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This Is What Commitment Actually Looks Like
I just want to take a moment to say this... I’m genuinely proud of you. Not because this is easy. Not because you have it all figured out. But because you’re leaning into the work anyway. Adapting is uncomfortable. Learning new tools stretches you. Changing how you think, move, and operate takes effort. And most people avoid that. Most people wait until it feels simple. Until it feels familiar. Until someone else proves it first. You didn’t. You are committed to the tools. You are staying in the room. You choose to get better instead of staying comfortable. That tells me everything I need to know. When things change and you don’t opt out… When you feel resistance and lean in anyway… That’s what separates the few from the many. This is how real growth happens. Not overnight. Not perfectly. But consistently. Keep going. You’re exactly where you should be.
love to being here🤠
Hello everyone,šŸ¤ My name is Masoud, and I’m based in the Northwest of the UK. I’m an experienced HGV driver, NOT AI or IT languages.Currently expanding my skills through online cybersecurity studies. I’m pleased to be part of the Skool community The AI Advantage , and look forward to learning, contributing, and growing alongside like, minded people. Family is very important to me—I’m a proud father of two—and I enjoy spending time outdoors whenever possible.šŸ«¶šŸŒ¹ā˜•
Help, please
I am using ChatGPT (paid version) to help me with some of my designs for something, but it's not quite getting it right after hours of back and forth and explicit instructions. I am hoping that somebody here can help direct me to some AI that will help me with my visual mockups. I don't know where to go.
Practical AI adoption: what actually works in real systems
A lot of AI ā€œadoptionā€ discussions stay at the mindset level. Useful, but I’ve found progress usually comes from much more boring mechanics. What’s worked best for me so far: 1. Pick a task with a small blast radius.Summarisation, classification, first-draft support. If it fails, it’s annoying — not dangerous. 2. Define ā€œgood enoughā€ upfront.Not ā€œbe smartā€, but constraints like: cite the source, ask clarifying questions when unsure, and never take actions without human confirmation. 3. Design for being wrong.Assume the model will misunderstand. Make uncertainty visible, log failures, and do a quick weekly ā€œwhat broke?ā€ review. 4. Only then scale.If one narrow use case isn’t reliable and repeatable, adding more prompts/agents just multiplies confusion. Confidence with AI has come less from mindset shifts and more from seeing the same small workflow work 10 times in a row without surprises. Curious what others here consider a ā€œsafe first winā€ use case — especially ones that still hold up after the novelty wears off.
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Tell Us Where You’re From Without Actually Telling Us šŸŒ
Tony says ā€˜Proximity is power.’ Let’s find out who’s in proximity... Tell us all where you’re from… without actually telling us where you’re from 🤣
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