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Adaptability Is the Quiet Advantage
One of the biggest advantages you can build in life isn’t more information. It’s adaptability. Not the dramatic kind. The disciplined kind. The kind that comes from being willing to look at what’s actually working… and what isn’t… without making it mean anything about you. Most people don’t get stuck because they’re incapable. They get stuck because they’re loyal to an old version of themselves. Old rules. Old patterns. Old ways of operating that once served them well. And here’s where it gets tricky. Resistance loves rigidity. It tells you that staying the same is integrity. That changing course means you failed. That letting go means you’re giving up. But adaptability isn’t quitting. It’s professionalism. It’s the ability to face reality as it is today and make a clean decision from there. No drama. No self-judgment. Just honesty and action. Growth doesn’t always ask you to push harder. Sometimes it asks you to release what no longer fits and keep moving. The people who grow aren’t the ones forcing the next step. They’re the ones willing to learn, unlearn, and choose again without turning evolution into a personal indictment. The future doesn’t belong to the most rigid. It belongs to the people who stay open and keep showing up. So my question for you today... where might Resistance be asking you to cling instead of adapt?
So grateful for AI Advantage!
Thank you, Tony, Dean, and Igor, for such high-quality training. The idea of creating a clone, much less actually doing it, has been mind-blowing. I even asked my clone the "what do you know about me" question that Igor discussed. The information opened up content-creation ideas I would never have thought of on my own. Your training and heart are classy, high-quality, and so user-friendly! I thank God I chose to participate in the AI Advantage! I have so much to learn, and I'm so grateful I can go back to the replays and keep trying.
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A lesson from building a recognition model
One nuance we ran into with food recognition specifically is that ingredients rarely appear in a clean, “textbook” state. In the real world, ingredients are sliced, diced, mashed, squeezed, crushed, cooked, or partially combined. That meant we had to train the model to recognise ingredients across many different forms and states, not just whole items. The biggest challenge wasn’t the concept, it was volume and variety: a lot of ingredients, in a lot of conditions, across a lot of images. It reinforced how important real-world data is versus idealised datasets when you want recognition to actually work day-to-day. Curious how others handling vision models deal with highly variable inputs like this.
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