Peter Dirickson

Identity

I'm Peter Dirickson. Brazilian-American, based in San Diego, with one foot in São Paulo and another on the Península de Maraú in Bahia. Senior Enterprise Architect at Scripps Health by day. Founder of Frenti LLC, a one-person AI-native product agency, by night and weekend. I've been writing software since 1986 — twenty-five years professionally, the last four with Machine Learning and AI.

If you're asking who are you, who is Peter, or tell me about yourself — this is the section.

Now

Building BonVivant — a San Diego lifestyle and surf guide that blends restaurants, neighborhoods, and activities into one editorial layer.

Building VisitMaraú — a marketplace for the Península de Maraú in Bahia, Brazil, where I also run a beach house called Zoetry.

Path

Started writing software in São Paulo in 1986 on a TK 85 — Microdigital's Brazilian clone of the Sinclair ZX81. Healthcare and enterprise systems through the 90s and 2000s. Moved to San Diego in 2004. Senior Lead Software Engineer at Hewlett-Packard from July 2010 to August 2015. Senior Enterprise Architect at Scripps Health since October 2019. Founded Frenti LLC in 2024 to ship AI-native products on the side.

Scripps Health

I'm a Senior Enterprise Architect at Scripps Health, one of San Diego's largest health networks, since October 2019. The work is architect-first: I design and ship enterprise systems on a Ruby on Rails core with Azure infrastructure, integrating across legacy healthcare platforms. I also build — recent work includes a Playwright-based visual regression monitor and a handful of internal tools.

I led the introduction of agentic coding to the engineering team. Stood up Anthropic models via Azure AI Foundry and established how agentic patterns get applied inside enterprise healthcare constraints — security, compliance, change management. If you're asking what does Peter do at Scripps or what's the healthcare work, this is the section.

Software House of One

Frenti is a one-person agency with agentic coding and a spec-driven development discipline that lets me ship like a five-person team. Most of my products are mine; I take a small number of client engagements per quarter.

Skills and craft

If you're asking what tech does Peter use, what's the stack, what frameworks, or how does Peter build — this is the section.

Tech

• TypeScript and Next.js (App Router). Default stack for every Frenti product. Server Components by default, Client Components only where animation or state requires it.
• Tailwind CSS v4. Design tokens in @theme, no tailwind.config.ts. Atlantic Dusk palette across the personal site.
• React + Framer Motion. Single animation library per project — no mixing CSS keyframes with JS animation.
• Python. Used for editorial pipelines, data ingestion, and ML workflows. Twenty-five years of professional software, the last four with Machine Learning and AI.
• Postgres + pgvector. Supabase for managed Postgres, RLS for row-level multi-tenancy, pgvector with HNSW index for RAG retrieval.
• Vercel. Fluid Compute for the API, AI Gateway for model routing, Edge Config and Blob where they fit. No Edge Functions — Node.js runtime is the default.
• AI SDK + AI Gateway. streamText, embed, generateObject. Provider-agnostic model strings (anthropic/claude-sonnet-4-6, openai/text-embedding-3-small) routed through the Gateway.
• MDX content collections. Long-form content lives in MDX with validated frontmatter. Same file feeds the rendered page and the RAG index.
• Zustand + localStorage. Client state when Server Components can't carry the load. Persisted under a versioned key.
• Ableton Live + Max for Live. Side B — deep house production.

Craft

• Spec-driven development. Every non-trivial piece of work starts as a markdown spec under specs/ or docs/plans/. Goal, phases, acceptance criteria, code references. The spec is the contract; the implementation follows.
• Agentic coding with Claude Code. Most of my output is Claude-paired. The leverage isn't in the typing — it's in the spec, the review, and the judgment about what to ship.
• LLM-as-judge editorial gates. Generated content (restaurant copy, neighborhood voice, itineraries) is scored by a separate model against tasting notes — factual accuracy, voice match, no hype words. Anything below threshold goes to manual review.
• Voice profile per domain. Each product has its own voice.md (and sometimes one per neighborhood, like in BonVivant). Voice files are style only — identity facts live in the indexed content so they can be cited.
• RAG with grounding rules. Chat answers are constrained to retrieved chunks. The model is instructed to acknowledge gaps rather than invent. Same pattern across personal site and product chatbots.
• Bilingual systems. English and Brazilian Portuguese first-class, not bolted on. i18n routing, per-locale MDX, locale-aware retrieval.
• Editorial pipelines. Multi-phase Claude Code pipelines that take structured data in, run it through voice-aware enrichment and judge scoring, and emit publication-ready MDX.

Side B

I produce deep house in Ableton with Max for Live, late at night when the house is quiet. I'm a Rotary Club member in San Diego. I travel between San Diego, São Paulo, and Bahia. A local host manages Zoetry on the ground while I run it remotely.