The Multiverse School
The AI Builder · live, plus 230.5 h filmed
Join us $250

For people who want to ship the system, not read about it

Getting an answer is easy. Getting it to stay up is the job.

Thirteen classes in the order the school teaches them. You start by getting a model to answer you the way you meant, and you finish running an agent unattended that can account for every action it took. 230.5 hours of it is already filmed, so you can be watching the first class ten minutes from now — and the next one runs live on 7 Sep, if you would rather be in the room for it.

Two ways in, both by the month: $250 for the recordings and the written curriculum; $500 for those plus a seat in every class on this path that runs this month. Either one renews monthly and you cancel it yourself, any time — and a class you finish stays yours after you stop.

path · build-ai-systemsmeasured

tms path build-ai-systems --stats

classes
13
capabilities
41
recorded
179 clips / 230.5 h
exercises
625
student apps
13 (1 fully guided)
tops out at
order 13 · Metasystematic
hours of material
~413 h
By the end you have built

An autonomous agent with memory.

One that runs without you watching it, remembers what happened last time, and picks up where it left off — behind a review gate, with a bill you can account for.

↑ 41 things you can do, stacked ↑
You start from

A laptop and an API key.

That is the whole entry requirement. The first class takes you from there.

The bill nobody models first


What will this thing cost you to run?

The demo is free and the bill is not. Set your traffic and your token mix, put today's prices in the rate fields, and see where a rented GPU stops being the expensive option.

inference-cost.benchyour numbers
how you serve it

Rates — change any of them

Claude Opus 4.1 list price
Cheaper hosted model typical mid-tier list price
Self-hosted open model one 80 GB GPU, rented

List prices to start with; everything on the right recomputes from whatever you type. Tokens a second means real traffic, not the benchmark chart. A month is 30.44 days.

Your bill, Claude Opus 4.1, 10,000 requests a day

$29,679

$355,875 a year · 9.750¢ per request

Claude Opus 4.1

$29,679 $355,875 a year $15.00 in / $75.00 out per million tokens

Cheaper model

$1,583 $18,980 a year $0.80 in / $4.00 out per million tokens

Self-hosted

$1,315 $15,768 a year 1 rented GPU at $1.80/hour, running all month

A rented GPU costs the same whether you serve ten requests or ten thousand. At your token mix and your rates, self-hosting overtakes the cheaper hosted model at about 8,300 requests a day, and beats Opus at about 450 requests a day.

You are at 10,000 a day, so self-hosting is the cheaper option here — and you have just taken on a GPU to keep alive.

Halve the prompt and the bill becomes $20,547 a month, $9,132 off, without changing vendor, model, or anything a user can see. That lever is the one nobody reaches for first, and pulling it is what most of this path is about.

Already on the calendar


The next 6 sessions on this path

The live plan is a seat in every one of them. Turn up with the traceback you are actually stuck on, ask about it out loud, and take the recording home afterward — it lands in the same login as the 230.5 hours that are already there.

schedule · build-ai-systemsa seat in each

tms schedule --path build-ai-systems

  1. 7Sep Agentic SDLC Monday · 26.5 h of it already filmed · 46 exercises · $500 on its own next up
  2. 12Sep Control AI Spending Saturday · 3.1 h of it already filmed · 12 exercises · $100 on its own
  3. 15Sep Intro to Agents Tuesday · 56.7 h of it already filmed · 32 exercises · $350 on its own
  4. 16Sep Context Engineering Wednesday · 35.7 h of it already filmed · 329 exercises · $400 on its own
  5. 17Sep Production Agent Engineering Thursday · 22.3 h of it already filmed · $200 on its own
  6. 25Sep AI Alignment Friday · 1 exercise · $300 on its own

Two ways in


Watch it all, or be in the room

Same curriculum either way. The difference is whether you are asking your questions out loud on the day, starting with Agentic SDLC on 7 September.

On your own clock

$250a month

Watch it all

  • 179 clips and 230.5 hours across 13 classes, open the minute you join
  • 625 exercises and the written curriculum for all of them
  • The 13 tools that come with the classes
  • Pause, rewind, and run the exercise with the class on the other screen
Join us $250
In the room

$500a month

Be in the room

  • Everything on the left, plus a seat in every class on this path that runs this month
  • Bring your own traceback and ask about it out loud — 6 sessions are already on the calendar
  • Every session is filmed as it runs, so the hour you miss arrives days later
  • The archive keeps growing while you are in it
Join us $500

Both renew every month and you cancel either one yourself, any time. A class you finish stays yours — the recordings, the exercises and the written curriculum for it remain in your login after the subscription stops. Classes can also be taken one at a time, at their own prices — this path runs from $100 to $500 a class.

What you walk out able to do


41 things you can prove you can do

Every one of these is a verb you can do afterward, and every one comes with the artifact you hand over to show you can. They stack: the step you are standing on is what lets you reach the next. The last one asks for a week of unattended agent runs and a replay of one of them reconstructed from the trace alone.

7 You get a model to answer you. 2 capabilities · Preoperational
  • follow a model quickstart model behavior
  • locate a tool in an open tool hub system construction

Prove itfollow a model quickstart — A terminal transcript or notebook showing a successful call you ran yourself, including the model's response.

8 You do it by hand, with no library in the way. 2 capabilities · Primary
  • execute a tool call round trip by hand system construction
  • run an open model locally infrastructure

Prove itexecute a tool call round trip by hand — A transcript of all four messages — request, tool call, tool result, final answer — from a script with no agent library in it.

9 You produce something another person can pick up and use. 11 capabilities · Concrete
  • configure an open model endpoint behind a provider interface infrastructure
  • configure inference hyperparameters model behavior
  • elicit output from a model model behavior
  • operate a model as a first pass editor model behavior
  • produce a list of where untrusted input enters a system security
  • produce a reusable system prompt model behavior
  • produce a rubric a model can apply evidence and verification
  • produce an account of what you depend on and who can revoke it infrastructure
  • produce an itemised bill for what your running system costs infrastructure
  • segment text into tokens and cost it model behavior
  • transform a corpus into an embedded index data shaping

Prove itconfigure an open model endpoint behind a provider interface — One client script, unedited, producing comparable output against both the hosted provider and your own endpoint, with only the base URL swapped.

10 You make it general — a pattern, not a one-off. 8 capabilities · Abstract
  • characterise a models failure modes model behavior
  • characterise an applications injection surface security
  • characterise what breaks when you cut the connection infrastructure
  • characterise what one user costs you to serve infrastructure
  • constrain model output to a schema model behavior
  • generalise a reasoning prompt pattern model behavior
  • generalise a tool interface for model use system construction
  • parameterise a prompt template model behavior

Prove itcharacterise a models failure modes — A table of failure modes, each with the input that reproduces it and the output it produced, reproducible by a second person.

11 You prove it works, with evidence somebody else can check. 7 capabilities · Formal
  • falsify a prompt with a benchmark evidence and verification
  • measure whether a fine tune changed behaviour model behavior
  • verify a program does what you claimed with a test system construction
  • verify a rubric against independent graders evidence and verification
  • verify an agents actions with a critic evidence and verification
  • verify model written code against its specification evidence and verification
  • verify you would know your system broke before a user tells you infrastructure

Prove itfalsify a prompt with a benchmark — A benchmark file with graded cases, a run showing a non-zero failure rate, and a written account of which failures are the prompt's fault.

12 You build the whole system, and it holds together. 5 capabilities · Systematic
  • construct a multi agent conversation with turn taking system construction
  • construct a retrieval system system construction
  • construct an agent that uses tools system construction
  • design a recursive summariser for oversized documents data shaping
  • design a review gate that catches what the doer cannot see organization and delegation

Prove itconstruct a multi agent conversation with turn taking — A transcript where the speaker order differs between two runs of the same task, both terminating, plus the selection rule in source.

13 You run systems without watching them, and can account for what they did. 6 capabilities · Metasystematic
  • reconcile agents from different toolchains into one run system construction
  • select among agent architectures system construction
  • synthesise a metalanguage for a problem domain data shaping
  • synthesise a self extending agent behind a review gate system construction
  • synthesise an accountability regime for an unattended agent system construction
  • synthesise an agent that carries notes across its own runs system construction

Prove itreconcile agents from different toolchains into one run — One transcript containing agents from at least two frameworks and one self-hosted backend, plus the script chaining its output into a second task.

Tagged by philosophy, this path is 32 make · 22 think · 14 own · 3 secure. A capability can carry more than one tag, so those add up to more than 41.

The sequence


Thirteen classes, from prompt to deployed system

This order is curated, not alphabetical and not chronological — each class stands on the one above it. The bar under each class fills up as you collect the 41 things you can do by the end: purple is what you walked in with, cyan is what that class hands you.

  1. 01

    Using Large Language Models

    • generalise a reasoning prompt pattern
    • produce a reusable system prompt
    • operate a model as a first pass editor
    • +2 more

    Prove itThe pattern applied to three tasks with paired outputs against a plain prompt, plus one task where it made the output worse.

    • 6 clips · 10.7 h
    • 55 exercises
    Adds 5 — after this class you can do 5 of the 41 things on this path
  2. 02

    Control AI Spending

    • segment text into tokens and cost it
    • run an open model locally

    Prove itA token count for a real document, with the resulting cost and the fraction of the model's context window it occupies.

    • 2 clips · 3.1 h
    • 12 exercises
    • Syllabus
    • next session 12 Sep
    • $100 on its own
    Adds 2 — after this class you can do 7 of the 41 things on this path
  3. 03

    AI Alignment

    • verify a rubric against independent graders
    • measure whether a fine tune changed behaviour
    • falsify a prompt with a benchmark
    • +5 more

    Prove itTwo rubric versions, the adversarial inputs that forced the revision, and inter-grader agreement measured before and after.

    • 1 exercise
    • Syllabus
    • next session 25 Sep
    • $300 on its own
    Adds 8 — after this class you can do 15 of the 41 things on this path
  4. 04

    Context Engineering

    • design a recursive summariser for oversized documents
    • segment text into tokens and cost it
    • configure inference hyperparameters

    Prove itA run over a document several times the context window, a fact list written beforehand, and the recall score of the output against it.

    • 27 clips · 35.7 h
    • 329 exercises
    • Guided app
    • next session 16 Sep
    • $400 on its own
    Adds 1 — after this class you can do 16 of the 41 things on this path
  5. 05

    Claude Model Context Protocol

    • generalise a tool interface for model use
    • execute a tool call round trip by hand
    • locate a tool in an open tool hub

    Prove itThe tool definition, a transcript of a model calling it correctly from the description alone, and a transcript of it recovering from a bad call.

    • 3 clips · 5.2 h
    • Syllabus
    Adds 3 — after this class you can do 19 of the 41 things on this path
  6. 06

    Intro to Agents

    • construct an agent that uses tools
    • constrain model output to a schema
    • produce a reusable system prompt
    • +2 more

    Prove itA run log showing tool selection the author did not script, a benchmark over held-out tasks, and a case where it correctly declined to continue.

    • 42 clips · 56.7 h
    • 32 exercises
    • Syllabus
    • next session 15 Sep
    • $350 on its own
    Adds 1 — after this class you can do 20 of the 41 things on this path
  7. 07

    Prompt Engineering

    • synthesise a metalanguage for a problem domain
    • falsify a prompt with a benchmark
    • parameterise a prompt template
    • +3 more

    Prove itThe notation's grammar, a work produced in it that exceeds the context window, and a consistency check a reader runs to catch contradictions.

    • 58 clips · 63.5 h
    • 142 exercises
    • Syllabus
    Adds 1 — after this class you can do 21 of the 41 things on this path
  8. 08

    RAG & Memory

    • synthesise an agent that carries notes across its own runs
    • construct a retrieval system
    • transform a corpus into an embedded index

    Prove itPaired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.

    Adds 3 — after this class you can do 24 of the 41 things on this path
  9. 09

    Advanced Retrieval Augmented Generation

    • construct a retrieval system
    • transform a corpus into an embedded index

    Prove itA running system, a benchmark of retrieval quality over held-out questions, and citations traceable to source documents.

    • 5 clips · 6.8 h
    • 7 exercises
    Goes deeper — after this class you can do 24 of the 41 things on this path
  10. 10

    Production Agent Engineering

    • synthesise a self extending agent behind a review gate
    • select among agent architectures
    • reconcile agents from different toolchains into one run
    • +3 more

    Prove itA diff the agent authored to its own prompt or toolset, the benchmark run that accepted it, and a logged rejection with the reason recorded.

    • 19 clips · 22.3 h
    • next session 17 Sep
    • $200 on its own
    Adds 6 — after this class you can do 30 of the 41 things on this path
  11. 11

    Agentic SDLC

    • synthesise an agent that carries notes across its own runs
    • synthesise an accountability regime for an unattended agent
    • design a review gate that catches what the doer cannot see
    • +4 more

    Prove itPaired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.

    • 17 clips · 26.5 h
    • 46 exercises
    • Companion
    • next session 7 Sep
    • $500 on its own
    Adds 6 — after this class you can do 36 of the 41 things on this path
  12. 12

    Agentic AI Security: Securing What You Build

    • verify model written code against its specification
    • characterise an applications injection surface
    • produce a list of where untrusted input enters a system

    Prove itThe diff, a review note per changed hunk, and a test run that is red on the parent commit and green on the child.

    • 1 exercise
    • Reference
    Goes deeper — after this class you can do 36 of the 41 things on this path
  13. 13

    Keep It Running

    • verify you would know your system broke before a user tells you
    • characterise what one user costs you to serve
    • characterise what breaks when you cut the connection
    • +2 more

    Prove itA real failure you learned about from your own alerting with the timestamps to prove it, and one you learned about from a user, with what you added so that one cannot happen the same way twice.

    Adds 5 — after this class you can do 41 of the 41 things on this path

Straight answers


What is behind the login

230.5 hours of this path, filmed

Prompt Engineering is 58 clips and 63.5 hours; Intro to Agents is 42 clips and 56.7 hours; Context Engineering is 27 clips and 35.7 hours. 179 clips and 625 exercises in all.

Pause it, rewind the part where the trace does not match the code, and run the exercise with the class still on the other screen. Join on a Tuesday and you can be three classes in by the weekend.

And the room they were filmed in

A recording answers the question the teacher expected. The room answers the one you brought about your own repo. The next one is Agentic SDLC on Monday 7 September, and your pass is a seat in it.

Every session is filmed as it runs, so the hour you could not make arrives in the same login a few days later, and the archive you joined keeps getting longer while you are in it.

Both plans, and what carries on

The recordings, the exercises, the tools and the written curriculum come with either one; the dearer one adds the live room. A class you finish stays yours after you stop paying.

Watch it all $250 Be in the room $500

The 13 tools that come with the classes

You open these next to your own work — your prompt, your repo, your bill — with the recording paused on the other screen.

  • Context Engineering 1 guided workbench. Nine stations that walk you from a bloated prompt to a context budget you can defend, on your own material.
  • Agentic SDLC 1 companion, 9 walkthrough decks. A companion for running the loop, plus decks on agent memory, the complexity ladder, context compression, git under agents, and shipping.
  • Agentic AI Security: Securing What You Build 1 reference, 1 walkthrough deck. A frameworks reference and a map of where untrusted input gets into the thing you built.

Read this before you pay


Who this path is not for

  • You will write code and live in a terminal. Not "a bit of Python eventually" — from Intro to Agents onward you are running processes, reading stack traces and configuring endpoints yourself.
  • You want to own the running system, including the bill, the outage and the injection surface. If you want the output and not the operations, this is the wrong door.
  • It is big. 413 hours of material and 625 exercises; Context Engineering alone has 329 of them. Come for the long haul.
  • You want to wander, not to climb. Both plans here are 13 classes deep and one track wide, in a curated order. Somebody who wants to sample across all nine paths should start from the paths index and pick the one they mean.

If you want AI doing your work without building the plumbing yourself, two other paths cover the same ground with no terminal in them:

One sequence, worked out already

Press play tonight, be in the room on 7 September

179 clips and 230.5 hours open the moment you join, in the order the school teaches them, and every class above tells you what you will be able to do — and what you hand over to prove it — before you spend an hour on it. That next session is Agentic SDLC, and the live plan is a seat in it.

Both plans renew every month and you cancel yourself, any time. A class you finish stays yours afterward.

The AI Builder $250/month
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