Study to the depth you'll be tested at.
Six-stage sessions on hard ML and SWE topics: the intuition, the derivation, code that runs, and a retrieval check you have to answer. Add a resume and that check asks about work you have actually shipped, not a generic example. The tutor stops after every stage, and every thread saves so you can resume days later.
157 topics · 10 domains · 2 tracks · 6 stages per session.
Why this beats asking a chatbot to explain it
six differencesIt stops and waits
A chat window hands you a wall of text and moves on. Here the session halts after every stage until you continue, ask, or take a card. That pacing is what turns reading into review.
The derivation is motivated
Every term gets a reason to exist. You learn what breaks when a term is removed, which is the question an interviewer actually asks.
Why the scale factor. Drop it and the dot products grow with dimension:
The code runs
Reference PyTorch next to the math, with the mask in the right place and a test at the end. Theory and implementation on one screen.
def attn(q, k, v, mask=None):
# scale before the softmax
s = q @ k.transpose(-2, -1) / q.size(-1) ** 0.5
if mask is not None:
s = s.masked_fill(mask == 0, float('-inf'))
return torch.softmax(s, dim=-1) @ vYou have to retrieve it
Stage 6 makes you reproduce the concept unaided, then grades the gaps. Recognition feels like knowing; retrieval is knowing.
You write it, then it gets reviewed
The practice lab is an editor where you implement the thing from scratch in PyTorch or NumPy. What comes back is a code review — what is wrong and on which line, what you got right, and one question about a design choice you made.
def attention(q, k, v):
s = q @ k.T / d_k ** 0.5
return torch.softmax(s, -1) @ vk.T transposes a 2-D tensor only — batched heads need transpose(-2, -1).It asks about your projects
Add a resume and the retrieval check stops quizzing you on a generic model. It asks you to defend the decisions you actually made and shipped — which is the version of the question an interviewer will ask.
A full session, exactly as it renders.
A recorded replay of all six stages — the same math, code, and diagrams a live session produces. No account, no model calls.
Try it right here
Watch a full session run.
A recorded replay of a real six-stage session — the exact pauses where the tutor stops and waits, plus two follow-ups you can ask. No account, no model calls.
6 stages · the same math, code, and diagrams a live session renders · ~3 minutes
Six stages, in order, never compressed — the pause after each one is the point.
Every topic, in the same six stages.
Never compressed, never reordered. The pause between stages is the product.
ML / Research
71 topics · 5 domainsAttention, transformers, optimization, RLHF, training systems, and the math behind modern models.
for Research Engineer · MLE · Research Scientist · Applied Scientist
Software Engineering
86 topics · 5 domainsRendering models, distributed systems, data structures, caching, databases, and production tradeoffs.
for Frontend · Backend · Fullstack · System Design · UI/UX
17 concepts written in the session format.
Open by default. Sign in for the parts that have to know you.
Read every concept, browse all 157 topics, and run 2 full six-stage sessions before you decide anything. An account raises that to 8 a month and turns on the parts that cannot work without knowing who you are.
Resume upload is optional, skippable, and deletable at any time. It exists so STAR stories and follow-ups can reference work you have actually shipped.
Sign in to set up context71 topics, 5 ML domains.
Try it before you exist to us.
Only Pro takes a card. The trial does not charge for seven days and cancels in one click.