diffusion model
diffusion model at Beyond Market Intelligence is a file of 5 stories. The newest of them: “Explore how AI terrain generation runs in your browser with just 23 million parameters”, “From Codebase to Dataset: Fine-Tuning AI on Your Own Projects”, and “Generating 32x32 images from a microcontroller with 264KB of RAM”. A single RTX 5060 trained Talus in about 4.5 hours, and now it generates 64x64 terrain heightmaps in your browser in roughly three seconds. Turning a well-built React or Next.js project into a fine-tuning dataset is a puzzle worth solving, especially when you're aiming for quality over generic code dumps. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every diffusion model story on Beyond Market Intelligence, newest first.
Explore how AI terrain generation runs in your browser with just 23 million parameters
A single RTX 5060 trained Talus in about 4.5 hours, and now it generates 64x64 terrain heightmaps in your browser in roughly three seconds. That is impressive efficiency. With just 23 million parameters, this model conditions on terrain type and any combination of five measured properties, making it genuinely flexible. The plains distance ratio dropped from 3.98 to 1.23, a concrete improvement. Open problems remain, ridges and smooth mountains, but the browser demo at talus.tersa.tech invites exploration.
From Codebase to Dataset: Fine-Tuning AI on Your Own Projects
Turning a well-built React or Next.js project into a fine-tuning dataset is a puzzle worth solving, especially when you're aiming for quality over generic code dumps. The real challenge isn't just extracting files; it's crafting instructions that mirror how you'd actually ask for a component or a page. Pairing screenshots with code and preserving cross-file context are the hard parts, but they're where the value lives.
Generating 32x32 images from a microcontroller with 264KB of RAM
Training an image diffusion model on a microcontroller with 264KB of RAM is a bold experiment, and the results are honest about the tradeoffs. The Shrike lite's FPGA added parallel INT8 MAC engines, but the memory wall from I/O operations made it slower than the MCU alone, at 220 seconds per image versus 70. That contrast is a useful lesson in hardware bottlenecks. Many outputs were noisy, yet some images landed.
From text to ASCII art, an accessible AI model you can build
Turning a text prompt like "build a cat" into a precise ASCII rendering is a clever twist on generative modeling, and it's exactly the kind of hands-on experiment that deepens your grasp of diffusion. You've got the right foundation from CS229 and CS230, but the leap from standard image generation to symbolic, grid-based output is where the real learning lives. Start with character-level tokenization and a simple U-Net backbone. For guidance, look into DALL-E's discrete VAE work and the original DDPM paper.
Discover how AI transforms stereo tracks into immersive spatial audio experiences.
For six months, a developer wrestled with a stubborn problem: most music lacks a quality spatial mix. The result is Stereo2Spatial, an open-source model that converts stereo tracks into spatialized binaural audio. The journey wasn't smooth, though. Latent-space attempts hit quality ceilings, and raw waveform training kept collapsing into instability. A clever fix from the WavFlow paper finally stabilized things.