It started as 30+ AI agents.
Now it’s an operator OS.

One person, operating like a team.

Simple parts, wired for leverage.

Agents fire on GitHub Actions, think with the right model for each job, speak through Telegram and email, remember in Supabase, and answer to a single secure dashboard.

Pillar two: hand it a goal.

The biggest leap. Instead of triggering one agent at a time, I give Migi a goal — typed or spoken — and a team of specialist agents takes it from there: a supervisor plans it, workers research, draft and critique. And this cycle it stopped just planning and drafting — it learned to do the thing, publishing live once I approve. It can compose my existing fleet agents as tools inside a mission, and runs fully isolated from the scheduled fleet — its own assistant, its own compute budget, its own space.

Pillar three: talk to your knowledge.

A personal, private RAG system — a second brain that actually knows my work and my life. I teach it anything (notes, documents, web pages, even my own code repositories), then I talk to it by text or voice; it answers from my own knowledge plus a live read of my data, cites where each answer came from, and reads it back aloud. Searching your notes is friction; conversing with them is leverage.

One model for everything? Not anymore.

Migi stopped using one AI for everything and started thinking like a team lead — assigning each job to the right brain, with a backup for every brain, on a budget, without ever risking a breakage. A premium model leads the heavy, quality-critical work; free models handle the routine; and every agent has an ordered fallback chain.

The fleet grew up as engineering.

Prompted by two independent audits, this cycle didn’t add a fourth pillar — it added the engineering-maturity layer. Migi went from “a fleet that runs” to “a fleet you can trust and build on”: it tests itself, heals itself, plugs into any AI assistant, safely bolts on premium-tier free tools, and starts to learn from how I use it — all still on $0 infrastructure, all human-in-the-loop.

I don’t outsource my voice. I edit with it.

A closed-loop content engine that keeps me visible on LinkedIn. It surfaces the day’s best stories, drafts in a voice that’s mine, and guards against repeating itself — I choose the topic, shape the angle, and give the final yes. Editorial control at the top of the funnel; leverage everywhere else.

From an SMS to a categorized ledger.

A privacy-first money agent I hardened the way you’d harden a real product, not a demo — trained on my own bank messages and shipped with a test suite. It captures both bank SMS and app notifications, de-duplicates the same transaction across them, learns my category corrections, and ignores everything that looks like money but isn’t — so only money that actually moved is logged, with manual cash entry, a 24-month history, budgets and a full audit trail. All behind my login.

The tedious 95% of applying, done.

It continuously scouts target roles, scores each against my CV, writes a tailored cover letter, and hands me an apply-ready packet with a direct link. I make the final click — deliberately no auto-submit, for quality and full compliance — then track the whole pipeline on the dashboard.

Reviewed the way it’s actually screened.

Anyone can paste a resume into a chatbot for one generic opinion. This parses the file like an applicant-tracking system, scores it against the actual live roles my job agent is tracking, and reviews it through the same three sets of eyes that screen it in real life — then hands back prioritized fixes and tracks the score over time.

What actually lands on my phone.

Not concepts — real messages the agents send me every day, as instant Telegram pings and designed email digests. Public content only; personal data stays private.

I had it independently security-audited. Twice.

Migi runs my private life — my journal, my finances, my content, my job hunt — so I treated it like any real product that holds sensitive data. This wasn’t a feature cycle; it was a security-maturity cycle: two independent AI-driven audits of the whole system, reconciled into one plan and fixed in phases, then hardened end to end against the specific ways AI systems get attacked. Handling private data responsibly is part of the build, not an afterthought — and this is proactive hardening, never a response to a breach.

Built solo — the AI-native way.

From architecture to 30+ agents to a multi-agent system, a knowledge brain and a secure dashboard: designed and shipped by one person orchestrating an AI coding agent, end to end, on entirely free infrastructure.