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“I hope that this topic is not very dry,” Nuri, an independent consultant on responsible AI, said to the room on a Friday afternoon. A few laughs. She was about to talk about governance, but her real topic was trust. With AI moving from decision support to conversational assistants and now to autonomous agents, a new question fills the room: when an agent makes a mistake, who is responsible? For builders, Nuri’s message was simple: responsible AI is not a compliance checkbox.

Hanyi, a dispute lawyer with ten years of experience, started with the words “AI LAWYER” on the screen. Then they blinked out. “That’s my crisis,” he joked. For a lawyer, the term suggests replacement, an existential threat that he said keeps him and his batchmates awake at night. On this Friday afternoon, a senior dispute lawyer and a computer science undergraduate are asking the same fundamental question about their craft. Hanyi’s answer isn’t to fight the technology. It’s

Haodan's explorations into this topic started with a question from a lead data scientist inside SAP, someone who should have had the least fear. He asked the visiting CAIO, “how can we stay on top in the age of AI?” That question from someone “working at the frontier” surprised Hao Dan, Lead Product Manager for one of SAP’s most successful AI products. In this week's session of The Stage, Haodan shared her thoughts on what she thinks is the recipe for differentiation in the

Six months. That is usually how long it takes. You build a clean demo. You walk the stakeholders through it. They are impressed. You leave the room with momentum and a follow-up meeting scheduled. Six months later, the product sits untouched. Not because it broke. Because nobody trusted it enough to keep using it. Bhavna opened her talk at Coworking Friday this way, and I recognised the room in her description. Most of us had watched this happen. Several of us from inside it.

It's 1:30 PM on a Friday and we're passing the mic around the room. I am sitting with builders researching emotional state in AI, a lawyer looking at knowledge graphs, performance marketers, and hardware design engineers. Yao Kai Jiang dials in from the US. It's 10:30 PM for him. He is the founder of Momen, a full stack application builder for non-technical founders. He frames the recent surge in prompt-to-app building as modality translation. He sees it as the process of tra

The room at SQ Collective is quiet, save for the hum of laptops and the occasional shuffle of chairs. At 1.30 the AI Labs came to life, and builders took turns stepping up to the screen to show what they have been hacking together over the past month. At SQ Collective, we organize these monthly demos to encourage everyone to move from vibe to real feedback. AI for Fashion, but one that actually understands you Yu Qing steps up first. She is building an AI styling and shopping

Mike Anderson, CTO of Covia Labs opens with a historical parallel: Back in the dawn of the industrial era, when people first harnessed electric power, there was no electricity grid. Factory owners built their own generators. They designed their own machines and figured out how to fuel them in isolation. It took time before the grid evolved and connected everyone. We are currently in the generator phase of artificial intelligence. Everyone is building their own self-contained

"Building has become super easy now," Matthew starts. "We can basically go to Codex and pull one, and we've brought out a POC in a few days." "What that means is that we are building software faster than we are figuring out how the business works right now." Today, the focus is the customer: customers, with daily pressures, existing solutions, and forces that shape how they behave. "We need to unbox our customers." The Person, The Problem, and The Context Matthew started off

What brownfield engineering teaches builders about reliable AI agents, context, reuse, sentinels, and behavior-driven evals at SQ Collective.

Why autonomous AI agents need identity, trust, and credential boundaries, from a Stage session with loomal.ai at SQ Collective.

How AI builders can design better products with Claude and Paper.design by clarifying direction before code at SQ Collective.

A SQ Collective Stage recap on dark labs, physical AI, and how automated chemistry labs change the bottlenecks for AI builders.

Demo Day S2 recap from SQ Collective: ten builders shared AI products, agent workflows, OpenClaw progress, Roblox coding agents, and forecasting tools.

Ivan from Manus.ai explains why reliable AI agents start with context before tools, using capability design, memory, and tool calling at SQ Collective.

Russell's SQ Collective talk explores how AI can support richer human connection instead of flattening people into platform categories.

Practical lessons from three years building AI agents, from flashy demos to agents that create real business value and revenue.

Farhad's Co-work Friday talk on why enterprise complexity is a translation problem, not just a technical one, and how teams can make it visible.

Why staff ignored an AI Telegram bot, and what changed when the workflow was redesigned around clarity, critique loops, and real human edge cases.

Clarence Lam traces intelligence from early nervous systems to human belief systems and AI, asking what patterns keep repeating across history.

A candid recap of Co-working Friday's first Demo Day, from shipping an unfinished AI community tool to building confidence through public demos.