title: SQLite to PostgreSQL Migration Directive

insight: Kinan explicitly directed migration of all SQLite databases to Elestio PostgreSQL, recognizing that local-file SQLite violates the cloud-native architecture (PostgreSQL + Qdrant + Redis)

reasoning_chain: Genesis reported SQLite audit finding 10 databases (72MB) and 9 Python files with sqlite3 imports

reasoning_chain: Kinan issued clear directive: migrate the sqlite databases to elestio postgresql

reasoning_chain: This aligned with Rule 7 (No SQLite) and the stated architecture goals

source_context: Kinan: migrate the sqlite databases to elestio postgresql


title: PostgreSQL Schema Design with Type Mappings

insight: Migration required explicit type mappings from SQLite to PostgreSQL types including TEXT dates→TIMESTAMPTZ, INTEGER bools→BOOLEAN, JSON→JSONB, and FTS5→TSVECTOR with GIN index

reasoning_chain: SQLite uses INTEGER (0/1) for booleans, TEXT for dates, FTS5 virtual tables for full-text search

reasoning_chain: PostgreSQL needs BOOLEAN, TIMESTAMPTZ, JSONB, and native full-text search

reasoning_chain: Agent created 10 tables with 36 indices including GIN indexes on TSVECTOR columns for FTS equivalent

source_context: Schema creator agent created tables with mappings like INTEGER (bool) -> BOOLEAN, TEXT (JSON blobs) -> JSONB, FTS5 VIRTUAL TABLE -> TSVECTOR + GIN index


title: Data Migration Strategy Execution

insight: Migration executed in three phases: schema creation → parallel data migration → code updates, ensuring data integrity with ON CONFLICT DO NOTHING for idempotency

reasoning_chain: Phase 1: Schema creator agent created 10 PostgreSQL tables with proper types and 36 indices

reasoning_chain: Phase 2: 3 parallel data migration agents moved 28,617 rows (conversation_index), 79 rows (episodic_memory), 5 rows (kinan_liaison)

reasoning_chain: Phase 3: 3 parallel code update agents modified Python files to use psycopg2 instead of sqlite3

reasoning_chain: All inserts used ON CONFLICT DO NOTHING for safe re-runs and idempotency

source_context: Dispatched schema creation agent → Created 10 tables, 36 indices → Dispatched 3 parallel data migration agents → Dispatched 3 parallel code update agents


title: Execution Layer Auto-Restoration

insight: Genesis automatically dispatched agent to fix broken execution layer when gemini_executor.py and gemini_rate_maximizer.py were found in legacy_v1/ but imported from core/, demonstrating Full Authority Protocol for critical system repairs

reasoning_chain: Post-compaction recovery revealed execution layer was broken (ImportError)

reasoning_chain: Files existed in legacy_v1/core/ but genesis_execution_layer.py imported from core/

reasoning_chain: Agent automatically copied files back to core/ and verified all imports resolve

source_context: The Genesis execution layer was broken because it imports two modules that had been moved to legacy_v1/ but the execution layer still expected them at core/


title: 4-Layer Context Defense System

insight: Built 4-layer context defense to prevent future context exhaustion: StatusLine (Layer 1) → Context Tracker (Layer 2) → PreCompact CTM (Layer 3) → Auto-compact at 85% (Layer 4)

reasoning_chain: Previous session hit context limits and got stuck

reasoning_chain: Recovery required building defensive systems

reasoning_chain: Layer 1: genesis_statusline.sh provides real-time context tracking

reasoning_chain: Layer 2: context_tracker.py with thresholds at 50/65/80%

reasoning_chain: Layer 3: pre_compact_ctm.py backs up transcript before compaction

reasoning_chain: Layer 4: Auto-compact triggers at 85% context usage

source_context: 4-Layer Context Defense System: StatusLine (genesis_statusline.sh) → Context Tracker (context_tracker.py) → PreCompact CTM (pre_compact_ctm.py) → Auto-compact at 85%


type: DIRECTIVE

title: Python Files Must Use PostgreSQL Instead of SQLite

insight: Kinan explicitly directed updating all Python files from sqlite3 to PostgreSQL, eliminating the 9 violations found in the audit

reasoning_chain: SQLite audit found 9 Python files with sqlite3 imports

reasoning_chain: Kinan issued directive: update the python files to use postgresql instead of sqlite

reasoning_chain: 3 parallel agents dispatched to update core/, AIVA/, and skills/ files

reasoning_chain: Pattern: import sqlite3 → from elestio_config import PostgresConfig; import psycopg2

source_context: Kinan: update the python files to use postgresql instead of sqlite


title: Table Prefix Convention for Migration

insight: PostgreSQL migration used table prefixes (cm_, em_, kl_, se_, rlm_) to avoid collisions with 86 existing tables in the public schema

reasoning_chain: PostgreSQL public schema already contained 86 tables

reasoning_chain: Needed to avoid naming collisions with existing data

reasoning_chain: Prefix convention: cm_ (creator mind), em_ (episodic memory), kl_ (kinan liaison), se_ (state episodic), rlm_ (reward learning model)

reasoning_chain: Created sqlite_migration_log table to track migration progress

source_context: Prefix convention prevents collisions with the 86 existing tables: cm_ (creator mind), em_ (episodic memory), kl_ (kinan liaison), se_ (state episodic)


title: Playwright System Dependencies Pending

insight: Browser automation deployment requires system libraries (libnspr4, libnss3, libasound2t64) that Kinan must install manually via sudo apt-get

reasoning_chain: Browser Automation Deploy plan (929 lines) created with Playwright MCP (22 tools)

reasoning_chain: Plan requires 3 system libraries to be installed

reasoning_chain: Cannot be automated - requires sudo access on the host system

reasoning_chain: Kinan needs to run: sudo apt-get install -y libnspr4 libnss3 libasound2t64

source_context: Playwright system deps: sudo apt-get install -y libnspr4 libnss3 libasound2t64


title: Jules CLI Authentication Required

insight: Jules DeepMind integration requires CLI authentication that must be run from Windows terminal, cannot be automated

reasoning_chain: Jules DeepMind Integration plan (1,726 lines) created with 5 paste-ready tasks and GitHub Actions

reasoning_chain: Jules CLI installed but requires authentication

reasoning_chain: Cannot be automated - requires human to run npx @google/jules login from Windows terminal

source_context: Jules CLI auth: Run npx @google/jules login from Windows terminal


title: Overnight Evolution Completed Successfully

insight: 8 overnight evolution agents produced 8,260+ lines of strategic output across 9 files, demonstrating autonomous system capability to plan and execute multi-agent missions

reasoning_chain: Previous session dispatched 8 overnight agents

reasoning_chain: Output: Jules DeepMind Integration (1,726), AIVA Queen Coronation (1,323), Duplex Voice Research (958), Alpha Evolve KG Cycle 21 (35 axioms), Memory Architecture Evolution (1,611), Sunaiva MVP Evolution (1,233), Browser Automation Deploy (929), Genesis System Audit (846)

reasoning_chain: All 9 files verified present on disk with expected line counts

source_context: All overnight deliverables survived and are intact. System evolved. 8,260+ lines of strategic output across 9 files.


title: Google GenerativeAI SDK Deprecation

insight: Execution layer fix revealed google.generativeai SDK is deprecated in favor of google.genai - minor issue now but requires future update

reasoning_chain: Fixing execution layer verified all imports work

reasoning_chain: Discovered FutureWarning: google.generativeai deprecated in favor of google.genai

reasoning_chain: Non-blocking currently - all functionality works

reasoning_chain: Future update needed to gemini_executor.py to use new SDK

source_context: There is a FutureWarning from the google.generativeai SDK indicating it has been deprecated in favor of google.genai. This is not a breaking issue currently but should be addressed in a future update to gemini_executor.py