Can Google Gemini be vibe coded?

Build brief — a focused alternative to Google Gemini

**Verdict:** Not faithfully · **Buildability:** 12/100 · **Category:** AI Assistant

**Source:** https://www.canitbevibecoded.com/gemini

Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Google Gemini. Verify current pricing and capabilities before acting.

Context

**Google Gemini** — Google AI assistant bundled with Gemini Advanced and Google AI Pro. It currently costs $19.99/mo.

A chat UI is easy, but Gemini's model access, Google ecosystem integration, multimodality, storage tie-ins, and platform distribution are not solo-buildable.

This brief describes a focused, single-operator replacement for the part of Google Gemini that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

What you are building

Build a multi-model chat app with document upload and optional Google API integrations.

Build a focused single-user workflow with real persistence, search, and export.

A responsive interface with real empty, loading, success, and error states.

Requirements

Functional

Hosted app.

Storage.

File parsing.

Data and integrations

LLM API.

Optional Google OAuth/API access.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

Non-functional

Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.

Security: server-side secrets, validated input, and no credentials in the client bundle.

Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.

Portability: the operator can export their data and leave without losing it.

Implementation brief

Build me a personal chat client on the Gemini API to replace my Google AI Pro

subscription with per-token billing. Requirements:

A local web app: Node + Express + better-sqlite3, one page, binds to

localhost.

Gemini API key in .env, with optional Claude and OpenAI keys too; a model

picker per conversation.

Streaming responses with Markdown and code-block rendering; history in SQLite

with FTS5 search across old conversations.

Upload PDFs and images and pass them through to models that accept them.

System-prompt presets as .md files in ./prompts/, selectable per chat.

Export any conversation to a Markdown file.

No accounts, no telemetry; history stays on my machine, only prompts go to

the APIs.

Out of scope: Gmail, Docs, and Drive integrations, and mobile apps. Do not

build Google Workspace OAuth; that ecosystem tie-in is the subscription.

README: where to create each API key, a rough cost table per model, and state

that per-token billing can land above or below $20/month depending on use,

the model itself is only rentable.

Delivery standard

Inspect the repository first, then write a short implementation plan before writing code.

Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.

Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.

Include responsive layouts plus genuine empty, loading, success, validation, and failure states.

Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.

Add structured logs around every external call and return actionable errors without leaking sensitive details.

Write unit tests for the core logic and one automated test of the main user journey.

Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

Acceptance criteria

A clean install starts the app using only the README and .env.example.

The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.

Invalid input, missing configuration, provider failure, and an empty database each have a usable state.

The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.

Tests, type checking, linting, and a production build all pass with no ignored failures.

No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

Non-goals

Do not build these, and do not claim to have replaced them:

Frontier models.

Google app integrations.

Mobile/native distribution.

Multimodal stack.

Model quality and inference operations are part of the product.

Connectors, OAuth flows, and vendor API changes require constant upkeep.

What you still own after launch

Secure credentials, rotate secrets, and handle provider rate limits.

Run migrations, backups, restores, and dependency updates.

Test the critical journey after every model, API, or hosting change.

Monitor failures and fix the edge cases a first prompt will miss.

Maintain every third-party integration as APIs and OAuth rules change.

Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

Prior art

Working open-source software you can read, fork, or borrow from before starting:

[Open WebUI](https://github.com/open-webui/open-webui) — Open-source self-hosted chat UI for model APIs and local models

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