
Nexus CommandHQ
Mission control for collaborative AI agents
Nexus CommandHQ launched on February 17, 2026 with 2 votes, #6,500 of 16,627 launches that month.
What it does
Nexus CommandHQ helps you run a team of specialized AI agents from one mission-control dashboard. Define the goal, assign roles, and let agents collaborate in structured rounds while you track progress in real time. Human approval checkpoints keep final decisions under your control. Bring your own Claude subscription and launch without the infrastructure headache.
Nexus CommandHQ - AI Agent Collaboration Dashboard
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Mission Control – Open-source task management for AI agentsFeb 2026 · github.com · ▲45I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification…

Cosmic AI AgentsDec 2025 · cosmicjs.com · ▲145Hey HN, We're Cosmic (YC W19), a headless CMS. Today we're launching Team Agents and the Cosmic Agent. Team Agents are AI-powered team members that live in Slack, WhatsApp, and Telegram. They have names, roles, persistent memory, and real capabilities. Not chatbots. Not one-off prompts. Persistent agents that work alongside your team. Here's what they can do: CONTENT AGENTS write blog posts, landing pages, and marketing copy. They generate images, add SEO metadata, and publish directly to the CMS. Message them in Slack, get a finished post back. CODE AGENTS read your codebase, create…
20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…


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