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

> The self-organizing workspace powered by AI

- Site: https://mem.ai
- Category: Productivity / AI Note-Taking
- Verdict: **Solid side project** (68/100 vibecodeable)
- Estimated effort: 2-3 weeks part-time

## Verdict

Build a personal web-based clone with Next.js and pgvector in a few weekends, but expect friction when wiring up background vector indexing and real-time audio transcription pipelines.

At $15/month ($180/year), Mem is relatively cheap, but building a personal-use clone of its core loop is a great weekend-to-side-project endeavor. You can easily replicate the core note-taking UI and vector-augmented search using Next.js, a PostgreSQL database with pgvector, and the OpenAI/Anthropic APIs. Where you will burn hours is setting up the background worker queue that chunks notes, generates embeddings on save, and hooks up Whisper for voice dictation without introducing latency into the editor UI. A personal web app drops the complex local-first multi-device CRDT sync headache entirely.

### What you can't replicate

- The massive historical training and fine-tuning data collected across hundreds of thousands of users
- Polished native OS-level menu bar capture and native iOS integrations

## What it does

An AI-powered knowledge management and note-taking application that automatically organizes notes, web clips, meeting transcripts, and voice memos using vector search and retrieval-augmented generation.

### Core features

- Frictionless note capture and Markdown editing
- AI vector search and RAG-powered chat across all notes
- Context-aware automatic note surfacing (Heads Up)
- Voice memo capture with automatic Speech-to-Text transcription
- Agentic web clipper for saving and parsing web pages
- Entity extraction and automatic tagging/linking

## The business

### Pricing

- Free: Free — Restricted tier capped at 25 notes and 25 AI chat messages per month.
- Mem Pro: $15/mo — Unlimited capture, AI chat, collections, and templates.

### Funding

$29.1M raised.
- Seed Round (~$5.6M - $6M, April 2021)
- Series A ($23.5M, November 2022)
Investors: OpenAI Startup Fund, Andreessen Horowitz (a16z), Floodgate, Shrug Capital, Unusual Ventures

Founded 2017.
Team size: 35-50.

## The hard parts

- Vector embeddings pipeline that indexes and updates semantic chunks asynchronously without blocking the UI
- Local-first SQLite storage with snappy multi-device sync and conflict resolution
- Real-time audio chunk streaming, transcription, and structural formatting via Whisper/LLMs
- Retrieval context window management ensuring relevant historical notes surface accurately during queries

## How to vibe code Mem

### Prerequisites

- Node.js (free): Required runtime for running the Next.js framework and local development packages.
- GitHub (free): Source control and seamless connection to deployment infrastructure.

### Recommended AI tools

- Claude Code: Best-in-class agentic coding tool for scaffolding full-stack features, managing database migrations, and debugging multi-file React apps directly from the terminal.
- Cursor: AI-native code editor ideal for iterative UI polish, writing Tailwind components, and reviewing inline code diffs.

### Stack

- Frontend: Next.js
- Backend: Next.js API Routes / Server Actions
- Database: Supabase
- Auth: better-auth
- Payments: None
- Other: Vercel AI SDK, OpenAI API (Embeddings & Whisper), Anthropic API (Chat & Agentic Routing), Tailwind CSS

### Hosting

- Vercel (Zero-config hosting for the Next.js frontend, API routes, and edge functions.): $0/mo (Hobby tier)
- Supabase (PostgreSQL database with pgvector extension enabled for storing notes and semantic vector embeddings.): $0/mo (Free tier)

### Build guide

1. **Project Initialization & Database Schema** — Initialize the Next.js application with Tailwind CSS, configure better-auth, and set up the Supabase PostgreSQL database schema with the pgvector extension.

```
Create a new Next.js 16 project using TypeScript, App Router, and Tailwind CSS. Configure better-auth with email/password authentication backed by a Supabase PostgreSQL connection. Create a migration script or SQL setup file that enables the pgvector extension and creates two tables: `notes` (id, user_id, title, content, created_at, updated_at) and `note_embeddings` (id, note_id, vector vector(1536), chunk_text). Set up basic environment variable validation using Zod.
```

2. **Note Capture & Markdown Editor UI** — Build a responsive split-pane dashboard with a sidebar for note navigation and a clean central Markdown editor for capturing thoughts instantly.

```
Build a dashboard UI using Tailwind CSS featuring a collapsible sidebar for navigating notes and a main workspace area housing a Markdown text editor. Implement real-time auto-saving with a debounce hook that persists note title and content changes to the `/api/notes` backend route. Ensure keyboard shortcuts (like Cmd+N for a new note) are wired up cleanly.
```

3. **Vector Embedding Pipeline & Background Indexing** — Implement a background processing pipeline that chunks note content, calls the OpenAI embeddings API, and stores vector representations in Supabase.

```
Implement a server-side embedding utility using the OpenAI API (`text-embedding-3-small`). Whenever a note is created or updated in the `/api/notes` route, chunk the markdown text into overlapping segments of roughly 500 characters, generate their vector embeddings, and upsert them into the `note_embeddings` table linked to the note ID. Ensure this runs asynchronously so the user's save action is not blocked by API latency.
```

4. **AI Thought Partner Chat & RAG Search** — Build the AI chat drawer using the Vercel AI SDK to query user notes via cosine similarity search over pgvector.

```
Implement a slide-over AI chat panel using the Vercel AI SDK and Anthropic API (`claude-3-5-sonnet`). When a user asks a question in the chat, generate an embedding of their query, perform a vector similarity search (`1 - (vector <=> query_embedding}`) against the `note_embeddings` table in Supabase, inject the top 5 matching note chunks as context into the LLM system prompt, and stream the cited response back to the client UI.
```

5. **Voice Memo Capture & Whisper Transcription** — Add audio recording capabilities directly in the browser that send audio blobs to an API endpoint for transcription via OpenAI Whisper.

```
Create a voice recording component that uses the browser's MediaRecorder API to capture audio clips. Add a 'Voice Mode' button that uploads the recorded audio blob to a Next.js API route. In the API route, forward the audio file to the OpenAI Whisper API (`whisper-1`) to get the transcript text, and automatically create a new note populated with the transcribed text and formatted timestamp.
```

6. **Polish, Error Handling, and Deployment** — Add error boundaries, toast notifications, loading skeletons, and deploy the application to Vercel.

```
Add comprehensive error boundaries, loading skeletons for note lists, and toast notifications for successful note saves and AI responses. Write a `CLAUDE.md` file summarizing project architecture, database tables, and build commands. Verify the application builds cleanly with `next build` and prepare configuration files for seamless deployment to Vercel and Supabase.
```

### Cost vs paying

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

- Domain name (optional): $12/year
- OpenAI/Anthropic API starter credits: $10 one-time
- Total: ~$22 one-time

**Ongoing costs (monthly):**

- OpenAI & Anthropic API usage (embeddings, chat, whisper for personal use): ~$3-5/mo
- Total: ~$4/mo

- Paying for the SaaS instead: $15/mo
- Build time: 16-24 hours
- AI tool credits: $20/mo (Claude Pro)
- Break-even: 2 months

## Sources

- [Mem Help Center - Platform Availability](https://help.mem.ai/)
- [Product Hunt - Mem 2.0 Launch Details](https://www.producthunt.com/posts/mem-2-0)
- [TechCrunch - Mem Series A Funding Announcement](https://techcrunch.com/)
- [PitchBook Profile - Mem Technologies](https://pitchbook.com/)