AI
Technical leverage
Turn capability into repeatable execution.
INDEPENDENT LEVERAGE STUDIO
Build leverage. Compound outcomes.
I turn repeated work, practical judgment and AI workflows into systems, products and experiments that can be reused, tested and improved. Builder × Investor × AI Operator × Business Thinker
01START
Choose where to enter.
02LEVERAGE
Six forms of leverage. One operating system.
Software
Scale leverage
Build once, serve repeatedly.
Business
Commercial leverage
Connect value, distribution and revenue.
Investing
Capital leverage
Allocate capital where asymmetry is strongest.
Systems
Time leverage
Replace repeated effort with durable workflows.
Content
Distribution leverage
Turn learning and proof into discoverable assets.
03PRODUCT SYSTEMS
Product systems
Four task-first systems, organized by workflow and tier so you can compare scope, status and evidence before choosing.
01 / PRODUCT SYSTEMSAI Operator
Build a personal AI operating system for clearer execution, context, verification and decisions.
3 TierCompare each tier's current status, price and version before choosing.
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02 / PRODUCT SYSTEMSSEO Autopilot
Turn SEO research, prioritization, implementation, QA and reporting into one repeatable workflow.
4 TierCompare each tier's current status, price and version before choosing.
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03 / PRODUCT SYSTEMSGEO Autopilot
Build a repeatable workflow for query mapping, source planning, citation visibility, content and measurement.
3 TierCompare each tier's current status, price and version before choosing.
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04 / PRODUCT SYSTEMSStarter Skills
Start with a small, inspectable skill before adopting a larger operating workflow.
1 TierCompare each tier's current status, price and version before choosing.
View edition04BUILDING NOW
Building Now
Experiments in progress, shown with their current state.
LAB 001
ModelPilot
LAB IDENTITY
A public beta that turns one pasted Codex task into a recommended model and reasoning effort.
01Task entry
02Task context
03Candidate recommendation
04Human decision
A research map for testing a lower-friction choice at task entry: Task entry → Task context → Candidate recommendation → Human decision
- Problem
- Users often do not know which Codex model and reasoning effort fit a task.
- Current hypothesis
- A single-paste transparent recommendation is useful enough to validate before building an automatic router.
- Next decision
- Measure whether real users follow the recommendation and come back to use the tool again.
05PROOF OF WORK
Case files from building real systems.
Evidence from building and validating real workflows.
CASE 001
RecordedFollowUp
Work type: MVP product build
A case study in making follow-up actions clearer and more consistent.
01Lead
02Next action
03Reminder
04Complete
05Next action
Next action
A recorded follow-up workflow map: Lead → Next action → Reminder → Complete → Next action
- Problem
- Follow-up actions become inconsistent and leads can be lost.
- Verification
- State-transition checks and automated tests provide reviewable evidence; no business benchmark is claimed.
CASE 002
RecordedAI Company OS
Work type: Governance architecture
A case study in giving agent workflows clear scope, approval, audit, and ownership boundaries.
01Inbox
02Agent
03Review
04Approve
05Execute
06Audit
A recorded governance workflow map: Inbox → Agent → Review → Approve → Execute → Audit
- Problem
- Multiple agents and people require clear scope, approval, audit, and ownership boundaries.
- Verification
- Review focuses on approval boundaries, ownership, and staged workflow checks; no autonomous business outcome is claimed.
CASE 003
RecordedSEO Autopilot
Work type: Codex skill workflow
A case study in turning repetitive, underspecified SEO requests into a durable operating method.
- 01Request
- 02Skill
- 03Work pack
- 04QA
- 05Report
A recorded request-to-report method map: Request → Skill → Work pack → QA → Report
- Problem
- SEO requests are repetitive and underspecified.
- Verification
- Durable skill structure, validation, and a QA workflow provide reviewable evidence.
06LATEST THINKING
Latest Notes
Practical writing on validation, systems, and product decisions.
- 01AI Systems / Product
What Makes an AI Skill Worth Paying For
A practical framework for distinguishing a durable Codex Skill from a long prompt.
2 min read - 02Product Validation
How I Validate AI Products Before Expanding Them
A practical note on testing a bounded workflow before adding more features, claims, or operating complexity.
3 min read - 03Human Control / AI Systems
Building With Codex Without Giving Up Human Control
Practical boundaries for using a coding agent as controlled leverage rather than an unreviewed authority.
2 min read
07STUDIO LOG
What changed in the studio.
A curated record of real product checks and active research.
- ProductAI Operator Workflow — FREEv1.0.0 · Tested
- ProductAI Operator Workflow — PLUSv1.0.0 · Tested
- ProductAI Operator Workflow — PROv1.0.0 · Tested
- LabModelPilotTesting
- ProductGEO Autopilot Pro — Generative Search Operating Workflowv0.1.0 · Tested
- ProductSEO Autopilot Pro — Codex SEO Operating Workflowv0.1.0 · Tested
08THE OPERATOR
Builder × Investor × AI Operator × Business Thinker
Built by CHU.
I approach products as a builder, markets as an investor and AI as an operator. The common goal is leverage: turning limited time, knowledge and capital into systems that keep producing value.
09EVIDENCE
Facts before claims.
The visible boundaries used to decide what is ready, what is checked, and what still needs approval.
Tested with real workflows
Claims stay tied to a real working context instead of a generic demo.
Versioned and last-tested
Product records show the version and the latest stated test date.
Exact contents before purchase
Scope, files and delivery boundaries are visible before a decision.
Human approval and QA
A person remains responsible where a workflow needs approval.