Frontier Lab Notes

Frontier Lab Readiness MOC

This is the umbrella map for the subjects adjacent to Frontier LLM Architectures MOC. The architecture sphere is the trunk. These spheres are the branches that make you useful in a real frontier-lab environment.

Start Here

Read in this order:

  1. Frontier LLM Architectures MOC
  2. ML Systems for Frontier Models MOC
  3. Evaluation and Benchmarking MOC
  4. Mechanistic Interpretability MOC
  5. Agents and Tool Use MOC
  6. Multimodal Foundation Models MOC
  7. AI Safety and Security MOC
  8. Research Engineering Practices

Why These Matter

Sphere Why frontier labs care
ML Systems for Frontier Models MOC Models are constrained by hardware, memory, distributed training, inference latency, and cost.
Mechanistic Interpretability MOC Labs need to understand what models represent and how behavior emerges or fails.
Evaluation and Benchmarking MOC Bad evals create fake progress; strong evals drive real research.
Agents and Tool Use MOC Models increasingly act through tools, code, browsers, and long-horizon environments.
Multimodal Foundation Models MOC Frontier models are no longer text-only; vision, audio, video, and action matter.
AI Safety and Security MOC Capability without reliability, misuse resistance, and monitoring is not deployable.
Research Engineering Practices Clean experiments, logging, ablations, and reproducibility are how research becomes knowledge.

The Frontier-Lab Skill Shape

You want a T-shape:

Good specialty choices:

The Minimum Serious Portfolio

Build at least four artifacts:

Stronger portfolio:

Weekly Study Rhythm

North Star

Aim to become the person who can say:

I understand the mechanism, I can implement a toy version, I know the systems bottleneck, I can evaluate it honestly, and I can explain what result would change my mind.

That is the research posture.