I ran an experiment that changed how I use AI forever. I took the SAME prompt and sent it to ChatGPT, Claude, and Gemini at the same time. Not to see which one was "best." I wanted to see where they DISAGREED. Here's what blew my mind: → ChatGPT gave me a confident, detailed plan. Sounded great. → Claude flagged two risks that ChatGPT completely ignored. → Gemini agreed with ChatGPT's plan... but used completely different reasoning to get there. So who was right? They all were. And they were all wrong. Each one had blind spots that the others caught. That's when it hit me — asking ONE model is like hiring ONE consultant and hoping they don't have blind spots. They always do. So I started doing this with every important decision. Three models. Compare the disagreements. The answer is always in the friction between them. A few things I've noticed after months of doing this: → When all three agree, you can trust the answer. When they don't, that's where the gold is. → ChatGPT is the most confident. Claude is the most cautious. Gemini is the fastest to spot patterns in large data. None of them will tell you they're wrong. → The biggest risk in AI isn't a wrong answer. It's a wrong answer that SOUNDS right and you have no way to know. Curious — is anyone else cross-checking between models, or am I the only one doing this the hard way?

I started with ChatGPT long before the bootcamp but now it seems that in their effort to not be as user friendly with answers like "your awesome, you're so smart, etc" it is giving me answers that are preachy, with presumptive psychoanalysis and quite frankly sound like a patronizing narcissist. Anyone else notice this?

Tell me something what you guys building? Will provide some few insights curious to know yáll answersss!!!!

When using AI, it's really important that we get good at prompt engineering, which is a fancy word for “giving instructions in a clear way”. The big issues I see with this are two things. One, we're actually pretty terrible at communicating. And two, we don't actually know what we want the agent to do. The first one is self-explanatory. We use big, vague words that don't clearly state what we want or what makes an output good versus bad. I'm guilty of this all the time, but honestly, it's a lot of work to write a detailed prompt (especially when all you want is to find the cheapest place to buy groceries). The second one is more important though. I've caught myself wanting AI to write good copy, but then I realize I've never actually defined what "good copy" means. I don't have enough experience to even understand the nuances of what makes a strong foundation for a good piece versus a bad one. I'm using copywriting as the example here because marketing and lead gen is where I'm struggling in business right now, but this applies to anything. If you're having trouble with writing and you try to get AI to write something, of course it's not going to understand what makes it sound human versus not human. You haven't taken the time to explain it. Same thing with AI-generated images and video. We know it can produce good results, but it also produces bad results a lot of the time because the defaults are things like waxy skin and unnatural body compositions. The issue is that we haven't told it the specifics (things like "use a light skin tone with some blemishes and natural imperfections"). These models are exceptional at what they can do. But a lot of the time when we don't get the output we want, it's user error, not a model problem. TLDR: Bad AI outputs is a skill issue.

Out of curiosity, has anyone here actually made money using AI yet? If yes, what are you doing with it?