Upload your resume. Get personalized STAR stories. Pick an ML or SWE topic and study it through a rigorous 6-stage session — from intuition to internals to production code — grounded in your own project experience.
Pick what you're interviewing for — or flip between tracks mid-week.
For Research Engineer, MLE, Research Scientist, and Applied Scientist loops. Stage 3 is full math — derivations with every term motivated.
Roles: Research Engineer · MLE · Research Scientist · Applied Scientist
For senior SWE, backend, frontend, and fullstack loops. Stage 3 swaps math for internals: data structures, complexity, tradeoffs, and failure modes.
Roles: Frontend · Backend · Fullstack · System Design · UI/UX
Every session follows a rigid 6-stage structure. You can't skip stages, and the AI won't let you rush the derivation or the system design.
Your resume and STAR stories are injected into every session. The retrieval check asks about your specific implementation choices.
Calibrated to someone who has read the papers or shipped the systems. Deeper, not shallower.
Every topic. Every session. Always in order. Never compressed.
Shown: ML track. The SWE track swaps Stage 3 “Math” for “Internals & Complexity”.
2–3 sentences on what the concept solves and where it appears in real production systems. No jargon, no math — just the mental frame.
Core idea in plain language, then a structured diagram with tensor dimensions and data flow annotated. Mandatory for all DL architectures.
Full step-by-step derivation with every term motivated. Not just what each symbol is, but what breaks if you remove it.
Production-quality PyTorch with type annotations, every non-obvious line commented, and an explicit test snippet at the end.
5 graded questions: conceptual, implementation, applied, systems-level (latency/memory/scale), and failure modes. Ordered from warm-up to hard.
Conversational drill. The AI asks, waits for your answer, then tells you precisely what was right, wrong, or missing — no score, just a senior engineer interviewing you.
After each stage the AI pauses: “Ready to continue, or any questions before we move on?” Type “continue”to advance, or ask any clarifying question — the AI answers it fully, then resumes from where it left off. A quick-action button also appears so you don't have to type.
What Engineers Are Saying
J.K.
ML Engineer, ex-Meta
“The 6-stage structure is exactly what I needed. I stopped trying to memorize definitions and started actually deriving things. Passed my loop at a top lab.”
A.P.
Research Scientist, Google DeepMind
“The resume-grounded retrieval check is genuinely useful. It asked me about my specific transformer project and I realized I couldn't explain my own work precisely enough.”
D.M.
Applied Scientist, Amazon
“I've tried every ML prep resource. This is the only one that actually forces you to understand the math before moving on. The stage structure doesn't let you fake it.”
Upload your resume PDF and the platform extracts your experience, projects, and skills, then generates 6–8 STAR stories mapped to common behavioral questions.
Every session injects your full profile into the system prompt. The Stage 6 retrieval check is grounded in your work — if you built an attention-based model or shipped a rate-limited service, the AI asks about your implementation choices, not a generic one.
Edit your STAR stories and extra context at any time on the Profile page. Changes apply to the next session immediately.
Resume upload
PDF → extracted text → STAR story generation
Profile injection
Full resume + stories injected into every session
Grounded retrieval
Stage 6 references your specific project work
Editable anytime
Update profile on Profile page, effective immediately
Every session is saved. Click any past session in the history sidebar to reload the full conversation and continue from where you left off. The sidebar shows topic, stage reached, and date.
Topic mastery is tracked locally — a colored dot in the topic browser shows topics you've studied and roughly how many sessions on each.
After Stage 2 or later, a “Revision card” button appears. Click it to generate a compact summary: core concept, key equations or tradeoffs, the one implementation detail that distinguishes strong candidates, and the two most likely interview questions.
Designed for rapid pre-interview review — the night before your loop.
Classical models to LLM internals to RL theory — calibrated for RE / MLE / RS / AS loops.
Foundations
Architectures
Language and LLMs
Multi-task and Transfer Learning
Foundations
Tabular Methods
Deep RL
Pricing
Start free. Upgrade when the monthly cap gets in the way. Cancel any time.
Generous monthly free tier. Ideal for casual prep or trying the platform.
For candidates running daily sessions in the weeks leading up to interview loops.
No charge for 7 days. Cancel anytime in your profile.