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
- Design a churn-prediction system for a subscription software company with 2 million customers. The retention team contacts at-risk customers weekly.senior40 min
- Design a daily demand-forecasting system for a grocery chain: 500 stores, 20,000 SKUs, forecasts 1 to 14 days ahead feeding automatic replenishment.senior40 min
- Describe how you would deploy a new version of a model (or a new prompt for an LLM feature) safely, and how you would decide to roll back. What must be in place before the first deployment?senior15 min
- A regulator asks your team to explain one specific loan decline from nine months ago: what exactly did the model see, which model version produced the score and why. Nobody can reproduce it.senior12 min
- A customer asks to have their data deleted. You discover it appears in raw logs, a feature table, a training set used by three deployed models, a vector index for an assistant, and the prompts and…senior15 min
- A ranking service must answer within 300 ms at the 99th percentile. For each request it calls five independent backend services in parallel (inventory, price, user profile, ML features, ad filters);senior15 min
- Whiteboard case. A payments-risk team and a recommendations team both want "fresh" per-user features, such as transactions in the last hour and average basket in the last 30 days, at serving time…senior35 min