How to test whether learning a new AI skill is worth your time
The simple test Greg Isenberg proposes for deciding whether to invest in learning any new skill is to ask yourself two questions: Did it find something you would have missed? And did it produce something you would have actually used? If the answer to both is yes, that skill is worth pursuing. This applies especially to AI-assisted workflows — if the output genuinely adds value beyond what you'd have caught or created on your own, you're building a real capability, not just playing with a tool.
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MembersUsing AI agents to autonomously handle low-stakes email replies
There's a subset of emails that are low-stakes enough that you'd want an AI agent to not only draft a reply but, if it feels confident the situation isn't sensitive, just go ahead and respond automatically. Simple examples include inbound m
Daily 30-minute practice beats sporadic sprints for mastering AI agents
The biggest mistake people make when learning to build with AI agents is trying too sporadically — picking it up occasionally, then dropping it. Greg Isenberg recommends committing to a set period — 30, 60, or 90 days — with a literal calen
The 'messy middle' of AI: most people quit before reaching great outputs
Getting great outputs from AI is like learning tennis — when you first start, you're bad at it, and you almost don't want to show up because you feel incompetent. But you go through the messy middle, you get better and better, and eventuall