userType: external

gitBranch: claude/zealous-liskov

slug: jaunty-zooming-globe

type: progress

hookEvent: SessionStart


type: user

role: user

content:
t5- i pay for instantly and t-5 launches warm up campaigns get customers ready for tradies voice suite with free heatmap audits or similar hook. which I beleive are native GHL.
t6- duplicates reviewharvest.com and also researches other similar tradies suite sites. also assess hwther we build sub-domains for each single product as a component. these can be sold cheap as entry pathway with low friction.
t7- actually build s and develops the websites/landing pages
t8- build out our brand dna fb pages
t9- and beyond ready for instant deployment as required- can be spawned with the system ready.

permissionMode: acceptEdits


content:
Your task is to create a detailed summary of the conversation so far, paying close attention to the user's explicit requests and your previous actions.
This summary should be thorough in capturing technical details, code patterns, and architectural decisions that would be essential for continuing development work without losing context.

Before providing your final summary, wrap your analysis in <analysis> tags to organize your thoughts and ensure you've covered all necessary points. In your analysis process:

1. Chronologically analyze each message and section of the conversation. For each section thoroughly identify:
   - The user's explicit requests and intents
   - Your approach to addressing the user's requests
   - Key decisions, technical concepts and code patterns
   - Specific details like:
     - file names
     - full code snippets
     - function signatures
     - file edits
  - Errors that you ran into and how you fixed them
  - Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
2. Double-check for technical accuracy and completeness, addressing each required element thoroughly.

Your summary should include the following sections:

1. Primary Request and Intent: Capture all of the user's explicit requests and intents in detail
2. Key Technical Concepts: List all important technical concepts, technologies, and frameworks discussed.
3. Files and Code Sections: Enumerate specific files and code sections examined, modified, or created. Pay special attention to the most recent messages and include full code snippets where applicable and include a summary of why this file read or edit is important.
4. Errors and fixes: List all errors that you ran into, and how you fixed them. Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
5. Problem Solving: Document problems solved and any ongoing troubleshooting efforts.
6. All user messages: List ALL user messages that are not tool results. These are critical for understanding the users' feedback and changing intent.
6. Pending Tasks: Outline any pending tasks that you have explicitly been asked to work on.
7. Current Work: Describe in detail precisely what was being worked on immediately before this summary request, paying special attention to the most recent messages from both user and assistant. Include file names and code snippets where applicable.
8. Optional Next Step: List the next step that you will take that is related to the most recent work you were doing. IMPORTANT: ensure that this step is DIRECTLY in line with the user's most recent explicit requests, and the task you were working on immediately before this summary request. If your last task was concluded, then only list next steps if they are explicitly in line with the users request. Do not start on tangential requests or really old requests that were already completed without confirming with the user first.
                       If there is a next step, include direct quotes from the most recent conversation showing exactly what task you were working on and where you left off. This should be verbatim to ensure there's no drift in task interpretation.

Here's an example of how your output should be structured:

<example>
<analysis>
[Your thought process, ensuring all points are covered thoroughly and accurately]
</analysis>

<summary>
1. Primary Request and Intent:
   [Detailed description]

2. Key Technical Concepts:
   - [Concept 1]
   - [Concept 2]
   - [...]

3. Files and Code Sections:
   - [File Name 1]
      - [Summary of why this file is important]
      - [Summary of the changes made to this file, if any]
      - [Important Code Snippet]
   - [File Name 2]
      - [Important Code Snippet]
   - [...]

4. Errors and fixes:
    - [Detailed description of error 1]:
      - [How you fixed the error]
      - [User feedback on the error if any]
    - [...]

5. Problem Solving:
   [Description of solved problems and ongoing troubleshooting]

6. All user messages:
    - [Detailed non tool use user message]
    - [...]

7. Pending Tasks:
   - [Task 1]
   - [Task 2]
   - [...]

8. Current Work:
   [Precise description of current work]

9. Optional Next Step:
   [Optional Next step to take]

</summary>
</example>

Please provide your summary based on the conversation so far, following this structure and ensuring precision and thoroughness in your response.

There may be additional summarization instructions provided in the included context. If so, remember to follow these instructions when creating the above summary. Examples of instructions include:
<example>
## Compact Instructions
When summarizing the conversation focus on typescript code changes and also remember the mistakes you made and how you fixed them.
</example>

<example>
# Summary instructions
When you are using compact - please focus on test output and code changes. Include file reads verbatim.
</example>

IMPORTANT: Do NOT use any tools. You MUST respond with ONLY the <summary>...</summary> block as your text output.


type: message

role: assistant

type: text

text:
<analysis>
Let me chronologically analyze every message and section of this conversation carefully.

**Context Recovery (from previous session summary):**
The conversation started in a previous session that ran out of context. Key points from that session:
- User (Kinan) initiated focus on Genesis memory infrastructure
- 7 research reports were read and committed to Supermemory
- CLAUDE.md was updated with Memory-First Mandate
- Discovery that only ~60 memories exist in genesis-kinan from 9 months of work
- Root causes identified: late setup, one-time event, no enforcement, fragmentation, unreliable MCP
- Web searches found claude-supermemory plugin, claude-mem, memory-mcp as solutions
- Market gap confirmed: no "export chat history → MCP memory" product exists

**This session begins with my synthesis of the web search results from the previous session.**

1. **My first message**: Synthesized research findings - claude-supermemory plugin discovery, state of the art memory systems (Feb 2026), market gap validation, 5 Gemini deep research prompts, and immediate action plan.

2. **Built memory_saver.py hook** at `.claude/hooks/memory_saver.py` - auto-saves session context to Supermemory on Stop events. Registered it in `.claude/settings.json` under the Stop hook.

3. **Testing the hook**:
   - `python3` not found on Windows → used `python` instead
   - Hook ran but got HTTP 403 from Supermemory API (urllib doesn't follow 308 redirects)
   - Later discovered `curl -L` works fine

4. **User message**: "i dont think we are yet on pro. at what point do we need scale plan? what is two separate accounts issue? what do you mean by this-Keep Supermemory as a customer-facing option in the product- do customers know its supermemory? we want to white label it.. how quick is quick setup? does it still take 24hrs for digestion? we should not offer sovereign for first few weeks. start with what we have.. everything we build must be universal and agnostic, work on pc or mac on phone with claude with chatgpt etc"

5. **My response**: Answered all questions - verified API works via curl (queued status returned), explained Pro vs Scale, two-accounts issue, white-labeling (operationally white-labeled but not data-sovereign), setup time (minutes for MCP, 1-2 hours for digestion via API), agreed on starting with what we have, defined universal/agnostic architecture principle.

6. **User message**: "the conflict I am in is I want the benefits/speed to market of using supermemory but dont really want to commit the $400 upfront but mostly dont want them to know what we are doing. I always like to operate by stealth and not let others know our approach. this is my dilemma."

7. **My response**: Proposed 3 options. Recommended Option 3 (stealth hybrid) - use Supermemory Pro ($20) for Genesis's own memory only, build the PRODUCT on sovereign stack (FalkorDB + Graphiti) from day one. Supermemory never sees customer data.

8. **User message**: "i worked out if we just use the mcp we dont even need to be on the scale plan but then what features do we miss out on?"

9. **My research**: Fetched Supermemory MCP docs, pricing docs, searched extensively. Found MCP exposes 3 tools (memory, recall, whoAmI). Pro ($20) adds Claude Code plugin + connectors. Scale ($400) adds high usage + "for startups building on top." Confirmed user's insight: MCP alone = normal user, doesn't need Scale.

10. **User message**: "lets do claude- why? its a bigger market. its where most of my stuff is. the current memory didnt work well so genesis-kinan and genesis-aiva incomplete anyway. what do you think?"

11. **My response**: Agreed - Claude is the right first format. Listed 5 reasons. Existing 18+427 scattered memories = "before" photo. Clean pipeline = "after."

12. **User feedback**: "build the basis and we can always make it better on lovable later. yes we own all these domains. its written on my list where they all live. get it up and running."

13. **User also mentioned**: sunaiva.com/ai as the brand, wants drag-n-drop hero website first, then sort out memory backend.

14. **I read Domain Portfolio files**: domain_portfolio.txt (100+ domains across GoDaddy, VentraIP, Namecheap), Domain Portfolio January 2026/*.docx files, KNOWLEDGE_GRAPH/entities/domain_portfolio_revenue_strategy.json (full strategic asset with 3-tier model, revenue services, pricing tiers, target markets).

15. **Saved to Supermemory**: Domain strategy memory + Memory product decision memory (both queued successfully).

16. **Built landing page**: `E:/genesis-system/sunaiva/ai-memory/index.html` - dark theme, drag-n-drop hero, 3-step process, platform compatibility grid, beta pricing, FAQ section. Opened in browser.

17. **User feedback on pricing**: "the pricing is wayyyy to low. $9/ mo doesnt position correctly at all and cost to serve is too high. think $47-$97/mo for something like this. who is the customer archetype?"

18. **Researched FalkorDB hosting**: Found FalkorDB is NOT on Elestio's managed catalog. Options: FalkorDB Cloud (free tier, $73/mo startup), Docker on Elestio VPS, Neo4j on Elestio. Recommended Docker on Elestio VPS as simplest.

19. **User insight**: "if supermemory is $20/mo and thats just one component but we provide the full seamless customer stack... this has tremendous value.. its virtually priceless and wrapped as a universal mcp!!!! wowsers.."

20. **My response**: Crystallized the value prop - 6 layers of value vs Supermemory's 1 layer. Target user spends $300-800/mo on AI tools with amnesia. $97/mo = no-brainer. Margins 80%+. Proposed pricing tiers: $47/$97/$197/Custom.

21. **User message**: "look I can easily get a dedicated docker going on elestio and then its kind of managed anyway?"

22. **My response**: Confirmed - Elestio gives managed VPS + Docker. Recommended 4GB RAM, 2 vCPU, ~$25-35/mo. Showed all 3 containers can run on one VPS.

23. **User message**: "lets set this up properly, so 3 separate docker containers? and cloudflare workers run on 3rd one?"

24. **My response**: Clarified architecture - Cloudflare Workers are separate from Docker. For V1, skip CF Workers entirely, everything runs on Elestio VPS. 2 containers (combined FalkorDB+Graphiti image + Pipeline API).

25. **Built the full deployment stack**:
   - `docker-compose.yml` - 2 services (memory-engine + pipeline)
   - `.env.example` - environment variables template
   - `pipeline/Dockerfile` - Python 3.12 slim
   - `pipeline/requirements.txt` - FastAPI, uvicorn, httpx, openai, redis
   - `pipeline/main.py` - Full pipeline API with parsers for Claude/ChatGPT/Gemini
   - `DEPLOY.md` - Step-by-step deployment guide

26. **User message**: "yes lets go. and most users that want this will be power users like me- claude $100-$200/mo, gemini ultra $400/mo(discounted to 50% off for 3 months), Manus AI $29-$200/mo (currently on hold) so $97/mo to get memory across these plus claude code, google antigravity, openclaw agents etc is massive win"

27. **User then shared GENESIS_CONCURRENT_SWARM_DEPLOYMENT.md** - a detailed 9-session swarm deployment plan from a Claude Chat conversation. This includes tmux-based persistent sessions across 4 tracks: Track 1 (George GHL), Track 2 (Spy→Clone→Scale), Track 3 (Memory Infrastructure = our product), Track 4 (Outbound).

28. **User's final message**: Described additional tracks t5-t9:
   - t5: Instantly.ai warm-up campaigns for Tradies Voice suite (free heatmap audits as hook, native GHL)
   - t6: Duplicate reviewharvest.com + research similar sites + assess sub-domain strategy for cheap entry products
   - t7: Actually build websites/landing pages
   - t8: Build brand DNA Facebook pages
   - t9+: Ready for instant deployment as required, spawnable

Key errors encountered:
- python3 not found on Windows (use python instead)
- urllib.request doesn't follow 308 redirects (Supermemory API returns 308) → use curl -L
- PowerShell variable interpolation issues ($r getting mangled) → switched to curl
- FalkorDB not available on Elestio managed catalog → use Docker on Elestio VPS instead

Key user feedback/corrections:
- Pricing too low ($9) → should be $47-97/mo
- Don't want Supermemory to know what we're doing → stealth approach
- Start with what we have, don't offer sovereign initially
- Everything must be universal and agnostic (PC, Mac, phone, Claude, ChatGPT)
- Build the basis, can improve on Lovable later
- Sunaiva brand for the product
- Claude format first (biggest market)
- User is user zero for testing
</analysis>

<summary>
1. Primary Request and Intent:

   Kinan's core mission in this session is **building a sovereign AI Memory-as-a-Service product** under the Sunaiva brand. The session evolved through several phases:

   **Phase 1 — Research**: Deep dive into memory systems (Feb 2026 state of the art), comparing Supermemory, Mem0, Zep/Graphiti, Letta, FalkorDB. Identified the 5 missing layers no one has built (Collection, Normalization, Dedup, Sync, Delivery).

   **Phase 2 — Strategic Decisions**:
   - **Stealth approach**: Don't let Supermemory know we're productizing. Stay on Pro ($20/mo) for personal use. Build product on sovereign stack.
   - **Brand**: Sunaiva (sunaiva.com) — enterprise agentic brand, patent-tracked
   - **First format**: Claude conversations (biggest market, most of Kinan's data)
   - **Kinan is user zero**: Test full pipeline on his own 615 conversations
   - **Pricing**: $47/$97/$197/mo tiers (NOT $9 — too low, wrong positioning)
   - **Target customer**: Power users spending $300-800/mo on AI tools (Claude Pro $100-200, Gemini Ultra $400, Manus $29-200)
   - **Universal & agnostic**: Works on PC, Mac, phone, Claude, ChatGPT, Gemini, Antigravity, any MCP client

   **Phase 3 — Build**: Created landing page, docker-compose stack, pipeline API, deployment guide

   **Phase 4 — Concurrent Swarm**: User shared GENESIS_CONCURRENT_SWARM_DEPLOYMENT.md describing 9+ concurrent autonomous sessions running 24/7. Our memory product is Track 3 (t3-ingest, t3-rag, t3-ui). Additional tracks t5-t9 were described for Instantly campaigns, ReviewHarvest duplication, website building, Facebook pages, and on-demand session spawning.

2. Key Technical Concepts:
   - **Sovereign Memory Backend**: FalkorDB + Graphiti MCP on Elestio Docker VPS (no Supermemory dependency for product)
   - **Graphiti MCP Server**: Combined Docker image `zepai/knowledge-graph-mcp:latest` bundles FalkorDB + Graphiti + MCP endpoint
   - **Multi-tenant isolation**: `group_id` parameter in Graphiti namespaces data per user
   - **Universal MCP delivery**: One URL per customer, works with any MCP-compatible AI tool
   - **Pipeline architecture**: Upload → Parse (platform-specific) → Extract → Chunk → Embed (OpenAI) → Store (FalkorDB graph)
   - **3 memory layers**: Episodic (what happened), Semantic (what things mean), Procedural (how to do things) + Temporal (how things change)
   - **Supermemory API v3**: `api.supermemory.ai/v3/memories` (save) and `/v3/search` (search), uses Bearer auth with `sm_` keys, containerTags for isolation
   - **Claude-supermemory plugin**: `supermemoryai/claude-supermemory` on GitHub — automatic conversation capture on session end
   - **Hook-based auto-save**: Custom `memory_saver.py` fires on Claude Code Stop event
   - **Cloudflare Workers**: $5/mo serverless compute option for future scaling (skipped for V1)
   - **Docker Compose on Elestio**: Managed VPS with Docker, 4GB RAM, ~$25-35/mo, handles all containers
   - **FalkorDB**: NOT on Elestio managed catalog; must run as Docker container. Free cloud tier available. GraphBLAS sparse matrices, sub-10ms queries
   - **Concurrent Swarm Deployment**: 9+ tmux sessions with Claude Code + Gemini fallback, persistent 24/7 loops with watchdog (genesis-hive)

3. Files and Code Sections:

   - **`E:/genesis-system/sunaiva/ai-memory/index.html`** — Landing page with drag-n-drop hero
     - Dark theme, Inter font, purple accent gradient
     - Drop zone accepting .json, .jsonl, .zip, .txt
     - Processing state with animated progress bar
     - Beta email signup form
     - 3-step "How it works" section
     - "Works everywhere" platform grid (Claude Code, Cursor, Windsurf, ChatGPT, Gemini, Antigravity, etc.)
     - Pricing card (needs updating from $9 to $47-97)
     - FAQ section with export guides
     - JavaScript: drag-n-drop handling, simulated processing stages, beta form submission
     - **NOTE: Pricing still shows $9/mo — needs updating to $47/$97/$197 tiers**

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/docker-compose.yml`** — Sovereign memory stack
     - 2 services: `memory-engine` (FalkorDB + Graphiti MCP combined image) and `pipeline` (custom FastAPI)
     - memory-engine: `zepai/knowledge-graph-mcp:latest`, ports 6379/3000/8000
     - pipeline: custom build from `./pipeline/`, port 8080, depends on memory-engine health
     - Volumes: falkordb_data, mcp_logs, pipeline_uploads, pipeline_logs
     - Bridge network: sunaiva-net
     ```yaml
     services:
       memory-engine:
         image: zepai/knowledge-graph-mcp:latest
         container_name: sunaiva-memory-engine
         environment:
           - FALKORDB_PASSWORD=${FALKORDB_PASSWORD:-sunaiva2026}
           - FALKORDB_URI=redis://localhost:6379
           - FALKORDB_DATABASE=${FALKORDB_DATABASE:-sunaiva_memory}
           - OPENAI_API_KEY=${OPENAI_API_KEY}
         ports:
           - "6379:6379"
           - "3000:3000"
           - "8000:8000"
       pipeline:
         build: ./pipeline
         container_name: sunaiva-pipeline
         environment:
           - FALKORDB_URI=redis://memory-engine:6379
           - GRAPHITI_MCP_URL=http://memory-engine:8000/mcp/
         ports:
           - "8080:8080"
         depends_on:
           memory-engine:
             condition: service_healthy
     ```

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/.env.example`** — Environment template
     - OPENAI_API_KEY, FALKORDB_PASSWORD, FALKORDB_DATABASE, GRAPHITI_GROUP_ID, CORS_ORIGINS

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/pipeline/Dockerfile`** — Python 3.12 slim, uvicorn
     ```dockerfile
     FROM python:3.12-slim
     WORKDIR /app
     COPY requirements.txt .
     RUN pip install --no-cache-dir -r requirements.txt
     COPY . .
     RUN mkdir -p /app/uploads /app/logs
     EXPOSE 8080
     CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
     ```

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/pipeline/requirements.txt`**
     - fastapi==0.115.0, uvicorn[standard]==0.32.0, python-multipart==0.0.12, httpx==0.28.0, openai==1.58.0, redis==5.2.0, pydantic==2.10.0, python-dotenv==1.0.1

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/pipeline/main.py`** — Core pipeline API
     - Endpoints: POST /upload, GET /status/{job_id}, GET /health, GET /jobs
     - Parsers: `parse_claude_jsonl()`, `parse_chatgpt_json()`, `parse_gemini_json()`
     - Auto-detection: `detect_and_parse()` tries all formats
     - Background processing: `process_upload()` — parse → extract memories → store via Graphiti MCP
     - `store_memory_via_graphiti()` — JSON-RPC over HTTP to Graphiti MCP endpoint
     - `extract_memories_from_conversation()` — chunks messages into groups of 10, creates atomic memory units
     - CORS configured for sunaiva.com + localhost
     - In-memory job tracking (noted for Redis swap in production)

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/DEPLOY.md`** — Step-by-step deployment guide
     - Create Elestio Docker VPS (4GB RAM, 2 vCPU, 40GB)
     - SSH, clone, configure .env, docker compose up -d
     - Security: firewall 3000/6379, Cloudflare proxy for 8000/8080
     - DNS: api.sunaiva.com and memory.sunaiva.com → Elestio IP
     - Customer MCP URL format: `http://memory.sunaiva.com:8000/mcp/?group_id=user_XXXXX`

   - **`E:/genesis-system/sunaiva/ai-memory/deploy/` directory structure**:
     ```
     sunaiva/ai-memory/
     ├── index.html                    ← Landing page
     └── deploy/
         ├── docker-compose.yml        ← 2-container stack
         ├── .env.example              ← Environment template
         ├── DEPLOY.md                 ← Deployment guide
         └── pipeline/
             ├── Dockerfile
             ├── requirements.txt
             └── main.py               ← Pipeline API
     ```

   - **`.claude/hooks/memory_saver.py`** — Auto-save hook (created but has API issue)
     - Fires on Stop event, extracts session summary, saves to Supermemory API
     - Uses urllib.request (doesn't follow 308 redirects properly)
     - Config loaded from `.claude/.supermemory-claude/config.json`
     - Logs to `E:/genesis-system/data/memory/save_log.jsonl`

   - **`.claude/settings.json`** — Updated with memory_saver hook in Stop section
     ```json
     "Stop": [
       { "hooks": [{ "type": "command", "command": "python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event session_stop" }] },
       { "hooks": [{ "type": "command", "command": "python3 /mnt/e/genesis-system/.claude/hooks/memory_saver.py --event stop" }] }
     ]
     ```

   - **`CLAUDE.md`** — Previously updated with Memory-First Mandate (from prior session)

   - **`E:\genesis-system\domain_portfolio.txt`** — Full domain list (100+ domains across GoDaddy, VentraIP, Namecheap)
     - Sunaiva brand: sunaivadigital.com, sunaiva.com, sunaiva.com.au, sunaivasites.com.au, sunaivacore.com.au, sunaivaflow.com.au
     - Voice AI: voiceassistant.com.au, receptionistai.au, phonebot.au, talkbot.au, bookingbot.au
     - Tradie domains: tradiechatbots.com.au, tradiesvoice.com.au, tradieautomation.com.au, aitradie.au
     - City chatbots: brisbanechatbots.com.au, sydneychatbots.com.au, etc.
     - Industry verticals: lawyerbot.au, dentalbotai.au, propertybotai.au, mechanicbot.au, etc.

   - **`E:\genesis-system\KNOWLEDGE_GRAPH\entities\domain_portfolio_revenue_strategy.json`** — 162-line strategic asset JSON with 3-tier domain strategy, revenue services pricing, target markets, tech stack

   - **`E:\genesis-system\KNOWLEDGE_GRAPH\creator_mind/`** — Local KG with 917 entities, 1867 relationships
     - Key files: `entities/all_entities.jsonl` (481KB), `relationships/all_relationships.jsonl` (347KB), `evolution/open_questions.jsonl` (2.7MB), `network/people_network.jsonl` (1.7MB), `revenue/business_ideas.jsonl` (205KB)

   - **`C:\Users\P3\Downloads\GENESIS_CONCURRENT_SWARM_DEPLOYMENT.md`** — 9-session concurrent swarm plan
     - Read by user request but I provided summary of what I read and connected it to our work

4. Errors and Fixes:

   - **python3 not found on Windows**: `python3` command doesn't exist, only `python`. Fixed by using `python` instead.

   - **Supermemory API 403 from memory_saver.py**: urllib.request doesn't follow HTTP 308 Permanent Redirect. The Supermemory API returns 308 redirects. Fix: use `curl -L` for API calls (works perfectly). The memory_saver.py hook still needs to be updated to use subprocess curl or the `requests` library instead of urllib.

   - **PowerShell variable interpolation**: `$r = Invoke-WebRequest...` gets mangled by bash→powershell piping. The `$` in `$r` is interpreted by bash. Fixed by switching to curl for all Supermemory API calls.

   - **Supermemory search returns max 10 results**: `topK:50` still returns limited results. Each semantic query returns at most ~10 relevant results. Fixed by running multiple queries with different angles and deduplicating by documentId.

   - **User feedback: Pricing too low**: Initial landing page had $9/mo beta / $29 regular. User said "the pricing is wayyyy too low. $9/mo doesnt position correctly at all and cost to serve is too high. think $47-$97/mo." Landing page still needs updating.

   - **FalkorDB not on Elestio**: Assumed FalkorDB would be on Elestio managed catalog. It's not. Only Neo4j is available as a managed graph DB. Fix: use Docker container on an Elestio Docker VPS instead.

5. Problem Solving:

   - **Solved**: Identified that Supermemory's MCP alone (on Pro $20) is sufficient for personal use without needing Scale ($400). The product runs on sovereign infrastructure completely separate from Supermemory.

   - **Solved**: Architecture for sovereign memory product: FalkorDB + Graphiti MCP + Pipeline API, all in Docker on single Elestio VPS ($25-35/mo).

   - **Solved**: Stealth approach — Supermemory never sees customer data. We use their Pro plan for Genesis's own memory only.

   - **Solved**: Universal/agnostic design — MCP endpoint works with any MCP client. REST API for tools that don't support MCP.

   - **Ongoing**: Landing page pricing needs updating from $9 to $47/$97/$197 tiers.

   - **Ongoing**: memory_saver.py hook needs fix for 308 redirect issue (currently logs error but doesn't save).

   - **Ongoing**: Pipeline API parsers are V1 (simple chunking) — need LLM-powered entity extraction for production quality.

   - **Ongoing**: Deployment to Elestio VPS not yet done — waiting for user to spin up the VPS.

6. All User Messages:

   - "i dont think we are yet on pro. at what point do we need scale plan? what is two separate accounts issue? what do you mean by this-Keep Supermemory as a customer-facing option in the product- do customers know its supermemory? we want to white label it.. how quick is quick setup? does it still take 24hrs for digestion? we should not offer sovereign for first few weeks. start with what we have.. everything we build must be universal and agnostic, work on pc or mac on phone with claude with chatgpt etc"

   - "the conflict I am in is I want the benefits/speed to market of using supermemory but dont really want to commit the $400 upfront but mostly dont want them to know what we are doing. I always like to operate by stealth and not let others know our approach. this is my dilemma."

   - "i worked out if we just use the mcp we dont even need to be on the scale plan but then what features do we miss out on?"

   - "is it the digestion pipeline? and if so, how can we make ours just as fast and efficient? what would be our processing power optimally for that job? cloudflare workers? check out moltworker is it called?"

   - "i think that I need to be the first test user of our entire pipeline. I almost want to find a way to start from scratch again. like erase all memories. or alternatively we could use my gemini chat export as test case but maybe we already ingested those too? not sure now.."

   - "lets do claude- why? its a bigger market. its where most of my stuff is. the current memory didnt work well so genesis-kinan and genesis-aiva incomplete anyway. what do you think?"

   - (User also expressed decisions about: sunaiva.com brand, website first, drag-n-drop hero, pricing feedback that $9 is too low, and the "formula 1 racecar" realization about shipping)

   - "build the basis and we can always make it better on lovable later. yes we own all these domains. its written on my list where they all live. get it up and running."

   - "if supermemory is $20/mo and thats just one component but we provide the full seamless customer stack, you know. this has tremendous value.. think about all the pain this solves. its virtually priceless and wrapped as a universal mcp!!!! wowsers.."

   - "look I can easily get a dedicated docker going on elestio and then its kind of managed anyway?"

   - "lets set this up properly, so 3 separate docker containers? and cloudflare workers run on 3rd one?"

   - "yes lets go. and most users that want this will be power users like me- claude $100-$200/mo, gemini ultra $400/mo(discounted to 50% off for 3 months), Manus AI $29-$200/mo (currently on hold) so $97/mo to get memory across these plus claude code, google antigravity, openclaw agents etc is massive win"

   - (Shared GENESIS_CONCURRENT_SWARM_DEPLOYMENT.md) "i just had a conversation with claude chat. I could use my voice for quick expression- I need concurrent autonomous sessions running 24/7. I have gemini credits. I also need a dedicated session for facebook aged accounts deployment based on the research project report outline. I will buy adspy. i will get docker compose managed service? on elestio."

   - "t5- i pay for instantly and t-5 launches warm up campaigns get customers ready for tradies voice suite with free heatmap audits or similar hook. which I believe are native GHL. t6- duplicates reviewharvest.com and also researches other similar tradies suite sites. also assess whether we build sub-domains for each single product as a component. these can be sold cheap as entry pathway with low friction. t7- actually build and develops the websites/landing pages. t8- build out our brand dna fb pages. t9- and beyond ready for instant deployment as required- can be spawned with the system ready."

7. Pending Tasks:
   - **Deploy sovereign stack to Elestio VPS** — docker-compose.yml is ready, waiting for user to create the VPS
   - **Update landing page pricing** — Change from $9/$29 to $47/$97/$197 tiers
   - **Fix memory_saver.py** — Replace urllib with curl subprocess to handle 308 redirects
   - **Test pipeline as user zero** — Run Kinan's 615 Claude conversations through the pipeline
   - **Expand swarm deployment** — Tracks t5-t9 described but not yet built (Instantly campaigns, ReviewHarvest duplication, website building, Facebook pages, on-demand spawning)
   - **Save session decisions to Supermemory** — Multiple strategic decisions made this session need to be committed

8. Current Work:

   The most recent exchange was the user describing the **concurrent swarm deployment vision** with tracks t5-t9 and confirming they want to proceed with Elestio Docker Compose managed service. The user shared `GENESIS_CONCURRENT_SWARM_DEPLOYMENT.md` which describes a 9-session tmux-based swarm system. I connected it to our memory product (Track 3) and laid out how the architecture fits together.

   The user's very last message added tracks t5-t9:
   - t5: Instantly.ai warm-up campaigns for Tradies Voice
   - t6: Duplicate reviewharvest.com + research similar sites
   - t7: Build websites/landing pages
   - t8: Build brand DNA Facebook pages
   - t9+: On-demand session spawning

   The docker-compose.yml, pipeline API, Dockerfile, and landing page are all built and ready at `E:/genesis-system/sunaiva/ai-memory/`. The deployment to Elestio VPS is the immediate next step once the user creates the VPS.

9. Optional Next Step:

   The user's most recent explicit action was sharing the swarm deployment doc and describing t5-t9, while also confirming "i will get docker compose managed service? on elestio." The logical next steps are:

   1. **Wait for user to spin up Elestio Docker VPS**, then deploy the docker-compose stack
   2. **Update landing page pricing** to $47/$97/$197 as user directed
   3. **Integrate the swarm deployment plan** — our Track 3 (memory product) is already built, the other tracks (t5-t9) need task briefs written

   The most immediately actionable task is updating the landing page pricing (doesn't require waiting for Elestio) and writing task briefs for the additional swarm tracks the user just described.
</summary>

service_tier: standard

type: assistant


hookEvent: SubagentStop

hookName: SubagentStop

command: python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event subagent_stop