Claude Code is my workhorse.
I build, run, and train with it.

01 / Infrastructure as code

I stopped hand-writing Ansible playbooks

My homelab spans an Oracle Cloud VPS, an HP EliteDesk G4 server, two Raspberry Pi 5s, and a scatter of ESP32 sensors. The four Linux hosts are managed as one Ansible repo, deployed automatically by self-hosted Gitea Actions on every push. I used to write the YAML myself, then spin up a spare host to find out what I'd gotten wrong. Now I describe the change and Claude writes the role, the task, the template — and reasons about idempotency and host-scoping better than I bothered to when I was doing it by hand.

02 / How I build apps

I describe. Claude codes. I review. CI ships.

Vibe coding — as Dennis Denuto would say, it's not strictly speaking a methodology, it's more... the vibe of the thing.

No IDE marathon sessions. Development is a conversation: I say what I want in plain English, Claude Code reads the codebase, writes the change, and explains it. My job shifts from typing to deciding — and the same loop runs whether I'm shipping a feature, fixing a bug, or standing up a whole new app.

6b181f8 Add dual-model selection layer for fast vs strong AI tasks     invest
a91c2e4 Add self-healing model picker with daily validation           training
f30d117 Add watchdog role: restart containers on failed healthcheck      infra
ab75482 Fix price forecast: handle empty LLM response and reframe prompt invest
c48e910 Replace Python best-picking with a SQL window function                training
7d2b6aa Fix idempotency gap in tailscale role on re-run                   infra

Different domains, same rhythm: add → fix → refine → harden. Small conversational increments, each one shippable. Context lives in CLAUDE.md and Claude's persistent memory, so every session starts already knowing the project — whether that project is a trading dashboard, a training log, or a fleet of home servers.

03 / When a subscription killed my training app

So I built my own. It's free, and better.

The app I'd used for years to plan marathon training quietly stopped working, and the fix the owner shipped was a subscription. Instead of paying for a worse version of what I already had, I asked Claude Code to help me build PMCDash: it syncs 18 years of Garmin history, models training load the way real coaches do — fitness, fatigue, form — and adds an AI coach that reads my actual data. It runs on my own server for nothing.

pmc.insightiq.au/pmc

The core view. Daily load, accumulated fitness, and form — the same physiology every training plan is built on.

/coach

The AI coach. Answers grounded in my real training history via tool calls, not generic advice.

I won't go deep on this one — the pattern is the same as everywhere else. What's worth noting: it isn't a lesser replacement. It's tailored to exactly what I want to see, with none of the features I never used and none of the recurring bill.

04 / The deep dive

Now, the one I'll actually walk you through

INVEST is an analyst that never sleeps: a full-stack monitoring app for my ASX and US holdings, running on an Oracle Cloud VPS alongside my other public services, priced live, assessed daily by an LLM, and advised weekly. It's the build where every architectural decision mattered enough to explain — so this is where the tour slows down.

invest.insightiq.au

The dashboard. Live watchlist across exchanges — USD positions converted to AUD, advancing / declining at a glance.

/stock/WBC

Stock detail. Price history plus the AI-written monitoring brief — five themes it watches for this stock every day.

/trends

Trends. Rolling 7/30/90-day sentiment per stock, with the AI's price forecasts and their scored outcomes.

05 / The loop, for real this time

The same rhythm from section 02 — here's the actual log

This is what "describe → implement → review → push" looked like for INVEST specifically: 49 commits in 11 days, each one a small, shippable increment.

3d79fca Add AI price range forecasts with outcome tracking
ab75482 Fix price forecast: handle empty LLM response and reframe prompt
0e5e01d Use fast model for price forecasts                    ← refine
4407c03 Add cash positions with interest rate tracking
352c7b1 Include cash positions in AI portfolio advice          ← extend
eba8eb8 Fix N+1 queries in portfolio summary and advisor      ← harden
6b181f8 Add dual-model selection layer for fast vs strong AI tasks

6,569 lines of code, 49 commits, 11 days. Context lives in CLAUDE.md and Claude's persistent memory, so every session starts already knowing the project.

06 / Architecture

Decisions were mine. Research was free.

AI didn't decide the architecture — it made every option cheap to evaluate. Each choice below started as a question to Claude, came back with trade-offs, and ended with me picking.

07 / The model decision layer

Not every task deserves the expensive model

The app makes hundreds of LLM calls a week. Reformatting a news query doesn't need the same brain as judging my portfolio — so I built a routing layer: every AI task declares whether it needs strong judgment or just fast transformation. The result: the entire AI layer runs on ~$2 a month.

async def _chat(messages, max_tokens=1024, fast=False):
    model = (settings.TOGETHER_MODEL_FAST
             if fast and settings.TOGETHER_MODEL_FAST
             else settings.TOGETHER_MODEL)
    ...

# startup: pick both tiers from the live list
strong = max(priced, key=lambda m: m["output_price"])
fast   = min(priced, key=lambda m: m["output_price"])

08 / The monitoring themes

Every stock gets 5 signals worth watching

The moment I add a ticker, the two model tiers go to work together. The strong model thinks like a long-term shareholder and writes five key themes — earnings drivers, growth bets, macro sensitivities, capital allocation. The fast model then compresses each theme into a Google News query phrased the way journalists actually write headlines.

Illustrative pair for Westpac (WBC). The query prompt carries hard rules: anchor to the instrument's base search term, ban abstract finance jargon like "valuation multiples", and no two queries may retrieve the same articles — each targets a distinct signal.

This is the feature that makes the trend assessment mean something: the AI isn't reacting to generic headlines — it's tracking the five signals it decided matter for this specific stock, every single day.

09 / AI on my portfolio

The LLM never guesses — it reads my numbers

The portfolio advisor isn't "ask ChatGPT about stocks." The app computes every fact deterministically — weights, P&L, sector concentration, cash yield — and hands the model a structured brief. Code does the arithmetic; the model does the judgment.

/portfolio

The portfolio page. Positions, cash accounts with interest tracking, and the one-click Portfolio Advisor. Amounts pixelated — the AI sees them, the audience doesn't.

Feeding it back: every day's Bullish / Neutral / Bearish verdict is stored as history, and the next assessment reads its own last five — the analyst has memory and accountability, the two things that make an opinion worth reading.

10 / Lessons

What actually transfers

Infrastructure, apps, a training log, an investment analyst — different problems, same handful of lessons carried across all of them.