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There are two ways to build AI agents: 1. The way that looks good in a demo. 2. The way that makes money. Most people start with #1. They build something impressive. They record a demo. They post it online and watch the views pile up. Zhu Liang built both. And in a recent sharing at The Stage, and he shared his take-aways from building agents these last 3 years. The Agent that Makes its Own Money This was a really interesting exploration that Zhu Liang did over the past few

When Farhad , the speaker at last Friday's Co-work Friday, was a consultant, he was part of a team building a large enterprise company from scratch, and building a digitally native platform on top of that. The ambition was large: own the entire customer journey, from the "why" all the way through booking and transaction. He spent years reflecting on his experience on that project. He initially thought it was a technical problem. Then he realised it was a translation problem.

Nobody wanted to use my Telegram bot, Rex ( -> https://rex.sq-collective.com/ ). I built it. I was proud of it. Rex could generate invoices, book calendars, check for double bookings. It could create Luma events, generate cover art, send drip invites, update descriptions. It even did Luma event invites based on "interest level" through the embedding database I built for Kai ( -> https://kai.sq-collective.com/ ) My staff opened it once, got a confusing response, and went back

Ancient Egyptians thought the heart did the thinking. Aristotle agreed. We've come a long way since then. Or have we...? Last Friday at the Stage, we shifted gears away from practical applications to history. Clarence Lam , who runs Chiasm, a Swiss-based startup focused on the design and optimization of complex systems, brought us on a brief journey from early life and nervous systems to present-day human belief systems and artificial intelligence, exploring intelligence as

I almost cancelled Demo Day. Not because something went wrong. Because I was scared my demos weren't good enough. That the whole thing would feel flat. That people would walk away thinking, what was the point of that? But I caught myself. I run a space where I tell people every single week to build in public, to ship messy, to show up before they're ready. And here I was, about to cancel because my own project  wasn't polished. So I didn't. I went first. I demoed Kai, my half

(and why the chime on my phone is my new coworker) A few days after John's talk at Stage: "One Person, One AI, One Product" (read it here ), I had an uncomfortable realisation: I've had a Claude Max subscription for a while, and I've never once hit the rate limit. I was using it like a tab I'd open only when I remembered. John's sharing planted a simple idea in my head: if one person can ship production-grade software without a team, the bottleneck isn't engineering capacity.

Last Friday, AI Speedup  and SQ Collective  hosted a session that brought together founders, GTM leaders and infrastructure leaders to discuss the reality of scaling globally in 2026. Key Speakers:   David (Wavespeed AI), Jack Chen (Akool) , Lusha Chen (Dify) , Ivan Tang (Zilliz) , and Sarah Wang (Moonshot AI/Kimi)  shared insights on enterprise video, open-source ecosystems, and vector infra. Panel Discussion:   “Where AI Globalization Actually Breaks — or Scales”  featured

Last Friday, John dropped a line that made half the room laugh and the other half go quiet: “Don’t talk to the engineers yet. Give the requirements to Claude. Let’s see what happens.” In November 2025, John ran the experiment with his team. He told his non-technical teammates exactly that. Two hours later, they had what used to take four engineers a full multi-week sprint. That moment changed how he builds software, and it might change how you think about it too. The setup: P

Trust in AI Isn’t Magic. It’s actually just expectation Alignment. Ask ten people how they feel about AI and you’ll get everything within the range from “this is AGI, we’re cooked” to “it’s all hype, my grandfather does this better.” Underneath all of that noise, there’s one simple idea that spells out whether people trust what you’re building: Trust = expectations and reality are in sync. In this week's Stage @ our Co-working Friday, Jeremy Soo , founder of emotional AI star

During SQ Collective's Stage session this week, which is a part of our co-working Friday: "The bad news is no one knows about their pricing. The good news is there's a way to think about it." Mohammed Sebti , former Simon-Kucher consultant turned startup mentor, dropped truth bombs at our recent Stage session on Pricing Strategy . Here's the reality for most of us building products: We guess. We look at a competitor, undercut them by 10%, and pray. Or we take our costs, add a

On The Stage this week, Henry from Smithery  said something that broke the whole conversation open: "We have a bit of a Claude Paradox. You have models that have big brains but no arms and legs." They can write a sonnet, but they can't check your calendar. They can solve math problems, but they can't ping your team on Slack. That’s where the Model Context Protocol (MCP)  comes in. And if you’re a builder, this is the part where you stop treating AI like a chatbot and start tr