Search Results for "activation code finder"
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Showing 61 open source projects for "activation code finder"
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Finder Component
Find files and directories via an intuitive fluent interface
The Finder component finds files and directories based on different criteria (name, file size, modification time, etc.) via an intuitive fluent interface. If you install this component outside of a Symfony application, you must require the vendor/autoload.php file in your code to enable the class autoloading mechanism provided by Composer. -
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QR Code generator library
High-quality QR Code generator library in Java, TypeScript/JavaScript
This project aims to be the best, clearest library for generating QR Codes. My primary goals are flexible options and absolute correctness. The secondary goals are compact implementation size and good documentation comments. This work is an independent implementation based on reading the official ISO specification documents. I believe that my library has a more intuitive API and shorter code length than competing libraries out there. The library is designed first in Java and then ported to... -
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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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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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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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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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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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legendary.nvim
A legend for your keymaps, commands, and autocmds
Define your keymaps, commands, and auto commands as simple Lua tables, building a legend at the same time (like VS Code's Command Palette). A legend for your keymaps, commands, and autocmds, integrates with which-key.nvim, lazy.nvim, and more. -
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Pearcleaner
A free, source-available and fair-code licensed mac app cleaner
Pearcleaner is a small, native macOS utility that helps you fully remove applications by finding and deleting the leftover files they scatter across your system. After you drag an app into Pearcleaner (or select it from a list), it scans common Library locations for related caches, logs, preferences, containers, launch agents, and support files. Results are presented with clear paths and sizes so you can deselect anything you want to keep before sending items to the Trash. The tool favors... -
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Spring Data JPA
Simplifies the development of creating a JPA-based data access layer
...Pagination support, dynamic query execution, ability to integrate custom data access code. -
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TorchCode
Practice implementing softmax, attention, GPT-2 and more
...It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. It runs in a Jupyter-based environment, allowing users to write, test, and debug their code interactively while receiving immediate feedback. An automated judging system evaluates correctness, gradient flow, and numerical stability, helping users understand both functional and theoretical aspects of their implementations. -
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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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Pika Skills
A collection of open-source skills for AI coding agents
Pika Skills is an open-source framework designed to extend the capabilities of AI coding agents by introducing modular, reusable “skills” that can be dynamically invoked during development workflows. Each skill acts as a self-contained unit composed of structured instructions, executable scripts, and dependency definitions, enabling agents to autonomously perform complex tasks without requiring manual configuration or orchestration. The system is tightly integrated with the Pika Developer... -
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fzf.vim
Things you can do with fzf and Vim
fzf.vim integrates the blazing-fast fzf fuzzy finder into Vim and Neovim, turning common navigation tasks into instant searches. With commands like :Files, :Buffers, :Rg, and :Commands, you can jump to files, lines, symbols, commits, or help tags using incremental fuzzy matching. It embraces Vim’s philosophy by composing with registers, marks, quickfix, and mappings, letting you build powerful flows from small pieces. Previews show file contents, diffs, or documentation alongside candidates,... -
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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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...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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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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Path Finder for Processing
2D/3D Path finding library
V1.0 is compatible with Processing 2 & 3 This library provides a set of classes to define a graph comprising of nodes and edges. Then allow the user to select from 4 different algorithms (Depth First, Breadth First, Dijkstra, A*) to find a path through the graph. Although suitable for games it can be used in a wide range of problems involving networks. Although the download is packaged as a Processing library the actual code has no dependencies on Processing except the examples. To use... -
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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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StatisticsTables
This is a Java APP for Probability and Statistics Tables
If you have Windows or a MAC , you can setup and run Statistics Tables with very little effort. You can use jdistabs in place of paper Probability Tables to get electronic probabilities. You can also download the Java code used to create this APP. There are 2 zips. One is for a working windows APP, and the other with complete code and file structure. I used Eclipse to develop the APP. Click the link you want to download to get the Statistics Tables Windows.exe or OS X zipped files. After saving Windows file, just click the jdistabs_win.exe file to install. ...
