20+ Years of Ideas. Articulation Is the Craft. Here's How I Write Now. ✍️
What this is. Opinion + Experience + Fact (45% opinion · 30% experience · 20% fact · 5% fiction) Written in collaboration with AI — I discuss, I do not outsource.
Chapter 1. Ideas Came Easy. Writing Took Practice.
20+ years of building 🛠️. 40+ products shipped 📦. Plenty of stories to share.
What took longer to develop was articulation. English came to me later in life. The ideas came fast 💡. Putting them down the way they lived in my head took work — passes, then more passes. That has always been the real craft.
▸ First principle. Ideas are abundant; the scarce resource is clear articulation that other people can actually use.
Chapter 2. Two Writing Desks in One Home
When our kids want to write something, they know exactly where to go. For what to write — the idea, the angle, the thing worth saying — they come to me. For how to write — grammar, structure, polish — they go to their mom. She is a technical writer.
Yes, our family literally has two service desks. And yes, the queue at her desk is always longer 😊. I have always been the what parent. The how has always lived at a different desk.
▸ First principle. Separating "what" and "how" works inside a family, but if you want to ship serious work, you eventually have to own both.
Chapter 3. Finding My Own "How" Partner
Today I finally have a "how" partner of my own — my AI agent 🤖. One I can talk to anytime.
The Age of Google taught me how to learn 🔍. Search, read, find sources of truth, then stitch those into whatever you are building. I started as an embedded engineer, but that approach helped me build complete products — hardware to cloud, bench to factory floor 🏭.
The Age of AI gives me a different mode. AI can answer questions too — that is a valid use. What makes AI genuinely new is discussion 🧠.
▸ First principle. Tools change, but the job is the same — turn information into working systems. Google did it with links; AI does it with dialogue.
Chapter 4. Why Discussion Beats One-Shot Answers
Under the hood, AI predicts the next best token from the context it has. A bare question gets a generic answer. A discussion does three things at once: it feeds richer context, it lets me correct as it pushes back, and it sends the AI to research and validate against real sources. Each turn, the context sharpens. The predictions sharpen with it.
What emerges is articulation ✨ — work the two of us did together. Google gives you results. AI discusses, validates, and builds an answer with you.
Those of us who survived a wall of Stack Overflow tabs know which one we prefer 😅.
There is another reason discussion matters. A recent MIT study compared writers who used an AI assistant to generate essays upfront with writers who wrote first and brought AI in afterward. The second group — the ones who led with their own thinking — showed higher brain engagement, better memory recall, and stronger ownership of their work.
Discussion sharpens you over time 🎯. Outsourcing sharpens the AI.
▸ First principle. If the tool does the thinking, your brain goes idle. To grow, you have to carry the reasoning and let AI amplify, not replace, your work.
Chapter 5. The Idea Lives in Your Head, Not in the Prompt
And there is still one more reason discussion works — one that has nothing to do with AI at all. The idea lives in your head. AI cannot read it.
That is the whole problem. When I ask a question — anyone's question — the words on the screen carry only a fraction of what is actually going on upstairs. The full picture, the constraints, the weird edge case I am worried about, the several products I have seen this fail on, the part I cannot quite put into words yet — all of that stays in my head.
Engineers have known this for a long time 🦆. Rubber duck debugging says: explain your bug to a rubber duck on your desk, and in the act of explaining, you solve it yourself. The duck never spoke. You did the work. What the duck did was force you to articulate.
AI works the same way, with one big upgrade. It listens, pushes back, and asks what you missed. Each question it asks is a nudge to surface more of what is already in your head.
This is how AI helps you think out loud. That is why the same AI tool gives different quality of answers to different people. The output depends on the quality of the conversation.
▸ First principle. The real bottleneck is not the model; it is your ability to externalise your own context so the model has something meaningful to work with.
Chapter 6. Four Lenses I Read (and Write) Through
Two decades of reading others' writing taught me to ask one question first: what kind of writing is this? 📖
Is it fact — something I can verify? Is it experience — something this person lived through? Is it opinion — a position, even an informed one? Is it fiction — a hypothetical, a scenario, a thought experiment?
The answer changes how much weight I give it. An engineer's lived experience building a sensor driver is worth more to me than an essay theorising about sensor drivers. A cited benchmark is worth more than a comparison opinion. A hypothetical has its own role — it is how we imagine futures.
All four have their place. They are just different 🎯. A post built on pure opinion is still useful, if I know it is opinion. Writing that tells you what it is earns the reader's trust.
When you are shipping products, you learn to sort quickly. What can I rely on? What is someone's view worth considering? What is storytelling making a point?
I still read that way. I just did it silently, for years, in my own head.
▸ First principle. Every reader silently sorts what they read into fact, experience, opinion, or fiction. Labeling that sort upfront speeds trust on both sides.
Chapter 7. The Responsibility Flip: Reader to Writer
Today I am writing 🎤. That changes my role.
As a reader, I could quietly judge what kind of writing each post was. The writer never knew. As a writer, the responsibility flips. If I am asking readers to spend a few minutes with my words, I owe them the same clarity I always demanded as a reader.
Saying "this is opinion" out loud is a small act. It lets the reader weigh it accordingly. Saying "this is experience from 40+ products" tells them: this is what I lived — one builder's view. Saying "this is a hypothetical" tells them I am imagining, not reporting.
So here is the commitment 🤝 — every post I write from today forward will tell you what it is. The labeling will be visible. The formula will be honest. If I get the mix wrong, please tell me. Accuracy matters to me more than appearing perfect.
Writing publicly is a privilege. The least I can do is be honest about what I put in front of you.
▸ First principle. Long-term trust comes from showing your confidence and perspective directly — letting readers see what kind of writing this is, not making them guess.
Chapter 8. The Four Categories and the Math
Here are the four categories and how the math works 📐.
Fact — something verifiable. A cited benchmark, a proven principle, numbers I can back up with a source.
Experience — something I lived through. A specific project, a specific bug, a moment on the factory floor.
Opinion — a position, even an informed one.
Fiction — speculation, hypothetical, a thought experiment. Humor counts — it is almost always a small fiction.
The formula is simple. My AI agent and I read the draft paragraph by paragraph. We tag each one by its dominant substance. We weight by word count. We round to the nearest 5%. Pretending to be 62.7% opinion would be its own kind of dishonesty 😊.
When I committed to this approach, I checked whether others do something similar. They do.
Scott Alexander (Astral Codex Ten) has used a short "epistemic status" line at the top of his posts for years — a one-line note about how confident he is in what follows. NPR clearly labels opinion content and distinguishes it from news, analysis, and reviews to help readers understand what they are seeing.
Each of us found a version of this idea. We arrived at the same place from different roads. Mine is four categories with percentages, built for a builder writing on LinkedIn in the age of AI.
▸ First principle. Posts travel further than their author. A clear contents label keeps the post honest wherever it lands.
Chapter 9. What My Stamp Will Look Like
So here is what the stamp will look like 📋. Words first, percentages in brackets — telling you the mix of fact, experience, opinion, and fiction. Sources where research is cited. A note that the post was built in conversation with AI, not handed off to it.
If you take one thing from this: writing online is more useful when readers know what they are reading 🎯.
Next: a pattern I have watched repeat on project after project — and what finally stops it.
▸ First principle. A visible commitment is more durable than a stated intention. The stamp at the bottom of every post is the commitment kept.
STAMP BLOCK
Labeled: Opinion + Experience + Fact (45% opinion · 30% experience · 20% fact · 5% fiction)
Sources:
Your Brain on ChatGPT — MIT Media Lab study (2025)
Scott Alexander / Astral Codex Ten — epistemic status tags
NPR Opinion labeling policy and transparency standards
Advanced Chess / Centaur Model — Kasparov, 1998
(Written in collaboration with AI — I discuss, I do not outsource.)
— Ritesh | ritzylab.com
#ThoughtLeadership #WritingPublicly #FirstPrinciples #AICollaboration #BuilderVoice
NOTES
This is the foundational labeling article. It does NOT carry the "New to this labeling? Read the framework → [link]" footer, because it IS the framework. Starting from the next long-form article, that link footer points back to this piece.
All nine chapters carry a
▸ **First principle.**line per Rule 10.Top declaration uses Option 1 locked format.
All repeats from earlier drafts resolved: "20+ years" appears once in Ch1; Ch6 uses "Two decades". "Discussion is how AI..." in Ch5 replaced with "This is how AI...". Ch9 describes the stamp rather than re-promising.
