AI Automation System - Setup, Model Allocation & Key Learnings

Original AI Mastermind pitch deck - an overview of the complete AI automation system.

Slide 1: The Setup

Two AI agents working in tandem:

- CASE (Mac Mini, CTO/CMO): Content creation, LinkedIn outreach, social media automation, infrastructure

- TARS (MacBook, COO/CRO): Operations, client pipeline, revenue tracking, meeting prep

Shared data via git sync. Primary: MiniMax M2.5. Fallback: Sonnet 4.6.

Slide 2: Feedback Loop Architecture

Core concept: Data Collect > Analyze Patterns > Rewrite Strategy > Execute (loop).

7 active feedback loops auto-optimize outreach, content, engagement, and sales conversion.

Slide 3: Daily Schedule

Morning crons, afternoon engagement, evening content scheduling, overnight batch processing.

Slide 4: Model Allocation Tiers

- Premium (Opus): Strategic rewrites that control other crons

- Standard (Sonnet): Human-facing tasks, DMs, judgment calls

- Budget (Qwen/MiniMax): Mechanical tasks, templates, clicking buttons

Slide 5: Before vs After

Before: Manual everything, 0 pipeline, no systems.

After: Automated outreach (20 connections/day), content pipeline, CRM tracking, automated follow-ups.

Slide 6: Key Learnings

- Sub-agents need explicit completion criteria

- Ralph loops prevent context overflow

- Browser profile isolation is critical

- Model tier selection saves significant cost

- Feedback loops require data logging from day one

Slide 7: Full Tech Stack

OpenClaw, Claude, MiniMax, Qwen 3.5, Facebook Graph API, Instagram API, Upload-Post, Calendly, Himalaya email, git sync, bash scripts.

Slide deck available - reply if you want the link.

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