In meditation, momentum is everything — not gripping the calm, but being willing to return to it again and again. And a different attitude to the mind: investigating why sadness or craving arises at all, until the mind's play loosens its grip.
When a child melts down, it feels like they're giving you a hard time — but really they're having one, caught in a storm their nervous system can't yet hold. Seeing that lets you lower your voice and simply stay. And the same is true of every difficult adult.
With AI, supposedly one prompt makes a polished UI. The way I actually do it is 400 prompts, late nights after my daughter sleeps, two-word stretches between her interruptions. When someone says it was *just AI*, they miss what is actually happening. Writing code is cheap now — the cost of trying an idea collapsed, and work I never would have shipped comes back in reach. The bottleneck moves to taste — also the part AI cannot do, because the genuinely new isn't in its data yet.
A phrase from a Vipassana book I found at a used bookstore — "the feeling of unworthiness" — and how much of what people do, the pleasing and the masks and the drama, runs on it.
The most important thing I could pass on isn't technology or knowledge or even morality — it's the ability to manage your own mind: to stay quiet in chaos, to guard against jealousy and anxiety becoming habits, and the honest catch that I can't give it without having it first.
The Rigveda's river hymn looks like it lists rivers in geographic order. Scholars said so for 150 years but never measured it. I did — and ran the same test on four more old lists. The Indian ones follow the map; the Zoroastrian Avesta and Genesis don't, or not simply. What a list is for seems to matter more than its language or its century.
A short take on the Vedic birth chart: read it backward, not as a forecast but as a record of the tendencies a person arrived with — the specification of a being fused from several streams of consciousness. The full argument is in the linked paper.
A strong memory for your own conclusions can quietly block fresh thinking — the mind says "I already know this" before it has looked again. Weak retention and the ability to re-derive may be two sides of one coin, and a way, too, of re-reading a difficult Mercury.
Kumar asked me what an AI agent costs to run. I didn't know. This is the story of how that question turned into a calculator, a paper, and a book — because the answer needed to work for the next team to ask, not just his.
A section-by-section walk-through of the methodology in "Cost Modeling for Public-Facing LLM Chat Applications" — the naive per-token formula and why it lies, the structural corrections that break it (caching, traffic shape, segments, daily caps), the six equations that replace it, and the worked geospatial-Q&A example that ties them together.
Twenty-three interactive chapters that walk language models end-to-end — from what tokens are and how attention works, through retrieval and context engineering, to the production techniques (caching, tools, evals), inference and serving, and the choice of model itself, closing with foundation models beyond language. Read in order or jump to the chapter that matches what you are building.
A single-page, progressive tutorial for engineers and analysts who want to build their own AI-agent cost calculator. Starts at "what is a token?" and ends at a reference architecture you can clone — data model, formula library, and a validation harness. Implementation-agnostic; one worked reference lives at calc.ajinkya.ai.