CreatorStudioBuilt with AI — the build, by the numbers

One engineer, one AI, one month.

CreatorStudio — the announcement engine, two Angular apps, the LED sign renderer, three vehicle clients and a ten-document handbook — was built between July 1 and July 31, 2026 by one engineer directing Claude in Claude Code. This page is the measured record of that build: every figure below is computed from the session transcripts, the git history and the test report — nothing is estimated.

31calendar days, Jul 1 – 31 · progress on every single one
~194 hof measured AI working time across 58 sessions + 124 subagents
≈ $10.4kAPI list-price equivalent for all compute — the whole product
102klines of code & tests in the shipped tree, plus 15.7k lines of docs
1,669automated tests · 0 failing · engine 93.0% line coverage

The shape of the month

Tokens the models generated per day, and hours the AI was actively working (wall-clock with idle gaps over 10 minutes excluded). The build never paused: even the quietest day moved the product forward.

Generated tokens per day · millions

38.8M tokens of code, tests and documentation generated in total — the peak, Jul 18, was the LED template editor push.

Active build hours per day

~194 hours total — a sustained ~6.3 h/day of AI working time, directed by 1,150 human prompts (~37 a day).

Daily data as a table
Day (Jul)Tokens (M)Active hours

Where the compute went

13.0 billion tokens were processed in total. 98.2% of them — 12.76B — were served from the prompt cache at a tenth of the input price; only 38.8M were newly generated. Cost is the API list-price equivalent of the measured usage, per model.

ModelResponsesGeneratedCache readsCost (list-price equivalent)
Prompt caching is what makes the price possible. The 12.76B cache-read tokens cost ≈ $7,440 at cache rates. Reprocessed at full input price they would have cost ≈ $74,400 — the same build, without caching, lands at roughly $77k instead of $10.4k. A 7× saving from architecture, not from doing less.
12.76Btokens read from cache (98.2% of everything processed)
239.7Mtokens written to cache
4.3Mfresh uncached input tokens
38.8Mtokens generated — the actual code, tests and prose

What the AI actually did

Not autocomplete — an agent operating the repository: 19,361 tool calls across the month, reading, editing, running tests and driving a browser against the real apps.

6,377shell commands (builds, test runs, git, Docker)
5,103precise file edits
3,713file reads
1,055files written
1,292searches (grep + glob)
129subagents launched for parallel work

What it bought

The shipped tree, verified — not a prototype. Every deliverable below is in production shape, covered by the same CI gates.

The announcement engine

15.5k lines of TypeScript: journey-state tracking, 33 trigger types + custom rules, the render pipeline, 13-supplier TTS layer with failover, LED sign rendering with FF and Hanover wire protocols.

Two Angular apps

39.8k lines: the CreatorStudio authoring app and the fleet cockpit — plus the 27.5k-line Creator mirror kept parity-tested against the engine.

Vehicle clients & portal

Go, Python and browser players closing the proof-of-play loop; the passenger screen; the documentation portal and this page.

11.7k lines of tests

1,669 cases — engine 738, front-ends 931 — 0 failing, 0 skipped. Engine 93.0% line coverage; parity guards that fail the build if the authoring app and engine drift.

A ten-document handbook

15.7k lines of maintained documentation — architecture, functional spec, security, scaling, test report — drift-checked by CI, with an honest gaps register.

The product story

A 28-slide presenter-ready deck, speaker crib sheet, leaflets and diagrams — all regenerable from the repository.

Efficiency, honestly computed

$0.10compute per shipped line of code & tests
$6.25compute per passing automated test
~525shipped lines per active AI hour — rewrites and dead ends already paid for
~$54compute per active AI engineering hour
208commits merged — every one through typecheck, tests, parity guards and doc-drift gates

How these numbers were measured

Sources. Time, tokens and cost come from the 58 Claude Code session transcripts and 124 subagent transcripts of this project — every model response carries exact token usage (input, output, cache read, cache write) and a timestamp. Code size comes from git ls-files line counts of the shipped tree; test and coverage figures from the regenerable TEST-REPORT.md.

Active time is wall-clock across all sessions with idle gaps capped at 10 minutes, measured on the merged timeline so parallel subagents aren't double-counted.

Cost is the API list-price equivalent: the measured tokens priced at each model's published pay-per-token rates (Opus 4.8/Opus 5 at $5/$25 per MTok, Fable 5 at $10/$50, Haiku 4.5 at $1/$5; cache reads at 0.1×, cache writes at 1.25× the input rate). It answers "what would this exact usage have billed" at public pay-per-token rates. The actual out-of-pocket was different — and far smaller: the build ran on a single monthly Claude Code subscription, so the real spend was one subscription month. The $10.4k is quoted because it is the reproducible, scrutiny-proof way to price the compute; the subscription price is a flat-rate bundle and not a cost model.

What's not counted. The engineer's own time — 1,150 prompts of direction, review and product decisions — is the human half of the build and is not in the dollar figure. Line counts are the surviving tree, not lines written: every rewrite, refactor and dead end is inside the $10.4k, not inside the 102k lines.