Timeline

[SUGGESTED_QUERY] Supabase embedding model compatibility issues

[SUGGESTED_QUERY] agent-memory.py Pieces data integration

Website: https://memz-silk.vercel.app

[Command-Line Output] Fleet Peer Mesh Implementation Dispatch (Merlin + Knox + Frankie): - Merlin: Fork broker, multi-runtime adapters, zombie fix. - Frankie: fleet.merlinoai.com dashboard. - Knox: Finished infrastructure. Manual steps (INFRA.md): 1. SSH mac /opt/homebrew/bin/cloudflared tunnel login 2. Paste ACL JSON at https://login.tailscale.com/admin/acls 3. Add 2 CNAMEs in Namecheap UI Petey Agent Report: - Classification: executable-process - Step count: 48 across 11 phases - Verified via SKILL.md and Supabase row a69d8286-e706-4269-be73-fc321049c02c - 5,416 skill rows remaining for classification pass.

[SUGGESTED_QUERY] manual infrastructure steps for Fleet Peer Mesh

[SUGGESTED_QUERY] Petey agent executable process classification results

[SUGGESTED_QUERY] Fleet Peer Mesh infrastructure setup status

Website: https://fleet.merlinoai.com

[Command-Line Output] Decision needed: Start with HTTP and add MCP later, or build MCP first? Phase 4: Replace "mad skills" with lean routers. Right now every agent loads 500 skill files into context. Replace with query-brain-skill, query-brain-sop, query-brain-chat, query-brain-transcript, query-brain-project. Three decisions needed for Phase 1: 1. Pinecone index layout: single master-brain index with scope filter, or per-scope indexes? 2. Embedding model: OpenAI text-embedding-3-large (3072), Gemini Embedding 2 (1536), or Voyage? 3. Smoke test target: SOPs (1,263 chunks), skills (19 MB), or chat backups (1.2 GB)? Fleet Peer Mesh Status: - Building cross-machine peer dashboard at fleet.merlinoai.com. - Knox finished infra; Merlin forking broker; Frankie building dashboard. - Manual steps in INFRA.md: SSH cloudflared login, paste Tailscale ACL JSON, add Namecheap CNAMEs.

[SUGGESTED_QUERY] Agent implementation dispatch Merlin Knox Frankie

[SUGGESTED_QUERY] Pinecone index layout and embedding model decisions

[SUGGESTED_QUERY] Fleet Peer Mesh infrastructure setup steps

Website: https://login.tailscale.com/admin/acls

[Command-Line Output] Phase 3: Agent access endpoint Two access shapes for /api/brain/search: 1. HTTP: agents call https://brain.merlinoai.com/api/brain/search?scope=skill:*&q=Y&top_k=5 2. MCP server: wrap search as an MCP server for native agent connectivity. Phase 4: Replace 'mad skills' with lean routers Currently, every agent loads 500 skill files into context. The plan is to replace these with 5 router skills (each ~30 lines) that call the search endpoint. Decisions needed for Phase 1: 1. Pinecone index layout: single master-brain index with scope filter, or per-scope indexes? 2. Embedding model: OpenAI text-embedding-3-large (3072), Gemini Embedding 2 (1536), or Voyage? 3. Smoke test ingestion target: SOPs (1,263 chunks), skills (19 MB), or chat backups (1.2 GB)? Corpora analysis: - Skills: 19 MB (fastest smoke test) - Chat backups (Project Chats): 1.2 GB (real workload) - Chat backups (By Date): 28 GB (skip, duplicates Project Chats)

Website: https://brain.merlinoai.com/api/brain/search?scope=skill:*&q=Y&top_k=5

[SUGGESTED_QUERY] Ingesting SOPs and chat backups for agent search

[SUGGESTED_QUERY] Pinecone index layout and embedding model selection

[SUGGESTED_QUERY] Master Brain agent architecture planning

Tag: pinecone-indexing Source: CLOUD_LLM

[SUGGESTED_QUERY] Merlino Marketing Hermes Agent API configuration

[SUGGESTED_QUERY] x-api-key header required error debugging

[SUGGESTED_QUERY] Merlino AI API authentication error

Website: https://brain.merlinoai.com/api/brain/search?scope=skill

[Web Browser Content] Master Brain Dashboard Master Brain — unified knowledge across all sources and agents. Dashboard SOURCES: 24 active ingestion sources AGENTS: 8 with classified memory HEALTH: 1d ago last ingestion TOTAL MEMORIES: 324,188 across all sources Agents: Graph, Galaxy, Merlin 3D By Source: Transcript: 65,070 Brain: 43,653 Chat: 42,661 Pieces: 34,521 Hindsight: 27,346 SOP: 20,105 Vault: 17,470 Claude: 16,357 Actions: Upload, Export

[SUGGESTED_QUERY] Skipper app knowledge management dashboard overview

[SUGGESTED_QUERY] AI agent memory statistics and data sources

[SUGGESTED_QUERY] Master Brain dashboard knowledge ingestion metrics

[Web Browser Content] Dashboard / Extension Management - Claude (MCP) - AWS - brain.merlinoai.com - Chase - GeoGr - MiniN'1 - SEO In - Archar - Creatii - Ask Gemini - CTR Geeks - Ads Manager - Management - Merlino Marketing - Dashboard - Hermes Agent - Dashboard - Developers Call Tracking

[SUGGESTED_QUERY] Ads Manager and marketing tools overview

[SUGGESTED_QUERY] Claude MCP browser extension configuration

[Command-Line Output] Today (May 16) on disk: 12 codex sessions. Root cause: The deduplication check for codex sessions was incorrectly slicing the session ID, causing all chats on the same day to be skipped after the first one. Fix: Update the regex in backup-pipeline.py to correctly extract the UUID from the session path. Infrastructure Plan: Migrating agent knowledge to Pinecone. Phase 1: Implement Pinecone fanout backend using scripts/fanout_to_pinecone.py. Phase 2: Back up all data (chats, skills, SOPs, transcripts, Pieces LTM) to Pinecone. Phase 3: Implement agent-side router pattern for brain queries. Master Brain server: https://brain.merlinoai.com/api/brain/search

[SUGGESTED_QUERY] Master Brain server architecture and fanout patterns

[SUGGESTED_QUERY] migrate agent knowledge to Pinecone backend

[SUGGESTED_QUERY] codex session deduplication bug fix

Website: https://brain.merlinoai.com/api/brain/search

Tag: pinecone-migration Source: CLOUD_LLM

Tag: codex-bug Source: CLOUD_LLM

[Web Browser Content] Chaser (trychaser.com/mcp) Connect Chaser to your AI agent. Let ChatGPT, Claude, and other AI agents assign tasks, run reports, and more. Copy-paste this into your favorite AI tool: "Connect me to Chaser (a Slack-native task management tool) via MCP. The server URL is https://api.trychaser.com/mcp. Give me step-by-step setup instructions."

[SUGGESTED_QUERY] How to use Chaser with Claude and ChatGPT

[SUGGESTED_QUERY] Chaser task management MCP setup

[SUGGESTED_QUERY] Connect Chaser to AI agents via MCP

[Web Browser Content] Chaser MCP Server — Connect Your AI Agent to Slack Task Management Connect Chaser to your AI agent Let ChatGPT, Claude, and other AI agents assign tasks, run reports, and more. Copy-paste this into your favorite AI tool: https://api.trychaser.com/mcp . Give me step-by-step setup instructions.

[SUGGESTED_QUERY] integrating AI agents with Chaser task management

[SUGGESTED_QUERY] how to connect Chaser to AI agents via MCP

[SUGGESTED_QUERY] AI agent integration with Chaser API

[SUGGESTED_QUERY] Chaser Slack task management MCP server setup

[SUGGESTED_QUERY] how to connect AI agent to Chaser MCP

Connect Chaser to your AI agent Let ChatGPT, Claude, and other AI agents assign tasks, run reports, and more. Copy-paste this into your favorite AI tool: Connect me to Chaser (a Slack-native task management tool) via MCP. The server URL is https://api.trychaser.com/mcp. Give me step-by-step setup instructions.

[Web Browser Content] Jason Lo @jason-lo-5996 Memberships: Chameleon Mode (378 members) Learning: AI and marketing Joined: Jun 22, 2025 Sidebar Tools: - Claude (MCP) - Ads Manager - Hermes Agent - Call Tracking - Ask Gemini - CTR Geeks - Merlino Marketing

[SUGGESTED_QUERY] Skool community member profile