Search Results for "parallel" - Page 2
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Showing 36 open source projects for "parallel"
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
HumanLayer is an open source development environment designed to help developers orchestrate and manage AI coding agents working within complex software projects. It provides a framework and tooling that allow AI agents to research, plan, and implement changes in large codebases while maintaining structured workflows. It focuses on enabling AI-assisted development through coordinated agent workflows rather than isolated code generation tasks. HumanLayer integrates with modern AI models and... -
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Every Code
Local AI coding agent CLI with multi-agent orchestration tools
...Every Code enhances the traditional coding assistant model by introducing multi-agent orchestration, allowing multiple AI agents to collaborate, compare solutions, and refine outputs in parallel. It supports integration with various AI providers, enabling users to route tasks across different models depending on their needs. Every Code also includes browser integration and automation capabilities, extending its usefulness beyond simple code generation into more complex development tasks. Customization is a key focus, with support for theming, configurable settings, and reasoning controls that allow developers to fine-tune how the agent behaves. -
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Velox Download Manager
A multi-threaded, cross-platform download manager
Velox — Segmented Download Manager Velox is a fast, modern, open-source download manager for desktop (macOS / Windows / Linux). Built with Tauri 2 + Rust and a React/Tailwind UI, it downloads files dramatically faster than a browser by splitting each file into parallel byte-range segments and fetching them simultaneously — the same technique used by IDM. Features - Segmented downloading — splits files into up to 32 parallel connections and assembles them byte-perfectly. Tune the connection count per download or globally. - Pause / resume— pause any download and pick it back up exactly where it left off, byte-for-byte (works on any server that supports HTTP byte ranges)... -
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8N1Term
High-speed serial terminal for embedded systems and protocol analysis
High-throughput serial capture without dropped data. Real-time plotting and visual inspection of incoming data. Combined ASCII / HEX views suitable for low-level debugging. Multi-window workflows for parallel monitoring. Cross-platform support (Windows, Linux). -
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Mindwtr
A complete Getting Things Done (GTD) productivity system for desktop a
Mindwtr: The Privacy-First GTD System Mindwtr is a Getting Things Done (GTD) productivity tool designed for "Mind Like Water." It runs completely offline—no accounts, no tracking, and no subscriptions. The Core GTD Workflow Capture: Instantly offload thoughts to your Inbox. Clarify: Process tasks rapidly with the built-in "2-Minute Rule" timer. Organize: Sort tasks by Contexts (@work, @home), Areas, and Projects. Reflect: Keep your system trustworthy with a guided Weekly... -
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OmniPull
Just pull anything
OmniPull is a powerful, cross-platform download manager built with Python and PySide6. It provides a modern, intuitive interface for managing downloads with advanced features like multi-threading, queue management, and media extraction. -
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LocustDB
Massively parallel, high performance analytics database
An experimental analytics database aiming to set a new standard for query performance and storage efficiency on commodity hardware. See How to Analyze Billions of Records per Second on a Single Desktop PC and How to Read 100s of Millions of Records per Second from a Single Disk for an overview of current capabilities. Download the latest binary release, which can be run from the command line on most x64 Linux systems, including Windows Subsystem for Linux. When loading .csv or .csv.gz files... -
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glommio
Thread-per-core crate that makes writing asynchronous apps easier
Glommio (pronounced glo-mee-jow or |glomjəʊ|) is a Cooperative Thread-per-Core crate for Rust & Linux based on io_uring. Like other Rust asynchronous crates, it allows one to write asynchronous code that takes advantage of Rust async/await, but unlike its counterparts, it doesn't use helper threads anywhere. -
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Amadeus
Harmonious distributed data analysis in Rust
Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. It emphasizes developer productivity... -
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exa
A modern replacement for ls
...Different types of file and data will be coloured differently, and the user and group columns will be highlighted for the current user. exa can display a file’s extended attributes, as well as standard filesystem information such as the inode, the number of blocks, and a file’s various dates and times. exa queries files in parallel, giving you performance on par with ls. Not only is the standard tree tool built-in, but it’ll show you your files’ information alongside the hierarchy. View the staged and unstaged status of every file, right there in the standard view. Also works in tree view for a high-level overview of your repository. -
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Weld
High-performance runtime for data analytics applications
...Instead of optimizing individual functions independently, Weld introduces an intermediate representation that allows different frameworks to share optimization opportunities. This approach reduces data movement between libraries and enables the system to generate highly optimized machine code for parallel execution. Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. By combining operations from multiple libraries into a single optimized execution plan, Weld can significantly improve performance in analytics and machine learning pipelines.
