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.
12 classes from first prompt to deployed agent. Start tonight with 230 hours of recordings, or join the next live session on 7 Sep.
$250/mo recordings · $500/mo with live classes. Cancel any time. A class you finish stays yours.
Secure what you ship
$400 suggested
Own your deployment stack
$750 suggested
Add on anytime — keeps your current path. One-time purchase, classes yours to keep.
tms path build-ai-systems --stats
- classes
- 12
- recorded
- 230.5 h
- tools
- 40
A laptop, an API key, and some code.
This path assumes you can write code. The first class takes you from there. Not sure where you stand? Take the diagnostic — 2 minutes, no code required.
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)
A · Pay-per-token API
B · Serverless GPU (RunPod / Modal / Replicate)
C · Dedicated GPU (Lambda / RunPod reserved / your box)
Every rate is editable — pick a model or type your own numbers. Everything on the right recomputes from whatever you type. Tokens per second is real throughput, not the benchmark number. A month is 30.44 days.
See live benchmark data for every major model →Cheapest option at 100 requests a day
—
A · API
—B · Serverless
—C · Dedicated
—At your traffic and token mix, the dedicated GPU is the cheapest option. The API overtakes serverless at about 100 requests a day, and the dedicated GPU beats both at about 450 requests a day.
A serverless GPU charges only while it runs, so it wins at low traffic. A dedicated GPU charges all month, so it wins at high traffic. The API is the simplest but the most expensive per token.
Halve the prompt and the bill becomes — a month, — 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.
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.
tms schedule --path build-ai-systems
- 7Sep Agentic SDLC Monday · 26.5 h recorded · 46 exercises · $350 on its own next up
- 12Sep Control AI Spending Saturday · 3.1 h recorded · 12 exercises · $75000 on its own
- 15Sep Intro to Agents Tuesday · 56.7 h recorded · 32 exercises · $350 on its own
- 16Sep Context Engineering Wednesday · 35.7 h recorded · 329 exercises · $400 on its own
- 17Sep Production Agent Engineering Thursday · 22.3 h recorded · $200 on its own
- 25Sep AI Alignment Friday · 1 exercise · $300 on its own
- 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.
$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
$500a month
Be in the room
- Everything on the left, plus a seat in every upcoming class on this path
- Bring your own traceback and ask about it out loud — 7 sessions are already on the calendar
- Every session is recorded as it runs, so the hour you miss arrives days later
- The archive keeps growing while you are in it
Both renew every month and you cancel either one yourself, any time. Classes can also be taken one at a time, at their own prices — this path runs from $200 to $75000 a class.
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.
-
01
Using Large Language Models
- 10.7h recorded
- 55 exercises
- Guided app
-
02
Control AI Spending
- 3.1h recorded
- 12 exercises
- Guided app
- next session 12 Sep
- $75000 on its own
-
03
AI Alignment
- 1 exercise
- Guided app
- next session 25 Sep
- $300 on its own
-
04
Context Engineering
- 35.7h recorded
- 329 exercises
- Guided app
- next session 16 Sep
- $400 on its own
-
05
Claude Model Context Protocol
- 5.2h recorded
- Guided app
-
06
Intro to Agents
- 56.7h recorded
- 32 exercises
- Guided app
- next session 15 Sep
- $350 on its own
-
07
Prompt Engineering
- 63.5h recorded
- 142 exercises
- Guided app
-
08
RAG & Memory
- Syllabus
- next session 9 Oct
- $400 on its own
-
09
Advanced Retrieval Augmented Generation
- 6.8h recorded
- 7 exercises
- Syllabus
-
10
Production Agent Engineering
- 22.3h recorded
- Guided app
- next session 17 Sep
- $200 on its own
-
11
Agentic SDLC
- 26.5h recorded
- 46 exercises
- Guided app
- next session 7 Sep
- $350 on its own
-
12
Agentic AI Security: Securing What You Build
- 1 exercise
- Reference
Straight answers
What is behind the login
230.5 hours of this path, recorded
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. Start any week and the sequence picks you up.
And the room they were recorded 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 recorded 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.
Watch it all $250 Be in the room $500The 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 4 guided workbenchs, 3 aids.
- Control AI Spending 1 guided workbench.
- AI Alignment 1 guided workbench.
- Context Engineering 4 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 to expect
What this path 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.
- 230.5 hours across 12 classes. This path moves fast and builds in sequence.
- Curated, not a buffet. Both plans are 12 classes deep and one path wide, in a deliberate order, so each one stands on the last. Want to browse first? See all learning paths.
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:
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.
The standups run every week on both plans. Both renew every month and you cancel yourself, any time.