Agentic Coding Capability Course — Adhoc
Off-cycle deep-dive episodes outside the topic-numbered curriculum.
AudioWilliam Liu
Technical podcasts
Conversational deep-dives on language-model architecture, training, and scaling — with concrete examples and practical engineering takeaways.
Agentic Coding Capability: From Coding Models to Coding Agents
The series overview lays out the shared mental models and expert disagreements that recur throughout the podcast.
Agentic Safety & Alignment: From Predictors to Governed Agents
A rigorous map of agentic safety and alignment, from the moment prediction gains tool permissions through scheming, simulated failure cases, corrigibility, prompt injection, AI control, evaluation science, safety cases, and deployment governance. Maya and Leo use an enterprise research agent to distinguish capability from propensity, harmful compliance from unauthorized goal pursuit, and early-warning evidence from real-world prevalence.
Mastering Language Models: From Architecture to Optimization
Maya and Leo open the series with the map: seven stops from the Transformer blueprint to the machinery under massive models, anchored by a three-person startup building an insurance-claims assistant on eight GPUs. They lay out the mental models every LLM expert shares — trust curves, find the bottleneck, separate capability from behavior — then stage the field's cleanest fight on air: bigger models versus more data, from OpenAI's 2020 scaling curves to Chinchilla's flip to the serving-cost era that ran past both camps. Plus trailers for the live attention debate and the alignment fight to come.