7 min read
Transformers vs. Diffusion Models: Not All AI Is the Same
We casually say “AI can write, AI can draw, AI can code” as if it’s one thing. It’s not. Two of the most talked-about model families in AI today solve …
17 pages
Llm
8 min read
Stochastic, Entropy & AI: From Thermodynamics to Information Theory to Modern Machine Learning
I was listening to a podcast the other day about AI and the mathematics behind it — especially stochastic processes, entropy, and probability — and it immediately drew me in. With …
7 min read
BeviaLLM: Building a Language Model From Scratch With Python and NumPy
There’s a gap between using language models and understanding them. You can call an API, get a response, and build a product on top of it — without ever knowing what happens …
9 min read
Reasoning Models and Deep Reasoning in LLMs: Chain-of-Thought, Tree of Thoughts, and Test-Time Compute
After reading Wei et al.’s work on Chain-of-Thought, the Tree of Thoughts paper from Princeton, and several recent studies on test-time compute scaling, I wanted to organize …
5 min read
AI as an Amplifier, Not a Replacement: Why Domain Expertise Matters More Than Ever
There’s a narrative floating around that AI will replace domain experts. That if the model can generate code, write architecture docs, and explain EMV flows, then maybe you …
6 min read
AI Sycophancy: Your Model Is Trained to Please You, Not to Be Right
There’s a quiet failure mode in AI-assisted engineering that most people don’t talk about: sycophancy. It’s the tendency of large language models to prioritize …
6 min read
Prompt Engineering for POS: Treating LLM Inputs as First-Class Architecture
When we talk about AI in payments, the conversation often jumps to chatbots and support agents. But the real leverage — especially in regulated environments like SmartPOS and …