Search Results for "parallel-sudoku-solver" - Page 5
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Showing 1929 open source projects for "parallel-sudoku-solver"
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Colly
Elegant Scraper and Crawler Framework for Golang
...With Colly you can easily extract structured data from websites, which can be used for a wide range of applications, like data mining, data processing or archiving. Clean API. Fast (>1k request/sec on a single core) Manages request delays and maximum concurrency per domain. Automatic cookie and session handling. Sync/async/parallel scraping. Distributed scraping. Caching, automatic encoding of non-unicode responses. Robots.txt support. Google App Engine support. -
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Playwright CLI
CLI for common Playwright actions
...Developers can scaffold test files, record browser interactions as code, and run tests in headless or headed modes without needing a complex setup, dramatically reducing the barrier to writing robust UI tests. Playwright-CLI includes utilities for running tests in parallel across multiple devices, capturing screenshots and videos on failures, and outputting detailed reports that help diagnose flaky behavior or regressions. It also integrates with CI/CD pipelines, making it simple to automate testing as part of build and deployment workflows. -
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Eigent
The Open Source Cowork Desktop to Unlock Your Exceptional Productivity
Eigent is an open-source cowork desktop application designed to help you build, manage, and deploy a custom AI workforce. It enables multiple specialized AI agents to collaborate in parallel, turning complex workflows into automated, end-to-end tasks. Built on the CAMEL-AI multi-agent framework, Eigent emphasizes productivity, flexibility, and transparent system design. You can run Eigent fully locally for maximum privacy and data control, or choose a cloud-connected experience for quick access. The platform supports a wide range of AI models and integrates powerful tools through the Model Context Protocol (MCP). ... -
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ComputeSharp
.NET library to run C# code in parallel on the GPU through DX12
ComputeSharp is a .NET library to run C# code in parallel on the GPU through DX12 and dynamically generated HLSL compute shaders. The available APIs let you access GPU devices, allocate GPU buffers and textures, move data between them and the RAM, write compute shaders entirely in C# and have them run on the GPU. The goal of this project is to make GPU computing easy to use for all .NET developers! -
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oh-my-claudecode
Teams-first Multi-agent orchestration for Claude Code
...It emphasizes natural language interaction, allowing developers to describe goals instead of memorizing commands or manually orchestrating workflows. The system integrates multiple AI providers and tools, enabling parallel execution and cross-validation of tasks across different models. It also incorporates persistent execution mechanisms, ensuring that tasks continue until completion without requiring constant user intervention. -
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
...It accepts input in WebAssembly-like form but also accepts a general control flow graph for compilers that prefer that. Binaryen's internal IR uses compact data structures and is designed for completely parallel codegen and optimization, using all available CPU cores. Binaryen's IR also compiles down to WebAssembly extremely easily and quickly because it is essentially a subset of WebAssembly. Binaryen's optimizer has many passes (see an overview later down) that can improve code size and speed. These optimizations aim to make Binaryen powerful enough to be used as a compiler backend by itself. -
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PRAXIST
Autonomous research system for measurable computer-executable research
PRAXIST is an autonomous research system for measurable, computer-executable problems. It turns an already runnable project into a persistent research process instead of a series of disconnected prompts. Parallel research peers explore competing hypotheses and implementations while evaluators convert outcomes into structured evidence. That evidence is carried across generations so later work can build on promising strategies and avoid repeating weak ones. The system supports multi-metric evaluation, quality-diversity methods, resource-aware scheduling, and durable evidence tracking. ... -
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AnySearch Skill
Unified real-time search engine skill for AI agents
AnySearch Skill is a real-time search engine skill for AI agents. It gives agents a structured way to search the web, run vertical searches, perform parallel batch searches, and extract full-page content. The project is packaged as a skill rather than a standalone search application, so it is meant to be installed into compatible AI-agent environments. It supports multiple domain-specific search categories, making it useful when general web search is too broad. The skill can also fetch page content after a URL is found, which helps agents answer questions from source material rather than snippets alone. ... -
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Superset LLM
Run an army of Claude Code, Codex, etc. on your machine
Superset is a development environment and terminal-based platform designed to orchestrate multiple AI coding agents simultaneously within a single workspace. The tool enables developers to run many autonomous coding agents in parallel without the typical overhead of manually managing multiple terminals, repositories, or branches. Each agent task is isolated in its own Git worktree, ensuring that code changes from different agents do not interfere with each other while allowing developers to track their progress independently. The platform includes built-in monitoring capabilities so users can observe the activity of each agent, receive notifications when tasks are completed, and quickly review changes produced by automated coding workflows. ... -
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SciMLBenchmarks.jl
Benchmarks for scientific machine learning (SciML) software
SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem. -
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Freebuff
The free coding agent
Freebuff is a suite of free AI tools for coding, application building, and research across desktop, terminal, browser, cloud, and chat interfaces. Its coding agents can inspect a project, locate relevant files, edit code, run commands, and review their own work. Specialized agents divide tasks such as context gathering, implementation, research, and validation. The desktop app can run multiple agents in isolated local workspaces. Web and cloud editions provide hosted sandboxes, previews,... -
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Roo Code
Roo Code gives you a whole dev team of AI agents in your code editor
...It combines a powerful VS Code extension with cloud-based agents that can take on real development tasks across GitHub, Slack, and the web. Designed to work on your terms, Roo Code gives you full control locally while enabling delegation and parallel execution at scale. Its model-agnostic architecture ensures flexibility as AI models and providers evolve, letting you choose or bring your own keys. Role-specific agent modes keep AI focused, reliable, and aligned with real engineering workflows. Open source, secure, and highly configurable, Roo Code fits seamlessly into both individual and team-based development environments. -
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Triton
Development repository for the Triton language and compiler
...Triton enables users to write optimized kernels for machine learning workloads while maintaining readability and control over performance-critical aspects like memory access patterns and parallel execution. The project leverages LLVM and MLIR to compile code into efficient GPU instructions, supporting both NVIDIA and AMD hardware. It is widely used in research and production environments where custom tensor operations are required, offering both high performance and developer-friendly syntax. -
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JavaCV
Java interface to OpenCV, FFmpeg, and more
JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, FlyCapture, Spinnaker, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract) and provides utility classes to make their functionality easier to use on the Java platform, including Android. JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), and more. -
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MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
...Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers only selected memory back to the accelerator. Memory Interleave alternates retrieval, context expansion, and generation to improve multi-hop reasoning across distant segments. The project reports experiments extending from 16K to 100M tokens, including inference on two A800 GPUs. -
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gnhf
Before I go to bed, I tell my agents: good night, have fun
...Runs can stop on request or when configured limits such as iterations, runtime, or token usage are reached. It supports multiple coding-agent CLIs and ACP targets and can run parallel agents in separate Git worktrees. Each run also produces live terminal status and a final summary with commits, token totals, branch statistics, logs, and review commands. -
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jlens
Companion code for the global workspace interpretability paper
...The transformed vectors are decoded through the model’s own unembedding into ranked vocabulary predictions. The package can fit new lenses, load saved ones, apply them to prompts, and merge results from parallel fitting jobs. Interactive layer-by-position views reveal how token rankings evolve across the network and compare them with the model’s final output. It supports open-weight Hugging Face decoder models, with Qwen used in the included examples. The repository also provides synthetic evaluation data and an end-to-end notebook, but it is not maintained. -
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security-audit
A coding-agent skill for multi-phase security audits
...It organizes the audit into multiple phases so the agent does not simply search randomly for vulnerabilities. The workflow begins with reconnaissance, then moves into parallel hunting across attack classes such as injection, access control, business logic, cryptography, feature abuse, and chained attacks. Findings are then challenged through separate validation agents to reduce false positives. The skill produces human-readable reports, detailed finding traces, structured JSON output, and independent verification against the actual source code. ... -
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Make It heavy
A Python framework that emulates Grok Heavy functionality
Make It heavy is a Python framework for producing deeper AI analysis through multi-agent orchestration. It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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forkd
Fork() for AI agent microVMs
...Instead of cold-booting a separate virtual machine for every worker, it forks children from a warmed parent snapshot and uses copy-on-write behavior to share the initial memory state. This makes it useful for spawning many isolated agent environments quickly during parallel research, testing, or code execution. The project is focused on runtime performance, sandbox isolation, and efficient branching from a live or prepared VM state. It is especially relevant for developers building agent infrastructure where many short-lived execution branches need to run safely. forkd is best understood as a low-level systems tool for scalable, isolated AI-agent workloads. -
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workerpool
Offload tasks to a pool of workers on node.js and in the browser
workerpool is a JavaScript library that simplifies the creation and management of worker threads and process pools for parallel task execution in Node.js and browser environments. The project provides an abstraction layer that allows developers to offload CPU-intensive operations to background workers without manually handling thread communication or lifecycle management. It supports task queuing, dynamic worker scaling, timeouts, transferable objects, and proxy-based APIs for interacting with worker functions as if they were local calls. workerpool is designed to improve application responsiveness and throughput in workloads involving heavy computation, data processing, or asynchronous execution. ... -
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Claude Ads
Comprehensive paid advertising audit & optimization skill
...The system generates structured reports, identifies inefficiencies, and suggests optimization strategies based on industry benchmarks. It supports platforms like Google Ads, Meta Ads, TikTok, LinkedIn, and more, offering a unified analysis workflow. The architecture uses parallel subagents to speed up audits and includes financial modeling and A/B testing guidance. It runs locally, ensuring privacy and control over sensitive advertising data. The project is aimed at replacing manual audit workflows with fast, automated, and repeatable analysis. -
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CodeMachine
CLI tool for multi-agent workflows and automated code generation
...It enables developers to transform high-level specifications into production-ready code by managing planning, architecture, implementation, testing, and validation within a unified environment. CodeMachine CLI supports parallel execution through multiple specialized agents, allowing faster development cycles and scalable automation. Built for flexibility, it can handle anything from simple scripts to complex, long-running workflows that span hours or days. CodeMachine also integrates with various AI engines, assigning roles such as planning, coding, and review to different models for efficient collaboration. -
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ChainForge
An open-source visual programming environment
...Instead of relying on isolated prompt experimentation, it introduces a dataflow-based interface that allows users to create complex prompt pipelines and evaluate them across different models, parameters, and datasets simultaneously. The platform enables rapid experimentation by generating permutations of prompts and inputs, making it possible to test hundreds of variations in parallel and analyze performance trends more effectively. It also includes evaluation nodes that allow developers to define scoring functions, enabling automated benchmarking of outputs based on custom criteria such as accuracy, formatting, or relevance. -
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OpenWorkflow
Open-source TypeScript framework for building durable workflows
...The framework supports long-running processes that can sleep for seconds or months, making it suitable for background jobs, automation pipelines, and event-driven systems. OpenWorkflow includes automatic retries with exponential backoff, parallel step execution, and scheduling capabilities, all while using the developer’s existing database instead of requiring dedicated orchestration infrastructure. A built-in dashboard provides visibility into workflow execution and debugging. Overall, it delivers production-ready workflow durability similar to temporal-style systems but with a simpler operational footprint.
