Search Results for "g-code" - Page 31
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Showing 1552 open source projects for "g-code"
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Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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Streamline Azure Security with Palo Alto Networks VM-SeriesImprove your security posture and reduce incident response time. Use the VM-Series to natively analyze Azure traffic and dynamically drive policy updates based on workload changes.
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Open Responses
Specification for multi-provider, interoperable LLM interfaces
...This makes it a powerful option for teams or individuals who want full control over their AI infrastructure, prioritize privacy, or need to standardize inference calls across multiple backends without rewriting their code. -
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AI-Job-Notes
AI algorithm position job search strategy
...The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. It ties technical study (ML/DL fundamentals) to real hiring signals like problem-solving, code quality, and experiment logging. The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. It’s designed to reduce uncertainty and decision fatigue during the often lengthy job-hunt cycle. -
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Deep Learning Is Nothing
Deep learning concepts in an approachable style
Deep-Learning-Is-Nothing presents deep learning concepts in an approachable, from-scratch style that demystifies the stack behind modern models. It typically begins with linear algebra, calculus, and optimization refreshers before moving to perceptrons, multilayer networks, and gradient-based training. Implementations favor small, readable examples—often NumPy first—to show how forward and backward passes work without depending solely on high-level frameworks. Once the fundamentals are... -
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Gemma in PyTorch
The official PyTorch implementation of Google's Gemma models
...The repository demonstrates text generation pipelines, tokenizer setup, quantization paths, and adapters for low-rank or parameter-efficient fine-tuning. Example notebooks walk through instruction tuning and evaluation so teams can benchmark and iterate rapidly. The code is organized to be legible and hackable, exposing attention blocks, positional encodings, and head configurations. With standard PyTorch abstractions, it integrates easily into existing training loops, loggers, and evaluation harnesses. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Open Infra Index
Production-tested AI infrastructure tools
...FlashMLA, DeepEP, DeepGEMM, 3FS, etc.) that together form DeepSeek’s infrastructure stack. The repo's README describes the project as sharing “humble building blocks” of their online service—code that is documented, deployed, and battle-tested in production. The timing of its opening matches DeepSeek’s “Open-Source Week” campaign (starting around February 2025) when they gradually released internal infrastructure components publicly. It is licensed under CC0-1.0 (Creative Commons Zero) to maximize openness. -
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Model Zoo
Please do not feed the models
FluxML Model Zoo is a collection of demonstration models built with the Flux machine learning library in Julia. The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for... -
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TorchDistill
A coding-free framework built on PyTorch
torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file. -
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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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NeuroMatch Academy (NMA)
NMA Computational Neuroscience course
...These videos are completely optional and do not need to be watched in a fixed order so you can pick and choose which videos will help you brush up on your knowledge. The pre-reqs refresher days are asynchronous, so you can go through the material on your own time. You will learn how to code in Python from scratch using a simple neural model, the leaky integrate-and-fire model, as a motivation. Then, you will cover linear algebra, calculus and probability & statistics. The topics covered on these days were carefully chosen based on what you need for the comp neuro course. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
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Guardrails
Adding guardrails to large language models
Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). At the heart of Guardrails is the rail spec. rail is intended to be a language-agnostic, human-readable format for specifying structure and type information, validators and corrective actions over LLM outputs. We create a RAIL spec to describe the expected structure and types of the LLM output, the quality criteria for the output to be considered valid,... -
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MCP Server Chart
A visualization mcp contains 25+ visual charts
...The server can run over stdio for desktop IDEs or via SSE/“streamable” HTTP transport, making it easy to plug into MCP-capable clients and platforms (including Dify) without custom glue code. A simple CLI and environment variables control behavior, including disabling specific tools, selecting a visualization request service, or tagging a service instance for multi-tenant setups. The README documents private deployment, record generation, and tool filtering, giving teams a path from local experimentation to managed usage. -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning... -
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Context Hub
Makes coding agents get smarter with every task
Context Hub is a curated documentation system built to help coding agents write more accurate code. It gives agents versioned, language-specific reference material instead of forcing them to rely on noisy web searches or stale model memory. The project includes a CLI called chub that agents can use to search for available docs, fetch specific API guidance, and request only the files they need. It also supports local annotations, allowing an agent to remember project-specific notes, pitfalls, or workarounds across future sessions. ... -
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adversarial-spec
A Claude Code plugin that iteratively refines product specifications
adversarial-spec is a framework focused on designing and testing systems using adversarial thinking to uncover weaknesses and improve robustness. It encourages developers to define specifications that anticipate failure modes, edge cases, and malicious inputs before implementing solutions. The project emphasizes proactive design, ensuring that systems are built with resilience in mind from the beginning. It provides structured approaches for identifying vulnerabilities and stress-testing... -
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Claude Codex Settings
My personal Claude Code and OpenAI Codex setup
Claude Codex Settings is a configuration-focused repository that provides curated settings, prompts, and workflow optimizations for improving AI-assisted coding environments. It is designed to help developers fine-tune how Claude and similar models behave within coding workflows, ensuring more consistent and high-quality outputs. The project emphasizes practical usability, offering ready-to-use configurations that can be directly integrated into development environments. It also includes... -
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Ollama JavaScript Library
Ollama JavaScript library
Ollama JavaScript is the official JavaScript client for integrating Ollama into JS and TS applications with a lightweight, developer-friendly API. It is designed around the Ollama REST API, so it feels consistent with the platform while making common tasks easier to handle in application code. The library supports standard chat interactions, text generation, embeddings, and model management, which makes it useful for both simple chat interfaces and more advanced AI-powered workflows. It works in Node.js and also supports browser usage through a dedicated browser import, which broadens where it can be deployed. Streaming responses are built in, returning an async generator so applications can render output progressively instead of waiting for a full response. ... -
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SynaBun
Persistent vector memory for AI assistants
...It functions as a local-first solution that stores and retrieves contextual knowledge across sessions using a built-in vector database powered by embeddings, eliminating the need for external APIs, cloud services, or Docker dependencies. The system integrates tightly with developer workflows by running alongside tools like Claude Code, enabling automatic memory capture, retrieval, and contextual augmentation through lifecycle hooks and commands. One of its defining characteristics is its Neural Interface, a browser-based 3D visualization that represents stored memories as nodes in an interactive graph, allowing users to explore relationships, edit entries, and manage knowledge visually. -
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SEO Machine
A specialized Claude Code workspace for creating long-form
SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It... -
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Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in... -
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Oh My OpenCode Slim
Slimmed, cleaned and fine-tuned oh-my-opencode fork
Oh My OpenCode Slim is a lightweight, optimized fork of the broader oh-my-opencode ecosystem, designed to deliver high-performance multi-agent coding workflows while significantly reducing token consumption and system overhead. It retains the core concept of orchestrating multiple specialized AI agents but streamlines their configuration, execution, and communication to make the system more efficient and practical for everyday use. The framework introduces a structured “pantheon” of agents,... -
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Rust Port
The Rust workspace under rust/ is the current systems-language port
Rust Port is an open-source reconstruction and experimentation framework derived from leaked or reverse-engineered versions of advanced AI coding agents, designed to replicate and extend the capabilities of agentic development systems. It functions as a programmable coding assistant that operates through autonomous workflows, enabling users to generate, modify, and analyze code with minimal manual intervention. The project emphasizes agent-based execution, where tasks are broken down into steps and handled iteratively, simulating how modern AI coding tools operate in production environments. It is often used as a sandbox for exploring how large-scale coding agents behave, including their decision-making processes, tool usage, and workflow orchestration. ... -
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Edgee
AI gateway with token compression for Claude Code, Codex, and more
Edgee is an edge-native execution platform designed to run AI-driven logic and data processing directly at the network edge, reducing latency and improving responsiveness for modern applications. It enables developers to deploy functions and workflows closer to users, allowing real-time processing without relying heavily on centralized cloud infrastructure. The platform is built to support event-driven architectures, where actions are triggered by incoming requests, user behavior, or... -
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markstream-vue
A Vue 3 renderer specifically built for AI-powered streaming Markdown
...The framework focuses on eliminating jitter and reflow issues that commonly occur when rendering progressively generated text, ensuring a smooth and readable user experience even during rapid updates. It integrates advanced rendering support for complex content types such as code editors via Monaco, diagrams via Mermaid, and mathematical expressions via KaTeX, all optimized for incremental updates. The architecture is tailored for reactive front-end environments, leveraging Vue’s reactivity system to efficiently update only the necessary parts of the DOM. -
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ZML
Any model. Any hardware. Zero compromise
...The system allows models to be compiled and executed across multiple types of accelerators, including GPUs and TPUs, even when distributed across different machines or locations. One of its key strengths is cross-compilation, enabling developers to build once and deploy across various platforms without rewriting code. zml provides example implementations of models and workflows, demonstrating how to run inference tasks such as image classification or large language models. It is designed to handle complex distributed setups, including scenarios where model components are split across devices connected via networks. -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation of workflows, PySpur makes it easier to debug interactions between components and identify failures in complex pipelines. ...
