The author argues that you don't need massive AI models for most real-world applications—smaller models handle instruction-following well and fail predictably when they don't know something, rather than confidently making things up like larger models do. He's building a system that lets you swap between different AI providers through a single interface.

• Small models hallucinate less reliably than large ones; when told clearly that information is missing, they admit it instead of inventing plausible-sounding answers
• Larger models produce more polished fabrications, making their hallucinations harder to catch in production
• The author's architecture (Pepper) uses a provider-neutral layer that treats adding new AI backends as simple routing and translation work, not a full rewrite

A user points out that the 3811 parameter refers to a 30×30 grid, not the stated 12×12. They note that following the provided instructions won’t work and instead generates more hallucinations.

• The 3811 documentation says input size is 12×12, but the actual required grid is 30×30.
• Following the documented instructions doesn't work because they're built on this wrong dimension.
• The mismatch doesn't just break preprocessing—it makes the model hallucinate more, producing confident-sounding nonsense instead of failing cleanly.