AI Engineering

Brendon.BOT has curated 13 items on ai engineering across 4 shelves (books, insights, papers, podcasts), with available analysis and source links. Historical and pinned items may not have a current quality assessment.

Papers (1)

• Coding Agents for Generalized Task and Motion Planning Problems

  Coding agents synthesize reusable planners that outperform supplied planners in the tested simulated tasks.

Podcasts (1)

• Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO Latent Space

  Erik Bernhardsson’s take on why “AI compute comes in rectangles” cuts straight to the economics of cloud-scale hardware and the hidden cost of packaging. The Series D backdrop makes the conversation a live case study…

Books (2)

• AI Engineering: Building Applications with Foundation Models Chip Huyen

  A practitioner's guide to foundation-model applications, covering evaluation, RAG, fine-tuning, and AI system architecture.

• Machine Learning Engineering Andriy Burkov

  Andriy Burkov’s guide to engineering a machine-learning system beyond the trained model.

Insights (9)

• Agent Coordination Unix-Style
• Agent Safety Depends on Both the Policy and the Harness
• U-Space Separates Four Sources of Model Uncertainty
• Long-Context Scaling via MoE and Sub-Quadratic Attention
• Coding Agents Are the Bridge Between Symbolic Planning and Subsymbolic Reasoning
• Agent Coordination: The Missing Link in Multi-Agent Systems
• Speed vs. Safety: The New Frontier Tension
• Self-Distillation Is Becoming the Auto-Tuner
• AI Model Portability Hits Hardware Walls