Welcome to Musings by Chris Hayduk
Hi—I’m Chris. I write rigorous, readable essays at the intersection of frontier machine learning, AI x biology, and industry/governance. The connective tissue is systems thinking: how methods, data, incentives, and history interact to produce real-world outcomes.
What I cover (the three pillars)
Frontier ML — reasoning LLMs, training signals (RLHF → RLAIF → GRPO), long-context, routing/MoE, evals that actually predict usefulness.
AI × Biology — the ESM line, structure/fitness modeling, how models interface with experiments, and what’s real vs. speculative.
Strategy & Governance — model economics, moats (data/compute/product), safety levers, and the policy/geopolitics that shape adoption.
What to expect
Cadence: one flagship explainer each week
Style: diagram-first; math when it clarifies; citations and links to primary sources; no performative hot takes.
Audience: ML engineers/researchers, biotech folks, and curious strategists who like crossing boundaries.
Start here
Frontier ML:
DeepSeek Series: routing, KV compression, and why V-series mattered.
AI x Biology:
Protein LMs Series: how we got from ESM2 to ESM3—and what changed.
ESM3 & the Future of PLMs: where capability is actually coming from.
Strategy & Governance:
The Strategic Implications of GPT-5 for OpenAI: how GPT-5 signals OpenAI’s shift away from the enterprise and towards the consumer
Managing Civilizational Tail Risks: how tail risks and ergodicity economics give insights into the rise and fall of civilizations
Free vs. paid
Free: all essays and the full archive.
Paid: subscriber-only chat and curated learning plans (math, physics, ML).
Consulting retainer: a higher tier that includes a 1-hour monthly session on a topic of your choosing.
(If you’re new, subscribe free—then read one piece from each pillar to see how the threads connect.)
About Me
In my day job, I am a Machine Learning Engineer at Meta working on applied AI & machine learning research for our ads ranking models. In my previous role, I served as the Lead Machine Learning Engineer for Drug Discovery at Deloitte, where I created biomedical knowledge graphs, trained & finetuned drug discovery AI models, and developed workflows for in-silico drug optimization.
My academic background includes:
A Bachelor of Science in Computer Science & Mathematics from Fordham University
A Master of Science in Mathematics from The City College of New York
A Master of Science in Applied Statistics from the Fordham Gabelli School of Business
[In progress] A Master of Science in Computer Science with a specialization in Machine Learning at the Georgia Institute of Technology
📫 How to Reach Me
Twitter: https://twitter.com/chris_hayduk1
LinkedIn: https://www.linkedin.com/in/chrishayduk/
GitHub: https://github.com/ChrisHayduk
Goodreads: https://www.goodreads.com/chris_hayduk
