Search Results for "cutting" - Page 3
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Showing 264 open source projects for "cutting"
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Lepton AI
A Pythonic framework to simplify AI service building
A Pythonic framework to simplify AI service building. Cutting-edge AI inference and training, unmatched cloud-native experience, and top-tier GPU infrastructure. Ensure 99.9% uptime with comprehensive health checks and automatic repairs. -
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Fastify
Fast and low overhead web framework, for Node.js
...The framework’s plugin system uses encapsulation to isolate routes, decorators, and hooks, which keeps large codebases modular and avoids accidental cross-pollution. Built-in hooks (such as onRequest, preHandler, and onSend) and lightweight decorators make it easy to insert cross-cutting concerns like auth, caching, and metrics. Fastify ships with thoughtful defaults like structured logging via Pino and AJV for schema validation, and it has first-class TypeScript types. The result is a server stack that scales to high throughput while remaining easy to reason about in production. -
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DreamFactory
DreamFactory API Management Platform
...Restrict capabilities using role-Based Access Controls. Easily manage API keys. Deploy behind firewalls and in air-gapped environments. Integrate legacy technology into cutting-edge applications, extending technology lifecycles and derisking legacy system replacements. -
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PyMC3
Probabilistic programming in Python
...PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets. -
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GLM-4.5
GLM-4.5: Open-source LLM for intelligent agents by Z.ai
GLM-4.5 is a cutting-edge open-source large language model designed by Z.ai for intelligent agent applications. The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. Both models unify reasoning, coding, and intelligent agent capabilities, providing two modes: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for immediate responses. ... -
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spartan
Cutting-edge tools powering Angular full-stack development
spartan is a modern, experimental full-stack toolkit designed to enhance Angular development by combining a flexible UI component system with a broader application stack. It is structured as a monorepo that includes both a UI library and a full-stack solution, enabling developers to build complete applications with consistent tooling and design patterns. One of its defining characteristics is its inspiration from libraries like shadcn/ui, emphasizing ownership of code rather than heavy... -
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Google Research
This repository contains code released by Google Research
...Rather than being a single framework, the repository serves as a centralized collection of experimental projects, reference implementations, and reproducible research artifacts. It is intended primarily for researchers and advanced practitioners who want to explore cutting-edge techniques directly from the teams that developed them. The repository includes datasets, training scripts, and model implementations that support academic study and applied experimentation. Because of its breadth, users typically clone only the subdirectories relevant to their specific research interests. Overall, google-research functions as a living archive of state-of-the-art research code supporting both academic and industrial AI innovation. -
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LongCat-Image
Foundation model for image generation
LongCat-Image is an open-source foundation model for image generation and editing created by the LongCat team at Meituan, designed to deliver high-quality visual outputs while remaining efficient and accessible for developers and researchers. Rather than relying on massive parameter counts typical of many cutting-edge models, LongCat-Image achieves strong photorealism, stable structure, and accurate bilingual (Chinese and English) text rendering with a more compact ~6-billion parameter architecture, making it competitive with much larger alternatives despite its relatively lean design. The model excels at both text-to-image generation and instruction-guided image editing, offering users versatile capabilities for creative and practical tasks—whether generating art, mockups, or adjusting existing visuals with fine control. -
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Swift OpenAPI Generator
Generate Swift client and server code from an OpenAPI document
...It removes boilerplate around request/response wiring so you focus on business logic while keeping strict type safety. The generated code uses a separate runtime package with pluggable transports and middlewares, enabling you to swap HTTP stacks or add cross-cutting concerns without touching the API layer. Swift-idiomatic naming and code structure in outputs. The project tracks ergonomic details like Swift casing conventions for generated types and properties. Releases provide a predictable upgrade path as the generator/runtimes evolve. Together, the plugin and runtime form a standardized pipeline from OpenAPI to production-ready Swift code. -
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MiniCPM-o
A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming
MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available. -
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Gradle Git Version
A Gradle plugin that uses `git describe` to produce a version string
...It works well in branching workflows by incorporating branch or distance information, making debug artifacts easy to trace back to source. By centralizing version logic around Git history, it eliminates manual bumps and reduces mistakes when cutting releases across multiple modules. -
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Stop Slop
A skill file for removing AI tells from prose
Stop Slop is a writing skill file designed to remove the obvious tells of AI-generated prose. It gives an assistant concrete editing rules for making text sound more direct, human, specific, and trustworthy. The project targets common AI habits such as filler openings, overused contrasts, unnecessary adverbs, vague language, passive phrasing, and metronomic sentence rhythm. It also includes a scoring rubric that rates drafts across dimensions such as directness, rhythm, trust, authenticity,... -
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DeepGEMM
Clean and efficient FP8 GEMM kernels with fine-grained scaling
...One distinguishing aspect is that DeepGEMM compiles its kernels at runtime (via a lightweight Just-In-Time (JIT) module), so users don’t need to precompile CUDA kernels before installation. Despite its lean design, it includes scaling strategies (fine-grained scaling) and optimizations inspired by cutting edge systems (drawing from ideas in CUTLASS, CuTe) but in a more streamlined form. -
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Semantic Kernel
Integrate cutting-edge LLM technology quickly and easily into your app
Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds. To help developers build their own Copilot experiences on top of AI plugins, we have released Semantic Kernel, a lightweight open-source SDK that allows you to orchestrate AI plugins. With Semantic Kernel, you can leverage the same AI... -
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AWS SDK for Java 2.0
The official AWS SDK for Java - Version 2
The AWS SDK for Java v2 is a modern, modular rewrite that reduces dependencies, improves performance, and embraces non-blocking I/O. Each AWS service ships as its own small artifact, so applications include only what they use, cutting footprint and cold-start time. The SDK provides both synchronous clients and a high-performance asynchronous stack built on event-driven HTTP, enabling reactive pipelines and high-throughput services. Models are immutable and builders are pervasive, leading to safer code and clearer intent. Credential providers, region resolution, retries, and paginators are redesigned with extensibility in mind, and integrations are available for common observability and HTTP client choices. ... -
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mlpack
mlpack: a scalable C++ machine learning library
...Written in C++ and built on the Armadillo linear algebra library, the ensmallen numerical optimization library, and parts of Boost. Aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack uses CMake as a build system and allows several flexible build configuration options. You can consult any of the CMake tutorials for further documentation, but this tutorial should be enough to get mlpack built and installed. -
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NG Bootstrap
Angular powered Bootstrap
...All the Bootstrap widgets you know like a carousel, modal, popover, tooltip, tablet plus some additional goodies like datepicker, rating and typeahead. All code is tested with almost 100% coverage, all changes are meticulously reviewed. We are not cutting corners. All the widgets are accessible. We use proper HTML elements and required aria attributes. Keyboard navigation and focus management work as expected. We've created angular-ui/bootstrap and have spent several years doing widget development. A number of team members are core Angular contributors. -
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dots.ocr
Multilingual Document Layout Parsing in a Single Vision-Language Model
dots.ocr is a cutting-edge multilingual document parsing system built on a unified vision-language model that combines layout detection, text recognition, and structural understanding into a single architecture. Unlike traditional OCR pipelines that rely on multiple specialized components, dots.ocr integrates these processes end-to-end, reducing error propagation and improving consistency across tasks. -
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Reco-papers
Classic papers and resources on recommendation
...The project organizes a large body of literature into thematic sections such as classic recommender systems, exploration-exploitation strategies, deep learning–based recommendation models, and cold-start mitigation techniques. It serves as a reference library for researchers and engineers who want to explore foundational and cutting-edge work in recommendation technologies. The repository includes papers from academic institutions and industry organizations and groups them according to topics such as retrieval and reranking, reinforcement learning in recommendation, and feature engineering infrastructure. By structuring these materials into categories, the project helps practitioners quickly discover relevant research for designing recommendation engines in production environments. -
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CVPR 2026
Collection of CVPR 2026 Papers and Open Source Projects
CVPR2026-Papers-with-Code is a community-maintained repository that collects research papers and corresponding open-source implementations from the CVPR 2026 conference and related computer vision research. The repository acts as a continuously updated catalog of cutting-edge research across a wide range of topics including computer vision, multimodal AI, generative models, diffusion systems, autonomous driving, medical imaging, and remote sensing. Each entry typically links to the research paper as well as the public code repository associated with the work, allowing researchers and developers to quickly access reproducible implementations. ... -
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KG-LLM-Papers
Papers integrating knowledge graphs (KGs) and large language models
...The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge graph-guided prompting, retrieval-augmented generation, reasoning over structured data, and hybrid architectures combining symbolic and neural systems. By gathering these papers into a single organized repository, the project helps researchers quickly discover relevant literature and track the evolution of the field. -
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Deep Learning Essay Reading
Read classic and new deep learning papers paragraph by paragraph
Deep Learning Essay Reading repository is a comprehensive collection of machine learning and deep learning research summaries designed to make cutting-edge academic work more accessible. Instead of reading entire dense academic papers, contributors provide structured breakdowns and insights into the most influential research from the past decade, often including explanation highlights and key takeaways. The content spans foundational models, architectures, and training methodologies across computer vision, natural language processing, generative models, and other machine learning domains. ... -
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TONL
TONL (Token-Optimized Notation Language)
TONL is a cutting-edge data platform built around a production-ready serialization format designed to be both compact and powerful, combining human readability with performance features that make it suitable for large-scale applications and AI workflows. 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. -
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Lyra 2
Project Lyra: Open Generative 3D World Models
...The architecture is designed to handle both 3D and 4D scene generation, making it suitable for applications such as simulation, gaming, and virtual environments. By emphasizing open implementations, the project provides researchers and developers with access to cutting-edge generative modeling techniques. -
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Front-End Performance Checklist
The only Front-End Performance Checklist that runs faster
...The checklist is organized by areas such as HTML, CSS, JavaScript, fonts, images, and network delivery, so teams can tackle bottlenecks systematically. Each section emphasizes practical outcomes like cutting render-blocking resources, optimizing critical path CSS, and leveraging modern compression and caching. The project is designed to be used in code reviews and pre-launch audits, making it easy to institutionalize performance culture across teams. It evolves through community contributions, ensuring it reflects current best practices and platform capabilities.
