In this video, I show off a prompt we created to make Claude's visuals even more useful. It's a simple trick that you'll definitely want to add to your prompting toolkit. Enjoy! :)

Resilience is probably the most underrated skill on the planet. Everybody wants the outcome. More money. More freedom. More confidence. More impact. But almost nobody talks about the emotional strength required to keep going while none of it is happening yet. Because THAT’S the part that breaks most people. Not failure. Not lack of talent. Not lack of opportunity. It’s the uncertainty. It’s doing the work and wondering if it’s ever gonna pay off. It’s showing up when you feel invisible. It’s putting yourself out there and not getting the response you hoped for. It’s having a bad week and still deciding not to quit on yourself. That’s resilience. And honestly… the people who win in life are usually just the people who got really good at getting back up faster. They stopped making hard seasons mean “this isn’t for me.” They stopped treating discomfort like danger. They stopped expecting confidence before action. They learned to keep moving anyway. I think a lot of people in this community are way closer than they realize. But they’re judging themselves because the results haven’t caught up YET. Don’t confuse slow progress with failure. Don’t confuse hard with impossible. And definitely don’t quit just because it’s taking longer than your ego wanted it to. Some of the biggest transformations happen in seasons where it feels like nothing is working. Keep going. Seriously. What’s one season in your life that almost broke you… but actually made you stronger? 👇

Ever notice how too many choices can make it harder to choose? AI can feel like standing in a giant candy aisle. Thousands of tools.Thousands of videos.Thousands of opinions. Many people don’t fail because they lack ability. They fail because they’re overwhelmed. Don’t focus on the whole aisle. Pick one thing and move forward.

There's a version of the AI solopreneur story that's everywhere right now. One person, a handful of AI tools, and the output of a small team. More content, faster proposals, better client communication, automated follow-ups. The throughput is real. For a lot of people, AI genuinely has multiplied what they can produce in a day. But there's a quieter version of the story that doesn't get as much attention. It goes like this: "I'm doing more than ever, but I feel more overwhelmed than I did before I started using AI." That version is also real, and it points to something important about the difference between scaling output and scaling your actual capacity. These two things are not the same. Confusing them is how you end up working harder with better tools. ------------- Context ------------- When AI arrived for everyday professionals, the most visible benefit was speed. Drafts that took two hours started taking twenty minutes. Research that required half a day compressed into thirty minutes of back-and-forth with a capable model. Emails, proposals, content calendars, onboarding documents, tasks that used to anchor entire afternoons started clearing in under an hour. The natural response was to do more. If a proposal takes twenty minutes now, write more proposals. If content takes a fraction of the former time, produce more content. If client research can be done quickly, take on more clients. The ceiling went up, so the workload expanded to fill it. This is where the trap lives. Output scaling and capacity scaling look identical from the outside. Both involve doing more. But they create completely different experiences of time, and they lead to completely different businesses. Output scaling means you're producing more of the same kind of work. Your calendar fills with the same types of tasks, just more of them. AI has made each task faster, but the total hours in your week haven't changed, so the increased volume consumes the time AI saved, and then some. You're running faster on the same treadmill.

The promise of AI agents is almost irresistible. Set them up once, point them at your biggest bottlenecks, and watch hours of work disappear. If you've spent any time in AI communities over the last year, you've probably seen the screenshots, inboxes managed automatically, research compiled without lifting a finger, entire workflows running while someone sleeps. What doesn't make it into those screenshots is the setup time, the debugging sessions, the broken handoffs, and the hours spent figuring out why the agent did something unexpected. The promise is real. But the gap between the promise and the reality is where most people quietly lose more time than they save. That gap deserves an honest conversation. ------------- Context ------------- AI agents are genuinely powerful. The concept is straightforward: instead of using AI to assist with individual tasks, you build systems where AI can take sequential actions, make decisions, and complete multi-step workflows with minimal human involvement. Done well, that shift is meaningful. Entire categories of repetitive work can be handed off in ways that weren't possible even eighteen months ago. But there's a pattern emerging that doesn't get discussed enough. Most people who struggle with agents aren't struggling because the technology is bad. They're struggling because they built an agent on top of a workflow they didn't fully understand. The automation made the confusion faster and more expensive, not simpler. Think about what that looks like in practice. A consultant builds an agent to handle their client onboarding sequence. It sends emails, creates folders, populates project templates. Three weeks in, they realize the agent is creating duplicate folders, sending follow-ups to clients who already responded, and occasionally attaching the wrong template. They spend four hours debugging. They rebuild parts of the sequence. They debug again. Two weeks later, the original manual process, the one that took 45 minutes and never had these problems, starts looking pretty good.