case study
3 stories filed under case study on Beyond Market Intelligence. The newest of them: “300K lines refactored for $4,000: what a C codebase taught AI agents”, “Generating 32x32 images from a microcontroller with 264KB of RAM”, and “Mapping City Airspace with Data to Plan Smarter Vertiport Networks”. Three hundred thousand lines of C, refactored in three weeks for $4,000 in tokens. Training an image diffusion model on a microcontroller with 264KB of RAM is a bold experiment, and the results are honest about the tradeoffs. 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 case study story on Beyond Market Intelligence, newest first.
300K lines refactored for $4,000: what a C codebase taught AI agents
Three hundred thousand lines of C, refactored in three weeks for $4,000 in tokens. CodeScene's case study is a practical stress test for AI agents, not a headline. The playbook of codebase-specific recipes the agents built is the real prize, it suggests these tools learn context, not just syntax. Practitioners are right to question the scope and the harness's role. That skepticism is healthy.
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.
Mapping City Airspace with Data to Plan Smarter Vertiport Networks
Lagos is crowded, complicated, and constantly moving, which makes it the perfect stress test for vertiport placement. This case study uses geospatial machine learning to balance population density, transport access, and airspace constraints, turning a daunting urban puzzle into a reproducible workflow. It is practical, not theoretical, and that is why it works. If you are curious about how machine learning handles spatial decision-making, this walkthrough offers a clear, actionable starting point.