Search Results for "k-melion"
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Showing 480 open source projects for "k-melion"
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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K-Pomodoro
Lightweight Pomodoro timer for Windows
K-Pomodoro is a lightweight desktop Pomodoro timer for Windows, built with WPF and .NET 8. Featuring a clean, minimalist dark UI, it guides you through 5 complete Pomodoro cycles (approximately 2.5 hours) to help you stay focused and productive. Features: - 25-minute work sessions + 5-minute breaks across 5 full cycles - Large MM:SS countdown display for at-a-glance timing - Per-cycle progress bars showing real-time completion status - Status badge indicating current mode: Working / Break / Completed - Borderless floating window — drag anywhere on screen - Zero dependencies, lightweight and resource-friendly Session Flow: Cycle 1–4: Work 25 min → Break 5 min → Cycle 5: Work 25 min → Done! ... -
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VimFx
Vim keyboard shortcuts for Firefox
VimFx brings Vim-inspired navigation and command mode to Firefox via WebExtensions. It allows users to control the browser with keyboard shortcuts (h/j/k/l), open tabs, follow links, find text, and execute commands—all without a mouse. It mimics Vim’s Normal/Insert modes, enabling power users to browse efficiently. It is open-source and integrates seamlessly with Firefox. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Hotkey
Trigger an action on an element with a keyboard shortcut
Trigger an action on a target element when the hotkey (key or sequence of keys) is pressed on the keyboard. This triggers a focus event on form fields or a click event on other elements. By default, hotkeys are extracted from a target element's data-hotkey attribute, but this can be overridden by passing the hotkey to the registering function (install) as a parameter. -
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GoAWK
A POSIX-compliant AWK interpreter written in Go, with CSV support
GoAWK now uses a bytecode compiler and includes native support for CSV files. AWK is a fascinating text processing language, and The AWK Programming Language is a wonderfully concise book describing it. The A, W, and K in AWK stand for the surnames of the three original creators: Alfred Aho, Peter Weinberger, and Brian Kernighan. Kernighan is also an author of The C Programming Language (“K&R”), and the two books have that same each-page-packs-a-punch feel. AWK was released in 1977, which makes it over 40 years old! Not bad for a domain-specific language that’s still used for one-liners on Unix command lines everywhere. ... -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
bitsandbytes is an open-source library designed to make training and inference of large neural networks more efficient by dramatically reducing memory usage. Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources,... -
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Nebula libp2p DHT
A libp2p DHT crawler, monitor, and measurement tool
A libp2p DHT crawler and monitor that tracks the liveness of peers. The crawler connects to DHT bootstrap peers and then recursively follows all entries in their k-buckets until all peers have been visited. The crawler supports the IPFS, Filecoin, Polkadot, Kusama, Rococo, Westend networks and more. The crawler can store its results as JSON documents or in a postgres database - the --dry-run flag prevents it from doing either. Nebula will print a summary of the crawl at the end instead. A crawl takes ~5-10 min depending on your internet connection. ... -
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DocArray
The data structure for multimodal data
...Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Zinnia
Runtime for worker nodes executing modules
Zinnia is a runtime for Filecoin Station modules. It provides a sandboxed environment to execute untrusted code on consumer-grade computers. Zinnia is a sandboxed and resource-limited runtime for distributed workers. This crate provides the zinnia executable to run modules locally, e.g. while developing. -
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kubeinvaders
Gamified Chaos Engineering Tool for Kubernetes
This project is part of the landscape of Cloud Native Computing Foundation in the Observability and Analysis - Chaos Engineering section. Backed by the teams at platform engineering.it and devopstribe.it, which provides enterprise-grade features and certified resilience services for your Kubernetes infrastructure. -
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better-escape.nvim
Map keys without delay when typing
A lot of people have mappings like jk or jj to escape insert mode. The problem with these mappings is that whenever you type a j, neovim wait about 100-500ms (depending on your timeoutlen) to see if you type a j or a k because these are mapped. Only after that time, the j will be inserted. Then you always get a delay when typing a j. -
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harmonypy
Integrate multiple high-dimensional datasets with fuzzy k-means
Harmony is an algorithm for integrating multiple high-dimensional datasets. harmonypy is a port of the harmony R package by Ilya Korsunsky. Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets. It is especially useful for large single-cell datasets such as single-cell RNA-seq. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on... -
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TreeQuest
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question... -
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H-K-Systems-Utils Android utils by H-K-Systems
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. 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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DashMap
Blazing fast concurrent HashMap for Rust
...DashMap tries to implement an easy-to-use API similar to std::collections::HashMap with some slight changes to handle concurrency. DashMap tries to be very simple to use and to be a direct replacement for RwLock<HashMap<K, V>>. To accomplish these goals, all methods take &self instead of modifying methods taking &mut self. This allows you to put a DashMap in an Arc<T> and share it between threads while still being able to modify it. DashMap puts great effort into performance and aims to be as fast as possible. If you have any suggestions or tips do not hesitate to open an issue or a PR. -
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httpstat
It's like curl -v, with colours
Windows/BSD/Linux supported, HTTP and HTTPS supported as well. For self-signed certificates use -k, and skip timing the body of a response with -I. Follow 30x redirects with -L. Change HTTP method with -X METHOD. Provide a PUT or POST request body with -d string. To supply the PUT or POST body as a file, use -d @filename. Add extra request headers with -H 'Name: value'. The response body is usually discarded, you can use -o filename to save it to a file, or -O to save it to the file name suggested by the server. ... -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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Kubie
A more powerful alternative to kubectx and kubens
kubie is an alternative to kubectx, kubens, and the k on prompt modification script. It offers context switching, namespace switching, and prompt modification in a way that makes each shell independent from others. It also has support for split configuration files, meaning it can load Kubernetes contexts from multiple files. You can configure the paths where Kubie will look for contexts, see the settings section. -
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TorchIO
Medical imaging toolkit for deep learning
...These transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity (bias) or k-space motion artifacts. TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity. -
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GLM-4.6V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.6V represents the latest generation of the GLM-V family and marks a major step forward in multimodal AI by combining advanced vision-language understanding with native “tool-call” capabilities, long-context reasoning, and strong generalization across domains. Unlike many vision-language models that treat images and text separately or require intermediate conversions, GLM-4.6V allows inputs such as images, screenshots or document pages directly as part of its reasoning pipeline — and... -
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Hotkeys JS
A robust Javascript library for capturing keyboard input
hotkeys-js is a tiny, framework-agnostic library for binding keyboard shortcuts in the browser, from simple key presses to complex combos and sequences. It provides a declarative API to register handlers like ctrl+k, shift+alt+p, or g g, and it normalizes key behavior across major browsers. The library includes scoping, so the same shortcut can trigger different actions depending on page context, and it offers filters to ignore inputs when the user is typing in form fields. You can enable or disable groups of shortcuts dynamically, making it well suited for SPAs and dashboard apps. ... -
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HyperTools
A Python toolbox for gaining geometric insights
...Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes, text or (mixed) lists. Applying topic models and other text vectorization methods to text data. HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot.
