Training Your Own AI: AutoResearch, Nanochat, and the Karpathy Stack

Andrej Karpathy keeps saying something that most people miss: the future of AI isn't one model to rule them all. It's your model — trained on your data, optimized for your tasks, running on your terms.

This sounds like a distant future. It's not. The tools are here now, and they're more accessible than you think.

The Personal AI Stack

There are three layers to building a personal AI system:

1. Research automation — tools that gather, synthesize, and organize information
2. Lightweight interfaces — chat UIs that you control and customize
3. Workflow agents — AI that takes action on your behalf

Let me walk through each.

AutoResearch: Deep Dives on Autopilot

AutoResearch is a pattern more than a specific tool. The idea: give an AI agent a research question, let it search the web, read papers, synthesize findings, and produce a structured report.

I use this for:

• Competitive analysis before strategy presentations
• Technology evaluation when choosing tools
• Market research for new product opportunities
• Due diligence on potential partners or acquisitions

The output isn't a replacement for human analysis. It's a starting point — a well-organized collection of facts and sources that would have taken me hours to compile manually.

Nanochat: Your Own Chat Interface

Nanochat represents a growing category of lightweight, self-hosted chat interfaces. Unlike ChatGPT or Claude.ai, these tools give you:

• Full control over system prompts — define exactly how the AI behaves
• Custom knowledge bases — load your own documents and data
• Privacy — conversations stay on your infrastructure
• Cost transparency — pay per token, not per subscription

For a strategist or marketer, this matters more than you'd think. You can build a chat interface that knows your company's brand guidelines, product catalog, competitive landscape, and historical campaign data. Ask it questions and get answers grounded in your context.

CoWorker by Anthropic: The Non-Technical Path

Not everyone wants to set up infrastructure. Anthropic's approach with tool-use and MCP (Model Context Protocol) is making AI automation accessible without engineering resources.

The idea: connect Claude to your existing tools — Google Sheets, Notion, Slack, your CRM — and let it take actions on your behalf. Need to generate a weekly report from your analytics dashboard? Create a prompt that pulls the data and formats the output.

This is the bridge between "AI is cool" and "AI saves me 10 hours a week."

What I'm Building

My personal AI stack currently includes:

• Research automation — Claude-powered deep dives that feed into a Supabase knowledge base
• Document analysis — PDF and document processing for contract review and competitive analysis
• Meeting prep — automated briefing documents before key meetings
• Content drafting — first drafts of articles, presentations, and strategy documents

Each of these started as a manual process. I noticed the pattern, built the automation, and now spend my time on judgment calls instead of information gathering.

Why This Matters for Marketing Leaders

Here's the strategic implication: the leaders who build personal AI systems now will have a structural advantage within 18 months. Not because the AI is magic — but because they'll have:

1. Better information — automated research means you're always current
2. Faster synthesis — AI-assisted analysis turns raw data into insights
3. Documented thinking — every research session creates a searchable artifact
4. Compounding knowledge — your knowledge base grows with every query

The gap between leaders who use AI as a toy and those who use it as infrastructure will be the defining competitive dynamic of the next decade.

If you're interested in building your own research automation pipeline, I'm happy to share technical details. Reach out via the contact form below.