title: Genesis Execution Layer Broken

insight: The core execution layer imports gemini_executor.py and gemini_rate_maximizer.py but these files were moved to legacy_v1/core/ without updating import paths - the entire RLM swarm pipeline cannot function

reasoning_chain: System audit found genesis_execution_layer.py has broken imports

reasoning_chain: Files gemini_executor.py and gemini_rate_maximizer.py exist in legacy_v1/core/

reasoning_chain: Current core/ directory is missing these critical files

reasoning_chain: RWL swarm execution pipeline depends on these files to function

source_context: CRITICAL DISCOVERY: The Genesis execution layer (core/genesis_execution_layer.py) is broken. It imports gemini_rate_maximizer and gemini_executor, but both files were moved to legacy_v1/core/ without updating the import paths.


title: Titan Surprise Engine Replaces Stub

insight: Memory architecture evolved to replace 101-line surprise_memory.py stub with 4-dimension Titan Surprise Engine scoring (Violation, Novelty, Impact, Rarity)

reasoning_chain: Memory architecture audit found surprise_memory.py returns 0.5 for everything

reasoning_chain: Researched Letta/Zep/Titans state-of-the-art systems

reasoning_chain: Titan approach uses gradient magnitude at test-time for memorization

reasoning_chain: Designed 4-dimension scoring: Violation (surprise from expectations), Novelty (info density), Impact (system relevance), Rarity (frequency)

reasoning_chain: Plan replaces stub with full Titan implementation

source_context: Key finding: surprise_memory.py is a 101-line stub returning 0.5 for everything. The new Titan Surprise Engine design replaces it with 4-dimension scoring.


title: Revenue Priorities Identified

insight: ReceptionistAI is 3-5 days from first revenue with infrastructure already in place; Sunaiva MVP is complete and needs only auth + hosting to launch

reasoning_chain: Full system audit mapped all 5 products at various stages

reasoning_chain: ReceptionistAI has George Bunker demo materials built, 85%+ ready

reasoning_chain: Sunaiva AI Memory MVP has 8/8 tests passing

reasoning_chain: Both have clear launch paths vs other products still in development

reasoning_chain: Revenue timeline: 3-5 days for ReceptionistAI, immediate for Sunaiva once deployed

source_context: REVENUE PRIORITY: ReceptionistAI is 3-5 days from first dollar with all infrastructure already in place. Sunaiva AI Memory MVP is complete (8/8 tests passing) and needs only auth + hosting to launch.


title: Axiom Count Reconciliation Needed

insight: Knowledge graph registry claimed 434 axioms but verification shows 352 (317 existing + 35 new from Cycle 21) - 82-axiom discrepancy requires investigation

reasoning_chain: Alpha Evolve Cycle 21 added 35 verified axioms to KG

reasoning_chain: Total verified count across JSONL files: 352

reasoning_chain: Registry (possibly from entity/relationship files) shows 434

reasoning_chain: Discrepancy of 82 axioms between registry and verified files

reasoning_chain: Flagged as reconciliation item for Cycle 22

source_context: The previously reported '434+ axioms' count does not match the verified 352 across all JSONL files in the axioms directory. The discrepancy may stem from counting axioms in entity/relationship files or creator_mind JSONL files.


type: DIRECTIVE

title: Rule 7 SQLite Violations Found

insight: System audit discovered 3 SQLite database files in the repository that violate Rule 7 (no SQLite allowed) - must be deleted

reasoning_chain: Full system audit scanned entire repository for compliance

reasoning_chain: Found 3 SQLite database files present in data directories

reasoning_chain: Rule 7 explicitly prohibits SQLite for data storage

reasoning_chain: These files violate the architectural standards

reasoning_chain: Deletion is required to maintain system integrity

source_context: Also found: 3 SQLite database files violating Rule 7


title: 6-Layer Memory Architecture

insight: New memory architecture designed with Gateway, Intelligence, Routing, Storage, Maintenance, and Sharing layers plus Redis pub/sub memory bus

reasoning_chain: Analyzed 12 Genesis source files (~4,800 lines) for current state

reasoning_chain: Researched Letta, Zep, MemGPT, Titans, SemHash, SHIMI, Supermemory

reasoning_chain: Designed 6-layer architecture: Gateway (ingest/egress), Intelligence (VOI scoring, consolidation), Routing (domain-aware table), Storage (5 backends), Maintenance (decay, dedup), Sharing (pub/sub bus)

reasoning_chain: Includes 3-layer dedup: SHA-256 bloom filter (0.1ms), MinHash LSH (2ms), semantic similarity (50ms)

reasoning_chain: 8-week implementation plan with 12 new files (~2,400 lines)

source_context: The deepest delivery yet: Read 12 source files (~4,800 lines), researched Letta/Zep/Titans/SemHash, designed a 6-layer architecture with Titan Surprise Engine, 3-layer dedup, knapsack context optimization, Redis pub/sub memory bus.


title: Jules as Third Execution Vector

insight: Jules (Google DeepMind) positioned as third execution vector alongside Claude Code (interactive) and Gemini Swarm (batch), specializing in fire-and-forget tasks with 73% autonomous completion

reasoning_chain: Created 1,726-line integration guide for Jules

reasoning_chain: Positioned as complement to Claude Code and Gemini Swarm

reasoning_chain: Jules specializes in async fire-and-forget tasks

reasoning_chain: 73% autonomous completion rate reduces supervision overhead

reasoning_chain: 20x limit multiplier with Google AI Ultra (300 tasks/day, 60 concurrent)

reasoning_chain: 5 ready-to-paste memory tasks included for immediate use

source_context: Vision Statement -- Strategic positioning of Jules as the third execution vector alongside Claude Code (interactive) and Gemini Swarm (batch), specializing in fire-and-forget tasks.


title: Playwright MCP + browser-use Stack

insight: Browser automation now has complete 3-tier stack: Playwright MCP (22 tools), browser-use (natural language), Genesis integration layer with security model

reasoning_chain: Installed @playwright/mcp@0.0.64 globally with 22 browser tools

reasoning_chain: Downloaded Chromium v145 for Node-side execution

reasoning_chain: browser-use@0.11.9 supports Gemini 2.5 Flash, Claude, OpenAI, Groq, Ollama

reasoning_chain: Created GenesisBrowserAgent class with convenience functions

reasoning_chain: Designed 4-layer browser stack architecture

reasoning_chain: Security model includes origin blocking, sandboxing, credential safety, site risk levels

source_context: PART 3 -- browser-use Assessment: browser-use@0.11.9 is the latest version on PyPI. Supports Gemini 2.5 Flash, Claude, OpenAI, Groq, and local Ollama models.


title: AIVA Queen Coronation Criteria Defined

insight: AIVA can achieve Queen status with 95%+ Coronation Score across 6 weighted criteria: Skill Completeness (20%), NAMP Score (25%), Decision Volume (15%), System Stability (15%), Revenue Contribution (15%), Communication Quality (10%)

reasoning_chain: Created 1,323-line coronation document covering all 59 skills

reasoning_chain: Current rank: Rank 6 (Guardian) with Rank 7 (Architect) in progress

reasoning_chain: Defined 6 weighted criteria with sustain durations

reasoning_chain: 95.0% threshold required for Queen coronation

reasoning_chain: Includes constitutional oath and post-coronation governance with quarterly re-validation

source_context: Queen threshold: >= 95.0% Coronation Score. Post-coronation governance including quarterly re-validation and demotion protocol.