# How to Vibe Code Your Own Pieces (and Stop Paying for It)

> The AI memory layer for modern work

- Site: https://pieces.app
- Category: Developer Tools & AI Productivity
- Verdict: **Don't bother** (15/100 vibecodeable)
- Estimated effort: 6+ months of full-time work

## Verdict

Keep paying or use native OS search — building a robust background capture daemon with multi-platform IDE and browser hooks is an immense systems engineering feat.

Pieces is not a simple wrapper; it requires writing a low-level native daemon in Flutter/Dart or Rust that continuously monitors screen activity, clipboard history, and browser state across three major desktop operating systems while interfacing securely with multiple IDE plugins. An AI agent can help scaffold individual plugins, but architecting the local daemon, managing local vector embeddings for months of work history, and executing real-time PII redaction without tanking system resources will consume months of painful debugging across OS permission boundaries.

### What you can't replicate

- Seamless zero-friction integration across every major IDE, browser, and communication app
- Optimized battery and memory performance of a background vision/context capture daemon across macOS, Windows, and Linux
- Ecosystem network effects and pre-built MCP extensions

## What it does

An AI-powered memory layer and context manager that runs locally in the background, capturing context across screens, clipboards, meetings, and development environments.

### Core features

- OS-level background daemon for continuous context capture
- Multi-source ingestion (clipboard, browser history, IDE activity)
- Local vector database and RAG pipeline
- Local LLM runtime and model orchestration
- IDE extensions (VS Code, JetBrains)
- Browser extensions (Chrome, Arc, Safari)
- MCP server integration for AI coding assistants

## The business

### Pricing

- Free: $0
- Pieces Pro: $10/mo
- Pieces Enterprise: Custom

Founded 2021.
Team size: Unknown.

## The hard parts

- Building a cross-platform background daemon that captures screen and clipboard data without draining laptop batteries
- Implementing real-time heuristic PII and credential redaction on local vision streams
- Managing a persistent local vector database indexing up to 9 months of contextual data
- Synchronizing inter-process communication between browser extensions, IDEs, and the core local daemon

## How to vibe code Pieces

### Prerequisites

- Node.js (free): Required for building companion extension toolsets and web dashboard views.
- GitHub (free): Repository hosting for your custom local agent and extension prototypes.

### Recommended AI tools

- Claude Code: Best-in-class agent for multi-file codebases and scaffolding complex local daemon architectures.

### Stack

- Frontend: Tauri
- Backend: TypeScript
- Database: Turso
- Auth: better-auth
- Other: Ollama, Vercel AI SDK

### Hosting

- Cloudflare (Hosting optional lightweight cloud sync endpoints and documentation.): $0/mo

### Build guide

1. **Project Scaffolding & Local Daemon Foundation** — Initialize a cross-platform desktop application using Tauri with a Rust backend core and TypeScript frontend to establish the local background service.

```
Scaffold a new Tauri desktop application project using TypeScript and Rust. Set up a persistent background tray service that runs silently on startup across macOS and Windows. Implement a secure local HTTP server inside the Rust core running on localhost to handle inter-process communication between browser extensions and IDE plugins. Ensure proper logging and error management for daemon lifecycle events.
```

2. **Clipboard and Context Capture Pipeline** — Build local event listeners to capture clipboard changes, active window titles, and browser URL changes.

```
Implement clipboard monitoring in the Tauri Rust backend that safely watches system pasteboard updates without leaking sensitive secrets. Create a background worker that polls the active OS window title and focused application name every 2 seconds. Store these captured events locally in an embedded SQLite database with timestamps, source application identifiers, and raw text payloads.
```

3. **Local Vector Database & RAG Pipeline** — Integrate local embeddings and vector indexing to make captured context semantically searchable.

```
Integrate local vector database capabilities using SQLite with embeddings generated via Ollama running local models. Build a chunking and embedding pipeline that processes captured clipboard snippets, browser text, and window contexts in the background. Implement a semantic similarity search endpoint within the local HTTP server so query requests can retrieve relevant historical work fragments.
```

4. **IDE Plugin & MCP Server Integration** — Create a Model Context Protocol (MCP) server that exposes the local context database to AI coding tools like Claude and Cursor.

```
Build an MCP (Model Context Protocol) server in TypeScript that connects directly to the local SQLite context database. Expose tools that allow AI assistants in VS Code or Cursor to query past snippets, active project research, and historical code context. Write comprehensive test cases verifying MCP tool request handling and JSON response formatting.
```

5. **Timeline UI & Search Interface** — Build a clean desktop UI to visualize the chronological timeline of captured work and query memories.

```
Develop a responsive Tauri frontend dashboard displaying a chronological timeline of captured work activities, snippets, and web research trails. Implement search filtering by time range, source application, and topic. Add interactive preview panels for inspecting code snippets and deep-diving into historical workflows.
```

### Cost vs paying

**Starting costs (one-time):**

- AI coding tools (Claude Pro): $20
- Total: $20 one-time

**Ongoing costs (monthly):**

- Local execution (Ollama): $0/mo
- Total: $0/mo

- Paying for the SaaS instead: $10/mo
- Build time: 120+ hours
- AI tool credits: $20
- Break-even: Never (built for learning)

## Sources

- [Pieces Official Website](https://pieces.app)