Vibecode Google Gemini

You are building a lean indie version of Google Gemini.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# Google Gemini indie build

## Goal

Build the smallest trustworthy replacement for the core Google Gemini workflow for one developer or a tiny team.

## Scope

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

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- frontier models
- Google app integrations
- mobile/native distribution
- multimodal stack
- reliability

If those capabilities are essential, use Open WebUI instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build 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.

## Required capabilities

- LLM API
- hosted app
- optional Google OAuth/API access
- storage
- file parsing

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a lean indie version of Google Gemini.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# Google Gemini indie build

## Goal

Build the smallest trustworthy replacement for the core Google Gemini workflow for one developer or a tiny team.

## Scope

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

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- frontier models
- Google app integrations
- mobile/native distribution
- multimodal stack
- reliability

If those capabilities are essential, use Open WebUI instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build 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.

## Required capabilities

- LLM API
- hosted app
- optional Google OAuth/API access
- storage
- file parsing

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a production product version of Google Gemini.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== PRODUCT.md =====
# Google Gemini product brief

## Problem

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

## Product outcome

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

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- LLM API
- hosted app
- optional Google OAuth/API access
- storage
- file parsing

## Explicit non-goals for v1

- frontier models
- Google app integrations
- mobile/native distribution
- multimodal stack
- reliability

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

===== ARCHITECTURE.md =====
# Architecture

## Starting 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.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

===== AGENTS.md =====
# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

===== MILESTONES.md =====
# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

===== OPERATIONS.md =====
# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted Google Gemini capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Google Gemini indie build

## Goal

Build the smallest trustworthy replacement for the core Google Gemini workflow for one developer or a tiny team.

## Scope

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

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- frontier models
- Google app integrations
- mobile/native distribution
- multimodal stack
- reliability

If those capabilities are essential, use Open WebUI instead of pretending the gap is solved.

# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

# Build plan

## Original build 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.

## Required capabilities

- LLM API
- hosted app
- optional Google OAuth/API access
- storage
- file parsing

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

# Google Gemini product brief

## Problem

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

## Product outcome

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

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- LLM API
- hosted app
- optional Google OAuth/API access
- storage
- file parsing

## Explicit non-goals for v1

- frontier models
- Google app integrations
- mobile/native distribution
- multimodal stack
- reliability

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

# Architecture

## Starting 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.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted Google Gemini capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR