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
- An airline wants an assistant that answers agents' questions about fare rules, baggage policy and rebooking procedures. Policies change monthly.senior15 min
- Explain how LoRA works. For one 4096 x 4096 weight matrix with rank 8, how many trainable parameters does LoRA add, and what are the practical trade-offs compared with full fine-tuning?senior12 min
- You must fine-tune a transformer classifier, but only 8 sequences of up to 512 tokens fit on one GPU at a time and the recipe you are reproducing used a batch size of 512.senior15 min
- A team fine-tunes an instruction-following model on 20,000 labelled airline customer-service replies. Task accuracy on their evaluation set rises (hypothetical numbers) from 71% to 89%, and they want…senior15 min
- You serve a 7-billion-parameter decoder-only model in 16-bit precision on one 80 GiB GPU for a support assistant with 8,192-token contexts. Throughput is far below the benchmark, and requests queue.senior15 min
- You compare two prompts for an LLM that extracts the refund reason from customer messages, on the same 200 labelled messages. Prompt A gets 156 right (78%), prompt B gets 164 right (82%).senior15 min