AI AGENTS · OPENCLAW · APPLE PLATFORMS · AUTOMATION · PRIVACY · HUMAN CONTROL
I build the layer between AI intent and real-world execution.
My work explores agents that can understand context, coordinate tools, operate across Apple devices, and remain accountable to the person in control. The goal is not another chat demo. It is dependable software with permissions, verification, recovery, and an audit trail.
2027 mode: local-first intelligence, explicit authority, observable execution.
01 OBSERVE Read the real state before touching it
02 FRAME Define the smallest complete outcome
03 AUTHORIZE Keep consequential decisions with the human
04 EXECUTE Use native tools with explicit boundaries
05 VERIFY Test the result and capture evidence
06 EVOLVE Ship the learning back into the system
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Privacy-first iOS keyboard intelligence. CARINA uses on-device models; MAYA provides deterministic contextual replies.
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A human-centered interface for coordinating multiple agents, permissions, tasks, and execution state.
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Risk-aware macOS voice orchestration with native feedback and an authenticated private relay.
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Native visual and spoken guidance for accessible, precise assistant-to-human handoffs.
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| Principle | What it means in practice |
|---|---|
| Private by default | Prefer on-device processing and narrowly scoped data access |
| Human authority | Consequential actions require clear permission |
| Verified execution | A system should confirm the result, not merely report an attempt |
| Native experience | Use platform capabilities instead of wrapping everything in a web view |
| Recoverable systems | Errors are visible, bounded, and designed for safe recovery |
LANGUAGES Swift · Python · TypeScript · JavaScript
INTERFACES SwiftUI · UIKit · App Intents · Apple Shortcuts
INTELLIGENCE OpenAI APIs · On-device Foundation Models · OpenCV
SYSTEMS WebSockets · REST · Docker · GitHub Actions
METHOD Build → Verify → Document → Improve
- Architecting CARINA OS as a controlled agent execution layer
- Connecting MAYA orchestration to native Apple workflows
- Building reliable handoffs between humans, agents, apps, and devices
- Turning experimental automation into testable product systems
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Architecture, prototypes, and the decisions behind CARINA and MAYA as they move from experiments into systems. |
Practical ideas for permission boundaries, verification, native automation, and human–agent handoffs. |
Issues and collaborations can influence what gets tested, connected, and shipped next. |
| Enter here | If you want to see |
|---|---|
| CARINA Command Center | Multi-agent control surfaces and execution state |
| CARINA × MAYA TYPE | Private, native intelligence on iOS |
| MAYA Orchestration Engine | Risk-aware tool and device coordination |
| Codex Click Guide | Accessible human–agent handoffs |
OpenClaw is an open-source personal AI assistant designed to run across operating systems and platforms. The compatibility direction I am exploring is a clean boundary between its agent runtime and the CARINA/MAYA control model:
OPENCLAW RUNTIME
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CARINA AUTHORIZATION → explicit scope · human approval · policy
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MAYA EXECUTION → native Apple workflows · tools · devices
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VERIFICATION → evidence · recovery · audit trail
This is a compatibility direction, not a finished integration. If you build with OpenClaw and care about safer execution, native Apple control, or verifiable agent actions, signal the use case through the integration link above.
