AI's missing layers - owned by the engineers improving them

Our first model is a task router for multi-model coding harnesses. Integrate it and earn a stake every time your decisions improve it.

What you capture when you integrate.

Hokusai routes coding tasks for a per-decision fee. The fees back the token. You capture a position in that fee stream — plus a smarter router that lowers your inference costs along the way.

Backed by a real fee stream

Every routing call through Hokusai pays a per-decision fee in USDC. Those fees flow into the router's bonding curve and back the token's redemption value. Your stake is a position in real network revenue, not in a performance metric.

A position in the router

Every meaningful improvement your outcome data produces mints HTASK tokens to you. The token is a position in the router's fee stream — held as ownership, or redeemed for USDC anytime.

Lower inference cost

A smarter router picks the right model for each task. You spend less on inference before counting any token rewards.

Configurable value share

Decide at integration whether to keep the token flow as revenue, pass it through to your users as an ownership feature, or split the two. Configurable later, not just at setup.

Coding / Multi-Model Routing

Coding Task Router

Hokusai's first router corpus comes from real autonomous coding evals, not synthetic routing examples.

696

Deduplicated router-training records

209

Challenge-mode routing examples

Head-to-head model comparisons

88%

Migration, feature, and bugfix work

The data spans migrations, features, bug fixes, infra, tests, refactors, and docs across TypeScript, JavaScript, Python, Bash, and mixed-language repos. This provides a decent starting point for learning routing behavior while leaving clear room to expand coverage across more repositories, languages, and task families.

Learn more about how the choice layer works

Integration

Drop-in middleware.

Route tasks through Hokusai, execute them in your harness, then report the result back so the shared router can keep improving.

import { route } from '@hokusai/router'

const { model, reasoning } = await route({
  task: userTask,
  context: harnessContext,
})

const result = await models[model].run(userTask)
await route.reportOutcome(result) // mints tokens proportional to performance lift

Learn how to contribute

Where does your routing data go today?

	Lab-owned auto-routing	Hokusai
Who pays for routing decisions	The lab keeps it as gross margin	Integrators pay; fees back the contributor token.
Who captures the optimization signal	The lab	You and the contributors
Who keeps the inference cost savings	The lab keeps margin	You
What you build over time	Nothing transferable	A token position in the router
Portability across harnesses	Locked in	Take your position with you
Auditability	Opaque	On-chain attribution

What else the factory makes

The router is the first decision layer built on Hokusai. The same protocol applies to every model where shared, incentive-aligned learning beats siloed effort.