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
- You lead a team of six. Leadership hands you eight AI/ML project requests for next year (a GenAI customer assistant, demand forecasting upgrade, fraud model refresh, dashboard automation, and four…staff25 min
- Over a year, your team's recommendation model improved click-through rate by 15% in successive A/B tests, but revenue per visitor is flat and returns increased.staff20 min
- Nine months into a demand-forecasting rebuild, the team has reached 70% of the accuracy-improvement target. The sponsor says "we have already spent 1.2 million; we must finish".staff25 min
- Your team of five engineers (about 100 productive engineer-days per month) operates 14 production models, each costing around 4 engineer-days a month in monitoring, retraining and incident work, so…staff25 min
- The CFO asks you to report the annual business value created by the company's ML systems. Five teams each submit a number from their own A/B tests: 6.0, 5.2, 4.1, 3.0 and 2.2 million (hypothetical).staff25 minin review