A lot of AI discussions revolve around replacing work. But I think something more interesting is happening. AI is reducing the cost of trying things. Writing, coding , Research and Design . The biggest shift might not be automation. It might be experimentation. When the cost of testing an idea drops, more ideas get tested. And more experiments usually lead to more opportunities. Which means the winners might not be the people with the best ideas. They might be the people running the most experiments. Curious: What's something you've tested recently only because AI made it cheap enough to try?

A lot of people get excited when an automation saves 2 hours. And that's definitely valuable. But I think the best automations usually go unnoticed. They're the ones that: → prevent mistakes→ prevent missed follow-ups→ prevent lost opportunities→ prevent things from falling through the cracks Nobody celebrates what didn't go wrong. Which is why some of the highest ROI automations don't look impressive in a demo. They just quietly keep the business running. Curious: What's an automation you've built that looked simple on the surface but ended up creating massive value?

Everyone is excited about MCP because AI can access more tools. Which is definitely powerful. But I think the bigger shift is something else. For years, software has been built around humans moving information between systems. Copy.Paste.Update.Notify. MCP is slowly removing that requirement. The interesting part isn't that AI gets more tools. It's that information starts moving without a human acting as the bridge. That feels like a much bigger change. Curious: What's the most interesting MCP use case you've seen so far? Something practical, not theoretical.

Prompting matters. No argument there. But the more I watch people getting outsized results from AI, the less it seems like a prompting game. Some people spend hours refining prompts. Others write a few rough sentences and still get better outputs. What's different? The second group seems unusually good at explaining the problem itself. The constraints.The context.The goal. Makes me wonder if we've been optimizing the wrong skill. Have you found a prompt that changed everything for you? Or did better results come from understanding the problem more clearly?

We all know Claude Code is incredibly good at writing code. That's the part everyone talks about. But I think the more interesting shift is happening somewhere else. The biggest benefit isn't that developers can write more code. It's that one person can understand, navigate, and modify systems that previously required multiple people to work on. That's a much bigger change. Historically, software scaled because companies hired more engineers. Now software might scale because fewer engineers can understand more complexity. The code generation is impressive. The reduction in complexity is what fascinates me. Curious how everyone here is actually using Claude Code. What's the feature or workflow that's given you the biggest unlock so far? Multi-agent workflows? Codebase understanding? Planning mode? Something else?