✳ The Multiverse School
The AI Builder · two ways in
Join us $250 Dashboard →

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.

Watch it all $250 Be in the room $500 Price your inference first

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.

🛡️ Defender Package

Secure what you ship

4 live sessions · 7 tools · recordings
$400 suggested / $300 floor
🔓 Independence Package includes Defender

Own your deployment stack

7 classes · cyberdeck build · self-hosting · security
$750 suggested / $500 floor

Add on anytime — keeps your current path. One-time purchase, classes yours to keep.

path · build-ai-systemsmeasured

tms path build-ai-systems --stats

classes
12
recorded
230.5 h
tools
48
You start from

A laptop and an API key.

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

Which sounds like you?

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. Your inputs stay here: the bench runs in this browser, and the bill it reaches is saved in it.

What are you building? (sets sensible defaults)

Control AI Spending

Segment text into tokens and cost it. Run an open model on your own machine. The class that makes the number above yours instead of somebody else's.

Context Engineering

Design a recursive summarizer for documents several times the size of the context window — 329 exercises aimed squarely at the input-token half of that bill.

Production Agent Engineering

Configure an open model endpoint behind a provider interface, so switching between hosted and self-hosted is a config change rather than a rewrite.

Keep It Running

Produce an itemized bill for what your system costs, and work out what one user costs you to serve — including the user who costs several times the median.

Already on the calendar

The next 7 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 · $350 on its own next up
  2. 12Sep Control AI Spending Saturday · 3.1 h of it already filmed · 12 exercises · $75000 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
  7. 9Oct RAG & Memory Friday · $400 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

  • 230.5 hours of recorded sessions across 12 classes, yours immediately
  • Exercises and the written curriculum for every class
  • The tools built for these 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 — 7 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 $200 to $75000 a class.

What you walk out able to do

What you can do by the end

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. 3 capabilities · Preoperational
  • follow a model quickstart model behavior
  • locate a tool in an open tool hub system construction
  • run a program someone else wrote and read its error 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. 4 capabilities · Primary
  • execute a tool call round trip by hand system construction
  • locate a claim in its primary source evidence and verification
  • locate the devices a passage uses on you distribution and influence
  • 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. 21 capabilities · Concrete
  • configure an assistants memory and outside connections interaction and interface
  • configure an open model endpoint behind a provider interface infrastructure
  • configure inference hyperparameters model behavior
  • elicit output from a model model behavior
  • express a data shape as a schema data shaping
  • operate a model as a first pass editor model behavior
  • produce a handoff that survives the author leaving organization and delegation
  • produce a list of where untrusted input enters a system security
  • produce a moderation score for an output alignment
  • produce a refusal boundary that fires on cases you did not list model behavior
  • produce a reusable system prompt model behavior
  • produce a rubric a model can apply evidence and verification
  • produce a stopping condition an agent can check on itself model behavior
  • produce a usable interface for an agent interaction and interface
  • produce an account of the gap between ideals and operating values alignment
  • produce an inventory of what you pay for and what it holds infrastructure
  • produce an itemised bill for what your running system costs infrastructure
  • recover a working state from your own version history system construction
  • segment text into tokens and cost it model behavior
  • transform a corpus into an embedded index data shaping
  • transform a transcript into a structured record data shaping

Prove itconfigure an assistants memory and outside connections — Memory on with one fact it retained across a fresh conversation, one connector enabled with the scopes it was granted written out, and one source you deliberately did not connect with the reason.

10 You make it general — a pattern, not a one-off. 21 capabilities · Abstract
  • characterise a models failure modes model behavior
  • characterise a plans weaknesses with adversarial review evidence and verification
  • characterise an applications injection surface security
  • characterise how a style works on a reader distribution and influence
  • characterise how an ai assisted attack unfolded security
  • characterise what a tool costs you beyond its price infrastructure
  • characterise what one user costs you to serve infrastructure
  • characterise where a teams work stalls organization and delegation
  • characterise who gains and who absorbs the cost of a deployment alignment
  • classify each recurring ai cost by keep downgrade or replace infrastructure
  • classify harmful outputs against a values statement alignment
  • classify models by fit for a workload model behavior
  • classify where ai can fill a shape and where it must not choose one interaction and interface
  • classify which framework applies where you are in the lifecycle security
  • constrain model output to a schema model behavior
  • generalise a reasoning prompt pattern model behavior
  • generalise a specification an agent can build from organization and delegation
  • generalise a tool interface for model use system construction
  • generalise several sources into a position you can defend evidence and verification
  • parameterise a prompt template model behavior
  • parameterise an agent persona 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. 19 capabilities · Formal
  • falsify a claim that a text was written by a person evidence and verification
  • falsify a models alignment guardrails alignment
  • falsify a prompt with a benchmark evidence and verification
  • falsify an applications defences against a named attack class security
  • hold several audience models on one surface interaction and interface
  • justify a safety case for a system that acts without you security
  • justify a vector index for a latency and corpus budget infrastructure
  • justify an embedding model and its dimensionality data shaping
  • measure a models value alignment against a stated standard alignment
  • measure whether a fine tune changed behaviour model behavior
  • measure whether a user pays more than they cost audience and market
  • verify a model output you thought was random model behavior
  • verify a program does what you claimed with a test system construction
  • verify a rubric against independent graders evidence and verification
  • verify a spending limit declines the charge you did not intend infrastructure
  • verify an agents actions with a critic evidence and verification
  • verify model written code against its specification evidence and verification
  • verify synthetic data preserves the property you need data shaping
  • verify you would know your system broke before a user tells you infrastructure

Prove itfalsify a claim that a text was written by a person — A judgement on passages of both kinds where you did not know the answer in advance, your score, and the cases you got wrong with the reason.

12 You build the whole system, and it holds together. 11 capabilities · Systematic
  • construct a multi agent conversation with turn taking system construction
  • construct a predictive account of a models behaviour model behavior
  • construct a retrieval system system construction
  • construct an agent that uses tools system construction
  • design a chunking strategy against measured retrieval data shaping
  • design a packaged assistant over your documents 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
  • design a system you can afford to keep running infrastructure
  • design an evaluation regime that decides model changes evidence and verification
  • design an interaction protocol for responsible use interaction and interface

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. 18 capabilities · Metasystematic
  • arbitrate between the measure a system optimises and the goal alignment
  • arbitrate which internal feature drives a models behaviour alignment
  • govern a swarm by information hierarchy organization and delegation
  • re architect an agent as a stateless reducer system construction
  • reconcile a failing retrieval by reranking and rewriting data shaping
  • reconcile a working set with the context window it must fit model behavior
  • reconcile agents from different toolchains into one run system construction
  • reconcile an agents run with a tool that failed system construction
  • reconcile what you built with what you can maintain alone infrastructure
  • select among agent architectures system construction
  • select among operating boundaries for inputs a system was not built for alignment
  • synthesise a long work that stays consistent across many generations interaction and interface
  • synthesise a memory hierarchy for an agent system construction
  • synthesise a metalanguage for a problem domain data shaping
  • synthesise a retrieval over relationships not just similarity 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 itarbitrate between the measure a system optimises and the goal — The measure, the outcome it stands in for, and a concrete behaviour that scores well on the first while failing the second.

Tagged by philosophy, this path is 63 think · 61 make · 37 own · 12 influence · 10 secure. A capability can carry more than one tag.

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 build on what came before: purple is what you walked in with, cyan is what that class hands you.

  1. 01

    Using Large Language Models

    • synthesise a metalanguage for a problem domain
    • reconcile a working set with the context window it must fit
    • design an interaction protocol for responsible use
    • +11 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.

    • 10.7h recorded
    • 55 exercises
    • Guided app
    Class 1 of 12
  2. 02

    Control AI Spending

    • verify a spending limit declines the charge you did not intend
    • classify models by fit for a workload
    • classify each recurring ai cost by keep downgrade or replace
    • +6 more

    Prove itA card with a monthly cap set below your previous spend, every subscription moved onto it, and the decline you caused deliberately. A limit nobody has tested is a preference, not a control.

    • 3.1h recorded
    • 12 exercises
    • Guided app
    • next session 12 Sep
    • $75000 on its own
    Class 2 of 12
  3. 03

    AI Alignment

    • originate oversight that holds when the system outpaces the reviewer
    • synthesise an accountability regime for an unattended agent
    • select among operating boundaries for inputs a system was not built for
    • +23 more

    Prove itThe scheme, a run where output exceeded what a reviewer could read, and an error the scheme surfaced anyway.

    • 1 exercise
    • Guided app
    • next session 25 Sep
    • $300 on its own
    Class 3 of 12
  4. 04

    Context Engineering

    • synthesise an agent that carries notes across its own runs
    • synthesise a memory hierarchy for an agent
    • select among agent architectures
    • +37 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.

    • 35.7h recorded
    • 329 exercises
    • Guided app
    • next session 16 Sep
    • $400 on its own
    Class 4 of 12
  5. 05

    Claude Model Context Protocol

    • generalise several sources into a position you can defend
    • generalise a tool interface for model use
    • produce a usable interface for an agent
    • +3 more

    Prove itThe position, the sources behind each claim, and the disagreement between two of them with the reading you chose and why.

    • 5.2h recorded
    • Guided app
    Class 5 of 12
  6. 06

    Intro to Agents

    • synthesise a self extending agent behind a review gate
    • select among agent architectures
    • reconcile agents from different toolchains into one run
    • +15 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.

    • 56.7h recorded
    • 32 exercises
    • Guided app
    • next session 15 Sep
    • $350 on its own
    Class 6 of 12
  7. 07

    Prompt Engineering

    • synthesise an agent that carries notes across its own runs
    • synthesise a self extending agent behind a review gate
    • synthesise a metalanguage for a problem domain
    • +41 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.

    • 63.5h recorded
    • 142 exercises
    • Guided app
    Class 7 of 12
  8. 08

    RAG & Memory

    • synthesise an agent that carries notes across its own runs
    • synthesise a memory hierarchy for an agent
    • select among agent architectures
    • +17 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.

    • Syllabus
    • next session 9 Oct
    • $400 on its own
    Class 8 of 12
  9. 09

    Advanced Retrieval Augmented Generation

    • reconcile a failing retrieval by reranking and rewriting
    • design a chunking strategy against measured retrieval
    • construct a retrieval system
    • +4 more

    Prove itA set of questions the first-pass retriever answered wrongly, the reranked or rewritten run, and the before-and-after scores on the same set.

    • 6.8h recorded
    • 7 exercises
    • Syllabus
    Class 9 of 12
  10. 10

    Production Agent Engineering

    • originate an operating model for managed agent teams
    • synthesise an accountability regime for an unattended agent
    • synthesise a self extending agent behind a review gate
    • +10 more

    Prove itA written operating model in use by at least two people, with a recorded instance of it catching a failure that the previous arrangement missed.

    • 22.3h recorded
    • Guided app
    • next session 17 Sep
    • $200 on its own
    Class 10 of 12
  11. 11

    Agentic SDLC

    • synthesise an agent that carries notes across its own runs
    • synthesise an accountability regime for an unattended agent
    • reconcile what you built with what you can maintain alone
    • +12 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.

    • 26.5h recorded
    • 46 exercises
    • Guided app
    • next session 7 Sep
    • $350 on its own
    Class 11 of 12
  12. 12

    Agentic AI Security: Securing What You Build

    • synthesise an accountability regime for an unattended agent
    • verify model written code against its specification
    • justify a safety case for a system that acts without you
    • +4 more

    Prove itA week of unattended runs with a per-action trace, and a replay of one run reconstructed from the trace alone.

    • 1 exercise
    • Reference
    Class 12 of 12

Straight answers

What is behind the login

230.5 hours of this path, filmed

Prompt Engineering is 63.5 hours; Intro to Agents is 56.7 hours; Context Engineering is 35.7 hours. 230.5 hours across 12 classes.

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 — the live plan 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 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.

  • Using Large Language Models 9 guided workbenchs, 3 aids.
  • Control AI Spending 1 guided workbench.
  • AI Alignment 1 guided workbench.
  • Context Engineering 7 guided workbenchs, 1 aid. Nine stations that walk you from a bloated prompt to a context budget you can defend, on your own material.
  • Claude Model Context Protocol 1 guided workbench.
  • Intro to Agents 2 guided workbenchs, 1 aid.
  • Prompt Engineering 3 guided workbenchs, 1 aid.
  • Production Agent Engineering 1 guided workbench, 1 aid.
  • Agentic SDLC 3 guided workbenchs, 1 companion, 1 aid, 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 walkthrough deck, 1 reference. A frameworks reference and a map of where untrusted input gets into the thing you built.

What you are signing up for

Know what this one asks of you

  • 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. If that sentence is the appeal, you are on the right page.
  • You end up owning the running system, including the bill, the outage and the injection surface. That is the point: nobody can take it away from you afterward.
  • There is a lot of it. 230.5 hours of recorded sessions across 12 classes — enough that you can keep going for months and still have somewhere to go.
  • It climbs rather than wanders. Both plans are 12 classes deep and one track wide, in a curated order, so each one stands on the last. If you would rather sample across the school, the paths index lays them all out side by side.

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

The AI Executive → The Founder →

One sequence, worked out already

Press play tonight, be in the room on 7 September

230.5 hours of recorded sessions 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.

Watch it all $250 Be in the room $500 See the other eight paths

The standups run every week on both plans, so neither one is a subscription to watching alone. Both 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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