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Ark Pandey

👋 hey, I'm Ark — an engineer who builds systems

I teach AI to stay on brand. Every prompt. Every time.

Give a language model my component library and it stops guessing. Same design, same tokens, same clean code — whether it's a fresh chat, a new feature, or a teammate who's never seen the system before.

say hi to TOK ✦ he keeps the AI in line

// the one-liner

It's Bootstrap for the AI era.

Developers already trust component libraries — Bootstrap, MUI, Tailwind. You pull them in, and everything looks right without thinking about it. I build the same kind of library, with one difference that changes everything: it governs what AI is allowed to generate, too.

Engineers get the library they already know how to use — drop-in components, typed props, clean architecture they can merge without a second look. But it isn't only for engineers: PMs, designers, and anyone who wants to build a feature or make a change can prompt against it and get real, on-system code back. And every AI tool pointed at it produces output that already fits. One source of truth for people and machines alike.

"Design consistency stops being a rule people have to remember. It becomes something the system enforces on its own."

// where it earns its keep

The four moments where AI usually breaks.

Consistency isn't impressive in a demo. It's impressive on the hundredth prompt, six weeks later, from someone who's never seen your design system.

01

You keep re-prompting to get it right

Normally, "make it a bit tighter," "no, the other blue," "smaller radius" — you nudge the model ten times and still babysit the output. With the library, there's nothing to nudge. The tokens are the only values it can reach for.

✓ prompt #1 and prompt #10 return the same on-system component
02

You start a brand-new chat tomorrow

Fresh context, zero memory of yesterday's rules. A raw model starts guessing from scratch and drifts. Mine doesn't carry the rules in the conversation — they live in the library, so a new chat is already caught up.

✓ no re-explaining the design system every session
03

A teammate ships a feature without asking you

Different person, different prompt style, same deadline pressure. This is where design systems quietly rot. Here, their AI is pointed at the same library as yours — so their feature lands on-brand without a single design review ping.

✓ five engineers, one visual language, zero coordination tax
04

The model wants to invent a component

Left alone, an LLM will happily hallucinate a shiny new dropdown that doesn't exist in your system. The contract won't let it. It has to compose what's already there — no phantom components, no one-off styles to clean up later.

✓ it builds from your library, never around it

// see it happen

Same prompt. Flip the library on.

Both sides were told: "a primary action button." The only difference is whether my library is in the loop.

“a primary action button” → two setups
✕ off-system — invented radius, spacing & color
Raw, it free-styles every property. Not broken, exactly — but nothing stops it from being wrong, and someone pays for that in review.
// the contract — it picks intent, never raw values
interface ButtonContract {
  variant: "primary" | "secondary" | "ghost";   // no free-form color
  radius:  typeof tokens.radius.control;        // on-scale, or it won't compile
  spacing: typeof tokens.spacing.control;
}

// the receipts

Running in production, across real teams.

65%
fewer tokens per generation — rules live in the library, not every prompt
50%
less design review — AI output lands on-system by default
3+
teams shipping on it in production, more onboarding