Search Results for "search engine optimization"
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Showing 153 open source projects for "search engine optimization"
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1
Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. ... -
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Search with Lepton
Lightweight demo to build a conversational AI search engine quickly
Search with Lepton is an open source demonstration project that shows how to build a conversational search engine using the Lepton AI framework. It combines traditional web search with large language models to provide natural language answers to user queries. It retrieves information from supported search engines and uses that context to generate responses through a retrieval-augmented generation approach. -
3
Whoogle Search
A self-hosted, ad-free, privacy-respecting metasearch engine
Get Google search results, but without any ads, javascript, AMP links, cookies, or IP address tracking. Easily deployable in one click as a Docker app, and customizable with a single config file. Quick and simple to implement as a primary search engine replacement on both desktop and mobile. Autocomplete/search suggestions. POST request search and suggestion queries (when possible). -
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SEO Machine
A specialized Claude Code workspace for creating long-form
SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It incorporates real data sources like Google Analytics and Search Console to guide decision-making and improve content effectiveness. ... -
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5
NVIDIA cuOpt
GPU accelerated decision optimization
NVIDIA cuOpt is a GPU-accelerated optimization engine designed to solve complex mathematical optimization problems at large scale. It supports a range of optimization models including linear programming (LP), mixed integer linear programming (MILP), quadratic programming (QP), and vehicle routing problems (VRP). Built primarily in C++, cuOpt leverages NVIDIA GPUs to deliver near real-time solutions for optimization tasks involving millions of variables and constraints. ... -
6
Optuna
A hyperparameter optimization framework
...You can check the optimization history, hyperparameter importances, etc. in graphs and tables. You don't need to create a Python script to call Optuna's visualization functions. Automated search for optimal hyperparameters using Python conditionals, loops, and syntax. Efficiently search large spaces and prune unpromising trials for faster results. Parallelize hyperparameter searches over multiple threads or processes without modifying code. -
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
8
KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms. -
9
theHarvester
E-mails, subdomains and names
theHarvester is a very simple to use, yet powerful and effective tool designed to be used in the early stages of a penetration test or red team engagement. Use it for open source intelligence (OSINT) gathering to help determine a company's external threat landscape on the internet. The tool gathers emails, names, subdomains, IPs and URLs using multiple public data sources. -
The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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10
MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. 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... -
11
AIDE ML
AI-Driven Exploration in the Space of Code
AIDE ML is an open-source research framework designed to explore automated machine learning development through agent-based search and code optimization. The project implements the AIDE algorithm, which uses a tree-search strategy guided by large language models to iteratively generate, evaluate, and refine code. Instead of relying on manual experimentation, the agent autonomously drafts machine learning pipelines, debugs errors, and benchmarks performance against user-defined evaluation metrics. ... -
12
Anna’s Archive
Comprehensive search engine for books, papers, comics, magazines
Anna’s Archive is a large-scale open-source search engine and data aggregation platform designed to index and provide access to a vast collection of books, academic papers, comics, magazines, and other digital texts through a unified interface. The project includes all the infrastructure required to run a full instance locally or in production, combining web servers, databases, and search indexing systems into a scalable architecture. -
13
Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. ... -
14
SearXNG
Free internet metasearch engine which aggregates
SearXNG is a free and open-source metasearch engine designed to aggregate results from multiple search engines while prioritizing user privacy and anonymity. Instead of maintaining its own index, it queries numerous external search providers and merges the results into a single interface, increasing coverage and diversity of information. One of its core principles is privacy, as it does not track users, store personal data, or create search profiles, making it a strong alternative to traditional search engines. . ... -
15
qBittorrent RuTracker plugin
qBittorrent search engine plugin for rutracker
qBittorrent RuTracker plugin is a lightweight search engine extension designed to integrate the RuTracker torrent index directly into the qBittorrent client, allowing users to search for torrents without leaving the application interface. The plugin follows qBittorrent’s official search plugin architecture and is implemented as a Python script that communicates with the RuTracker website to retrieve and display search results. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
17
SiteDorks
Automate search engine dorking across hundreds of websites
SiteDorks is a command line tool designed to automate advanced search queries across multiple search engines and websites. It allows users to perform search engine “dork” queries against a large set of predefined domains, making it easier to discover publicly available information across different platforms. SiteDorks supports several major search engines including Google, Bing, Brave, Ecosia, DuckDuckGo, Yahoo, and Yandex. -
18
MCP Server Qdrant
An official Qdrant Model Context Protocol (MCP) server implementation
The Qdrant MCP Server is an official Model Context Protocol server that integrates with the Qdrant vector search engine. It acts as a semantic memory layer, allowing for the storage and retrieval of vector-based data, enhancing the capabilities of AI applications requiring semantic search functionalities. -
19
OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
...OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget. Certified FinOps solution with the best cloud cost optimization engine, providing rightsizing recommendations, Reserved Instances/Savings Plans, and dozens of other optimization scenarios. ... -
20
Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
...The engine supports training models with hundreds of billions of parameters and enables long-context training with sequence lengths reaching tens of thousands of tokens. Its architecture incorporates memory-efficient optimizations that allow researchers to train large models even when computational resources are limited. XTuner is also designed to integrate with modern AI ecosystems, supporting multimodal training, reinforcement learning optimization, and instruction tuning pipelines. -
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Sunfish
Sunfish: a Python Chess Engine in 111 lines of code
sunfish is a minimalist yet surprisingly strong chess engine written in Python, designed to demonstrate how powerful algorithms can be implemented in a highly compact codebase. Despite being only around a hundred lines of core logic, the engine achieves competitive performance, reaching ratings above 2000 on online platforms. It implements classic chess engine techniques such as alpha-beta pruning and efficient board representation while maintaining readability and simplicity. ... -
22
SeaGOAT
local-first semantic code search engine
SeaGOAT is an open-source semantic code search engine designed to help developers explore and understand large codebases more efficiently. Instead of relying solely on traditional keyword search, it uses vector embeddings to represent the meaning of code and queries, allowing users to perform semantic searches that find relevant code even when the exact keywords are not present. -
23
Pruna AI
Pruna is a model optimization framework built for developers
Pruna is an open-source, self-hostable AI inference engine designed to help teams deploy and manage large language models (LLMs) efficiently across private or hybrid infrastructures. Built with performance and developer ergonomics in mind, Pruna simplifies inference workflows by enabling multi-model orchestration, autoscaling, GPU resource allocation, and compatibility with popular open-source models. It is ideal for companies or teams looking to reduce reliance on external APIs while... -
24
Nano-vLLM
A lightweight vLLM implementation built from scratch
...Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. Its API closely mirrors that of the original vLLM framework, allowing developers familiar with vLLM to adopt the tool with minimal changes. -
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Google Kubernetes Engine (GKE) Samples
Sample applications for Google Kubernetes Engine (GKE)
Google Kubernetes Engine (GKE) Samples repository is a comprehensive collection of sample applications and reference implementations designed to demonstrate how to build, deploy, and manage workloads on Google Kubernetes Engine (GKE). It serves as a practical companion to official GKE tutorials, providing real, runnable code that illustrates how containerized applications are packaged, deployed, and scaled within Kubernetes clusters.
