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#4557 Domain Specific Language for Prompting

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#4557 Domain Specific Language for Prompting https://www.jetbrains.com/mps/ #4557  Define a domain specific language for LLM prompting Designing a Domain-Specific Language (DSL) for LLM prompting—let’s call it PromptScript —is an excellent way to move away from messy, unstructured text and toward predictable, reproducible, and structured AI interactions. A great prompting DSL needs to handle variable injection , role definition , few-shot examples , and output constraints cleanly. Here is a blueprint for a declarative, human-readable DSL designed for LLM workflows. The Core Blueprint: PromptScript 1. Keywords and Syntax @agent : Defines the persona, system instructions, and temperature. @context : Injects background data, documents, or variables. @examples : Groups few-shot input/output pairs. @execute : The main prompt instruction or user query. @schema : Enforces a structured output format (e.g., JSON, YAML). {{variable}} : Standard syntax for dynamic data binding. Example 1: Cu...
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#4556 Python Application-Driven Waldorf Protein Sequences

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#4556 Python Application-Driven Waldorf Protein Sequences #4556  Since the Waldorf Protein has 250 memory slots where 32-step sequences can be stored, can a Python application sequence the reply of several sequences? Yes, absolutely. You can use Python to build a program that sequences, chains, or triggers those sequences. Because the Waldorf Protein features standard USB-MIDI and traditional TRS/DIN MIDI I/O , any Python program utilizing a MIDI library can control it. However, exactly how you program the Python application depends entirely on what you mean by "sequencing the reply" of those memory slots. Depending on your creative goals, there are two main ways to approach this with Python: Method 1: The "Program Change" Approach (Chaining Onboard Sequences) If the 32-step sequences are tied directly to the presets themselves (since the Protein saves its sequencer patterns as part of the patch data across its 250+ memory slots), you can use Python as a Maste...
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#4555 Image to Music

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#4555 Image to Music #4555  Describe how AI analysis of images can be used to produce beautiful music based on the image. Example: image of a flower or land scape. Translating a visual image—like a delicate flower or a sweeping desert landscape—into a beautiful piece of music is an incredible intersection of computer vision, generative AI, and cross-modal mapping (often referred to as AI-driven digital synesthesia). Here is a breakdown of how AI analyzes an image to compose an expressive, harmonically rich musical piece. 1. Deconstructing the Canvas (Computer Vision) Before a single note is played, the AI must "see" and unpack the image. It breaks the visual data down into distinct categories using convolutional neural networks (CNNs) and vision-language models: Color Histograms & Dominant Hues: The AI samples the color palette. Bright, warm colors (yellows, soft pinks) might map to major keys and higher frequencies, while deep blues, earthy terracottas, or muted greens ...
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