6 min read
The Hidden Mathematics of Multi-Agent AI: Why Agent Communication Does Not Scale Linearly
A simple counting formula explains why fully connected multi-agent systems become hard to control long before they become impressive.
26 pages
AI
LLMs, agentic systems, prompt engineering, and AI in payments
8 min read
Maximum Entropy Inverse Reinforcement Learning: Understanding the Trajectory Formula
Inverse reinforcement learning (IRL) asks a different question from classical RL: instead of assuming a reward function and learning a policy, you observe expert behavior and infer …
8 min read
Markov Decision Processes: The Mathematical Foundation of Reinforcement Learning
The Markov Decision Process (MDP) is the standard formal object for sequential decision-making under uncertainty. It separates problem definition — states, actions, how the world …
14 min read
Edge AI: Why Intelligence Is Moving to the Boundary — and What It Takes to Get There
There is a quiet architectural shift happening beneath the surface of the AI conversation. While the public discourse fixates on data center GPU clusters and trillion-parameter …
5 min read
I Spent Years on Adaptive Filters. I Was Already Training Neural Networks.
I spent years implementing LMS-based equalizers and echo cancellers in telecommunications. Only later did I fully appreciate what I had been doing mathematically: the same family …
12 min read
Feasibility Assessment: Q-Learning Suitability for Organizational Decision Systems
Q-learning for production decision systems: when tabular or deep Q-networks (DQN) make sense, state–action limits, stationarity, exploration cost, convergence risks—and when to say …
11 min read
Syntactic Fluency, Semantic Fragility: Why AI Masters Form but Stumbles on Meaning
Your favourite AI can compose a flawless sonnet, generate syntactically perfect ISO 8583 messages, and produce compilable C++ on the first attempt. Ask it whether that ISO message …
4 min read
Why Parents Still Matter in the AI Era
For most of us, the first things we learned did not come from a feed or a model. They came from people. Parents taught us how to speak, how to behave, how to apologize, how to tell …
9 min read
Reasoning Models and Deep Reasoning in LLMs: Chain-of-Thought, Tree of Thoughts, and Test-Time Compute
Language models don't reason. Not in the way humans do.
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 …