Ledényi Norbert

Executive Delivery Leadership

I run the nearshore Data and AI delivery organization at HCLTech Starschema, around 200 people, mostly working for US enterprise clients. I got here in steps rather than in one move: four roles in nine and a half years at the same company, through its acquisition. Ten engineers first, then a delivery unit of seventy against a $7M portfolio, now the full $20M one, with budget, staffing and execution attached. Each step moved me further from the code and closer to deciding what the teams work on. HCLTech filmed a short piece with me about that, if you would rather hear it than read it.

I did not stop being technical on the way up. I still review architectures, and I still write code most evenings, much of it the projects further down this page. That is partly self-interest: it keeps me able to read the work rather than only hear about it.

Enterprise Solution Architecture

The deliveries I learned the most from were too big for one architect. On end-to-end programs there are several of us, and the hard part sits in the seams between our designs, and in keeping those seams agreed while months of decisions land on top of them. A lot of the role turns out to be scheduling and translation: cutting the work into milestones somebody can actually finish, and spotting where everything has started queueing behind one person. Translation mostly means the sponsor sees the same picture the team does, early, because what they know about the business usually changes the design.

I want to know how something works underneath before I believe I can improve it. The next level of optimization is usually down there. The question I end up asking is what it costs if we are wrong, and how long before we find out. On a framework we built for a Fortune 50 conglomerate, the client turned down our cheaper always-on cluster because it would have blurred the per-request numbers they were about to bill each other with. It cost more and it was the right call. Two rules, in that order: do the right thing, and do things right.

Data Engineering & Infrastructure

For nearly four years in London I looked after the central BI reporting platform at Royal Bank of Scotland, the one the whole bank reported on. The reports were the visible half. The rest was keeping environments alive, and that is where I started writing automation for builds and backend jobs, because anything I did by hand (well before the AI era) I would be doing again six weeks later. At Starschema the work moved to cloud migrations, warehouses and ETL pipelines, first as the architect and engineer on client projects, later with a team of up to ten around me.

The habit I kept from those years is reviewing other people's work. Walking a developer through why a pipeline will break teaches me more about it than writing it myself would have, and the habits outlast the project. Fifteen years of consultancy also means almost every system I built belonged to someone else, ran on their data, and had to keep working after we left. That constraint shaped how I build far more than any technology choice did.

AI Transformation Strategy

I got hooked on this early, and a lot of what I do now is trying to get other people hooked too. I lead a track in our AI center of excellence and run trainings, and I spend most of that time with delivery teams working out where AI actually helps them as opposed to where it demos well. Some of that turned into internal process automation. Some turned into measurement, because adoption is easy to claim and harder to count. I am working through certifications myself at the same time.

I have built a multi GPU homelab to learn and explore. It runs local models for the things I would rather not send anywhere: object detection on the cameras, document parsing, and part of what keeps the house automated. I have written my own skills and MCP servers, and I run reviews of my own code as adversarial passes between models from different vendors, on the theory that Opus and GPT fail in different places and will catch each other. RAG, vector databases and model training are the parts I have not got to yet. That is what the GPU is for next.

Digital Design & Fabrication

It started with an Ender 5 that I modified until not much of the original was left, which taught me more about printers than the prints did. Then a Prusa MK3, later an MMU3 on it for multi-material, now upgraded to a MK3.5. A Core One with a Bondtech INDX is sitting in a box waiting for me to assemble it, and a Makera Z1 CNC mill is on its way. There is a K40 laser cutter and engraver too. I model in Fusion 360, parametric, so the part changes when a measurement does.

During covid the printers briefly stopped being a hobby. I printed protective face shields with a local shop, on every machine we could line up. It is still the most useful thing I have made with any of them.

What I get out of this is the same thing I get out of the AI work. Something exists only in my head, and between a parametric model and a machine that can cut or print it, I have what I need to put it in the world. The distance between wanting a part and holding it has become very short, which is probably why I keep buying machines.

Home Infrastructure & Automation

The systems I built professionally belonged to someone else. This one is mine, and the people I live with are the users. Proxmox hosts run the containers and virtual machines, Home Assistant runs the house, and around thirty Docker services sit behind a reverse proxy with single sign-on in front of them. The parts I like most are the ones wired to physical things: the gas boiler over a wired OpenTherm link through an ESP32 rather than the vendor cloud, the ventilation unit through a CAN gateway, the solar inverter, the sprinkler valves, and cameras doing object detection on a TPU sitting on a shelf.

It is also the only place where I get to take a decision all the way through. I wanted irrigation that reacts to the weather instead of a timer, so I built it: soil moisture probes, an evapotranspiration model, and the valves at the end of it. There is a family finance and health app that pulls in our bank statements, which is the one thing here I would honestly call production. Two of the Home Assistant pieces are public on GitHub now, an integration for a pool sensor and a dashboard card, because if I had to write them somebody else did too. Snapshots, replication and alerting are in place because losing any of it would be a real problem at home, not because it is good practice.

What is the page?

This is a personal portfolio page, with a twist. The serious parts are the cards on the main page: six areas covering how I lead delivery, design systems, and what I build at home when nobody is paying me to. The rest is the twist.

The page was written by an AI, and I am not going to pretend otherwise. I described what I wanted, argued over the details, and it produced every line of HTML, CSS and JavaScript. Worth saying rather than hiding, because the page is also the demo: AI does not supply the vision, it removes the excuse. Describe the thing precisely enough, keep pushing until it is right, and you can have it. Framework-free, three files, no build step, because I asked for that.

Which is also how the games got here. A portfolio does not have to be a dry read, so there is something on it you can actually play. Top left corner there is a dropdown and an eye icon. Pick a background, click the eye (or press F), and the canvas takes over the whole screen. A couple are just ambient, the rest are real games, each one a classic bent into something from the day job:

• Ping - Pong, except it is a TCP/IP packet bouncing between two ethernet ports until it drops.
• Clusteroids - Asteroids, where you shoot apart a database cluster and then the tables inside it.
• Flappy Moby - Flappy Bird with the Docker whale, swimming through a sea of containers.
• Netscaper - the Chrome offline dinosaur, hurdling vendor logos instead of cacti.
• Root Ninja - Fruit Ninja, except the fruit are vendor logos. Slice them.
• Finding Neo - matrix rain, with someone hiding in it.

They want a keyboard and a big screen, so desktop is where these are actually fun. Go break my high scores.

What does my AI think about me?

Inspired by a Reddit post, I asked my most frequently used AI buddy to write a letter about me. Instead of just using the most recent session, I asked it to draw on the 10 biggest recent sessions. The text below is unaltered, and I left the negatives in too. (PS: I know about the /insights command, but this one is funnier.)