$~:whoami> matheo

Secure systems.
Reliable data.
Useful AI.

I’m Matheo, a security engineer working across data platforms and applied AI. I turn fragmented information into useful software, with clear checks on what gets trusted, automated, and shipped.

Explore my workLet’s talk

A little curiosity goes a long way. Press ~ for a terminal.

$~:ls work/

Engineering case studies.

Products and tools I’ve built: the problem, the stack, the decisions, and the evidence. Project identities stay private; the engineering is here to examine.

Data platforms & applied AI

A decision-support data product

Keeping searchable records, calculated answers, and generated explanations consistent as the underlying facts change.

  • Next.js
  • TypeScript
  • React
  • PostgreSQL

Product engineering & information quality

An AI-assisted briefing product

Turning a stream of incoming information into searchable records and reviewed briefings, with usable behavior when dependencies fail.

  • Next.js
  • TypeScript
  • Python
  • PostgreSQL

Operational tooling & measurement

An operational measurement pipeline

Collecting usage and cost without confusing missing data, observed activity, and estimates of value.

  • Python
  • JSON
  • unittest

Real-time application engineering

A real-time collaborative application

Keeping a shared session coherent across phones, reconnects, timed phases, and external-service failures.

  • Python
  • FastAPI
  • WebSockets
  • JavaScript

Browser product engineering

A local-first learning platform

Giving multiple learning activities a consistent shell, shared preferences, and progress that stays on the device.

  • TypeScript
  • Vite
  • DOM APIs
  • Vitest

$~:cat principles

How I build.

Tools change. Clear thinking, useful constraints, and honest verification keep earning their place.

Start with the problem.

Understand the people, constraints, and useful outcome before choosing a tool.

Keep evidence close.

Make facts and calculations traceable, and leave uncertainty visible.

Separate making from checking.

Give implementation and review distinct roles. Observe the result where people use it.

Own the decisions.

Automate bounded work, make tradeoffs explicit, and keep human judgment where it matters.

Explore an illustrative engineering workflow

Illustrative workflow patterns, not a live team roster or the architecture of a particular product.

Matheo

Frames the problem · reviews consequential decisions

Start with evidence

Make the input understandable before asking a model to interpret it.

  • Evidence review

    Checks whether the available material supports the question.

    • Preparation

      Organizes inputs and flags gaps for review.

Separate the jobs

Use generated language where it helps and explicit rules where precision matters.

  • Output review

    Checks the result against the evidence and the task.

    • Drafting

      Proposes an explanation using the material provided.

    • Calculation

      Produces numeric results from explicit inputs and rules.

Verify before shipping

Keep implementation and review separate, then observe the result.

  • Independent review

    Assesses the change against its stated goal and constraints.

    • Implementation

      Makes a bounded change with clear acceptance criteria.

    • Checks

      Exercises the change and makes failures visible.

  • AI
  • Automation
  • Deterministic
  • Checker
  • Reports to the checker above

$~:ls notes/

Things I’m thinking about.

Security, software, product decisions, and whatever else I’m figuring out.

Activity is a starting point.

A workflow running for longer doesn’t mean it did more useful work. Before counting tasks, I want to know what a good result looks like and how someone checks it.

Notes on measurement →

Sometimes less software helps.

A small tool can keep preferences on the device, work without an account, and leave fewer things to maintain. Choosing what a product doesn’t need is part of the engineering.

More on the blog ↗

$~:cat about

An engineer.
Still curious.

Security is my starting point. I’m interested in how data, software, and AI fit together—and in making the result useful to the people who rely on it.

World explorer, mentor, and tinkerer. Pushing to prod since Win95. I make room for community work, playful experiments, and learning something outside my usual lane.

More about my background ↗

$~:ls community/

Security people can use.

Helping older adults recognize scams, protect themselves online, and know where to turn when something goes wrong.

Community & resources

$~:ls playground/

A little room to play.

Games, desk accessories, and a terminal with a few secrets. All inside a little computer from a different era.

Enter Arcade 95