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You Keep Telling AI What You Want. Start Showing It Instead.
When AI output doesn't sound like you, the instinct is to explain harder. You add more adjectives. Professional but friendly. Confident but not salesy. Warm, but concise. And the output still comes back not quite right, because words describing a style are a poor substitute for the style itself. Think about how this plays out. You want AI to draft something in your voice: a client update, a proposal, the kind of thing you write every week. So you describe how you want it to sound. You type out "keep it clear and warm and professional," maybe throw in a few more instructions, and hit send. What comes back is generic. It's technically what you asked for, but it reads like anyone wrote it. So you rewrite it by hand until it sounds like you again. Next week, same task, same vague description, same disappointing draft, same manual rewrite. You're describing your style over and over and never quite landing it. ---------- THE REAL PROBLEM ---------- The problem is not "AI can't match my voice." The problem is "I keep describing my style in words when I should be showing it real examples." Your style isn't really capturable in a list of adjectives. The way you open a client email, how long your paragraphs run, where you land between formal and casual, the phrases you reach for: that lives in the writing you've already done, not in a description of it. When you only describe it, the AI has to guess at all the specifics, and it guesses generically. That's not a capability gap. It's an input gap. You're handing it a description when you could hand it the real thing. ---------- WHY THIS MATTERS ---------- Describing your style instead of showing it costs you twice. First, the output is never quite usable, so you rewrite every draft by hand. The AI saves you less time than it should, because you're always doing the last mile yourself to put your voice back in. Second, it never gets better. Because you're describing from scratch each time, in slightly different words, the results stay inconsistent. One day it's close, the next it's off, and you can't build any reliability on top of it. You start to think AI just can't do your kind of writing, when the truth is it was never actually shown what your kind of writing looks like.
You Keep Telling AI What You Want. Start Showing It Instead.
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This New AI From SpaceX Is The Future (First Look)
Grokbot just released an entirely new way to interact with AI and I think that this is the next phase for people who want agents to get actual work done. Let me know what you think below. Want to save time, get more leverage, and stop figuring this AI stuff out from scratch? I put the clearest map and support inside the AI Advantage Club Enjoy! :)
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What if it getting harder means you’re getting closer?
There’s a point in almost every breakthrough where things don’t feel like they’re working. You’re putting in the reps. You’re doing the work. You’re trying to make better decisions. But the results haven’t caught up yet. And THIS is where it gets dangerous. Because your brain starts looking for an escape hatch. Maybe I picked the wrong thing. Maybe I should change direction. Maybe this just isn’t working. Maybe I’m not cut out for it. But sometimes nothing is wrong. Sometimes you’re just in the part where the work is asking more of you before it gives you something back. Think about something you’re working toward right now. Are you actually stuck? Or are you just uncomfortable because you haven’t gotten the payoff yet? What’s one thing you know you need to keep going on, even though it feels harder right now?
Progress Doesn’t Always Have to Be Big
Today I worked on a rough concept for my first website. Nothing is finished, and there’s plenty I’ll change, but now I can actually see what I’ve been imagining. One thing I’m learning with AI is that progress doesn’t always mean completing something. Sometimes it’s simply taking an idea out of your head and making the first version real. Small daily actions create massive results.
Let your AI grab its Configuration Files from your Project Repo
Maybe its a nobrainer, but along my review work I have found that many project-leaders have edited or uploaded their AI configuration files (for Kimi.com it is VISION.md, SPEC.md, TECH.md, BOUNDARIES.md, PATTERNS.md and DECISIONS.md) directly into the AI (where those files often got lost, when switching platform, the platform was ditched (for cost reasons) or when the project was left). The better approach is to compile those files into the GitHub Repo (or the local folder) you will need anyway to store the vibecoded code. Then, in your firsts prompt tell your AI from where to pick them up. Sideeffect: you are assured that your Repo is accessible.
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