Search Results for "programming learning system"
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38 projects for "programming learning system" with 2 filters applied:
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Taste Skill
Taste-Skill - gives your AI good taste. stops the AI
...The repository likely includes tools or interfaces that support content organization, tracking, and user engagement. Overall, it positions itself as a lightweight, adaptable system for continuous learning and skill discovery. -
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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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Flutter Agent Skills
Agent skills for Flutter, maintained by the Flutter team
Flutter Skills is an educational repository from the Flutter ecosystem that provides structured hands-on exercises, tutorials, and learning paths for improving Flutter and Dart development skills. The project focuses on practical learning through guided coding challenges, interactive examples, and real-world mobile application scenarios rather than purely theoretical documentation. It covers topics such as UI construction, state management, animations, asynchronous programming, architecture patterns, and testing workflows within Flutter applications. ... -
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OpenClaw-RL
Train any agents simply by 'talking'
...The system incorporates reinforcement learning techniques to refine the agent’s policies for tool use, decision making, and task completion over time. It also explores approaches such as online policy distillation and hindsight feedback signals to strengthen training signals from real interactions. The framework operates asynchronously and does not require external API keys, making it easier to experiment with local agent training workflows. -
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Youtu-Agent
A simple yet powerful agent framework that delivers with models
...The framework supports automated generation of agent components, enabling the system to synthesize prompts, tool interfaces, and workflow configurations automatically. Youtu-Agent also incorporates hybrid learning strategies that combine experience accumulation with reinforcement learning to improve agent performance over time. These learning mechanisms allow agents to refine their reasoning, coding, and search capabilities as they interact with environments and tasks. -
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AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
...The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes. -
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verl-agent
Designed for training LLM/VLM agents via RL
verl-agent is an open-source reinforcement learning framework designed to train large language model agents and vision-language model agents for complex interactive environments. Built as an extension of the veRL reinforcement learning infrastructure, the project focuses on enabling scalable training for agents that perform multi-step reasoning and decision-making tasks. The framework supports multi-turn interactions between agents and their environments, allowing the system to receive feedback after each step and adjust its strategy accordingly. ... -
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PilottAI
Python framework for building scalable multi-agent systems
pilottai is an AI-based autonomous drone navigation system utilizing reinforcement learning for real-time decision-making. It is designed for simulating and training drones to fly safely through dynamic environments using AI-based controllers. -
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Understand Anything
Turn any codebase into an interactive knowledge graph
Understand-Anything is an AI-driven tool designed to help users deeply understand any topic by generating structured explanations, summaries, and breakdowns. It focuses on transforming complex or unfamiliar subjects into clear, step-by-step explanations that are easier to grasp. The system leverages language models to provide layered insights, allowing users to explore topics at different levels of detail. It is particularly useful for learning, research, and quick comprehension of new concepts across various domains. The project emphasizes accessibility, making advanced knowledge more approachable for a wider audience. ... -
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RecursiveMAS
Offical Implementation for "Recursive Multi-Agent Systems"
...Instead of treating agents as independent units exchanging text outputs, it connects them through a shared latent computation loop, allowing internal “thought states” to be passed and refined iteratively. This recursive structure enables agents to build on each other’s intermediate reasoning, leading to deeper and more coherent solutions. The system uses a lightweight module called RecursiveLink to transfer and transform latent representations between agents, enabling seamless interaction even across heterogeneous models. It also incorporates an inner–outer loop training approach that optimizes the entire system collectively rather than tuning each agent separately. This design improves efficiency, reduces token usage, and stabilizes learning during iterative reasoning. -
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Hindsight
Hindsight: Agent Memory That Learns
Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works. ... -
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Compound Engineering
Official Compound Engineering plugin for Claude Code, Codex, Cursor
The Compound Engineering plugin project is an AI-driven workflow system designed to improve software development by turning each unit of work into a reusable and compounding asset. 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. ... -
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Auto-Commenter
A Claude skill that automatically posts personalized comments
Auto-Commenter is a Claude-oriented automation project built to help users write and post comments that sound natural and context-aware in targeted online communities. It centers on learning a user’s writing style from their real comment history, then applying that style to generate responses that feel consistent with the user rather than generic template text. The workflow emphasizes deeper post analysis so the system can respond to what is actually being discussed, instead of replying with shallow engagement bait. ... -
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. The framework includes a collection... -
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OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. ... -
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Cheat on Content
Workflow that turns every post into a calibrated experiment
...The project is built around the loop of score, predict, publish, retro, and improve. It is aimed at creators, marketers, and operators who want to build a repeatable system for learning from every published piece. Its value is strongest for people who already create consistently but need a better way to extract insight from their output. -
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Pal
A personal context-agent that learns how you work
Pal is an open-source AI personal agent built within the Agno ecosystem that functions as an intelligent digital assistant designed to learn from user activity over time. The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can... -
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Sprix SAGE Router
State-aware SELF/COLLABORATE/HANDOFF routing for A2A agent networks
Sprix SAGE Router is an open-source routing layer for coordinating agents in Agent2Agent networks. It decides whether an active agent should continue alone, recruit collaborators, or hand a task to another specialist. Routing decisions account for current progress, capability coverage, context-transfer costs, permissions, budgets, and deadlines. The system assigns remaining requirements to agents through task dependency graphs and estimates execution schedules. It learns contextual... -
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anti-distill
Anti-distillation for employee Skills
...The system is particularly relevant in contexts where models are exposed via APIs and risk being reverse-engineered through repeated querying. Its design reflects growing concerns around model security and competitive advantage in AI systems. It may also include experimental benchmarks to evaluate how resistant a model is to distillation attempts. -
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
...It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. The project is organized as a learning resource rather than a standalone framework, making it particularly useful for developers who want to move beyond surface-level prompt engineering into full agent system design. It explores how agents interact with environments, execute tasks, and maintain context over time, highlighting both strengths and limitations of current approaches. ... -
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ClawTeam
ClawTeam: Agent Swarm Intelligence (One Command → Full Automation)
ClawTeam is an advanced multi-agent orchestration framework that enables AI agents to form collaborative swarms capable of solving complex tasks autonomously. Instead of relying on a single agent, the system allows a leader agent to spawn and coordinate multiple specialized sub-agents, each responsible for different aspects of a problem. These agents communicate, share insights, and dynamically adapt their strategies based on real-time feedback, creating a form of collective intelligence. The framework supports a wide range of use cases, including software development, machine learning research, financial analysis, and content production. ... -
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Claude Subconscious
Give Claude Code a subconscious
Claude Subconscious is an experimental plugin that enhances AI coding workflows by introducing a persistent “memory layer” for Claude Code through integration with Letta’s agent framework. It operates as a background agent that continuously observes user interactions, reads project files, and processes session transcripts to build long-term contextual memory. Unlike standard AI interactions that reset between sessions, this system accumulates knowledge over time, allowing it to recall user... -
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TypeAgent Python
Structured RAG: ingest, index, query
...Instead of relying solely on free-form prompts, the architecture emphasizes converting natural language interactions into structured representations that can be processed by deterministic software components. This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. The repository is intended primarily as a research prototype and sample code rather than a production-ready framework, allowing developers to experiment with building AI agents that maintain structured memory and perform tasks through defined actions. -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation. OWL (Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation) is an advanced framework designed to enhance multi-agent collaboration, improving task automation across various domains. By utilizing dynamic agent interactions, OWL aims to streamline and optimize complex workflows, making AI collaboration more natural, efficient, and adaptable. It is built on...
