~/architecture

The system behind the system.

Six architecture diagrams: a personal multi-agent OS run by Claude Code, distributed microservices, multi-tenant SaaS, and an ML product. Every node was extracted from the real codebases, not sketched.

built on Claude Code

The brain of the whole system is Claude Code. The orchestrator is an interactive Claude session that fans work out to custom Skills via the Agent tool; email watchers spawn headless claude -p workers; the mobile bridge runs the Claude Agent SDK; MCP servers wire it into the vaults. I build with Claude, not just on top of a model API.

Custom Skills (~22)Headless claude -p workersAgent-tool sub-agent fan-outClaude Agent SDK (Python)MCP serversDomain-tier policy gatesHooks & slash commandsMulti-agent Workflows

These six diagrams were generated and verified by Claude Code multi-agent workflows: one sub-agent authored and rendered each diagram, then independent agents audited it against the real codebase and knowledge base for accuracy and reviewed the render for legibility. This page was built by the system it documents.

Grand Unified Architecture

system landscape
Grand Unified Architecture: architecture diagram

Every domain as a column, every shared platform drawn once as a capability band. Scan down a column to see what a project depends on; scan across a band to see who shares a platform. This is how five very different systems compose into one engine, now coordinated through a shared orchestration layer of specialist agents, saved workflows, and policy-as-code.

Claude CodeOpenAI gpt-4.1 / 4o / 5.1Ollama / WhisperNeonRabbitMQInngestAWSVercelTailscaleSentry

Personal Agentic OS: the brain

built on Claude Code
Personal Agentic OS: the brain: architecture diagram

An always-on multi-agent operating system run by Claude Code. The orchestrator dispatches through a versioned orchestration layer (seven specialist subagents, a saved multi-agent workflow library, policy-as-code hooks) out to custom Skills via the Agent tool; systemd-timed watchers spawn headless claude -p workers; a layered memory (session → project → vault) compounds research and ops, and a weekly self-verification loop audits the docs against the live system. Local models on an RTX 3080 handle the free tier; the iOS bridge runs the Claude Agent SDK.

Claude CodeClaude Agent SDKSkillsspecialist subagentsworkflow librarypolicy-as-code hooksheadless claude -pMCP serverssystemdOllama (gemma3n / bge-m3)faster-whisperLanceDBFastAPIarq + RedisTailscale

PodcastHub: distributed microservices

distributed systems
PodcastHub: distributed microservices: architecture diagram

A polyglot, event-driven recording platform built for graduate distributed-systems work (CAS 735). Six services across Node and Python, each a hexagonal ports-and-adapters bounded context owning its own data, choreographed over a RabbitMQ topic exchange plus a dedicated work queue for FFmpeg media processing.

Next.js 16Node / ExpressPython / FastAPIRabbitMQPostgreSQLMinIOFFmpegDocker Composehexagonal / DDD

Booking SaaS: access & role model

multi-tenant SaaS
Booking SaaS: access & role model: architecture diagram

Four roles (member, instructor, admin, platform owner), each with distinct authentication and capabilities, gated through a single pipeline: role-scoped JWT → edge middleware → multi-tenant subdomain routing → app-level studio isolation. The isolation boundary is enforced in the data layer by a scoped-Prisma wrapper, not by database RLS.

NextAuth (JWT)edge middlewaremulti-tenant subdomainsscopedPrisma isolation4-role RBAC

Booking SaaS: data-flow

multi-tenant SaaS
Booking SaaS: data-flow: architecture diagram

The same product traced by data movement: a synchronous request spine (booking, auth, card checkout) split cleanly from a durable, event-driven async tier (payment webhooks with HMAC verification, Inngest functions, scheduled cron, realtime push). One shared serverless Postgres, every row studio-scoped.

Next.js 16Prisma 7NeonSquareInngestVercel CronAWS SES / SNS / KMS / AppSync

Applify AI: resume-tailoring pipeline

ML product
Applify AI: resume-tailoring pipeline: architecture diagram

A resume-tailoring ML product with paying users. A JWT-gated request enqueues a durable, step-checkpointed Inngest pipeline that drives the OpenAI Responses API with Zod-validated structured outputs. Shown sparse on purpose: only the publicly self-reported stack, nothing inferred.

Next.js 15InngestOpenAI gpt-4.1 / 4o / 5.1 + ZodPrisma 7StripeSentry

M.Eng research project

Verified study notes: the M.Eng project

Mac Study Companion was my M.Eng Software Engineering research project at McMaster, supervised by Dr. William Farmer and Dr. Richard Paige, built for neurodivergent learners. It turns lecture recordings into study notes with a guarantee attached: every sentence it releases carries a citation back to the minute of audio it came from, and any sentence the checker cannot prove is held back in place with its reason shown, never quietly dropped. A fail-closed release gate discharges the per-response verification conditions in Z3, and the pipeline state machine is model-checked in TLA+.

mac-study-companion / today
Mac Study Companion home screen showing today's review session and the next problem to work.
Today opens on one session and one problem, not the whole backlog.
mac-study-companion / map
Course map showing lectures and concepts as connected nodes with their sealed claim counts.
The course as a map of lectures and concepts, with where you are standing marked.
mac-study-companion / lecture notes
Lecture notes page with a coverage bar and a ledger of sealed sentences, each showing its audio timestamp range.
Each released sentence carries the stretch of lecture audio it came from.
mac-study-companion / claim ledger
Claim ledger showing withheld sentences redacted, each labelled with the reason it was held back.
What the checker could not prove keeps its place, redacted, with the reason it was held.
mac-study-companion / cards
A multiple choice card with an option selected and the reveal button not yet pressed, showing no correctness signal.
You commit to an answer before anything on screen tells you whether it is right.
mac-study-companion / guidance
Guidance view showing established steps along a pipeline and a tutor reply that asks a question instead of giving the answer.
The tutor holds only the steps your own typed work has established, so it cannot hand over the rest.

Every design decision traces to a 25-item evidence-graded ADHD learning-design checklist distilled from the literature, in the spirit of Nielsen’s heuristics, and strict enough that it caught the product’s own quiz flow revealing correctness on the same click as the pick, which was rebuilt into answer-then-reveal. Both supervisors accepted the project in September 2026 as sufficient for the degree, and the build concluded in October 2026.

Z3TLA+FastAPIRedis StreamsNext.jsDeBERTa NLI