Highlights: Can you score color quality algorithmically? Not as taste — as math. This post builds a scoring system from first principles: two adjacent color squares, then triplets, then a real room with three spatial regions. We walk through every formula with brand and flag examples you already know, then prove that geometry alone can move the score by five points on an identical palette. Four pair scoring dimensions: contrast (WCAG luminance), harmony (hue peaks),…
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Highlights: Every computer vision pipeline that touches color starts with the same mistake: using RGB. RGB is built for screens, not for human perception. In this post we build a complete color scoring system for living room images — from the right color space (Lab), through palette extraction (K-means), to a two-color harmony scorer tested on 10 global brand palettes. We discover why luxury brands deliberately score low, and what that means for your model.…
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Highlights: Every modern LLM agent — from ChatGPT plugins to Claude tools — relies on a single learned skill: outputting a structured JSON function call instead of free text. In this post we teach that skill to a 3-billion parameter model using QLoRA on a free Google Colab T4. We start from the fundamentals — why fine-tuning, when LoRA, how quantization works — then build the full training pipeline from scratch. By the end, your…
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Highlights: Stable Diffusion doesn’t paint an image in one shot — it sculpts one from static, guided by your words. In this post we disassemble the entire machine. We start with the VAE that compresses pixels into a tractable latent space, walk through the forward and reverse diffusion processes, open up the UNet to see how cross-attention physically connects text tokens to spatial regions, and finish with the complete Latent Diffusion architecture diagram that ties…
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