AI Engineer · Full Stack · 15 years

Matt Barlow

Senior software engineer available for contract work. AI-native development, full-stack TypeScript, blockchain integration. 15+ years shipping production systems. Remote, EU timezone.

Available for contract work · Remote · EU timezone

What I Do

Services

AI agent integration (Claude API, autonomous systems)

React/TypeScript frontend development

.NET backend and API architecture

Full project builds (spec to production)

Selected Work

Projects

Crypto Clash

Received $5K USDC ecosystem grant

$5K Grant

Full-stack Web3 tournament platform on INK L2. Players mint hero NFTs, build decks, and compete in tournaments powered by real-time crypto market moves. Hero progression, rarities, and minting all handled by NFT smart contracts.

One of the first projects where I developed my spec-driven agentic workflow: every feature spec'd before implementation, then handed to AI coding agents to build. Awarded a $5,000 USDC grant from the Ink Foundation Echo Program.

Built on Ink

React

TypeScript

Remotion

MongoDB

Solidity

EVM

NFTs

Live

Nadoscan

Real-time DeFi analytics platform

Analytics

DeFi analytics dashboard. Real-time protocol data, transaction tracking, and portfolio insights across multiple chains.

React

TypeScript

Node.js

REST APIs

Chart.js

Live

AutoMon

Multi-agent orchestration system

Agent Orchestration

Autonomous multi-agent system. Multiple AI agents coordinated to perform complex tasks with tool-use, memory, and error recovery. Minimal human intervention. Built to explore reliable agent orchestration patterns at scale.

TypeScript

Claude API

OpenClaw

Node.js

Tool-use

Live Source

Colony Zero

Solo build, 14-day sprint

AI + Web3

AI-powered colony survival game. An LLM-based Storyteller evaluates colony state in real-time and generates dynamic narrative events: droughts, raids, discoveries, all contextual. Procedural worlds with AI-generated colonist personalities. Deployed on MegaETH.

React

Phaser 3

TypeScript

Claude API

Solidity

MegaETH

Live Source

Process

How I work

Research first

Before any code, a research agent maps every feature, interaction, and data model. The output is a research document — your 'what are we building' source of truth.

CTO builds the spec

A CTO session reads the research and produces architecture, data models, component structure, one spec per feature with acceptance criteria, and an ordered task list with dependencies.

Agents implement feature by feature

Each developer agent gets handed one feature spec. No knowledge of anything else. They explore the codebase, write the test, implement, pass, commit.

QA validates against the spec

A QA agent tests against the acceptance criteria in the feature specs. Nothing ships until lint, tests, and build pass. Every deliverable is production-ready.

Background

About

15 years as a software engineer. MSc Computer Science. Based in Germany.

Spent seven years at The Travel Corporation building platform infrastructure: message brokers (SQS/SNS), platform modernisation. .NET, React, and Azure. Led the Tour Agent Booking Platform team.

Now focused on AI-native development. I build systems where AI agents do the heavy lifting. Orchestrated, constrained, and reliable. The methodology for steering AI to produce production code is more valuable than any single project.

Currently exploring how autonomous agents can operate on-chain and taking on contract work across AI, full-stack, and blockchain.

Experience

Technology Platform Manager

The Travel Corporation

2018 - 2025

Platform modernisation, message broker architecture (SQS/SNS). C#, ASP.NET Core, React, TypeScript, Azure. Led the Tour Agent Booking Platform team.

Stack

TypeScript

React

Next.js

C# / .NET

Node.js

Claude API

Solidity

Azure

Docker

PostgreSQL

Let's talk

Available for contract work from March 2026. Remote, EU timezone.

matt@deepbuild.dev

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