The Pretraining Shadow: The Hidden Constraint Your Fine-Tuning Plan Ignores
Fine-tuning changes how a model talks, not what it fundamentally knows or believes. Here's what the research says about the ceiling practitioners keep hitting — and how to build around it.
SFT, RLHF, and DPO: The Alignment Method Decision Matrix for Narrow Domain Applications
A practical decision framework for choosing between supervised fine-tuning, RLHF, and DPO when aligning LLMs for narrow domain applications — including how to diagnose whether your alignment gap is a data problem, a reward problem, or a missing capability.
The Metrics Translation Problem: Why Technically Successful AI Projects Lose Funding
80% of AI projects fail to deliver business value — not because the models don't work, but because engineering teams never translate technical metrics into language executives can evaluate. A practical framework for mapping F1 scores, latency, and eval results to outcomes that keep projects funded.
The Cold Start Problem in AI Features: Why Week One Always Fails
Why behavioral ML systems fail on day one — and the layered bootstrapping architecture that keeps them useful before real training data arrives.
The Curriculum Trap: Why Fine-Tuning on Your Best Examples Produces Mediocre Models
Curating only high-quality, confident outputs as fine-tuning data creates distribution mismatch, destroys uncertainty awareness, and produces models that are confidently wrong. Here's why—and what to do instead.
Model Merging in Production: Weight Averaging Your Way to a Multi-Task Specialist
A technical deep dive into model merging techniques—weight averaging, SLERP, task arithmetic, TIES, and DARE—covering when merging beats ensembles, common failure modes, and how to deploy merged LLMs in production.
The Infinity Machine: How Demis Hassabis Built DeepMind and Chased AGI
From chess prodigy to Nobel Prize co-winner, Demis Hassabis built DeepMind into the world's most ambitious AI research lab. Sebastian Mallaby's biography traces the scientific breakthroughs, corporate battles, and existential dilemmas behind the quest for artificial general intelligence.