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  • 1
    Get Shit Done

    Get Shit Done

    A light-weight and powerful meta-prompting, context engineering

    Get Shit Done is a high-impact, open-source meta-prompting and spec-driven development system designed to streamline building software with AI assistants like Claude Code, OpenCode, and Gemini CLI. It solves “context rot” — the degradation of AI quality as a chat session grows — by structuring your idea into precise, context-engineered steps that are researched, scoped, planned, executed, and verified with clear commands and outputs instead of ad-hoc prompts. The project emphasizes...
    Downloads: 23 This Week
    Last Update:
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  • 2
    BMad Method

    BMad Method

    Breakthrough Method for Agile Ai Driven Development

    BMad Method is a comprehensive AI-driven software development framework that structures the entire lifecycle of building applications through coordinated agent workflows and agile methodologies. It transforms AI from a reactive assistant into a structured team of specialized roles such as product manager, architect, developer, and QA, each operating within predefined workflows. The system guides users through phases including analysis, planning, solution design, and implementation, ensuring...
    Downloads: 7 This Week
    Last Update:
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  • 3
    GSD Core

    GSD Core

    Meta-prompting, context engineering, spec-driven development system

    GSD Core is the core framework behind Open GSD, a workflow system for AI-assisted software engineering. It is designed to keep agent-driven development organized by turning vague goals into structured research, plans, tasks, implementation phases, verification steps, and handoffs. The project focuses on cleaner context, verified work, and more honest project history when working with coding agents. It supports installation across several AI coding runtimes and offers profiles for different...
    Downloads: 2 This Week
    Last Update:
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  • 4
    GNNPCSAFT Web App

    GNNPCSAFT Web App

    Smart Thermodynamic Modeling with Graph Neural Networks

    The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository.
    Downloads: 9 This Week
    Last Update:
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