Quick practical comparison for anyone building or selling AI agent systems (especially with Hermes, OpenClaw, or coding agents) for local businesses. Cloudroom Multi-harness (Claude Code, Codex, Pi, Hermes, etc.), open-source Rust core, use your own model subscriptions (no markup). Free limited cloud agents + self-host option. Early-stage but strong on flexibility. Orgo Full persistent cloud computer per agent. Works with almost any agent. Good isolation model when you’re running agents for multiple clients. OpenHands Fully open-source, strong self-hosting, model-agnostic. Highest control, more ops work if you self-host. Official platforms (Cursor Cloud, Claude Code Cloud, Codex Cloud, Devin) More polished and lower friction, but higher lock-in. Better if you stay inside one ecosystem. Recommendation for this community: Orgo for client isolation + Cloudroom or OpenHands for the agent work. Self-host the open-source options when you want control over data and cost. What are you currently using to run agents for clients or internal systems? Drop your stack below. Image

Big agent news out of Meta Connect this week, and it's worth a few minutes of your time. Muse, Meta's personal AI agent (it lives in their apps, WhatsApp, and on the web), just got a lot more reach: • Mac computer use, available now. With your permission, Muse can drive any app on your Mac and keep working through a list of tasks after you walk away. • Its own email address, so it can send and receive messages for you. Meta announced this as coming. It isn't shipping yet. • New connectors, including Notion, GitHub, Box, and Granola, plus retail and payments. • Voice that keeps talking while it works, and a voice you can customize by describing it. Credit to @ibuildwith_ai on X for the clean rundown. Meta's full announcement is here: https://www.meta.com/blog/meta-connect-2026-everything-we-announced/ Why this matters for us: you don't have to be a developer to use any of this. If you already live in Notion, Box, or Granola, an agent can now pick up the busywork between those tools and your inbox. Here's my take, from years of running enterprise support at AWS. Every time a system got more access, the first question we asked was: "What's the blast radius if this goes wrong?" Same question applies here, and it matters more now that an agent can click anything on your Mac and, soon, email people as you. Where I'd start: 1. Give it one boring, low-risk job first, like pulling Granola meeting notes into a Notion page. 2. Keep it away from payments, client email, and anything with passwords until you've watched it work for a while. 3. Read what it did before you trust it with the next job. Let's compare notes: 👉 What's the first task you'd hand an agent that can use your computer? 👉 What's the one thing you'd never let it touch? Drop it in the comments. — Keith

Corey Ganim shared a clean price list this week for the AI services small businesses actually buy: 1. AI Tools Assessment: $999 2. Process Redesign: $3,500 3. Knowledge System: $3,000 4. Custom Workflows: $3,000 to $5,000 5. Full Implementation: $5,000+ 6. AI Concierge: $1,500+ a month Save it. But if you're building an AI services business, the list order is also the delivery order, and skipping steps is where projects go sideways. At AWS, the rule before anything touched production was simple: know what you're changing and what breaks if it fails. Same thing here. The assessment earns the right to the next conversation. Process redesign comes before any tool, because automating a process nobody wrote down just makes the mess faster. The knowledge system is what your agents will stand on, so it comes before workflows. Then you automate one workflow at a time, with a human approving actions until it proves itself. The concierge retainer is where the business actually lives. It's recurring revenue for you, and it means someone is still watching the agents for the owner. Let's make this useful for everyone: 1. Which of these six are you selling today? 2. What are you actually charging, and is it landing? 3. Which one do clients push back on hardest? Drop your numbers below. Real pricing from real deals helps everyone here price with confidence. — Keith

Every coding agent most of us use today depends on a big model running in someone else's cloud. This challenge asks what it would take to get that same capability offline, on consumer hardware. The task is to post-train Gemma 4, Google's open model, into a coding agent that can find its way around a real repository and draft fixes to real software engineering tasks on its own. Humans still handle architecture, logic, and review. There are two tracks: 1. Developer Agent Competition ($65K). You post-train Gemma 4 into an agent that resolves real engineering tasks. 2. Paper Track ($35K). You submit new, unpublished research, or you write up how you approached the competition. Total prizes are $100,000, and the entry deadline is November 25, 2026. This lines up with what we keep saying in here. An agent is only as useful as the loop around it: a plan before it touches files, tests it has to pass, and a human at the gate. A local agent makes that loop cheaper to run, and you don't have to send your code to a third party. A few questions for the group: - Is anyone entering? I'd like to put a small team together. - Who has done fine-tuning or reinforcement learning on an open model before? - What hardware are you running locally? Not a vendor brochure. Just a good problem. — Keith

One thing I keep coming back to: Most business owners don’t really care about the tech at first. They don’t wake up thinking, “I need an AI automation stack.” They wake up thinking: “I’m buried.” “I’m missing follow-ups.” “My team keeps dropping the ball.” “I can’t keep everything in my head anymore.” “I need more time.” “I know we’re growing, but the backend is starting to break.” That’s the real sales conversation. AI, automation, agents, workflows, CRMs, integrations… all of that matters. But only because it should solve a business problem. If you lead with the tech, you make the owner do the translation. If you lead with the business outcome, they immediately understand why it matters. Example: Weak: “We can build an AI automation that updates your CRM and triggers a follow-up sequence.” Stronger: “We can make sure every new lead gets followed up with automatically, so fewer opportunities slip through the cracks and you don’t have to personally chase every conversation.” Same solution. Completely different conversation. The tech is the vehicle. The outcome is the sale. Once you help them save time, recover missed revenue, improve follow-up, reduce manual work, or get better visibility into the business, then they may want to understand how the system works. But in the beginning, they mostly want to know: “Can you solve the thing that’s slowing me down?” That’s the mindset shift I think every AI builder needs to make. Don’t sell AI. Sell time back. Sell fewer dropped balls. Sell cleaner handoffs. Sell more capacity. Sell the owner not being the bottleneck anymore. That’s where the value is. Question for the group: When you explain what you do, are you leading with the technology or the business problem you solve?