Tags: lemonade*

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  1. Joe Rice-Jones writes about how he used a local LLM to automate the organization of his cluttered Downloads folder. By connecting a small model with Lemonade to a PowerShell script, he created a two-tiered system where boring rules handle easy tasks like sorting installers by file extension, while an AI (specifically Qwen3.5-9B) handles more complex naming for screenshots and documents via localhost. This setup ensures privacy because all data stays on his machine, avoids the chaos of automated deletions through strict safety protocols, and has resulted in a consistently tidy folder.

    - The system uses Lemonade to run models locally on the same PC via an OpenAI-compatible API.
    - To prevent errors or loss of important files, the script requires 75% confidence from the model before renaming anything.
    - A "safety list" prevents the AI from creating new folders outside of approved directories.
    - The process is set as a scheduled task to run once per week.
  2. Joe Rice-Jones writes that his Asus ROG Flow Z13 tablet with a Ryzen AI Max+ 395 APU and 128 GB unified memory now runs the same local LLMs as his desktop mini PCs via Lemonade, using a model router to automatically send prompts to different sized models for everyday use and agentic coding with Crush, making it his preferred device.

    - Tablet is the Kojima Productions Ludens special edition ROG Flow Z13
    - Radeon 8060S iGPU has 96 GB of memory allocated in Adrenalin software
    - Model tiers include Qwen3.5-2B to gpt-oss-120b MXFP4 ~59 GB on disk
    - 120B model loads in 27.6 s, first token in 0.86 s, ~52 tokens/s on iGPU
    - Misrouted CPU-only run dropped to 0.44 tokens/s, over 100x slower
    - Router uses Qwen3.5-0.8B classifier and logs which rule fired per response
    - Crush coding required raising Lemonade context window from 4,096 to 32,768 tokens

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