hugo@rohach:~/portfolio$

$ hugo --whoami

I orchestrate AI tools
to ship products,
workflows, and integrations.

Shipping production AI at Charmies. Co-developing Kompass. Based in Belgium.

$ hugo --status

status         : ● Open to AI engineering roles
location       : Hybrid · Belgium
daily_driver   : Claude Code
favourite_stack: Next.js · Postgres

$

$ hugo --now

  • >Shipping AI integrations at Charmies
  • >Co-developing Kompass — late-prototype SaaS, in active investor talks
  • >Building Personal Helper — my own Claude Code skill library

$ hugo --featured

$ hugo --next

  • kalai COMING SOON

    iOS calorie tracker, camera-first, sub-10-second logging.

    Swift 6 · SwiftUI · Vision · SwiftData

  • sports-event-hub COMING SOON

    Started as an airsoft hub. Reframing to a hub for any local sports event.

    Next.js · TBD

$ hugo --stack

AI / AGENTIC

────────────
  • Agentic coding (Claude Code, Codex)
  • LLM SDKs · OpenRouter
  • Custom MCP servers
  • Agent harnesses + skills
  • Context engineering · evals

PRODUCT ENGINEERING

────────────
  • TypeScript
  • Next.js · React
  • Node.js · Python
  • PostgreSQL
  • Tailwind · shadcn
  • Any language the problem needs

INFRASTRUCTURE

────────────
  • Cloud: Azure · AWS
  • VPS-first (Docker · Coolify)
  • CI/CD · observability
  • Git

$ hugo --takes

01

LEAN OVER OVER-ENGINEERED

Most “agents” are one well-written LLM call plus retrieval. Add orchestration when a named failure forces it — long-horizon state, human-in-loop, fan-out you've already hit in production. Frameworks aren't wrong; they're premature.

02

ORCHESTRATION COMPOUNDS

The harness is the codebase now — SKILL.md, program.md, the context window, the agent graph. Sharpen this primitive and everything downstream multiplies. The catch: orchestration without evals is vibes with extra steps. Build the harness on top of eval discipline, not instead of it.

03

HUMAN-IN-THE-LOOP AT THE CHECKPOINTS

Models flip from correct to incorrect under casual pushback ~15% of the time. Manage AI like a mid-level report: trust it on execution, gate it on judgment. Continuous supervision is unsustainable; checkpointed approval before irreversible writes is the discipline that ships.

$ hugo --origin

At school we had a client in Leuven who wanted to automate scraping indicators like “what percentage of Belgians are obese.” Until then it was manual work. I'd seen something about n8n's AI agent feature, plugged OpenRouter into the workflow, and watched it scrape and reason about pages we'd have spent weeks on.

A few hours later I realised n8n was just making API calls — we could call OpenRouter directly from our own code. The whole flow migrated to native TypeScript. Around the same time we built the client's site with Bolt and I sat watching it generate a real custom website in minutes.

I haven't manually written code from scratch since. I'd rather spend the time learning to orchestrate agents. Coding ones, image ones, video ones, voice ones. Orchestration compounds across every sector.

EDUCATION

UC Leuven Limburg

BSc Applied Computer Science · 2022–2025

FIRST AI PROJECT

Ecofoodmap · n8n → code-native

CURRENT MODEL

Orchestrator, not writer

$ hugo --archive

  • prospect-enginenext.js · prisma · KBO datashelved
  • telegram-botpython · telegram apishipped
  • agent-orchestratorpython · langgraphresearch
  • ais-webscraperpython · playwrightshipped
  • youtube-summarizerpython · openaishipped

$ hugo --contact

EMAIL

hugorohach@gmail.com

LINKEDIN

linkedin.com/in/hugo-rohach

TWITTER / X

x.com/hugo_rhh

$exit
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