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Hi Reader, You don’t learn this stuff by reading more. You learn it by doing. And doing it with other people. What I’m Noticing AI is moving fast, and a lot of people are trying to keep up all on their own. Solo learning looks productive. New tools, new prompts, new threads. But it often turns into parallel play: everyone is experimenting separately, and nobody is getting meaningfully better together. I see this in teams all the time. Five people “trying AI”. Each person learning different things, in different tools, with no shared language or baseline. Nothing sticks. Information isn’t the bottleneck anymore. Integration is. And integration happens faster when there’s shared practice, shared standards, and shared reflection. The issue isn’t lack of tools. It’s lack of shared practice. Your Next Move From “everyone tinkers in private” to “we learn in public, in small loops.” You don’t need a formal program. You need a repeating container. I learned this at MIT: the best learning happened in Problem Set Parties, not lectures. A few people sit down with the same problem, try it independently, then share what worked. Simple. No curriculum required. Here’s a 20-minute version for your team: Step 1: Pick a tiny, real prompt Choose one work question that matters this week:
Make it yours: add the messy details. Who's the audience? What's the context? What does "done well" look like? The best prompts are specific to your situation. Step 2: Run a 15-minute show-and-tell With 2-4 people (peers or teammates):
Step 3: Capture the house rules As a group, write down:
Do this next week: Schedule one 25-minute Problem Set Party. Then do it again at the same time next week. Consistency beats intensity. Life Beyond the Screen That phrase, “parallel play,” keeps coming back to me. Not just at work. Like when my kids are in the same room doing different activities. They’re technically together, but not really interacting. I realized how often adults do a version of that too. We’re side by side, but each in our own little worlds. Phones out, tabs open, half present. The moments that feel best are the ones with a tiny bit of shared focus. Cooking with someone, walking with someone, learning something together, even for just 20 minutes. Where could you trade parallel play for actual togetherness this week? Let’s Chillaborate, Listen to the Automate Yourself podcast: YouTube | Apple Podcasts | Spotify |
Chill Labs is a boutique consultancy helping companies think strategically, solve business problems, and streamline operations utilizing Product Management, Software Engineering principles and AI. Combining a decade of experience running complex, globally distributed software products with expertise in product discovery, user research, and strategy, Chill Labs helps companies build products that users want and do so in a way that supports growth and scale. Dina Levitan, Founder and Principal at Chill Labs, based out of Seattle, WA, brings over 15 years of experience as a product and technical leader ranging from startups to companies like Google.
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