Search Results for "design analysis algorithm"
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Showing 124 open source projects for "design analysis algorithm"
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. -
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Lama Cleaner
Image inpainting tool powered by SOTA AI Model
Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, or people from your pictures or erase and replace(powered by stable diffusion) anything on your pictures. Lama Cleaner is a free, open-source and fully self-hostable inpainting tool powered by state-of-the-art AI models. You can use it to remove any unwanted object, defect, or people from your pictures or erase and replace anything on your pictures. Many AICG creators are using Lama Cleaner to clean-up their... -
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Orange Data Mining
Orange: Interactive data analysis
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for... -
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
...It is intended to facilitate reproducible RL training on reasoning / agentic tasks, supporting scaling from single nodes to large GPU clusters. It can streamline the development of AI agents and reasoning systems. Support for algorithm and system co-design optimizations (to improve efficiency and stability). -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Torch Pruning
DepGraph: Towards Any Structural Pruning
...The library focuses on reducing the size and computational cost of neural networks by removing redundant parameters and channels while maintaining model performance. It introduces a graph-based algorithm called DepGraph that automatically identifies dependencies between layers, allowing parameters to be pruned safely across complex architectures. This dependency analysis makes it possible to prune large networks such as transformers, convolutional networks, and diffusion models without breaking the computational graph. Torch-Pruning physically removes parameters rather than masking them, which results in smaller and faster models during both training and inference. ... -
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sktime
A unified framework for machine learning with time series
...It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another. -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
...The repository emphasizes features such as threaded execution, logging, and notification integrations, which position it as an automation-oriented project for controlled security assessment environments. From a software design perspective, its distinguishing idea is the use of language models as a front-end analysis layer that interprets a target page before the rest of the workflow proceeds. -
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MathModelAgent
An Agent Designed for Mathematical Modeling
MathModelAgent is an AI agent system designed specifically for assisting with mathematical modeling tasks and academic problem solving. The platform automates the process of analyzing mathematical problems, constructing models, generating code for simulations or computations, and producing a complete research-style report. The project uses a multi-agent architecture where different specialized agents handle tasks such as problem interpretation, modeling design, programming implementation,... -
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BioNeMo
BioNeMo Framework: For building and adapting AI models
BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
...In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). -
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Perf Book
The book "Performance Analysis and Tuning on Modern CPU"
This project is a practical guide to performance analysis and tuning on modern CPUs, bridging microarchitecture details with hands-on profiling. It explains how caches, TLBs, prefetchers, branch predictors, and out-of-order execution influence real program speed, then connects those concepts to concrete optimization strategies. Readers learn how to design trustworthy benchmarks, avoid measurement traps (warmup, turbo, frequency scaling), and interpret hardware performance counters. ... -
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ffmpy
Pythonic interface for FFmpeg/FFprobe command line
...It is designed for developers who want to automate media processing tasks without manually writing complex command strings. ffmpy supports flexible argument configuration, enabling a wide range of transcoding and analysis workflows. Its lightweight design makes it suitable for scripting, automation, and integration into larger Python applications. -
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Anthropic Cybersecurity Skills
754 structured cybersecurity skills for AI agents
Anthropic Cybersecurity Skills is a collection of structured prompts, tools, and workflows designed to enhance the cybersecurity capabilities of AI systems. It focuses on defining reusable “skills” that guide AI models in performing tasks such as vulnerability analysis, threat detection, and security auditing. The project is intended for experimentation and development of AI-assisted cybersecurity workflows, providing templates that can be adapted to different environments. It emphasizes... -
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ValueCell
Community-driven, multi-agent platform for financial applications
...ValueCell supports integrations with multiple language model providers and market data sources, giving developers flexibility in customizing agents and incorporating external APIs to enhance insights. Sensitive user data is stored locally, a design choice that prioritizes privacy and security while still enabling rich analytic workflows. -
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Claude Skills
Public repository for Agent Skills
...Rather than relying on handcrafted prompts every time, Skills teach an AI agent procedural knowledge and task-specific workflows so it can apply that expertise reliably, whether the task involves document creation, data analysis, design generation, or technical automation. Each Skill lives in its own directory with a SKILL.md file containing metadata and instructions, and can include supplemental scripts or assets that the agent uses to perform complex operations when relevant. -
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BettaFish
Public opinion analysis system
...With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. -
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MemU
MemU is an open-source memory framework for AI companions
...Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC integration and a dedicated algorithm team for scenario-specific optimization. User behavior analysis, real-time monitoring, and automated agent optimization tools. 24/7 dedicated support team, custom SLAs, and professional implementation services. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. This... -
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PocketFlow Tutorial Codebase Knowledge
Codebase to Tutorial
...The repository builds on a lightweight 100-line LLM framework and uses natural language models to inspect repository structures, identify core abstractions, map dependencies, and articulate the reasoning behind code design and interactions. By crawling code files, extracting higher-level patterns, and using large language models to narrate explanations, the system aims to help developers — especially those new to a codebase — understand unfamiliar projects without manual deep reading. It supports both GitHub URL crawling and local directory analysis, and can tailor output tutorials to different languages, making it accessible for international developers. -
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Biomni
Biomni: a general-purpose biomedical AI agent
...It integrates retrieval-augmented generation with code-based execution, allowing it to access external knowledge, process data, and generate testable hypotheses in scientific workflows. The system is built to support researchers by automating repetitive and time-consuming tasks such as literature review, data analysis, and experimental design. Biomni operates within a comprehensive environment that includes tools, APIs, and datasets, enabling it to execute multi-step research processes rather than just generating text responses. It supports integration with multiple AI models, allowing flexibility in selecting the most appropriate model for specific tasks. -
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Real-ESRGAN GUI
Cross-platform GUI for image upscaler Real-ESRGAN
...Real-ESRGAN can only enlarge the input image with a fixed 2-4x magnification (related to the selected model). This functionality is achieved by downsampling using a conventional scaling algorithm after multiple calls to Real-ESRGAN. Split each frame of the GIF and record the duration, zoom in one by one and then merge. Drag an image file or directory to any position in the window, and its path can be automatically set as the input. -
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ffmpeg-normalize
Audio Normalization for Python/ffmpeg
ffmpeg-normalize is a command-line utility designed to normalize audio levels in media files using FFmpeg, ensuring consistent volume across multiple tracks. It supports both EBU R128 loudness normalization and peak normalization methods, allowing users to choose the appropriate standard for their needs. The tool analyzes audio streams and applies adjustments to achieve target loudness levels without introducing distortion. It can process multiple files in batch mode, making it suitable for... -
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
SLM Lab is a modular and extensible deep reinforcement learning framework designed for research and practical applications. It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently. -
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FlexLLMGen
Running large language models on a single GPU
...This design allows organizations to deploy powerful language models for high-volume tasks without the infrastructure costs typically associated with large-scale AI systems. The project is particularly useful for workloads that prioritize throughput over latency, including benchmarking experiments and large corpus analysis. -
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OpenClaw Medical Skills
The largest open-source medical AI skills library for OpenClaw
...This modular design allows developers and researchers to build AI systems that can access specialized medical reasoning processes, retrieve relevant biomedical information, and generate structured outputs suitable for analysis or downstream processing.
