Search Results for "keygen activation code"
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Showing 36 open source projects for "keygen activation code"
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Flox
Developer environments you can take with you
...Instead of snowflake machines, teams define environments that layer or replace dependencies exactly where needed, then activate the same environment locally, in CI, or on production hosts. The GitHub repo and docs present a developer-first UX, plus integrations such as a VS Code extension that makes activating and working within Flox environments seamless. The ecosystem includes CI building blocks—for example, a CircleCI Orb—to install and activate environments as part of builds and tests. Flox’s messaging focuses on faster onboarding and “time-to-activation,” reducing friction for new contributors and multi-repo organizations. ... -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. -
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Ling
Ling is a MoE LLM provided and open-sourced by InclusionAI
...Model inference and API code (e.g. integration with Transformers). This collaborative approach accelerates development and ensures that the models remain at the forefront of technology, addressing emerging challenges in various fields. -
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fvcore
Collection of common code shared among different research projects
...It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. Because it is small, stable, and well-tested, fvcore is frequently imported by projects like Detectron2 and PyTorchVideo to avoid duplicating infrastructure and to keep research repos. -
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NextChat
Light and Fast AI Assistant
NextChat is a browser extension that injects a floating ChatGPT-style chat sidebar into webpages, enabling instant AI assistance during web browsing. It supports context-aware chatting using page content, quick queries, and code assistance without opening a new tab. Developers appreciate its customization options—like hotkeys, themes, and engine selection—while everyday users enjoy handy tools for writing, summarizing, or brainstorming on the fly. -
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limelight.vim
All the world's indeed a stage and we are merely players
limelight.vim is a minimalist plugin that enhances focus by subtly dimming all but the paragraph or block surrounding the cursor. It’s designed to complement distraction-free modes such as goyo.vim, helping writers and coders keep attention on their current context. The plugin adjusts highlight groups dynamically so the active text remains clear while surrounding text fades into a low-contrast tone. It supports fine-grained control over opacity and integrates with any colorscheme without... -
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Automated Interpretability
Code for Language models can explain neurons in language models paper
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature,... -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. -
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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. Typical use cases include turning analytical utilities—like probability mass functions, activation formulas, or recurrence relations—into equations suitable for papers, notebooks, and slide decks. ... -
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denuvo
DENUVO (VMProtect 2.6x) VM research tool
This is part of the original source code of DENUVO_Profiler, written by 386 Team in 2014-2017 to investigate the first versions of DENUVO (eq VMProtect 2-3), when there were no lifters and other modern devirtualisation tools. DENUVO_Profiler is a subsequent development of the SecuROM_Profiler utility - the simplest tool for visualising the operation of a virtual machine and effectively controlling the data it uses in the byte-code tape (p-code). -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model. This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments. -
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IEC60870-5 101 tools code library
IEC 101 Server and Client Simulator Source code Library, Win, Linux
v21,06.018 IEC101 RTU Server Simulator, IEC101 Client Master Simulator, Static and Dynamic Libraries, Source Code Library. Driver for Windows, POSIX Linux, ARM , PowerPC, QNX & more * Make your RTU, protocol converter, Gateway, HMI, Data concentrator compatible with iec 101. * Download Free Evaluation Kit - IEC 60870-5-101 Development Bundle. * In the Bundle included IEC 101 Server & Client Simulator, Windows and Linux SDK, C# projects, Doxygen , Raspberry Pi, ... -
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...Change-log for MathMod-11.1 (16/06/2021) 1) Support of graphing functions with complex numbers Z=u+iv in 3D and 4D spaces (demo scripts: "Complex3D_xx" and "Complex4D_Saddle") 2) Added support for HSV (hue, saturation, brightness) coloring model (script: "Complex_Domain_Coloring") 3) Texture definitions (RGB and HSV) for parametric surfaces can now be expressed as functions of complex numbers 4) Added a software implementation for the OpenGL variable gl_FrontFacing (fixed an issue with GPU drivers on MacOSX Catalina). The activation of this patch is available from mathmodconfig.js ("OpenGlConfig"-->"GL_FrontFacingSupport"=false) or the GUI("Appearance"-->"OpenGL Front Surface Support") 5) Bug-fixes, code optimization and documentation update.
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is... -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
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PandaOCR
Multifunctional OCR Image and Text Recognition
At present, the newly refactored PandaOCR.Pro professional version has been released. It is faster and more stable, with richer interfaces and easier operation. It is recommended for you to use it! The normal version will continue to be maintained, and all interfaces will be retained but no new functions will be added. The reason why the version number of the professional version starts from 5.x is that the normal version will be updated in the future, so a period of version number is... -
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CAM
Class Activation Mapping
This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
...By pairing code examples with conceptual explanations, the tutorials make abstract machine learning ideas accessible and encourage experimentation with TensorBoard visualization and distributed training. -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights, biases, activation functions, and gradient descent, as well as more advanced techniques like convolutional networks, recurrent networks, and reinforcement learning. It includes multiple hands-on projects, such as handwritten digit recognition, airplane detection in images, and text generation using recurrent neural networks, which demonstrate how different architectures solve real-world problems. -
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phpRegister Project
Account management PHP Script with a software infracstructure
php login and user management script with an admin part to manage accounts, tickets, translations. Do you have a new web project to develop? By using phpRegister, you will have directly all the essential elements for the development of your platform: Account creation, identification, profile management, user support, contact page and even a license template! phpRegister is also a software infrastructure written in PHP with as main purpose to help developers to start a new web... -
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wklaas
JMeasurement is a api for monitoring java production code
JMeasurement is a free and simple java api for monitoring runtime and usage (count, parallel activation, last activation) of user defined points in java production code. It is simple to use and extended. JMX is supported. Actual version is always in maven central. http://mvnrepository.com/artifact/net.sourceforge.jmeasurement2/JMeasurement -
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NASH OS
Nash Operating System for Modern Ecommerce
The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you. -
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Git Time Machine
Atom package that allows you to travel back in commit history
git-time-machine is a user interface (often as an editor plugin or UI extension) that allows users to browse a file’s history visually, stepping back and forth through revisions in Git like a “time machine.” It shows changes to a file over time, lets users compare older states, and often provides diff and blame views to understand how the file evolved. Instead of just opening a commit log or diff, git-time-machine gives an interactive, incremental experience where you can slide through... -
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Easy-HotSpot
A super easy WiFi hotspot user management utility for Mikrotik router
Easy HotSpot, A super easy WiFi hotspot user management utility for Mikrotik RouterOS based Router devices and works using the PHP PEAR2 API client by boenrobot. This PHP/MySql/Bootstrap based package can be installed in any supporting OS in a local webserver of the client or in any internet based webservers of choice. Put the contents/source code in the root or in a sub folder of the host/domain, as required. A MySql database need to be created in order to work, but the package will... -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels...
