Use cues, state, invariants, and spaced rebuilding to turn coding problems into recognizable mental models.
Guides
Long-form pieces
14 guides, newest first. Opinionated notes on senior ML interviews, system design, and applied practice.
- How to learn coding interview problems without memorizing them
- Annotated senior-principal mock: ecosystem ranking
A synthetic ten-turn mock showing how an upper-IC candidate handles a watch-time win, creator damage, causal uncertainty, rollback, governance, and portfolio change.
- Annotated upper-IC mock: reasoning under a fixed budget
A synthetic ten-turn interview showing how fixed-capacity arithmetic, verification, serving, recovery, portfolio choices, and delegated leadership affect upper-IC calibration.
- Annotated principal architecture mock: enterprise agents
A synthetic interview transcript showing how technical framing, authority, failure semantics, portfolio judgment, delegation, and reversal move an answer from staff to senior principal.
- Frontier AI lab interviews in 2026: prepare for the format, not the logo
OpenAI, Anthropic, DeepMind, Meta, and xAI now test different combinations of codebases, presentations, research work, AI tools, and technical depth.
- Frontier-lab proof of work: the 100-word claim, artifacts, and references
Make one exceptional contribution easy to verify, then choose references who can independently describe the same decisions and working style.
- Designing a RAG system that actually works
RAG fails most often at retrieval, not generation. A practitioner's guide to the architecture, the failure modes, and what production teams actually do in 2026.
- LLM Evals: The hardest part of shipping LLMs, and why most teams get it wrong
Your model is only as good as your eval. Your eval is a product. Treat it like one. The patterns that separate teams that ship from teams that thrash.
- Senior through senior-principal ML scope
Calibrate upper-IC ML interviews by problem ownership, technical depth, portfolio judgment, delegated authority, durability, and evidence rather than inconsistent company levels.
- How to think about LLM inference cost
Most teams calculate inference cost by multiplying token price by token count. The actual cost structure has five layers and most of the optimization wins are in the bottom four.
- System design case study: personalized search ranking
Design personalized search from retrieval through ranking, counterfactual evaluation, serving, experiments, feedback control, migration, and staff-level operating decisions.
- The 5 things every applied scientist interview is actually testing for
Strip away the questions and the role-specific jargon. Every senior AS loop is checking the same five things. If you know what they are, the prep gets sharper.
- Applied Scientist vs MLE vs Research Engineer: what these roles actually do
Companies use the same ML titles for different work. Compare the modeling, research, engineering, and product mix before choosing a target role.
- Lessons from Marin 8B: what an open pretraining log actually teaches you
Marin trained the first open-source 8B model to beat Llama 3.1 8B and published every mistake. The transferable lessons aren't about TPUs. They're about how to run pretraining like a science.