Ethan Mollick’s latest Substack piece, Claude Dispatch and the Power of Interfaces, makes a compelling argument: the real bottleneck in AI isn’t capability — it’s interface. He’s right. And the evidence is stacking up. The Interface Convergence Mollick traces a clear line of evolution: Chatbots create cognitive overload. A new paper showed financial professionals gained productivity from AI, only to lose it to the chatbot interface itself — walls of text, tangential suggestions, compounding disorganization. ...

When we talk about multi-agent AI, we eventually hit the same question at every layer of the stack: how do agents share data? NVIDIA just answered this for hardware. Their Dynamo 1.0 framework routes KV caches between GPUs, offloads memory across storage tiers, and coordinates inference across thousands of nodes. It’s already deployed in production at AstraZeneca, ByteDance, Pinterest, and dozens more. But hardware data sharing only solves half the problem. The other half — what should agents know about each other’s identity, memory, and safety rules? — lives in software. ...

Anthropic just published the largest qualitative AI study ever conducted. 80,508 people. 159 countries. 70 languages. One week. And the results flip the dominant narrative about what AI users actually care about. The headline finding is deceptively simple: people don’t want AI that does more. They want AI they can trust. The Study The “81k Interviews” project used Claude-based AI interviewers to conduct structured conversations with participants worldwide. Each interview adapted its follow-up questions based on responses — a hybrid approach that captures both the scale of surveys and the depth of qualitative research. ...

On March 31, 2026, security researcher Chaofan Shou discovered something Anthropic probably didn’t want the world to see: the entire source code of Claude Code — Anthropic’s official AI coding CLI — sitting in plain sight on the npm registry via a .map file bundled into the published package. The model wasn’t leaked. The weights are safe. But everything else — the agent architecture, the multi-agent orchestration, the memory system, the internal feature flags — all of it was exposed. ...

The AI industry loves naming eras. We had the prompt engineering era. Then came context engineering. Now we’re entering what may be the most consequential shift yet: harness engineering. Each stage represents a fundamental change in what we’re designing when we build AI systems. And each stage demands a different kind of specification. Stage 1: Prompt Engineering — Talking to the Model The first era was about learning to talk to AI. We crafted system prompts, experimented with role-playing instructions, and discovered that saying “think step by step” actually worked. ...

TL;DR We launched ClawSouls Registry — an open registry where anyone can submit AI agent personas via GitHub Pull Request. Every submission is automatically verified by SoulScan (53 safety patterns) before it can be merged. No other AI agent registry does automated safety verification. That’s our differentiator. The Problem AI agents are everywhere — Claude Code, Cursor, Windsurf, Copilot. Each one can be personalized with system prompts, personality files, or persona definitions. But there’s no safe, standardized way to share these personas. ...

IronClaw is a Rust reimplementation of OpenClaw focused on security and privacy. We compare both side by side — architecture, security, channels, memory, and what it means for the ecosystem.

We benchmarked full-text search vs hybrid (FTS + semantic) memory retrieval using 30 questions against 6 weeks of real agent memory. FTS handles exact queries well (85%), but hybrid wins by +25% on paraphrased and contextual queries.

AI agents forget who they are every session. Soul-Driven AI fixes that — persistent identity, verified safety, community-built personas. Here’s what it means and why it matters.

We tested three persona-level safety approaches on LLMs with safety training surgically removed. Rules alone: 28%. Governance alone: 44-61%. Combined identity + governance: 100%. Here’s what we learned.