15 Days with Fable

Fable 5 came out two weeks ago. I’m a solo founder in Singapore, and I decided the honest way to show what this model changes is not a hot take. Receipts. So: 15 projects, one article, updated as each build lands, every build answering the same four questions. What it is. What I actually did. What you can do with it. Who it’s for.

”How’s Fable so far?”, honestly

Since everyone asks, here’s my actual answer before the receipts.

It may not be an Opus upgrade at all. A community similarity analysis (this one) found Fable doesn’t cluster tightly with Anthropic’s other models the way siblings usually do. The author’s own speculation: a new base model on a different data mix, a different animal rather than a bigger one. Speculation, flagged as such. But it matches the driver’s seat: it doesn’t fail where I’d calibrated for failure.

The real value for me is unknown unknowns. People keep saying the models are really good. But if you can’t accomplish your goals with them, how good are they really? My pattern for two years: stuck, and either not knowing why, or knowing why and still not getting past it. Every model before gave me a partial diagnosis: “your problem is selling,” “your problem is how you build.” Partial diagnoses feel true and change nothing. Fable’s read of everything I’d done was the first one complete enough that things actually started moving. Most of this series exists because of that.

“Just send it” was never the problem. Earlier models would draft something and tell me it’s ready. Just send it. But a send with my name on it has standards: is it up to my bar, is every claim verified, is it my voice, do I understand it deeply enough to defend it in the reply? A draft I can’t defend is not a send. What finally worked is a pattern I think of as the barrier breakdown: an artifact holding the message, every file it’s built on, why each one is the way it is, and the exact steps for me to verify and approve. A memo deep enough to give me real understanding in minutes. Then the send happens. That’s how a client send finally fired, how an application I’d sat on for weeks left the building, and how I shipped my first video demo ever. I don’t know how to make videos. It made the video; I reviewed it; it went out. (The public, copyable version of this idea is the firepage build below.)

The meta-move underneath it all I learned from Jordan Peterson years ago: when a problem is too big to solve, break it down until the pieces are too small to procrastinate on. These artifacts just mechanize that, at the exact moment of maximum avoidance.

Ops notes for builders: I run two Claude Code Max accounts in parallel now, plus Azure credits running GPT-5.6 for the bulk work. I use Fable to write the skills, harnesses, and instructions so cheaper models can operate closer to its level when it isn’t around, and to direct lower models (and Codex) rather than doing everything itself. And I tried the recurring-loop pattern everyone recommends; an audit of my own usage showed my work is mostly one-off diagnosis and fixes, not recurring pipelines, so I stopped forcing it. Know which kind of user you are.

Ground rules for the series: real numbers over adjectives, the failure log stays in, clients and communities stay anonymous, and every build ends with either something you can use or something you can ask me. If a build reads like a report card, I’ve failed. Tell me.

The builds

Ranked roughly by how much I think you’ll enjoy them. Each becomes a full section of this article as it lands; the list shows what’s live so far.

My Todo Board Argues Back: the AI operating system that runs my company of one and quotes my own constitution back at me. (live, below)
“Live Mode Has Never Worked”: a 34-Hour Rewrite: a live call copilot: 0.04s captions, 37.5MB flat RAM, and the checkbox that’s still honestly unticked. (coming)
The Seller’s Bot Promised a Refund. The Human Disowned the Bot.: AI as family consumer-rights lawyer; full ¥2,199 recovered. (coming)
Zero Bans, Zero Closes: 1,327 DMs, TCP-style congestion control for outreach, the API that hid 7 of 8 replies, and the bottleneck that turned out to be me. (coming)
Cite or Shut Up: One Pattern, Three Products: the centerpiece: 6,080 filings, 32 citations, 0 fabrications, in 34 minutes. Then the same spine on health data and live chat. (coming)
Seven Gates and a 95-Cent Receipt: the outreach engine you can run on any company, with a fully costed run published. (coming)
11 Months, 12 Repos, 0 Customers. Then One Day.: the tax-tool redemption arc; the answer key came first this time. (coming)
He Already Fired a Human for This Job: a daily SGX ledger on a brutal deal: 20-30 days, zero misses, before it’s allowed to have a price. (coming)
The Plan Refuses to Render: the paid training-plan engine: 47 checks, a blind AI-vs-AI review, and the day it correctly refused me too. (coming)
My Feed Filter Admits When It Gets Worse: rai, the local-AI feed filter, and the eval score that dropped 16 points when the test got honest. (coming)
One Folder Reads 14 Inboxes: comms triage with a fresh-eyes critic and a hard invariant: no approval record, no send, ever. (coming)
The Draft Was Never the Bottleneck: firepage, one HTML file that gets stuck messages sent, extracted from a real multi-week failure. (coming)
Two Kinds of Waiting: the family medical-ops system, and the red/green split that stopped years from leaking. (coming)
Small Tools, Hard Rules: a dividend screener that caught a fake 35% yield, and a watcher whose busiest code path is its own safety boundary. (coming)
My Running Coach Argues With a 1998 Textbook: VDOT verified to the second, dew point priced into your pace, deliberately unlaunched. (coming)

The scoreboard

I’m tracking this series in public: reads (site analytics), repo traffic (GitHub clones/views, baselined the day before this article went up, effectively zero), and the number that actually counts on my board: paying customers using something weekly. The series doesn’t move that number until a stranger uses or buys something. That’s the honest frame: this article went up on day 27 of a 100-day public build, and the scoreboard updates weekly.

Build 1: My Todo Board Argues Back

The system that planned, argued about, and then built this very series.

Why it exists

My Claude Code setup right now: two Claude Max accounts in parallel, plus Azure credits running GPT-5.6 for the bulk work. For practical purposes, the token ceiling is gone.

Everyone assumes that’s the dream. It’s where the real problem starts. When execution is unlimited, the binding question flips from “can I build this?” (always yes now) to “is this the thing worth building today?”, and the model cannot answer that one. It optimizes for the request in front of it, not the mission. Left alone with infinite hands, I will research forever, polish what nobody asked for, and ship nothing to a stranger, all with perfect craftsmanship.

founder-home exists to answer the question the tokens can’t: what should today actually do?

What it is

Think: a chief of staff who has read your constitution, and holds you to it.

founder-home is the operating system for my company of one. Not a productivity app. A repo with three load-bearing parts:

One board. A single state.json holds today’s goals, the task stack, every open send, and the one metric that counts. Everything else reads from it. There is no second source of truth to hide in.
A ledger I don’t write. Every Claude Code session in every repo (nineteen and counting) writes a dated entry via hooks: what was asked, what was committed. The morning board reads yesterday back to me. I can’t misremember what I did, because I’m not the one keeping the record.
A constitution the AI enforces. My failure modes are written down and named. “The Loop”: research → build → abandon, twelve archived projects of evidence. “Vanity Theatre”: polishing the mirror instead of asking a stranger to pay. Rung priority: paying customers before prospects before interesting work. A NOT-DO list. And one binding constraint above everything: a send leaving the building outranks build, research, or harness work.

Every morning the AI reads the stack, proposes one needle goal and one support goal, debates me, and compiles the winners into structured prompts with a done-when and an explicit Anti line: the failure mode this goal must not become. Then it fires.

The part that actually matters

The board prints my worst number first, every day: COIN, paying customers using something weekly. On purpose. The system is designed so I can’t grade myself on effort, cleverness, or how good the tooling looks. This article you’re reading counts for nothing on that board until a stranger pays and comes back.

And it argues. The morning this series launched, I arrived with fresh energy and a new plan, one day after a discouraging customer signal. The system’s job in that moment was to say: this pattern has a name in your constitution, the sends still outrank it, here’s the goal restructured so the sends fire anyway. It did. They did. The new plan became this series. After the sends, not instead of them.

What you can steal

The pattern is portable and none of it is secret sauce:

One state file. Any second board is a place to hide.
Hook-written ledgers: your AI logs every session automatically; you never self-report.
A constitution file with your failure modes named. Naming is what makes them catchable in the moment.
An Anti line on every goal: what this must not turn into.
One honest metric printed first, especially while it’s ugly.

The uncomfortable prerequisite: you have to write down your own failure modes, in your own words, and give the AI standing permission to quote them back at the worst possible moment. That permission is the product.

Who it’s for

Solo founders and builders whose AI produces endless work but no accountability; anyone whose productivity system has never once told them no.

I’ll publish a sanitized starter template (hooks + empty board + constitution skeleton) if enough people want it. Tell me: @phuakuanyu.

If one of these makes you think of your own document pile, feed, or workflow: @phuakuanyu. First reply is a real look at your problem, not a pitch.