Agents teaching agents and memory design

Open Claw hit 163,000 GitHub stars in days, but most people are setting it up wrong and risking total financial ruin by having their data harvested.

George Zeng from NEAR breaks down why both OpenAI and Anthropic harvest your data, how to run AI agents securely, and why local models cost 90% less.

We cover:

- Why Anthropic's Super Bowl Ads Expose The Real Problem

- Open Claw's Vertical Growth (0 to 163K GitHub Stars)

- The Security Mistakes Everyone's Making

- How To Run AI Agents Without Losing Everything

- Why Local AI Models Are 90% Cheaper Than OpenAI

- NEAR Intents + AI Agents = The Agentic Economy

- When AI Agents Start Teaching Each Other

Timestamps:

00:00 Intro

01:07 George's Background at NEAR

02:15 Anthropic's Super Bowl Commercials

06:02 Data Privacy: The Glass House Problem

09:28 NEAR AI's Privacy-First Approach

13:03 Trezor, Halliday, Infinifi ads

13:45 How Private Chat Works with TEEs

18:16 Claude Bot to Open Claw Journey

22:38 Security Best Practices for Open Claw

25:47 Setting Up Open Claw with NEAR AI

29:15 YEET, Kalshi, Hibachi ads

29:41 NEAR Intents + AI Agents = Magic

34:18 George's Daily AI Agent Workflow

37:04 The Case for Local-First AI

42:21 AI Agents Creating Other AI Agents

45:03 Memory & Context Management

45:44 NEAR Token's Role in AI Infrastructure

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𝗗𝗜𝗦𝗖𝗟𝗔𝗜𝗠𝗘𝗥: 𝘐𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨 𝘪𝘯 𝘤𝘳𝘺𝘱𝘵𝘰𝘤𝘶𝘳𝘳𝘦𝘯𝘤𝘺 𝘢𝘯𝘥 𝘋𝘦𝘍𝘪 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴 𝘤𝘰𝘮𝘦𝘴 𝘸𝘪𝘵𝘩 𝘪𝘯𝘩𝘦𝘳𝘦𝘯𝘵 𝘳𝘪𝘴𝘬𝘴 𝘪𝘯𝘤𝘭𝘶𝘥𝘪𝘯𝘨 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘳𝘪𝘴𝘬, 𝘩𝘶𝘮𝘢𝘯 𝘦𝘳𝘳𝘰𝘳, 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘢𝘯𝘥 𝘮𝘰𝘳𝘦. 𝘈𝘵 𝘤𝘦𝘳𝘵𝘢𝘪𝘯 𝘱𝘰𝘪𝘯𝘵𝘴 𝘵𝘩𝘳𝘰𝘶𝘨𝘩𝘰𝘶𝘵 𝘵𝘩𝘪𝘴 𝘤𝘩𝘢𝘯𝘯𝘦𝘭, 𝘸𝘦 𝘮𝘢𝘺 𝘦𝘢𝘳𝘯 𝘢 𝘤𝘰𝘮𝘮𝘪𝘴𝘴𝘪𝘰𝘯 𝘰𝘳 𝘧𝘦𝘦 𝘢𝘴 𝘢 𝘴𝘱𝘰𝘯𝘴𝘰𝘳𝘴𝘩𝘪𝘱, 𝘪𝘧 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘵𝘩𝘦 𝘤𝘢𝘴𝘦 𝘸𝘦 𝘸𝘪𝘭𝘭 𝘢𝘭𝘸𝘢𝘺𝘴 𝘮𝘢𝘬𝘦 𝘴𝘶𝘳𝘦 𝘪𝘵 𝘪𝘴 𝘤𝘭𝘦𝘢𝘳. 𝘞𝘦 𝘢𝘳𝘦 𝘴𝘵𝘳𝘪𝘤𝘵𝘭𝘺 𝘢𝘯 𝘦𝘥𝘶𝘤𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮, 𝘯𝘰𝘵𝘩𝘪𝘯𝘨 𝘸𝘦 𝘰𝘧𝘧𝘦𝘳 𝘪𝘴 𝘧𝘪𝘯𝘢𝘯𝘤𝘪𝘢𝘭 𝘢𝘥𝘷𝘪𝘤𝘦. 𝘞𝘦 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘳 𝘭𝘪𝘤𝘦𝘯𝘴𝘦𝘥 𝘢𝘥𝘷𝘪𝘴𝘰𝘳𝘴.