Search Results for "g-code" - Page 32
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Showing 1552 open source projects for "g-code"
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JobWinner
Curated directory of thousands of generative AI tools by category
AI Collection is a curated repository that aggregates a large number of generative AI applications into a single organized directory. It serves as a discovery platform where users can browse and explore AI tools across a wide range of categories and use cases. Instead of providing software code for a single application, AI Collection acts as a structured index that lists AI tools along with brief descriptions and visual previews. It organizes thousands of AI applications into dozens of categories, allowing users to easily locate tools related to areas such as image generation, writing assistance, chatbots, productivity, and automation. ... -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets. -
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GPU Puzzles
Solve puzzles. Learn CUDA
...Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively more complex puzzles, learners gain a practical understanding of how parallel algorithms operate on graphics processing units. The project emphasizes experimentation and problem solving, encouraging learners to discover GPU programming techniques through trial and exploration. ... -
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Grounded Docs
Open-Source Alternative to Context7, Nia, and Ref.Tools
...By acting as an intermediary layer between documentation sources and AI tools, the server enables models to access structured documentation in a consistent and machine-readable format. This makes it easier for AI systems to answer technical questions, generate code examples, or retrieve reference material without requiring developers to manually integrate documentation into prompts. The architecture follows the MCP specification, which allows AI assistants and agent frameworks to connect to external tools through standardized protocols. -
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LLMs-Zero-to-Hero
From nobody to big model (LLM) hero
LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement... -
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LlamaDeploy
Deploy your agentic worfklows to production
...The project provides an asynchronous architecture that allows developers to deploy complex multi-agent workflows as scalable microservices. It enables teams to move from experimental prototypes to production systems with minimal changes to existing LlamaIndex code, making it easier to operationalize AI agents. The system supports orchestrating multiple services, handling communication between agents, and managing workflow execution in distributed environments. Developers can define workflows that involve multiple steps such as data retrieval, reasoning, tool invocation, and response generation, then deploy them using the framework’s infrastructure tools. ... -
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Floneum
Instant, controllable, local pre-trained AI models in Rust
Floneum is an open-source platform for building AI-powered workflows using large language models through a visual and extensible interface. The system allows users to design complex AI pipelines using a drag-and-drop workflow builder rather than writing extensive code. It focuses on enabling developers and researchers to create language model applications that combine different tools, data sources, and AI capabilities into automated workflows. Floneum supports a plugin architecture that allows external components to extend the platform while maintaining isolation and security. Many plugins can be written in different programming languages and compiled to WebAssembly modules, allowing them to run safely within the system. ... -
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AI Engineering Transition Path
Research papers and blogs to transition to AI Engineering
...Instead of presenting isolated tutorials, the repository provides a structured pathway that guides engineers through the technical knowledge needed to build and deploy large language model systems. The materials include curated research papers, blog posts, and code examples that explain both theoretical foundations and practical implementation strategies. By consolidating these resources into a single repository, the project helps developers navigate the rapidly expanding AI ecosystem without needing to search through scattered materials. -
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All Agentic Architectures
Implementation of 17+ agentic architectures
All Agentic Architectures is an open educational repository that provides hands-on implementations of modern AI agent architectures. The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works... -
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LLM Agents Papers
Must-read Papers on LLM Agents
...The project organizes academic literature that explores how language models can act as agents capable of reasoning, planning, and interacting with external tools or environments. Rather than providing software code, the repository functions as a structured knowledge base that helps researchers navigate the rapidly expanding field of agent-based AI research. Papers are categorized into thematic groups covering topics such as tool use, planning algorithms, reasoning strategies, and multi-agent collaboration. The repository helps readers understand how agent architectures are evolving and how they are applied in domains such as robotics, software automation, and decision-making systems. ... -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
...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. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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Tokscale
A CLI tool for tracking token usage from OpenCode, Claude Code
Tokscale is a CLI and terminal UI tool that tracks token usage and estimated cost across multiple AI coding assistants and development workflows. It treats tokens like a measurable resource, helping developers understand how much “AI energy” they are consuming over time and where it is being spent. The tool aggregates usage across supported platforms and presents it through interactive views that let users filter, sort, and explore trends without leaving the terminal. Tokscale also includes... -
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Polyaxon
MLOps tools for managing & orchestrating the ML LifeCycle
...Polyaxon integrates seamlessly with Kubernetes and container orchestration so that workloads can be scheduled efficiently, GPU and CPU resources are shared, and distributed training across multiple nodes is straightforward. It supports connection to external Git repositories for source-controlled experiments, making it easy to pull code directly for runs and enabling continuous integration workflows with tools like GitHub Actions. -
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FireRedTTS-2
Long-form streaming TTS system for multi-speaker dialogue generation
FireRedTTS2 is a next-generation open-source text-to-speech (TTS) system focused on long-form, streaming speech synthesis for multi-speaker dialogue, delivering stable natural speech with context-aware prosody and reliable speaker transitions that support real-time and conversational applications. It features a specialized streaming speech tokenizer and a dual-transformer architecture that enables low latency and high-quality synthesis, making it suitable for interactive systems like... -
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Maestro AI Orchestration
Agent Orchestration Command Center
Maestro is a cross-platform desktop application designed for power users to orchestrate and manage fleets of AI agents and project workflows from a keyboard-centric interface. It provides a high-performance experience for running multiple agent sessions in parallel, integrating with tools such as Claude Code, OpenAI Codex, and other agent tooling to automate tasks, perform unattended execution, and organize long-running work flows. Users can collaborate with AI to draft specifications, break tasks into playbooks, and run them sequentially or concurrently in isolated contexts, each with clean session history. Maestro includes advanced features like Git worktrees to isolate sub-agent tasks, command-line interfaces for integration into CI/CD pipelines, remote control via phone, and comprehensive session analytics. -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
...It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. The framework also supports plugin extensions that add things like rate limiting, caching layers, and telemetry without cluttering core code. -
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TONL
TONL (Token-Optimized Notation Language)
...It provides a serialization format that significantly reduces token usage compared with traditional JSON, which can result in lower costs and more efficient prompt size utilization in LLM-driven systems. TONL isn’t just a format — it includes a rich API for querying, indexing, modifying, and streaming data, along with tools for schema validation and TypeScript code generation. The platform comes with a complete command-line interface that supports interactive dashboards and cross-platform usage in browsers and server environments, and its high test coverage gives developers confidence in stability. -
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DFlash
Block Diffusion for Ultra-Fast Speculative Decoding
...This approach has been shown to deliver lossless acceleration on models like Qwen3-8B by combining block diffusion techniques with efficient batching, making it ideal for applications where latency and cost matter. The project includes support for multiple draft models, example integration code, and scripts to benchmark performance, and it is structured to work with popular model serving stacks like SGLang and the Hugging Face Transformers ecosystem. -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
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Beads
A memory upgrade for your coding agent
...By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
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Archon
The knowledge and task management backbone for AI coding assistants
...It acts as a backend (including an MCP server) that allows different AI coding tools and assistants to share the same structured context, knowledge base, and task lists, improving consistency, productivity, and collaboration across multi-agent interactions. Users can import documentation, project files, and external knowledge so that assistants like Claude Code, Cursor, or other LLM-powered tools work with up-to-date, project-specific context rather than relying on limited prompt memory. Archon’s UI and APIs are intended to streamline how developers interact with their agents, whether for exploratory coding, automated task execution, or integrated RAG workflows, helping reduce friction between manual coding tasks and AI-generated suggestions. -
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DeployStack
Centralized credential vault, governance, and token optimization
DeployStack is an open-source framework that helps developers and teams define and deploy production infrastructure stacks using modular, reusable templates, often with IaC (infrastructure as code) principles. It provides a structured way to compose resources such as cloud networking, compute, and managed services into coherent deployment blueprints that can be versioned and reused across projects. By abstracting common deployment patterns and capturing them as templates, Deploystack reduces duplication of effort that typically occurs when setting up stacks for different applications or environments. ... -
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Kong Konnect MCP
A Model Context Protocol server for interacting with Kong Konnect
...By bridging MCP clients to Kong’s control plane, the project allows autonomous agents to retrieve data on traffic metrics, route definitions, services, plugins, and consumer settings without writing custom API integration code. It also groups tools into categories—such as analytics, configuration, and control plane management—to organize capabilities logically for AI workflows. -
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MedGemma
Collection of Gemma 3 variants that are trained for performance
MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and... -
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D4RL
Collection of reference environments, offline reinforcement learning
D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm...
