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230B Open Model to Power Next-Gen Agents as OpenClaw Ships Daily

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Welcome back! OP here again, helping you with another addition of Agent Pulse - your go-to spot for agentic news, insights and more.

In today’s:

  • 👉 TOP Agentic News

  • ✨ Featured Agents

  • 🎙️ What to Watch this Week

  • ⚔️ Agent Arena Battleboard

  • 🏆 Agents Leaderboard

  • 🗺️ Agents Landscape Map

NVIDIA published MiniMax M2.7 on April 11 as an open-weights release aimed directly at agentic workloads, including reasoning, ML research workflows, software engineering, and office work. The model uses a 230B-parameter MoE architecture with only 10B active parameters per token and a 200K context window, which matters because it pushes long-horizon agent tasks toward lower-cost inference. NVIDIA also tied the release to NemoClaw, OpenShell, vLLM, SGLang, and NIM, making this less of a raw model drop and more of a deployable agent stack update. Strategically, this is another sign that the competition is shifting from “best model” to “best open, optimized, production-ready agent substrate.”

OpenClaw posted a tightly packed release cadence across April 11–12, with version 2026.4.10 on April 11, a 2026.4.11 beta later that day, and 2026.4.11 as the latest signed release on April 12. That pace matters because OpenClaw is one of the most visible open-source personal agent platforms, and rapid shipping around memory, provider support, and runtime behavior signals how quickly the open-agent layer is evolving in production. Even without overreading any single patch, the verified release history shows the project continuing to behave more like a live agent operating system than a static framework. Strategically, this reinforces that the open-source agent race is now being fought through weekly operational upgrades, not just occasional model announcements.

Hermes Agent surfaced as a new open-source autonomous agent from Nous Research built around a learning loop rather than static prompt configuration. According to the official repo and site, it can create skills from experience, improve those skills during use, maintain persistent memory across sessions, run scheduled automations, and operate across Telegram, Discord, Slack, WhatsApp, Signal, email, and CLI. The repo also shows unusually strong early developer traction, with more than 65,000 GitHub stars at the time of capture. Strategically, Hermes matters because it frames the next open-agent battleground as memory, adaptation, and multi-surface persistence, not just tool calling.

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Featured AI Agents
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What to Watch this Week

 🎙️AI on Fire: Real Builders. Real Heat.

Stories from people building AI Agents. Explore all episodes here

Building with AI Agents? Come talk about it.

AI on Fire is the podcast where we speak with founders and builders shaping the agent economy.

🏆 The Leaderboard Never Sleeps

The global ranking of AI agents is shifting every day. Who’s on top? Who just dropped?

🗺️ The Map of AI Agents (Live & Growing)

We’re charting the entire AI agent ecosystem — thousands of options across categories.
Your next agent is already on the map.

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