Super Individual / Solo Company / AI Leverage Systems
From learning reviews and platform audits to multi-agent collaboration, I turn AI into durable production systems with memory, evidence, and handoff, so one person can operate with small-team leverage.
I am building a personal lab around one question: in the AI era, how can one person turn reading, learning, audits, writing, and agent collaboration into stable production capacity instead of short-lived excitement inside chat windows? The answer I keep testing is long-term memory, evidence receipts, reviewable decisions, and learning loops that keep improving.
Working Materials
From learning to audits to agent collaboration
They are not scattered projects. They are one route: helping a solo builder preserve thought, organize action, verify results, and keep iterating after failure. Public work links to repositories or live products; private systems show capability boundaries only.
A Markdown operating system for coordinating AI workers with founder decisions, durable worklogs, handoffs, dashboards, and reusable skills. This is the public anchor project.
A state-aware collaboration protocol for AI agent work receipts, with a reference doctor that checks claims, missing evidence, ownership, and decision boundaries.
A companion knowledge system for turning reading into structured reflection, reusable insight, and long-term founder learning loops.
A controlled OTA price-transparency audit system with repeated sampling, screenshot anchors, evidence manifests, hash references, and explicit claim boundaries. The public package includes a redacted sample and methodology-facing summary.
A live IELTS study assistant for reading review, writing coaching, OCR-assisted material processing, and review-card based learning.
An evidence-constrained thinking coach that helps users reason better, ground claims in source material, and preserve useful learning traces.
I do not treat AI as a chat tool. I organize it into long-running work systems that can be reviewed, audited, and handed off.
AI should help define and decompose the problem, then test whether it is worth solving, before rushing to produce an answer.
Turning scattered model chats into decisions, worklogs, handoffs, and reviewable memory.
Designing systems that keep claims tied to source material, screenshots, manifests, or structured traces.
An agent should not merely say it finished. It should show what it did, what it did not do, where the evidence is, and what still needs human judgment.
Personal thinking, build notes, and lessons from the systems as they evolve. For now, this section keeps one anchor essay.