Practice
Workplace simulations and interview practice. Optional beyond the weekday lessons; studios embed selected scenarios. Everything simulated is labelled.
- 100scenarios
- 25capabilities covered
- 62 hof simulated work
- 150interview questions
Scenarios by capability100
Production incident
Senior code review
Stakeholder simulation
Messy real-world data
ADR centre
Live coding and SQL
- SQL gym: rolling and same-weekday features with window functions30 min
- Python gym: entity-safe lag and rolling features in pandas30 min
- SQL gym: consecutive nearly-full streaks and why the first query lies35 min
- Python gym: turning cumulative booking curves into pickup in pandas35 min
- SQL gym: booking pace against the same weekday last year40 min
ML system design
Ambiguous business case
Verbal defense
Statistical traps
Ablations
Decision theory and value
Robustness and stress tests
Paper reproduction
RAG evaluation workbench
LLM failure lab
- Unsupported citations and broken JSON in a claims assistant35 min
- Injected instructions and a looping helpdesk agent40 min
- A claims summariser that never saw its last document35 min
- A refund assistant that paid twice, in the wrong unit35 min
- An oversized voucher late in a long baggage-claim conversation40 min
Multi-agent company
End-to-end lifecycle
Golden test set and regression
Hiring committee
AI cost–quality
Agents vs workflows
Teach-back and Socratic interviewer
Executive presentation
Research reading protocol
- Read a churn paper before believing its abstract35 min
- Read an agent benchmark paper before adopting its framework35 min
- Does long context make retrieval obsolete? Read the claim40 min
- A language-model judge that agrees with humans 85% of the time35 min
- LoRA matches full fine-tuning: how much can 400 invoices show?35 min
Interview gym159
- In a gradient-boosted model of booking cancellations, SHAP says "days to departure" is the top driver and that higher price lowers cancellation.senior12 min
- A no-show model trained on a dataset where no-shows were undersampled to 50/50 is now used to compute expected no-shows per flight for overbooking.senior12 min
- A team tunes a gradient-boosting model with a random search of 200 configurations on one validation set and reports the best validation AUC (0.83) as the expected production performance.senior12 min
- In a no-show model, the baggage-weight field is missing for 40% of high-risk-segment bookings and 5% of the rest (hypothetical). An analyst wants to drop rows with missing values;senior15 min
- Marketing asks for "four customer segments" from a k-means model run on 120,000 loyalty members, using total spend (range 0 to 50,000), number of trips (0 to 30), days since last trip, and share of…senior15 min