FutureMaster · Technical edition
For kids who already ship: the 12-week operating spec.
You've deployed something, or you're one weekend away. You've argued with a model about its own hallucination. What you don't have is a systematic practice: mission design, context architecture, verification protocols, model evaluation methodology, and a deployment cadence that compounds. This is the spec.
// Residual human value
## The core claim, stated precisely
> 1. Problem selection
Deciding what is worth building. No objective function exists; requires domain obsession.
> 2. Specification quality
The fidelity with which you transmit intent to an execution system. Engineering skill, not writing skill.
> 3. Verification
Detecting when fluent output is wrong. Asymmetric: generation is cheap, validation is scarce.
> 4. Taste
Discriminating competent from exceptional, and forcing the gap closed.
> 5. Distribution
Moving other humans. The one market AI cannot flood, because trust is human-denominated.
// Weeks 3-6
## The dense phase
Week 3 is context engineering, the unglamorous 10x. Deliverable: a reusable context briefcase for your lane, plus an A/B of the same task with and without it. Numbers, not vibes.
Week 4 is verification protocol. A coach induces a confident, fluent, wrong output in your domain, live. Then the drill: source, seam, sanity-check. You build a wrong-log, every hallucination documented against primary sources, and wrong-log depth becomes a tracked stat. Trust architecture gets explicit: always-verify, sample-verify, never-delegate.
Week 5 is the One-Shot Canon, mission design as a spec discipline: state the mission not the steps, grant autonomy explicitly, define done as evidence, front-load context handles not dumps, declare trade-off priorities, set budgets and stop rules, plan before payload, fence the invariants, batch the whole ask, debrief into memory. Tracked stat: one-shot success rate, shipped with zero follow-up corrections.
Week 6 is the Problem Selection Studio, the midpoint gate. Domain scan, problem mapping, human interviews, opportunity sizing, pick ONE. The pitch is graded harder than the eventual solution, deliberately.
// Weeks 7-10
## Ship phase
$ W7 Deploy: Ship Zero, smallest real version, public by demo day. Feedback does not exist pre-deployment; everything before is simulation.
$ W8 Taste pass: study the greats of your lane, then push AI output to the standard they set in you. The decision log is the deliverable as much as the improved creation.
$ W9 Model Race: identical task, two frontier models, same agent scaffold. Compare quality, speed, factuality, failure modes, tool-use behavior, correction load. You keep a Model Scorecard of your own evals. When the next frontier model drops, you characterize it in days instead of consuming takes.
$ W10 The Director's Exam: cold brief, unseen problem, 20 minutes, live, paired with a rubric observer. Briefing quality, verification under load, recovery from bad output, honest ownership.
// Weeks 11-12
## The part technical kids skip, at maximum cost
When everyone has access to the same intelligence, the machine side of every field flattens. The residual competition is entirely human-side: trust, teaching, recruiting, moving a room. Technical kids who skip this cap their own ceiling regardless of build skill.
Week 11: Cold Ten, ten real outreach messages to professionals in your lane, response rate analyzed like a campaign. Plus the Human Edge thesis: run AI honestly against a great you admire and map the capability gap. Concluding AI closes the gap somewhere is a legitimate result; the skill graded is honest capability mapping.
Week 12: capstone ships in final form. Portfolio: 3+ live creations, wrong-log, mission briefs, decision logs, outreach record. Closed-laptop Q&A before external practitioners. The after recording is diffed against week one.
// Output is not graded. AI made output free.
## The grading function
SPEC
one-shot success rate
VERIFY
wrong-log depth
SHIP
cadence, W7-12
MOVE
humans moved
// Depreciating assets
## Deliberately excluded
$ Prompt tricks: expire with every model release. Invariants compound.
$ Coding syntax: contrarian but reasoned. You have a fleet of world-class engineers on tap awaiting a clear mission; the scarce skill is decomposition, delegation, specification, system connection. If you already code, nothing stops you. It is simply not what is being trained.
$ Tool-of-the-month: skills anchor to components and systems, not product names. The engine under JARVIS rotates on purpose.
$ Output grading, abstract ethics, theory without shipping.
# Each loop deposits three non-transferable assets: a live creation, a sharper judgment, and the identity of someone who finishes. A decade of the loop produces something historically new: one person with the agency of a team.
That is the spec. The rest is reps.
// applying = joining the vetting queue. we reply to fits.
FutureMaster · model + memory + tools + orchestration