I literally wrote a book about the best way to learn via coregulation mentorship, using attunement and attachment (Heart) to let mammals know they are safe and can experiment, drawing heavily on cultural learning rather than top-down (Rider, "mind") command, thus allowing the movement (Horse) to emerge from the motivation and safety of the emotional conditions. This works on fractal levels. (Rider - leader, Horse - follower...Rider - parent, Horse - child....Rider - your wise self, Horse - the part of you that just wants to eat junk and binge watch/scroll.) I'm fascinated with riding horses, and the asymmetry of the pair - the rider wants to ride, the horse wants to be safe and flee any possible danger. If the rider adds weight, the horse's movement is negatively affected unless, through careful, progressive, systematic physical and mental devleopment of the horse, you change the horse to better carry the weight. Humans all "know" we should have better posture, but do we? No. Horses aren't that much different. So to "teach" a horse to carry a rider well is the responsibility of the rider. I'm a parent, manager, and teacher. Traditional horsebackriding teaching is all instructional commands. Lot of talk about "feel" but little guidance on how to get there. I do things differently, although I follow methods that come from institutions that are centuries old - tradition figured out CLA principles worked best. I wrote my book before really diving into CLA but it's a final piece of the Centuar - the Arena. I'm fleshing out the model. The Rider then not only uses the Heart to motivate and create learning safety, the Rider also designs the Arena for the Horse to move. So I'm eager to learn more about CLA!

Finding your way through the AI maze? Me too. I’m a small-town tech guy with a passion for making technology work for us. Think of me as your curious neighbor - testing AI tools, sharing what works (and what doesn’t) for rural businesses like ours. My goal? Let’s discover together how to save 5 hours a week using straightforward AI tools. No tech degree required - just practical solutions in plain language. Let’s explore this together, And don’t try this at home

I’m Sam Elsner. I’m just a guy who’s obsessed with how things grow. For as long as I can remember, I’ve been fascinated by the human machine—how it adapts, how it breaks, and how it rebuilds. That obsession started at 13 on a football field. I wanted to go pro. I spent years inventing new ways to optimize myself in the margins, outside of practice. Eventually, that focus shifted to the discus. And on paper? I made it. - 2x NCAA DIII National Champion. - 6x All-American. - 2016 Olympic Trials Qualifier. But here’s the thing: I was a drill sergeant to my own talent. I drilled the movements over and over in isolation. But when the lights came on and the crowd roared? It didn’t transfer. The skill stayed in the practice ring. In 2019, I hung up the shoes and went all-in on coaching. I trained my athletes exactly how I had trained myself: rigid structures, high instruction, zero variability. And the pattern repeated. They were world-class in the weight room. They were average on game day. I couldn’t wrap my head around it. Until I changed the lens. In 2020, a friend introduced me to the Ecological Dynamics Model. It’s a fancy way of saying that you can’t separate the organism from its environment. Skill isn’t a program you download; it’s a relationship you negotiate with the world around you. That idea shattered everything I thought I knew. I spent the next three years rebuilding my systems from the ground up. I stopped trying to control the athlete and started designing the environment. And suddenly, the results showed up when it actually mattered. Now, I’m taking that framework out of the gym and into the rest of life. That is the genesis of Calibrate. We are constantly calibrating to the world around us. Solutions don’t come from force; they emerge from the mess. So, welcome. This isn’t a classroom. It’s a playground for messy learning. Because that’s the only kind that sticks. — Sam