No-Code Agentist

Claude Fable Has Been Delayed Another Week — and That’s Your Opportunity. Everyone clipped Andrej Karpathy talking about vibe coding. Almost everyone missed the most important part. The real opportunity is no longer making AI write code faster. It is building a system that remembers your business, connects scattered information, and becomes smarter with every new task. As Karpathy explains in this clip, no traditional codebase could build a knowledge layer from thousands of disconnected facts. An LLM can. That is exactly why Fable 5 matters: long-term tasks, agent orchestration, and file-based memory that can become a real second brain for your business. Most people will waste the delay waiting for access. You can use this extra week to prepare everything Fable will need: your notes, documents, bookmarks, client folders, decisions, research, and workflows. I turned the entire process into a practical step-by-step guide — from an empty knowledge base to a working second brain. Watch the clip, then read the full article below.

Want to build your own AI agent for WhatsApp? Here's a practical roadmap. Option 1: No-code (Fastest) Best if you want an assistant that answers questions from your own WhatsApp number. What you need: • A Meta Business account • A WhatsApp Business Account (WABA) • A phone number connected to WhatsApp Cloud API • n8n • OpenAI or Claude • A knowledge base (Notion, Google Drive, Markdown files, PDFs, etc.) Basic flow: 1. Create a Meta Business account. 2. Create your WABA and add your phone number. 3. Enable the WhatsApp Cloud API. 4. Create a webhook in n8n to receive incoming messages. 5. Send the message to OpenAI or Claude. 6. Give the model access to your knowledge base. 7. Return the response through the WhatsApp API. Architecture: WhatsApp → n8n → LLM → WhatsApp You can have a working prototype in a few hours. Great for: • Personal assistants • FAQ bots • Internal company tools --- Option 2: Low-code (My favorite) If your agent needs memory, business logic, or integrations, add a backend. Example stack: • FastAPI • PostgreSQL • Redis • OpenAI or Claude • n8n (optional) Now your agent can: • Remember previous conversations • Book appointments • Query databases • Call external APIs • Execute business workflows • Manage customer information Instead of only generating text, it can perform real actions. --- Option 3: Multi-agent architecture This is the direction I'm currently exploring. Instead of building one massive agent, create specialized ones. • Sales Agent • Support Agent • Booking Agent • Knowledge Agent • CRM Agent An orchestrator decides which agent should handle each request. From the user's perspective, it's still one WhatsApp conversation. Behind the scenes, multiple agents collaborate to generate the response. This architecture is easier to maintain and scale as your project grows. --- Tools worth exploring: • WhatsApp Cloud API • OpenAI • Claude • n8n • FastAPI • PostgreSQL • Redis • MCP Servers • Notion One lesson I've learned: The WhatsApp integration is usually the easy part. The quality of your agent depends on the quality of the information it can access. A solid knowledge base consistently outperforms an elaborate prompt.