Second Brain AI Assistant

The product is a chat box with a memory. The user uploads or connects a corpus they already care about — their Kindle highlights, Readwise exports, saved articles, internal company handbook, or their own notes. Then they ask questions: "What did Naval say about leverage?" or "What books mentioned the concept of antifragile?" or "Summarize what I've read on Bitcoin policy this month." Every answer streams in token by token, with inline citations linking to the exact passage in the source document. When the corpus doesn't have the answer, the product says so instead of making something up.

The target user is a power reader — someone who consumes 50+ pieces of long-form content a month. They use Readwise, Pocket, Instapaper, or Reader. They've highlighted thousands of passages they meant to revisit but never go back to. Their second brain is locked in a search-only interface. What they actually want is synthesis: cross-document, cross-time, and conversational. They'd pay $9/month for a chat that can synthesize across everything they've read.

Multiple real startups occupy this exact space. Mem.ai built a self-organizing workspace on top of personal notes (acquired by Reflect AI in 2025). Glean raised $260M to build the enterprise version. Readwise added an AI chat layer on their reading library. The category is real and proven — the gap is the indie / personal-corpus version that doesn't require an enterprise contract. 50,000+ daily Readwise users and 100,000+ Pocket / Reader users are the addressable audience for a personal version.

Technically you build a document ingestion pipeline (parse → chunk → embed → store with metadata), a vector similarity search layer, a streaming chat endpoint that injects retrieved context into the system prompt, refusal prompting for retrieval misses, and inline citation rendering with clickable links. This template — RAG + streaming + citations — is the single most-tested skill set in AI Engineer interviews: RAG appears in 43% of job posts AND 44% of interview questions.

What you ship: a public GitHub repo with 4 reviewable Pull Requests (one per checkpoint), a live deployed URL, a 2-minute Loom that shows the full flow in one take, and 3+ real beta users who used it weekly for at least two weeks. When the interviewer asks "walk me through something you built," this is the answer that gets you past the first round.

“Develop and optimize RAG pipelines, agent architectures, and LLM-powered systems”

“You have experience building AI products using LLMs, embeddings and other ML technologies.”

“Develop hybrid ML solutions combining statistical models, LLMs, and embeddings-based retrieval techniques”

“Production experience with LLMs: prompt engineering, agent development, evaluation frameworks, MCP, deployment at scale”