01 / 08
HUGO.CHARMIES(1)
PRODUCTION
$ 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
02 / 08LIVE ↗
HUGO.KOMPASS(1)
LATE PROTOTYPE
03 / 08
HUGO.AA(1)
PROTOTYPE
04 / 08
HUGO.PROSPECTING(1)
HOBBY PROJECT
05 / 08LIVE ↗
HUGO.LOODGIETER(1)
LIVE DEMO
06 / 08
HUGO.BOT(1)
PRODUCTION
07 / 08LIVE ↗
HUGO.PSWEB(1)
LIVE
08 / 08
HUGO.HELPER(1)
PRIVATE
$ 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