterprise-grade (40m+ LOC) codebase intelligence
SocratiCode is an AI-assisted learning tool that applies the Socratic method to programming education, guiding users through problem-solving rather than directly providing answers. It encourages critical thinking by asking structured questions that lead developers toward discovering solutions on their own. The system is designed to improve understanding of programming concepts through iterative reasoning and dialogue. -
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jcode
Coding Agent Harness
jcode is a lightweight developer tool designed to streamline Java coding workflows by simplifying compilation, execution, and testing processes. It provides a structured interface for managing Java programs without requiring complex IDE setups, making it ideal for quick experimentation and learning. The tool focuses on reducing friction for developers who want to run code snippets or small projects efficiently. It supports automation of repetitive tasks such as compiling and executing Java files in a consistent environment. JCode is especially useful for beginners or developers working in minimal setups where full IDEs are unnecessary. ... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
CUDA Agent is a research-driven agentic reinforcement learning system designed to automatically generate and optimize high-performance CUDA kernels for GPU workloads. The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. ... -
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RA.Aid
Develop software autonomously
...It integrates seamlessly with various development environments, providing intelligent code suggestions, automated documentation generation, and real-time error detection. By leveraging advanced machine learning models, RA.Aid aims to reduce development time and improve code quality. -
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free-code
The free build of Claude Code
Free-code is an open-source platform aimed at providing accessible coding resources, tools, or templates that help developers learn, build, and share projects without barriers. It typically focuses on simplifying the development process by offering prebuilt components, example projects, or utilities that can be reused across different applications. The project is designed to encourage collaboration and knowledge sharing within the developer community, making it easier for beginners and... -
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Ornith-1.0
Ornith-1.0 is a self-improving open-source models for agentic coding
...The project presents 9B dense, 31B dense, 35B mixture-of-experts, and 397B mixture-of-experts variants. These models are post-trained on top of Gemma 4 and Qwen 3.5 foundations. Its training approach uses reinforcement learning to optimize both the solution and the scaffold that guides the solution process. The repository emphasizes benchmark performance on Terminal-Bench, SWE-bench, NL2Repo, OpenClaw, and SWE Atlas while keeping the project MIT licensed and globally accessible. -
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Claude-Mem
Claude Code plugin that automatically captures everything Claude does
...Claude-Mem runs automatically in the background with no manual workflow changes required. Designed for serious developers, it transforms Claude Code into a continuously learning, project-aware coding assistant. -
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Letta Code
The memory-first coding agent
Letta Code is a memory-first CLI coding agent built on the Letta platform that offers developers a persistent AI assistant capable of learning and improving over time rather than resetting state each session, giving agents a sense of continuity and context across coding tasks. Unlike traditional session-based coding tools, Letta Code attaches a long-lived agent to a working directory so that the agent accumulates memory about a project’s structure, preferences, and history, effectively acting as a collaborative partner rather than a stateless helper. ... -
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Open Vibe
Open Vibe turns Claude Code into a SaaS-building assistant
...The agent then works as both tutor and pair programmer, explaining the system while helping the user build features from plain-language requests. Progress is tracked through JSON files written into the project, making the learning path structured while still letting the user build their own app idea. -
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Compound Engineering
Official Compound Engineering plugin for Claude Code, Codex, Cursor
...It provides a structured set of commands and agents that guide developers through stages such as brainstorming, planning, execution, review, and knowledge capture. The core philosophy is to reduce technical debt by emphasizing thorough planning and continuous learning, ensuring that each iteration improves future work rather than increasing complexity. The plugin integrates with multiple AI coding environments, including Claude Code and other tools, enabling consistent workflows across platforms. It also supports automated code review and ideation processes, leveraging multiple agents to enhance quality and decision-making. ... -
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Ditto
The simplest self-building coding agent
...The project is intentionally lightweight and experimental, making it easier to understand than larger agentic coding platforms. Its modular structure separates generated Flask components into cleaner directories for routes, templates, and static files. It is best suited for prototyping, learning, and exploring how natural-language app generation can work in a small local project. -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions, enabling quantitative assessment of skill quality and ensuring that only improvements are preserved over time. ... -
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Claude Code Architecture Study
Research on Coding Agents
...It likely provides step-by-step examples, conceptual explanations, and practical implementations that guide users through creating their own agents. The framework emphasizes learning by doing, allowing users to experiment with agent behavior, prompt design, and workflow structuring. It also explores how agents interact with tools such as file systems, terminals, and APIs, giving a holistic view of real-world applications. The project is particularly valuable for developers transitioning from traditional programming to AI-assisted or autonomous development paradigms. -
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Learn Claude Code
Bash is all you need, write a claude code with only 16 line code
Learn Claude Code is an educational repository that teaches how modern AI coding agents work by walking learners through a sequence of progressively more complex agent implementations, starting with a minimal Bash-based agent and culminating in agents with explicit planning, subagents, and skills. It emphasizes a hands-on learning path where each version (from v0 to v4) adds conceptual building blocks like the core agent loop, todo planning, task decomposition, and domain knowledge skills, illuminating the patterns behind what makes a true AI agent tick. The goal is to demystify agent architectures like Claude Code by having learners build simplified versions themselves and observe how tools, memory management, planning constraints, and context isolation contribute to reliable agent behavior. ... -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before generating code. ... -
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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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1loc
What's your favorite JavaScript single LOC (line of code)?
1loc is a community-maintained collection of useful JavaScript operations expressed as a single line of code. Each entry demonstrates a compact solution to a focused programming task. The collection is designed for quick reference, learning, and discovering language techniques that may simplify everyday scripts. Topics span arrays, objects, strings, dates, browser behavior, validation, and other common utilities. Examples are stored as individual Markdown files, making the repository easy to browse and extend. Contributors can add their own favorite one-liners through pull requests. ... -
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Codeball AI
AI Code Review that finds bugs and fast-tracks your code
...Fully customizable and programmable with GitHub Actions. Codeball Actions are built on multiple smaller building blocks, that are heavily configurable through GitHub Actions. Codeball uses a deep learning model that has been trained on over 1 million Pull Requests. For each contribution, it considers hundreds of inputs. Codeball is optimized for precision, which means it only approves contributions that it's really confident in. -
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Kite
Primary Kite repo, private bits replaced with XXXXXXX
...However, we do have cloud instances & VMs available for running larger jobs and for testing our cloud services. We bundle a lot of pre-computed datasets & machine learning models into the Kite app through the use of a custom filemap & encoding on top of go-bindata. The data, located in kite-go/client/datadeps, is kept in Git-LFS. -
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Tabnine
Vim client for TabNine
...Whether you call it IntelliSense, intelliCode, autocomplete, AI-assisted code completion, AI-powered code completion, AI copilot, AI code snippets, code suggestion, code prediction, code hinting, or content assist, you probably already know that it can save you tons of time, easily cutting your keystrokes in half. Powered by sophisticated machine learning models trained on billions of lines of trusted open source code from GitHub, Tabnine is the most advanced AI-powered code completion copilot available today. And like GitHub, it is an essential tool for professional developers. -
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vue-tetris
Use Vue, Vuex to code Tetris
...The design emphasizes performance and responsiveness, ensuring smooth gameplay even within a web environment. It also serves as an educational resource for developers learning Vue.js, illustrating how to structure real-time applications. The project may include additional features such as keyboard controls, game speed adjustments, and visual styling. Overall, vue-tetris combines entertainment with practical frontend development techniques. -
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Ornith-1.0
Open reasoning model for agentic coding and tool workflows
...The model focuses on coding-agent performance across benchmarks such as Terminal-Bench, SWE-Bench, NL2Repo, OpenClaw, and ClawEval. Its training uses a self-improving reinforcement learning framework that optimizes not only solution attempts but also the scaffolds that guide those attempts, helping the model discover better search paths and produce higher-quality solutions. Ornith-1.0-397B is a reasoning model by default, generating <think> blocks before final answers, and supports tool calling through OpenAI-compatible endpoints. ...
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