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    Z80-μLM

    Z80-μLM

    Z80-μLM is a 2-bit quantized language model

    Z80-μLM is a retro-computing AI project that demonstrates a tiny language model (Z80-μLM) engineered to run on an 8-bit Z80 CPU by aggressively quantizing weights down to 2-bit precision. The repository provides a complete workflow where you train or fine-tune conversational models in Python, then export them into a format that can be executed on classic Z80 systems.
    Downloads: 2 This Week
    Last Update:
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    Clippy

    Clippy

    Clippy, now with some AI

    Clippy is an open-source desktop assistant that allows users to run modern large language models locally while presenting them through a nostalgic interface inspired by Microsoft’s classic Clippy assistant from the 1990s. The project serves as both a playful homage to the early days of personal computing and a practical demonstration of local AI inference. Clippy integrates with the llama.cpp runtime to run models directly on a user’s computer without requiring cloud-based AI services. It...
    Downloads: 36 This Week
    Last Update:
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  • 3
    Sprite Fusion Pixel Snapper

    Sprite Fusion Pixel Snapper

    A tool to snap pixels to a perfect grid

    ...The tool works by adjusting sprite rendering coordinates and texture sampling so that every pixel aligns cleanly to the screen’s pixel grid, avoiding blurring, distortion, or unintended smoothing artifacts. This is especially important in pixel art games, retro-styled interactive media, or precise UI designs where crisp edges and predictable alignment are essential. SpriteFusion Pixel Snapper integrates with popular game engines and rendering pipelines to ensure that assets remain sharp across a broad range of resolutions and aspect ratios without requiring manual fiddling from artists or developers. ...
    Downloads: 7 This Week
    Last Update:
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  • 4
    Cheat on Content

    Cheat on Content

    Workflow that turns every post into a calibrated experiment

    ...Rather than generating posts for the creator, it focuses on sharpening judgment and helping users understand why certain content performs better. The project is built around the loop of score, predict, publish, retro, and improve. It is aimed at creators, marketers, and operators who want to build a repeatable system for learning from every published piece. Its value is strongest for people who already create consistently but need a better way to extract insight from their output.
    Downloads: 2 This Week
    Last Update:
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