axiom: VAPI adds $0.05-0.10/min markup for capabilities already available natively in Telnyx TeXML (STT, TTS, call control, recording) -- eliminating VAPI saves 60-80% per minute with zero capability loss when you own the voice pipeline

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (v2.0 architectural decision)

genesis_action: VAPI is permanently eliminated from Genesis stack. All voice work uses Telnyx Direct (TeXML). References to VAPI are forbidden in new work.


axiom: Two-product-line strategy beats single-product launches: Ship B2C personal assistant (ClawdTalk, 7-day sprint, no dashboard needed) FIRST to validate the memory moat, then ship B2B receptionist (ReceptionistAI, needs full dashboard) SECOND with proven tech

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Product A vs Product B strategy)

genesis_action: Ship ClawdTalk (Product A) in 7 days, ship ReceptionistAI (Product B) in 30 days. B2C validates, B2B monetizes at higher price.


axiom: The memory moat is THE competitive advantage in voice AI, not the voice technology -- accumulated entity/decision/preference graphs create exponentially increasing switching costs (60% retention at Week 1 to 98% at Month 12) that make cancellation feel like losing a personal AI brain

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 3: The Memory Moat)

genesis_action: Every product decision must prioritize memory depth. The moat compounds: more calls -> more memory -> more value -> more calls. After 3 months, switching cost becomes extreme.


axiom: Three-tier memory architecture (Redis hot <5ms, PostgreSQL warm <50ms, Qdrant deep <100ms) provides optimal latency stratification for voice agents -- hot memory for instant recall, warm for structured facts, deep for semantic 'that thing we talked about last month' queries

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 2.3 Memory Layer)

genesis_action: Implement three-tier memory for ClawdTalk: Redis (last 3 conversations, 7-day TTL), PostgreSQL (all entities/decisions/preferences), Qdrant (semantic search across all conversation history).


axiom: Memory-moat SaaS achieves 2.17x revenue retention vs traditional SaaS over 12 months because churn drops from industry-standard 8%/mo to 1%/mo as users accumulate irreplaceable personal context in the knowledge graph

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 3.3 Memory as Revenue Multiplier)

genesis_action: Model all revenue projections with declining churn curve: Month 1 (8%) -> Month 3 (4%) -> Month 6 (2%) -> Month 12 (1%). This is the financial thesis for memory-first products.


axiom: Post-call async memory extraction using Gemini Flash costs ~$0.001 per call and is non-blocking -- extract entities, decisions, preferences, facts, summary, sentiment, and topics from transcript, compare against existing memory for dedup, merge only genuinely new information

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 2.4 Memory Extraction Pipeline)

genesis_action: Build async extraction worker: call ends -> queue transcript -> Gemini Flash extracts -> dedup against existing memory -> merge to PostgreSQL -> embed to Qdrant -> refresh Redis hot cache.


axiom: Telnyx Direct architecture achieves $0.025-0.035/min total cost (voice + STT + TTS) vs $0.10-0.12/min with VAPI -- at 1,000 users this saves $6,600-33,000/month, and breakeven occurs at just 1 paying customer at $47/mo

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 4 Cost Model)

genesis_action: Track per-minute costs rigorously: Telnyx voice ($0.01-0.02), STT ($0.005), TTS ($0.01), Gemini ($0.01-0.02). Total per 10-min call: $0.28-0.40.


axiom: No major tech company currently ships a personal voice AI agent with persistent memory at scale -- OpenAI has shallow key-value memory, Google Gemini has no persistent memory, ElevenLabs has zero memory infrastructure, Siri is years behind in LLM capability -- this is a first-mover window measured in months not years

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 3.4 What Stops Big Tech)

genesis_action: Ship ClawdTalk before the window closes. By month 6, users will have irreplaceable context even if OpenAI/Google announce memory features.


axiom: Enterprise production hardening on a $46/mo Elestio stack adds only $2/mo incremental cost using free-tier tools: UptimeRobot (50 monitors free), n8n (already running), Redis (already running) for feature flags/rate limiting, GitHub Actions (2000 min/mo free) for CI/CD and backups

source: PRODUCTION_HARDENING_PLAN.md (Section 6 Cost-Effective Implementation)

category: infrastructure

genesis_action: Deploy production monitoring stack for $2/mo additional: UptimeRobot external checks + n8n Genesis Pulse deep checks + Telegram alerts + GitHub Actions CI/CD.


axiom: For Elestio-managed infrastructure you cannot modify internals -- you must build monitoring AROUND services (external HTTP/TCP checks, deep health queries, latency thresholds) not INSIDE them, and react to degradation rather than prevent it at the infrastructure level

source: PRODUCTION_HARDENING_PLAN.md (Executive Summary truth #1-2)

genesis_action: All monitoring is external: UptimeRobot for reachability, n8n Genesis Pulse for deep health (PG query latency, Redis memory, Qdrant collection status, Telnyx API), Telegram for alerts.


axiom: Four-level voice failover chain provides enterprise-grade resilience: L1 (Gemini down) -> GPT-4o-mini via Telnyx, L2 (all LLMs down) -> pre-recorded IVR menu, L3 (Telnyx down) -> forward to customer mobile, L4 (nuclear) -> static page with direct number

source: PRODUCTION_HARDENING_PLAN.md (Section 4.2 Failover Chain)

genesis_action: Pre-configure all 4 failover levels before launch. L1 is a single API call to swap Telnyx assistant model. L2-L4 require pre-built assets.


axiom: Blue/green deployment on shared infrastructure uses namespace isolation instead of duplicate instances: PostgreSQL staging schema, Redis key prefixes (prod:/staging:), Qdrant separate collections, separate Telnyx assistant -- same $46/mo infrastructure, full deployment safety

source: PRODUCTION_HARDENING_PLAN.md (Section 2.2 Staging Environment)

genesis_action: Create staging namespace: PostgreSQL schema 'staging', Redis prefix 'staging:', Qdrant collection 'genesis_vectors_staging', separate Telnyx staging assistant and phone number (~$2/mo).


axiom: Zero-downtime database migrations require 4 rules: never drop columns in same release that removes code, always ADD columns as nullable first then backfill, always use CREATE INDEX CONCURRENTLY, and use two-phase column removal (Release 1: stop using, Release 2: drop)

source: PRODUCTION_HARDENING_PLAN.md (Section 2.4 Zero-Downtime Migration Rules)

genesis_action: Enforce these 4 rules in all PostgreSQL migration files. Add to pre-commit checks if possible.


axiom: Row-Level Security (RLS) in PostgreSQL is superior to schema-per-tenant for multi-tenant SaaS at scale (87-500+ clients) -- single schema with RLS policies provides data isolation with simpler migrations, simpler queries, and zero management overhead vs hundreds of schemas

source: PRODUCTION_HARDENING_PLAN.md (Section 5.1 Tenant Isolation Strategy)

genesis_action: Use PostgreSQL RLS for customer data isolation. SET app.current_customer_id per connection. Admin role bypasses RLS for operations.


axiom: Hardcoded credentials in source code are the single biggest security risk in a codebase -- move ALL secrets to environment variables with .env for development (gitignored) and GitHub Secrets for CI/CD, with production mode that raises RuntimeError if secrets are missing

source: PRODUCTION_HARDENING_PLAN.md (Section 3.6 Secrets Management)

category: security

genesis_action: Migrate all Elestio credentials from elestio_config.py to .env (gitignored). Create .env.example (committed). Update config to require env vars in production mode.


axiom: SLA targets for voice AI SaaS: 99.9% uptime (max 43 min downtime/month), p95 API response <500ms, p95 memory query <1000ms, call setup <3 seconds, onboarding <24 hours -- with graduated credit table from 10% (43 min-4hr) to 100% (>24hr downtime)

source: PRODUCTION_HARDENING_PLAN.md (Section 4.3 SLA Targets)

category: operations

genesis_action: Publish SLA targets in customer contracts. Automate SLA calculation from health check data stored in Redis/PostgreSQL.


axiom: Post-incident review protocol after any outage >15 minutes: write incident report within 24 hours (timeline, root cause, resolution, action items), implement action items within 1 week, commit report to git + supermemory (CTM protocol)

source: PRODUCTION_HARDENING_PLAN.md (Section 4.5 Post-Incident Review)

genesis_action: Create plans/incidents/ directory. Template: date, duration, severity, service, timeline, root cause, resolution, action items, SLA impact.


axiom: Kimi K2.5 by Moonshot AI achieves 50.2% on tool-use benchmark (HLE) vs Claude 43.2%, GPT-5.2 45.5%, Gemini 40% -- while costing $1.07/1M tokens (9x cheaper than Claude at $9.60/1M) with 256K context window, 100 parallel sub-agents, and 1,500 tool calls per task

source: docs/KIMI_K25_ACTIVATION.md (Part 5 Benchmarks)

genesis_action: Route agentic swarm tasks and batch operations to Kimi K2.5 via NVIDIA NIM (free). Reserve Claude for complex reasoning, Gemini for real-time voice.


axiom: Tiered model routing (Kimi for swarms -> Claude for code review -> GPT for reasoning) achieves 82% cost reduction vs using a single frontier model for all tasks, because each model excels in a different capability dimension

source: docs/KIMI_K25_ACTIVATION.md (Part 5 Key Insights)

genesis_action: Formalize model routing table: Gemini Flash (real-time voice, $0.30/MTok), Kimi K2.5 (batch swarms, $1.07/MTok), Claude Opus (complex reasoning, $9.60/MTok), GPT-5.2 (math/reasoning, $3.44/MTok).


axiom: NVIDIA NIM provides free API access to Kimi K2.5 with no documented rate limits and instant API key generation -- zero friction signup makes it the recommended entry point for Genesis swarm integration

source: docs/KIMI_K25_ACTIVATION.md (Part 1 Free API Access)

genesis_action: Sign up for NVIDIA NIM free tier at build.nvidia.com/moonshotai/kimi-k2-5. Use OpenAI-compatible SDK. Test with swarm tasks before routing production workloads.


axiom: Gemini Ultra plan ($250/mo) provides 19+ actionable capabilities but Genesis currently uses only 3-4 at partial capacity (15-25% value capture) -- 12 capabilities were completely undocumented including Jules, Deep Think, Aletheia, NotebookLM Ultra, 25K AI credits, and YOLO mode

source: GEMINI_ULTRA_UNTAPPED_POTENTIAL.md (Executive Summary + Cross-Reference Matrix)

genesis_action: Activate immediately: Jules (async coding), Deep Think (architecture reasoning), GEMINI.md (persistent context), YOLO mode. Then: Mariner, NotebookLM, Cloud Credits.


axiom: Jules async coding agent works on GitHub repos up to 30K lines, produces PRs in background without stalling UI -- effectively doubles code throughput at zero additional cost since it runs while Claude Code orchestrates in parallel

source: GEMINI_ULTRA_UNTAPPED_POTENTIAL.md (Section 3.1 Jules)

genesis_action: Install Jules CLI extension: gemini extensions install. Assign first task: build a module. Claude orchestrates strategy, Jules executes code in background.


axiom: Project Mariner provides autonomous web intelligence gathering with 200 agent actions/day, 3 concurrent tasks, cloud VM anonymity -- replaces paid tools like AdSpy ($100-300/mo) for competitor funnel mapping, and can 'Teach & Repeat' a process across 100+ competitor sites

source: gemini_conversation_extension_NEW.md (Mariner intelligence sections)

genesis_action: Activate Mariner for weekly competitive intelligence sweeps. Build JSON schema for structured funnel data export into Genesis memory. Replace all paid spy tool subscriptions.


axiom: Voice humanization requires 5 specific techniques: mandatory contractions ('I'm' not 'I am'), filler words ('uh-huh', 'got it', 'let's see') during LLM processing, SSML prosody tags for pauses/emphasis, 250ms reaction time target (decouple turn detection from transcription), and guided prompts that narrow user intent to one action per turn

source: gemini_conversation_extension_NEW.md (Voice Agent Humanization section)

genesis_action: Implement all 5 humanization techniques in ClawdTalk and ReceptionistAI voice agents. Add to voice agent prompt template as mandatory requirements.


axiom: Social media engine architecture uses OpenClaw as persistent brain (heartbeat scheduler, local memory, IM integration) + Mariner as hands (web navigation, form filling, group discovery) + Gemini 3 for polymorphic content generation that bypasses Meta's RETVec transformer-based spam filters

source: gemini_conversation_extension_NEW.md (Social Media Engine sections)

genesis_action: Deploy OpenClaw to VPS for 24/7 social media operations. Use Docker containers per account for isolation. Mariner for intelligence, Gemini for unique content.


axiom: Mac Mini serves as 'Safe Vault' (master config, encrypted keys, MCP validator) while VPS serves as 'Engine' (24/7 heartbeat, IP integrity via static residential proxies, Docker sandboxing per social account) -- this separation provides security + scalability

source: gemini_conversation_extension_NEW.md (Mac Mini vs VPS Strategy)

genesis_action: Maintain Mac Mini as AIVA controller + key vault. Deploy VPS for always-on agent operations (social media, monitoring, background processing).


axiom: Pre-mortem thinking as operational protocol: before any launch, systematically identify every way it could fail (Telnyx latency, memory extraction garbage, no beta signups, concurrent call conflicts, cost overruns, US number provisioning blocks, data privacy complaints) and pre-build mitigations

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 12 Risk Register)

genesis_action: Add pre-mortem section to every shipping plan and PRD. List 8-12 failure modes with likelihood, impact, and specific mitigation for each.


axiom: The 'I'll remember that' moment is the product -- when a voice agent unpromptedly recalls a detail from a previous conversation, it creates an emotional reaction that no feature list can replicate, making it the primary demo moment and marketing hook

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 1.4 Demo Script + Appendix D)

genesis_action: Every ClawdTalk demo must showcase the 'I'll remember that' moment. The demo video, Reddit posts, and Twitter threads all center on this single magical interaction.


axiom: Deep Think mode in Gemini uses System 2 reasoning with 10-50x more compute, scoring 84.6% on ARC-AGI-2 and gold-medal level in math/physics Olympiads -- purpose-built for complex architectural reasoning, memory system design, and validation logic problems that stump standard models

source: GEMINI_ULTRA_UNTAPPED_POTENTIAL.md (Section 3.2 Deep Think Mode)

genesis_action: Use Deep Think for: Bloodstream MCP architecture, memory system edge cases, prompt engineering optimization, Alpha Evolve axiom validation. Command: gemini --thinking deep


axiom: Aletheia (DeepMind, released Feb 14 2026) uses balanced prompting to simultaneously prove AND refute statements -- this self-verification pattern can validate Genesis axioms, architectural decisions, and agent outputs before committing to action

source: GEMINI_ULTRA_UNTAPPED_POTENTIAL.md (Section 3.8 Aletheia)

category: verification

genesis_action: Research Aletheia access. Build balanced prompting template for axiom validation. Integrate into Alpha Evolve VERIFY step.


axiom: NotebookLM Ultra allows 600 sources per notebook -- uploading entire Genesis codebase (CLAUDE.md, Master PRD, protocols, research reports, KG axioms) creates a permanent 'Genesis Brain' that answers architectural questions grounded in source docs with zero hallucination risk and no context window limits

source: GEMINI_ULTRA_UNTAPPED_POTENTIAL.md (Section 3.6 NotebookLM Ultra)

genesis_action: Create 'Genesis Architecture Brain' notebook on notebooklm.google.com. Upload all PDFs, MDs, JSONLs. Test: 'Find contradictions between CLAUDE.md and Master PRD.'


axiom: Redis-based feature flags provide instant capability toggling at zero cost: simple key-value flags (prod:feature:{name} = 0|1) enable progressive rollout of features like outbound calling, advanced analytics, and multi-location support without code deployment

source: PRODUCTION_HARDENING_PLAN.md (Section 2.5 Feature Flags)

genesis_action: Deploy core/feature_flags.py with pre-defined LAUNCH_FLAGS. Seed defaults in Redis. Use is_enabled() checks in all new feature code paths.


axiom: Sliding window rate limiting via Redis sorted sets protects infrastructure from runaway agents or abuse -- pre-defined limits per service (Telnyx 100/min, PG 500/min, Qdrant 200/min, voice calls 50/min, onboarding 10/hr) prevent cascading failures

source: PRODUCTION_HARDENING_PLAN.md (Section 3.7 Rate Limiting)

genesis_action: Deploy core/rate_limiter.py with LIMITS dict. Integrate check_rate_limit() into all API endpoints and agent execution paths.


axiom: The one-liner that sells ClawdTalk: 'An AI that answers your phone, searches the web for you, and gets smarter every time you call' -- the 'gets smarter every time you call' differentiator is what no competitor currently offers

category: marketing

genesis_action: Use this exact one-liner as the tweet, Reddit title, elevator pitch, and landing page headline. Test variations but keep the 'gets smarter' hook.


axiom: Intelligence pipeline for competitive advantage: Mariner scouts competitors autonomously -> Deep Research analyzes findings into thematic reports -> Genesis distills into recursive heuristics and axioms -> Jules implements superior versions in code

source: gemini_conversation_extension_NEW.md (Genesis Integration Patterns)

genesis_action: Build this pipeline as a reusable Genesis workflow. Weekly cadence: Monday Mariner sweep, Tuesday Deep Research analysis, Wednesday synthesis, Thursday-Friday implementation.


axiom: Kimi K2.5 visual-to-code synthesis converts design mockups directly to React/Vue/HTML components using multimodal input (image + text) -- 256K context window handles full codebase analysis (200K lines), making it ideal for screenshot-to-working-prototype workflows at commodity pricing

source: docs/KIMI_K25_ACTIVATION.md (Part 7 Best Use Cases)

genesis_action: Use Kimi K2.5 for design-to-code workflows via execute_with_vision(). Feed Figma screenshots, get production-ready components. Test quality against Claude code output.


axiom: Data retention policy for voice AI SaaS must balance privacy and compliance: call recordings 90 days, voicemail 30 days, transcripts 90 days, account data service+30 days, audit logs 2 years, payment records 7 years -- with automated daily cleanup n8n workflow enforcing all TTLs

source: PRODUCTION_HARDENING_PLAN.md (Section 5.5 Data Retention Policy)

category: compliance

genesis_action: Implement automated data cleanup n8n workflow at 03:00 AEST daily. Log all deletions to audit_log. Telegram notification with cleanup stats.


axiom: Multi-agent voice orchestration separates concerns for optimal performance: Agent A (Greeter, Gemini Flash) handles first 10 seconds for trust-building at ultra-low latency, Agent B (Specialist, Deep Think) handles complex reasoning once intent is identified, Agent C (Validator, MCP) audits conversation in real-time for compliance

source: gemini_conversation_extension_NEW.md (Multi-Agent Orchestration for Voice)

genesis_action: Design ClawdTalk with multi-agent architecture: fast greeter for sub-200ms first response, deep specialist for complex queries, background validator for compliance.


axiom: Viral content about AI voice + memory has extremely high shareability because people immediately understand the value ('My AI remembers me') -- unlike most AI products that require explanation, a demo video of voice recall creates instant emotional impact and word-of-mouth

source: VOICE_MEMORY_MVP_SHIPPING_PLAN.md (Section 8.4 Aggressive Path)

genesis_action: Create 60-second demo video showing: call in, ask question, get answer, hang up, call back, agent recalls detail unprompted. Post to r/ClaudeAI, r/ChatGPT, Twitter. Target 100K+ views.


axiom: Daily Listen-Identify-Diagnose-Adjust loop for voice agent optimization: review call logs daily to find unnatural moments, trace error to specific prompt instruction or missing context, make targeted prompt tweak, redeploy immediately -- systematic refinement beats guesswork

source: gemini_conversation_extension_NEW.md (Voice Humanization section 4)

genesis_action: Establish daily voice quality review cadence from Day 1 of beta. Log all 'unnatural moments' with timestamps. Fix root causes in prompts, not symptoms.