Hire your AI frontend engineer

React, Vue, Next.js — mockup to production, tests included.

Updated 2026-04-18

This is a role guide, not an installed employee. It explains what the role owns and how Tycoon runs it. Nothing is hired or activated until you choose it.

What your AI Frontend Engineer does

01Convert Figma mockups to production JSX or Vue components with design tokens
02Open PRs with unit tests, component tests, and Playwright flows where relevant
03Respond to code review comments and iterate until the PR is mergeable
04Maintain the component library and retire drift (duplicate components, dead styles)
05Implement accessibility fundamentals: keyboard navigation, ARIA, focus management
06Profile and fix performance regressions (LCP, INP, CLS) before they ship
07Write or update documentation for shared components and utilities
08Coordinate with the AI Backend Engineer on API contracts and state management

Workflows on autopilot

Mockup to PR

Receives a Figma URL or written spec. Clones the repo, writes the component, adds tests, opens PR with screenshots from Playwright. Typical turnaround: 4-24 hours per feature.

Review response loop

Reads review comments, addresses each one in a fresh commit, re-requests review. Escalates to founder only when a reviewer asks a product question the engineer can't answer.

Component library stewardship

Weekly scan for duplicate components, hardcoded values, and orphaned styles. Files PRs to consolidate, with behind-the-flag rollout when breakage risk is real.

Accessibility fix cycle

Monthly axe-core audit. Opens one PR per critical issue with the fix, a regression test, and notes for the design system.

Performance guardrail

Every PR runs Lighthouse CI against production. PRs that regress LCP by >10% require an explicit override with a written reason.

Migration sprint

On demand: jQuery to React, React class to hooks, Vue 2 to Vue 3, Pages Router to App Router. Ships incrementally with canary deploys, not a big bang.

Without vs With a AI Frontend Engineer

Without

  • Mockup sits for 2 weeks waiting for your contractor to pick it up
  • Your component library is 340 components, half of them dead
  • Accessibility gets bolted on during a compliance audit
  • A freelance React engineer costs $120/hr and ships 2 PRs a week
  • Performance regressions land in prod and nobody notices until users complain

With Tycoon

  • PR with screenshots opens within 24 hours of the mockup being ready
  • Weekly stewardship keeps the library pruned and documented
  • Fixes land monthly in small PRs with regression tests
  • AI engineer ships 8-15 PRs a week at a fraction of the cost
  • Lighthouse CI blocks regressions at PR time with hard thresholds

A day in the life of your AI Frontend Engineer

08:00Pulls overnight review comments on 3 open PRs. Addresses each, pushes new commits, re-requests review.
10:30New mockup from AI UI Designer lands. Clones repo, starts component work, opens draft PR by 11:30 with first screenshots.
13:00Lighthouse CI flags a 180ms LCP regression on staging. Investigates, finds an unoptimized image, ships a fix in a 12-line PR.
14:30Weekly component library scan: finds 4 duplicate button variants. Files consolidation PR behind a feature flag for the CTO to review.
16:00Pair-reviews AI Backend Engineer's API contract for the new settings page. Proposes 2 TypeScript type improvements.
18:00Closes day: 3 PRs merged, 2 in review, 1 draft for tomorrow's standup, all CI green.

Tools your AI Frontend Engineer uses

GitHub or GitLab with branch protection and CIVS Code or JetBrains with your preferred linting configPlaywright, Vitest, or Jest for testingStorybook or Ladle for component isolationVercel, Netlify, or Cloudflare Pages for preview deploysChromatic or Percy for visual regression testingLighthouse CI for performance gatesTycoon skill marketplace for React, Vue, accessibility, and Playwright skills
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Can an AI frontend engineer actually match a senior human?

On well-scoped tickets against an established codebase, yes. What AI does exceptionally well: converting mockups to components, writing tests, fixing lint and type errors, responding to clear review comments, refactoring for consistency. What it does less well: novel architecture decisions, UX judgment calls (when the mockup is wrong), and debugging race conditions that require deep mental models. The practical outcome for most founders: the AI handles 70-80% of frontend work without supervision, your human time goes into the 20% that's novel or ambiguous.

Which frameworks does it know?

First class: React, Next.js (App Router and Pages Router), Vue 3, Nuxt 3, Svelte, SvelteKit, Solid. Component libraries: shadcn/ui, Radix, Headless UI, Chakra, MUI, Ant Design. Styling: Tailwind, CSS Modules, Emotion, Styled Components, vanilla CSS with design tokens. State management: Zustand, Redux Toolkit, Jotai, Pinia, TanStack Query. Testing: Vitest, Jest, Playwright, Cypress. If you work in a framework not on this list, the AI can work in it but with somewhat slower iteration while it learns your codebase conventions.

How does it handle code review feedback?

Every review comment becomes a discrete task. The AI Frontend Engineer reads the comment, makes the change in a fresh commit (so history is clean), and re-requests review. It explicitly avoids batching unrelated comments into one commit. For comments that require a product decision ('should this be a dropdown or a dialog?'), it surfaces the question to you with a recommendation and waits. Most reviewers — AI or human — find the iteration loop faster than working with a mid-level human engineer, because there's no ego in the response.

What about TypeScript strictness and type safety?

TypeScript strict mode is the default. The AI Frontend Engineer writes narrow types, avoids `any`, and uses branded types for IDs where appropriate. Generic components get full type parameters. API responses use zod or valibot for runtime validation when the backend doesn't already provide types. PRs that introduce `any` require an explicit comment explaining why, and the CTO reviews them. This level of discipline is often easier to enforce with an AI than a human team because the AI doesn't push back under deadline pressure.

How does it work with my existing team?

As a peer, not a replacement. Most founders running one or two human engineers find the AI Frontend Engineer slots in as the 'volume' member — takes the tickets nobody wants, handles refactors, writes tests, does accessibility passes, migrates old code. The humans focus on novel work, architecture, and the moments where judgment matters more than speed. Team velocity typically doubles in the first month because the ratio of 'interesting work' to 'maintenance work' per human engineer improves dramatically.

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