Mark Barnes / MarkVizion

Original Protocols for Creative AI Systems

A knowledge moat of named methods and implementation patterns.

Universal BlakCode Protocol

A protocol for turning messy creative/technical intent into modular AI-buildable components without losing the human taste layer. Most AI workflows either preserve creative vision but stay manual, or automate aggressively and flatten the taste. BlakCode keeps intent, constraints, proof, and iteration visible.

  • Name the real user outcome
  • Split the system into proof modules
  • Assign each module a visible artifact
  • Route work through the right AI or human capability
  • Evaluate the artifact against the original intent
  • Capture the lesson as reusable memory

Local-First Agent Architecture

A desktop AI agent should run close to the work: local models, local files, local tools, and cloud escalation only when the task truly needs it. Always-cloud agents are expensive, fragile, and disconnected from real user state. Local-first agents can run longer, see more context, preserve privacy, and compound memory.

  • Keep routine reasoning local
  • Expose desktop/file/tool state explicitly
  • Use persistent memory for repeated workflows
  • Escalate to cloud models only for high-value reasoning
  • Make tool execution observable
  • Evaluate whether the workflow became easier next time

Human-Like AI Interaction Layer

Human-like AI interaction is not about pretending the machine is human. It is about perception, timing, memory, feedback, and readable intent. Users trust AI systems when the system shows what it sees, what it thinks is happening, what it is about to do, and what changed after it acted.

  • Show perception state
  • Explain intent before action
  • Use timing and staging to reduce confusion
  • Remember user preferences and repeated friction
  • Confirm risky actions
  • Make the result inspectable

Digital Embodiment vs Tool Use

Tool use is calling an API. Digital embodiment is perceiving an environment, choosing action from state, using tools, and adapting after feedback. Many agent demos stop at tool calls. The harder and more valuable layer is environmental awareness: the agent can tell where it is, what changed, and what to do next.

  • Perceive environment
  • Map available actions
  • Choose the next action based on state
  • Execute through tools
  • Observe the changed state
  • Store the lesson for future work