title: CLAUDE.md/MEMORY.md Anti-Pattern Fix

insight: Our CLAUDE.md and MEMORY.md consume ~22% of context window before any work begins - this is the exact anti-pattern IndyDevDan warns about

reasoning_chain: IndyDevDan's video on Elite Context Engineering explains bloated claude.md files waste 10-22% of tokens

reasoning_chain: Our MEMORY.md alone is 525+ lines - way over Dan's recommended 43 lines

reasoning_chain: Fix: Shrink to 43 lines essentials, move task-specific to /prime slash commands, use file-based context bundles

source_context: Dan's fix: Shrink CLAUDE.md to ~43 lines / 350 tokens of universal essentials. Move task-specific instructions into `/prime` slash commands. Token math: MCP tools (~12%) + bloated claude.md (~10%) = 22% wasted before any work begins


title: Best-of-N Agent Pattern

insight: At scale, the bottleneck shifts from creation to review - running 9 parallel agents for 9 landing page variants simultaneously

reasoning_chain: Architecture ladder shows progression: Single agent → Better agent → Multiple agents → Custom agents → Orchestrator managing sandboxes

reasoning_chain: At scale, planning and reviewing become the key engineering activities - directly relevant to our 200-agent swarm

source_context: Best-of-N again — Dan ran 9 parallel agents (3 prompts x 3 forks) for 9 landing page variants simultaneously. Insight: At scale, the bottleneck shifts from creation to review.


title: 6-Level Agent Hierarchy Pattern

insight: Agent interaction patterns follow a 6-level hierarchy from simplest to most complex

reasoning_chain: Level 1: Ad-hoc Prompt - First encounter with a problem

reasoning_chain: Level 2: Reusable Prompts - After doing something 3+ times (80/20 rule)

reasoning_chain: Level 3: Sub-Agents - Need specialization AND parallelization

reasoning_chain: Level 4: Prompt-to-Sub-Agent - Orchestrated multi-agent workflows

reasoning_chain: Level 5: Wrapper MCP Server - Multi-service integration, deterministic code

reasoning_chain: Level 6: Full Application - Long-term product vision

source_context: The video presents a 6-level hierarchy of agent interaction patterns: 1. Ad-hoc Prompt → 2. Reusable Prompts → 3. Sub-Agents → 4. Prompt-to-sub-agent → 5. Wrapper MCP server → 6. Full Application


title: Bureaucracy Principle

insight: Every agent team member must earn their spot - bloating creates overhead just like in real businesses

reasoning_chain: Sub-agents are faster, cheaper, no inter-agent communication - best for focused parallel tasks

reasoning_chain: Agent Teams have higher token cost (communication tax) - agents can talk to each other

reasoning_chain: Agent Teams only worth it for complex interdependent systems (multi-stack dashboards with auth/payments)

reasoning_chain: For simple projects, single Claude or sub-agents beat Agent Teams on both speed and quality

source_context: Bureaucracy Principle: Every agent team member must earn their spot. Bloating the team creates bureaucratic overhead, just like in real businesses. Agents are not immune to bureaucracy.


title: Composio OAuth-as-a-Service

insight: Composio connects to our products - solves customer OAuth for Talking Widget and ReceptionistAI

reasoning_chain: Composio = OAuth-as-a-service for AI agents with 850+ SaaS apps

reasoning_chain: Replaces/complements Zapier MCP - free tier 20K calls/month vs Zapier's pricing

reasoning_chain: Startup credits available: up to $25K free

reasoning_chain: Solves customer OAuth: customers connect calendar/CRM/email to Talking Widget without custom code

reasoning_chain: Framework agnostic - works with Claude Agent SDK, OpenAI, Vercel AI SDK, LangChain

source_context: Customer-facing OAuth for products: Composio's OAuth flow can be embedded in applications, solving the 'how do customers connect their calendar/CRM/email' problem for Sunaiva Talking Widget and ReceptionistAI.


type: DIRECTIVE

title: User Zero Protocol

insight: Every pipeline gets tested on Kinan FIRST before launch - no public launches without green light

reasoning_chain: If Kinan isn't at the terminal → AIVA calls +61477786847: Pipeline ready, awaiting your approval

source_context: Kinan = User Zero. Every pipeline gets tested on Kinan FIRST before launch. Protocol locked in: 1. Email campaign ready → show Kinan here first for approval 2. If Kinan isn't at the terminal → AIVA calls 3. ZERO public launches without Kinan's green light


title: draw.io Over Excalidraw

insight: draw.io XML is more token-efficient than Excalidraw JSON for diagram generation

reasoning_chain: Excalidraw JSON is extremely verbose and burns through tokens fast

reasoning_chain: draw.io XML is more compact, supports multi-page documents, better VS Code integration

reasoning_chain: Claude Code advantage: diagrams are local files Claude can read, modify, iterate without download/upload cycles

source_context: draw.io > Excalidraw for token efficiency — Excalidraw JSON is verbose, draw.io XML more compact. Claude as Visual Thinking Engine - Claude can generate Excalidraw (JSON) and draw.io (XML) files that render as professional business diagrams.


title: MCP Apps Pattern

insight: MCP servers can return interactive HTML UIs, not just text - opens dashboard rendering, KG visualization, architecture diagrams

reasoning_chain: Excalidraw MCP Server generates interactive architectural diagrams inside Claude

reasoning_chain: MCP Apps pattern: MCP servers returning interactive HTML UIs

reasoning_chain: Diagrams are portable - copy/paste into Obsidian, Excalidraw standalone, presentations

source_context: MCP Apps pattern — MCP servers returning interactive HTML UIs, not just text. Opens up: dashboard rendering, KG visualization, architecture diagrams, memory visualization.


title: AI Business Blueprint Premium Deliverable

insight: Auto-generated swim lane business process maps as premium deliverable during customer onboarding

reasoning_chain: Business X-Ray Agent interviews business owner, auto-generates visual process map

reasoning_chain: Swim Lane Diagnosis shows unleveraged vs leveraged (owner does 5+ tasks vs 3 high-value decisions)

reasoning_chain: Premium deliverable: Your AI Business Blueprint swim lane diagram included with Sunaiva/ReceptionistAI setup

reasoning_chain: This becomes a differentiator vs competitors

source_context: Premium deliverable idea: Your AI Business Blueprint as a swim lane diagram included with Sunaiva/ReceptionistAI setup — differentiator vs competitors. The 4 filters framework: For every business process, ask — Eliminate? Delegate? Simplify? Automate?


title: Complexity Should Be Earned

insight: Start simple and scale incrementally - complexity should be earned not assumed

reasoning_chain: Otherwise stay at reusable prompts level

reasoning_chain: Information flow between agents is the #1 source of failure in multi-agent systems

source_context: Complexity should be earned, not assumed — start simple, scale incrementally. Sub-agents ONLY when you need both specialization AND parallelization — otherwise stay at reusable prompts.


title: Rule of Three

reasoning_chain: After doing something 3+ times, the 80/20 rule applies

reasoning_chain: Must become a reusable prompt/skill rather than repeated ad-hoc work

reasoning_chain: This applies to Genesis skill registry approach

source_context: Rule of Three: Any task done 3+ times must become a reusable prompt/skill. MCP prompts (slash commands) are the most underrated, highest-value MCP feature — we should add workflow prompts to our MCP servers.


title: Cost Optimization Pattern Validated

insight: Opus as orchestrator, Sonnet/Haiku as cheaper workers - matches existing Genesis architecture

reasoning_chain: Video shows Opus as the brain/orchestrator

reasoning_chain: This matches our existing Genesis architecture with tiered model assignment (Opus/Sonnet/Haiku/Kimi/MiniMax)

reasoning_chain: Our 64-skill registry approach also validated

source_context: Cost optimization pattern: Opus as the brain/orchestrator, Sonnet/Haiku as cheaper workers underneath — exactly matches our existing Genesis architecture. This video validates our multi-agent orchestration and tiered model assignment.