Old code isn't the problem. Expired approvals are
Old code isn’t the problem. Expired approvals are. Kernel lifecycle is really compliance alignment: whether the Level 2 (L2) kernel on your terminals still sits inside an active EMVCo approval, …
Corebaseit
I’m Vincent Bevia. I work in payments at MultiSafepay (part of Ant Group), and I’ve spent years focused on POS architecture, EMV, and cryptography. I’m also the author of Point-of-Sale Systems Architecture — Volume 1 and The Obsolescence Paradox: Why the Best Engineers Will Thrive in the AI Era.
This blog is my space to share what I’m thinking about, learning, and discussing: POS systems, EMV, Payment Security, and, increasingly, about AI in general and how AI is reshaping this field. I also draw on my background in Electrical Engineering and Telecommunications — information theory, stochastic processes, and thermodynamics — the technical foundations that sit beneath much of this work.
Less corporate, more personal. Opinions, reflections, and the kind of things I’d talk about over coffee.
Topics I care about
- POS & EMV — how terminals, chips, and contactless payments actually work
- Cryptography & security — DUKPT, HSMs, and key management in the real world
- AI in payments — where it helps, where it doesn’t, and what’s coming next
- Engineering foundations — telecommunications, information theory, and stochastic processes
If you’re building payment technology — or just curious about how it all fits together — welcome.
FIR Filter Design on FPGA: Manual Engineering vs AI-Assisted Workflows
Designing an FIR filter on FPGA still starts with fundamentals: convolution, z-domain structure, fixed-point limits, and synthesis constraints. AI can accelerate the workflow, but it does not remove …
Adaptive Filters and Stochastic Gradient Descent: One Update Rule, Two Vocabularies
Modern AI is often framed as a clean break from classical engineering. For anyone who has worked in adaptive signal processing, that framing is misleading. The mathematical spine of stochastic …
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 what reward would …
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 evolves, what you …
Reversals, Refunds, and Chargebacks: Decoding the Payment Lifecycle
Three words every payment professional uses — and confuses. Each lives at a different point in the settlement lifecycle, under a different protocol, with radically different consequences.
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 models, a different …
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 no. Reinforcement …
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 actually makes …
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 right from wrong. …