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
- What is overfitting? How would you detect it? And in a production forecasting system, how would you tell overfitting apart from temporal leakage?mid10 min
- Your insurance-claims fraud classifier has 99.2% accuracy and 0.93 ROC-AUC. Fraud is 0.8% of claims. The investigation team can review 200 claims a week.mid8 min
- A teammate adds a target-encoded travelagencyid (about 4,000 agencies, many with fewer than ten bookings) to a no-show model. Validation ROC-AUC jumps from 0.71 to 0.86.mid10 min
- A model predicts whether a customer will rebook within 90 days. The table has 10,000 rows from 2,000 customers (five bookings each, on average).mid8 min
- You model ancillary revenue per booking with linear regression. Revenue is strongly right-skewed, so you fit it on the log scale and get a good fit.mid8 min
- You fit a logistic regression for flight cancellations on bootstrap resamples of the same data. Two features, distancekm and scheduledminutes, correlate at 0.98.mid10 min
- A teammate classifies 100 labelled claims as fraudulent or not using 2,000 candidate features extracted from claim text and metadata.mid10 min