ARTICLE · 39 MIN READ · JANUARY 17, 2026
Chapter 4: Reflection
First drafts are rarely final. Reflection gives agents the ability to critique their own outputs, find what's wrong, and iterate toward better results — before returning anything to you.
KOHSHEEN TIKU
Blogs
ARTICLE · 25 MIN READ · JANUARY 14, 2026
Chapter 5: AI Governance — Approval Regulation, Technical Levers, and the Coordination Problem
Alignment is what you build into the model. Governance is the institutional scaffolding that decides which models get built, who gets to run them, and what evidence we demand before they ship. This chapter walks through the technical AI governance toolkit and the FDA-style approval-regulation proposal.
ARTICLE · 40 MIN READ · JANUARY 13, 2026
Chapter 3: Parallelization
Sequential is clean. Parallel is fast. The art is knowing which tasks can run at the same time — and wiring the plumbing to make it happen.
ARTICLE · 33 MIN READ · JANUARY 09, 2026
Chapter 2: Routing
Prompt chains are predictable. The real world isn't. Routing gives agents the ability to make decisions — picking the right tool, sub-agent, or workflow based on what's actually in front of them.
ARTICLE · 93 MIN READ · JANUARY 05, 2026
Chapter 1: Prompt Chaining
LLMs choke on complex tasks. Prompt chaining fixes that — by breaking one hard problem into a sequence of simple ones, each feeding the next.
ARTICLE · 28 MIN READ · DECEMBER 26, 2025
Chapter 4: AI Security — Jailbreaks, Adversarial Examples, and Model Theft
Alignment is what you want the model to do. Security is what an adversary can make it do anyway. This chapter walks through the attack surface — model weights, API endpoints, training pipelines, and the model itself — and the security disciplines that already know how to defend each one.
ARTICLE · 24 MIN READ · DECEMBER 08, 2025
Chapter 3: Deception, Inner Alignment, and Mechanistic Interpretability
Even a perfect training objective can produce a model that learns the wrong goal — and behaves well only while it's being watched. Inner alignment is what's left of the alignment problem after outer alignment is solved, and it's the part we can't address with reward tuning alone.
ARTICLE · 23 MIN READ · NOVEMBER 20, 2025
Chapter 2: Outer Alignment — Specification Gaming and Learning from Human Preferences
You get what you measure, not what you mean. Outer alignment is the engineering problem of writing down a goal that, when optimized hard, still produces the behavior you actually wanted.
ARTICLE · 18 MIN READ · NOVEMBER 01, 2025
Chapter 1: Scaling Laws, Superintelligence, and Instrumental Convergence
Bigger models keep getting predictably better. That single empirical fact — combined with the logic of instrumental convergence — turns AI safety from a science-fiction concern into a present engineering problem.