Sui Prototype Seal MPC Targets Secure On-Chain AI Agent Markets
Sui Prototype Seal MPC Targets Secure On-Chain AI Agent Markets: key Sui Seal MPC context, verified claims, market impact, and risk notes for crypto reader
InfoWorld AI·
Before Tobi Lütke ran Shopify, he learned programming through Germany’s apprenticeship system, the way people have learned trades forever: in a shared workshop, watching people who already knew what they were doing. More recently, describing Shopify’s River, he reached for a related word: Lehrwerkstatt, a teaching workshop where “the whole shop floor is the classroom.” X has been agog by the numbers around River, Shopify’s Slack-native AI agent. In total, 5,938 Shopify employees worked with River across 4,450 different Slack channels, and River now coauthors roughly one in eight merged pull requests across the company. It’s a big deal, but understanding why it works that way is the most important part. River can read code, run tests, open pull requests, query the data warehouse, inspect production traces, and sometimes push back on a plan it thinks is bad. Great. Lots of companies will have clever coding agents someday soon. Some already do. The interesting part is that River doesn’
Read full articleSui Prototype Seal MPC Targets Secure On-Chain AI Agent Markets: key Sui Seal MPC context, verified claims, market impact, and risk notes for crypto reader
EverMind has open-sourced EverOS, a local-first memory runtime that stores AI agent memory as plain Markdown indexed by SQLite and LanceDB. It combines hybrid BM25 + vector retrieval, multimodal ingestion, and self-evolving Skills under an Apache 2.0 license. Here's what it is, how the architecture works, where the benchmarks stand, and where it still falls short — plus a runnable code walkthrough and an interactive demo. The post Meet EverOS: An Open Source Markdown-First Agent Memory Runtime With Hybrid BM25 + Vector Retrieval and Self-Evolving Skills appeared first on MarkTechPost.
Sui Prototype Seal MPC Targets Secure On-Chain AI Agent Markets: key Sui Seal MPC context, verified claims, market impact, and risk notes for crypto reader
Fernando Irarrázaval posted his OpenClaw assistant's inbox to Hacker News and watched Claude Opus 4.6 hold off thousands of attackers.
PLUS: Give your AI agent a credit card (safely)
In this tutorial, we build a lightweight personal AI agent inspired by the architecture of nanobot, runnable entirely in Google Colab. We start from a provider abstraction, then add tool registration, session memory, lifecycle hooks, skills, and an MCP-style tool server. Rather than rely on an external framework, we recreate each building block ourselves to see how messages, tools, memory, and model responses fit together. The result is a provider-agnostic agent loop we can extend toward real LLM providers and production tools. The post Build a Nanobot-Style AI Agent in Google Colab with Tool Calling, Session Memory, Skills, and MCP Servers appeared first on MarkTechPost.
AI-driven match analysis could level the playing field, enhancing strategic insights for all teams, but may also intensify competitive pressures. The post FIFA gives every 2026 World Cup team a bespoke AI agent for match analysis appeared first on Crypto Briefing.
Claude Tag is Anthropic’s latest attempt at getting Claude out of your DMs and into your team’s Slack channels. AI assistants are increasingly showing up in the workplace to perform research, coding, writing, and analysis, but the results of those interactions typically remains tied to individual conversations rather than being shared across projects and teams. That limitation is what Anthropic is addressing with Claude Tag, a new Slack channel-based experience for its Enterprise and Team customers, designed to give them a shared AI collaborator that retains context across conversations and participates in work with multiple employees. Tag will replace Anthropic’s previous attempt at this, Claude in Slack, would only interact with one person (although it’s responses were visible to all in a channel) and its context was limited to the last 20 messages in a channel. Claude Tag has a much larger context and can be asked to complete tasks on its own, returning with results and a log of how