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Your success in life is directly tied to how quickly you face problems.
Not whether you have them. Not whether they’re fair. Just how fast you move toward them. Every time you deal with something right away, your capacity grows. You trust yourself more. You stop carrying the mental weight. You get stronger without even realizing it. Every time you delay, it gets heavier. It takes more energy. It starts to feel bigger than it actually is. Over time, that difference compounds. Solving small problems quickly builds confidence. Solving bigger ones consistently builds identity. And that capacity — the ability to handle hard things without hesitation — is what actually allows you to build something great. What’s one thing you know you need to face this week instead of pushing it off?
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A Fun Little Exercise...
The team and I did this and some of the results were very interesting! Go into ChatGPT and post this prompt: "Based on all the conversations we've had, can you analyze my behavioural tendencies. Keep it short and concise" Post your response in the comments below and let us know if any of it was a surprise to you! Here is mine: Here are the behavioural tendencies you consistently show in our chats: - Iterative builder: You like shipping a version, then tightening it fast with specific edits and constraints. - Framework-first thinker: You gravitate toward checklists, systems, repeatable steps, and clear structure over vague advice. - High signal standards: You push for longer, fuller answers with real substance, not surface-level answers. - Precision + optimisation bias: Whether it’s macros, pricing, positioning, or scripts, you like measurable targets and tuning variables. - Creative but directed: You enjoy big imaginative swings, but you still want them organized and usable. - Action orientation: You regularly steer toward “what do I do next” and practical outputs you can deploy immediately.
A Fun Little Exercise...
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⏳ The Hidden Cost of “Almost Clear” Requirements, and How AI Shrinks Rework Cycles
Most teams do not lose time because they move slowly, they lose time because they move forward without alignment. “Almost clear” requirements feel like speed, but they quietly inflate cycle time by creating rework, decision churn, and a constant drip of clarifying conversations that arrive too late. If we want real time back, we stop treating requirements as paperwork and start treating them as a time strategy. AI becomes powerful when we use it to turn vague intent into usable clarity early, so we stop rebuilding the same work in different versions. ------------- Context: Where Requirements Become a Time Leak ------------- In most organizations, the requirement stage is where time either gets protected or gets mortgaged. When we skip the hard thinking upfront, we do not eliminate work, we just push it downstream where it is more expensive. We see this in everyday micro-scenarios. A manager asks for “a quick overview deck” for leadership. Someone creates slides, adds charts, writes copy, and shares it. The feedback is not “this is wrong,” it is “this is not quite what I meant.” Now we are not just revising slides, we are revisiting the definition of the request. The work becomes a discovery process that should have happened before production. Another common pattern is the “invisible stakeholder.” We think the request is between two people, but the output is actually meant for five audiences with different needs. The moment that stakeholder appears, the work shifts. The assumptions that were harmless in a narrow context become costly in a broader one. More revisions appear, and the cycle time stretches. Then there is the “requirements teleport.” The brief says one thing, but the review conversation references a different goal, or a different constraint, or a new deadline. Everyone is still trying to be helpful, but the target is moving. That movement is time loss in disguise because it creates churn without accountability. What makes this so painful is that rework does not arrive as a single event. It arrives as repeated touches. We revisit the same doc, the same deck, the same plan, each time paying a context switching tax. It is not the minutes of editing that hurt, it is the hours lost to mental reload and coordination.
⏳ The Hidden Cost of “Almost Clear” Requirements, and How AI Shrinks Rework Cycles
📰 AI News: OpenAI Hires OpenClaw Creator To Build Next-Gen Personal Agents
📝 TL;DR Sam Altman just announced that OpenClaw creator Peter Steinberger is joining OpenAI to drive the next generation of personal agents. OpenClaw is moving into an independent foundation and staying open source, with OpenAI continuing to support it. 🧠 Overview This is a big signal about where AI is headed next. The race is shifting from “who has the smartest chatbot” to “who can deliver agents that actually do things,” like managing your inbox, booking travel, handling forms, and working across apps on your behalf. OpenClaw exploded because it made agents feel real, not theoretical. OpenAI bringing Steinberger in suggests they want to move faster on personal, multi step, multi agent systems, and they want the builder who already proved it can work in the wild. 📜 The Announcement OpenAI CEO Sam Altman said Peter Steinberger is joining OpenAI to lead the next generation of personal agents. He also said OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. Separately, Steinberger reinforced the same direction, he is joining OpenAI to bring agents to everyone, while OpenClaw remains open and independent under a foundation structure. ⚙️ How It Works • Personal agents, not just chatbots - The goal is an assistant that takes actions across tools, not one that only drafts text. • Multi agent direction - Instead of one giant assistant doing everything, the future looks like multiple specialized agents coordinating to complete tasks. • OpenClaw as proof of demand - OpenClaw gained massive traction because it showed real workflows like email, scheduling, and online tasks, not just demos. • Foundation structure for OpenClaw - Moving the project into a foundation is meant to keep it open source and reduce fear that it gets locked into a single company. • OpenAI support without full ownership - The public promise is continued support while the open source project stays independent in governance.
📰 AI News: OpenAI Hires OpenClaw Creator To Build Next-Gen Personal Agents
Guidance Feeling Operationally Stuck
Hey everyone, I’m Jason an entrepreneur running a real estate team of 40+ agents, and I’m hitting a wall operationally. I feel stuck and overwhelmed because I’m still doing too many manual, repetitive tasks in the business things I know could be automated or delegated. I just don’t know where to start first. I’m: - Still handling repetitive workflows - Touching things I probably shouldn’t be touching - Using ChatGPT mostly like Google instead of leveraging it strategically - Just started learning about Zapier and automation - Curious about building internal AI agents to support operations 🙋‍♂️ For those who’ve scaled: - What did you automate first? - How did you audit your time? - Where did you start with Zapier or AI? - Did you build SOPs first before automating? I don’t want more hustle. I want better systems. Any guidance on where to begin would help. Thank you
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