You don’t have to figure it all out at once. Life evolves—one small shift at a time.
Some days don’t land the way you hoped. You snap at someone you care about. You skip the workout you promised yourself. A small decision leaves you feeling off-center. It’s easy to write it off as a bad day. But what if that moment was trying to teach you something?
In the previous article, we explored The System of You—the idea that your body, mind, and habits form an interconnected network. A system that responds to inputs, learns from feedback, and adapts over time. Today, we’re zooming in on one of the most powerful tools behind that kind of learning: backpropagation.
It’s how a neural network trains itself—by taking what didn’t work, tracing the path that led there, and making small, targeted adjustments. Quiet. Precise. Iterative.
What if you could move through your life with that same kind of learning loop? Notice what didn’t work, make a subtle shift, and carry that insight forward—not once, but continuously.
Let’s look at how this algorithm—originally built and designed for machines—can become a tool for everyday clarity.
Backpropagation 101 (Without the Math)
At the heart of nearly every modern AI system is a simple idea: learn from what went wrong.
Backpropagation is the engine behind that process. It’s how a neural network figures out what to do differently next time—by looking backward.
Here’s the short version:
When an AI makes a prediction (say, recognizing an image or suggesting a next word), it runs its input forward through layers of logic—this is called the forward pass. At the end, it compares its prediction to the actual result and calculates how far off it was. That difference is called the loss.
Now comes the key move: instead of just noting the error and moving on, it sends a signal backward through the system—tracing the steps that led to the mistake.
Let’s walk through a concrete example:
Imagine an AI trying to recognize cats in photos. It sees a picture of a tabby and confidently says “dog.” The system calculates how wrong it was, then traces backward: Was it the shape detection layer that misread the ears? The pattern recognition that confused stripes with spots? The final decision layer that weighted dog features too heavily?
Each layer that contributed to the mistake gets a small adjustment—the ear detector becomes slightly more sensitive to pointed shapes, the pattern layer learns to better distinguish stripes, the decision layer rebalances its weights. The next time it sees a similar cat, it’s incrementally more likely to get it right.
This process—tweaking each layer based on how much it contributed to the error—is what backpropagation does.
The power isn’t in perfection—it’s in iteration. One pass won’t fix it, but multiple passes will: small, quiet, consistent feedback.
And that’s the lesson: learning doesn’t always mean adding more. Sometimes it means tracing things back, listening to the signal, and changing just enough to move in a better direction.
This isn’t just an algorithm—it’s a way of living. A method for turning every mistake into momentum, every misstep into data for your next move.
“Instead of shame spirals, you get learning loops.”
Instead of starting over, you iterate forward.
If You’re Curious… (About the Math)
The resources below are not required reading, but here if you want to explore the math behind the backpropagation algorithm:
Neural Networks and Deep Learning – Ch. 2 – An intuitive walkthrough with visuals and code.
Stanford CS231n Notes – Clear breakdowns of gradients, loss, and backprop.
3Blue1Brown Video Series – Stunning visual intuition for gradient descent.
DeepLizard Explainer – Quick, focused videos on how networks learn.
Now that that’s taken care of… let’s continue where we left off.
What If Humans Had a Loss Function?
In machine learning, a loss function defines what “wrong” looks like. It’s the signal that tells the system how far off it was from the desired outcome.
But in real life, we rarely define our version of “wrong.” We just feel off—anxiety, frustration, regret—and move on without tracing it back to a specific metric.
What if you defined your own loss function? Not to punish yourself, but to create clarity.
- Is your “loss” when you feel drained instead of energized?
- When you break alignment with your values?
- When your response didn’t reflect who you want to be?
Defining your personal loss signal could turn vague discomfort into meaningful feedback. And once you know what you’re optimizing for, those daily misfires become data—not just emotion.
That same loop—loss, trace, adjust—can work in your life too. The key is learning to notice when it’s happening.
Mapping Backprop to Everyday Life
So how does any of this apply to you—to your relationships, routines, or the way you navigate your day?
It starts with recognizing that we all do have error signals. They’re just quieter and more emotional than numerical. A skipped workout, a sharp tone in a conversation, a drained feeling after saying yes when you meant no. These aren’t just bad moments—they’re direct feedback.
Think of each one as your own version of “loss.” Not failure. Just a signal that something’s out of alignment with what you were aiming for.
And just like in a neural network, the key isn’t to overreact or blow it all up—it’s to trace the moment back. What was the input? What decisions or conditions led to that output? And what small tweak might nudge things in a better direction next time?
Let’s break it down through the lens of how backpropagation actually works in practice:
The Backprop Loop
First, you notice the signal. You don’t need a red warning light. Your body already tells you—tightness in your chest, hesitation in your voice, a lingering sense that something felt off. These moments are rich with feedback if you pause long enough to catch them.
Then, you trace the gradient. In machine learning, the gradient tells the system which direction to change—not by overhauling everything, but by identifying the path of least resistance toward a better outcome. In life, gradients are subtle clues: the conversation that flowed better when you led with curiosity, or the extra energy you felt when you gave yourself a slow morning.
Small observations, repeated over time, start to map the terrain.
Finally, you make the micro-adjustment. You don’t have to fix it all at once. You don’t even have to fix it right. You just have to adjust in the direction that reduces the misalignment. That’s what a neural network does—it updates slightly, then tries again. You can too.
The goal isn’t perfection. It’s movement.
And the more often you engage the loop—notice the error, trace it back, shift forward—the more naturally the process integrates. Over time, it becomes less about “getting it right,” and more about tuning in early, so the feedback doesn’t have to get loud to be heard.
Where This Loop Shows Up in Real Life
The beauty of backpropagation isn’t in the algorithm—it’s in the rhythm it creates. Catch the signal. Trace the path. Make the shift. That same loop can show up in nearly every corner of your life, once you learn to look for it.
Here are some key areas where this shows up:
Habit Formation: When the Routine Slips
You commit to journaling each morning. The first few days go well. Then one morning you skip it. Then another. You feel scattered, a little unmoored.
Instead of labeling it as failure, apply backprop: What caused the drop-off? Was your notebook buried? Did you check your phone first? Maybe the solution isn’t more discipline—it’s moving the notebook to your nightstand or starting with one line instead of a full page.
Decision Reviews: Learning from the Aftermath
You make a quick decision in a meeting that doesn’t land well—with your team or your own sense of integrity. That’s a loss signal. Trace it back: What belief or pressure shaped that choice? What information was missing? Maybe the adjustment is adding a short pause before answering, or building in a check-in before decisions that affect others.
Communication Tuning: Refining the Human Signal
You say something that doesn’t quite land. A misunderstanding. A raised eyebrow. A pause that feels heavier than it should. Instead of moving on, you trace the moment: Was it timing? Tone? Topic? The adjustment might be choosing a different entry point next time, or leading with a question instead of an assumption.
Energy Management: Reading Your System’s Feedback
You notice you’re consistently drained after certain types of meetings or activities. Instead of pushing through, you trace the pattern: What specifically depletes you? Is it the people, the topics, the time of day? The adjustment might be scheduling these differently, preparing with different energy, or setting clearer boundaries.
Relationship Dynamics: Updating Your Social Algorithms
A friendship feels off lately—conversations feel forced, energy feels mismatched. Instead of avoiding or overanalyzing, you trace back: When did the shift happen? What changed in how you both communicate? The adjustment might be having an honest conversation, changing how you connect, or simply accepting that relationships evolve.
Whether you’re building a habit, navigating a decision, or deepening a relationship, the loop is the same: recognize the loss, trace the pattern, make the smallest meaningful shift forward.
Recognizing these patterns is powerful, but without structure, insights fade back into good intentions. The difference between occasional awareness and genuine learning lies in building systems that capture and act on these signals consistently.
Tools & Techniques: Build Your Own Backprop Loop
Learning from feedback is powerful. But unless it becomes a practice—something with shape, rhythm, and intention—it usually stays in the realm of good ideas. This section is about giving your inner system the tools it needs to backprop your life in a grounded, consistent way.
Here are some practical ways to structure your learning loop:
Capture the Signal: Daily Micro-Retro
A simple practice that takes 2 minutes. At the end of your day, grab a notebook or open a notes app and run through:
- Input: What did I try today? (Focus on one key interaction, decision, or habit)
- Error: What felt off? (Not judgment—just signal. Drained? Reactive? Disconnected?)
- Update: One small shift for tomorrow? (Emphasis on small—change your morning routine by 5 minutes, not 2 hours)
The power isn’t in perfect answers—it’s in consistent signal collection. You’re training your awareness to notice patterns before they become problems. After a week of this, you’ll start seeing themes you never noticed before.
Externalize the Pattern Recognition
Use an AI tool or journaling assistant to process patterns with you—but frame it correctly. You’re not looking for advice; you’re looking for reflection.
Instead of: “What should I do about my energy crashes?”
Try: “I’ve been tracking my energy for two weeks. What patterns do you notice in when I feel drained versus energized?”
Instead of: “How do I fix my communication problems?”
Try: “Looking at these interaction notes, where do I seem to consistently misread social situations?”
The AI becomes a computational mirror—helping you see patterns in your own data that might be invisible to you in the moment. It’s like having a neutral observer who never gets tired of helping you trace your feedback loops.
You keep the interpretation and decision-making. The AI just helps you see more clearly.
Create a Learning Cadence
Instead of waiting for crisis, schedule regular “backward passes”:
- Daily signal checks (1-3 minutes) – What felt off? What helped?
- Weekly pattern reviews – Where did your system feel smooth? Where did it snag?
- Monthly meta-analysis – Are your feedback loops aligned with what you care about?
- Quarterly evolution checks – Are you growing the systems you’re running, or just optimizing them?
This isn’t about perfect tracking—it’s about rhythm. A gentle cadence of noticing, tracing, and adjusting.
Why Feedback Loops Beat New Year’s Resolutions
Most resolutions are made in isolation: one big goal, chosen under pressure, with no real system behind it. No tracking. No reflection. No course correction. Just hope.
That’s why most fade within weeks.
Feedback loops work differently. They’re:
- Sustainable — built into your rhythm, not forced onto a single date
- Traceable — you learn what’s working and why
- Natural — anchored in how you actually live and feel, not how you should be
“You’re not setting goals to impress your future self. You’re tuning your system to support who you already are—and who you’re becoming.”
Small adjustments. Honest signals. Long arcs.
That’s how real change sticks.
But here’s what I’ve learned from both AI systems and human experience: the model is only as good as the signals you give it. And there are some common ways this powerful framework can lead you astray if you’re not careful.
Pitfalls & Ethical Guardrails
Backpropagation is elegant in its simplicity—but applying it to human life adds complexity. You’re not just adjusting code. You’re navigating feelings, values, relationships, and goals that evolve dynamically over time. If you’re not careful, the same loop that helps you grow can start optimizing you in the wrong direction.
Optimizing the Wrong Loss Function
In machine learning, if you define the wrong objective, your system gets really good at producing the wrong result. The same thing happens in life.
If you’re optimizing for productivity at the expense of rest, or external validation instead of internal alignment, your feedback loop will keep pushing you—just not where you actually want to go.
Here’s what this looks like in practice: You start tracking how many tasks you complete each day. Your ‘loss’ becomes any day where you don’t hit your target number. So you optimize—cutting conversations short, skipping breaks, saying no to spontaneous moments of connection. Technically, you’re ‘improving’ your metric. But you’re also systematically removing the things that actually energize you.
Or you optimize for social media engagement as a proxy for meaningful work. Your feedback loop starts pushing you toward more controversial takes, catchier headlines, faster content production. You get better at the metric while drifting further from your actual values.
The system works exactly as designed—it’s just designed for the wrong outcome.
Take time to ask: What am I truly optimizing for?
Then check: Is that still right for me now?
Analysis Paralysis: When the Loop Takes Over
Some people discover backpropagation and turn it into a productivity system on steroids—tracking everything, analyzing every interaction, optimizing every decision. The loop becomes a trap instead of a tool.
Here’s the thing: neural networks don’t spend forever analyzing each error. They adjust quickly and move on to the next training example. The learning happens in the iteration, not the overthinking.
If you find yourself spending more time reviewing your day than living it, you’ve overcorrected. Set a time boundary—5 minutes for daily review, 20 minutes for weekly. Make the shift, test it, then come back to the loop. The goal is motion, not perfection.
The purpose of backprop isn’t perfection. It’s motion.
“The iteration is where change happens, not the overthinking.”
Set a limit on how long you stay in review. Make the shift. Test it. Then come back.
Outsourcing Too Much to Data
AI systems rely on metrics because they have no intuition. You do.
It’s tempting to start quantifying everything—steps, sleep, emotions—and lose touch with the quiet wisdom underneath it all. The part of you that just knows when something is off, even before you measure it.
Use tools, but don’t hand them the keys. Keep human judgment in the loop. Make space for instinct, reflection, and values—not just feedback curves.
Backpropagation is a framework, not a rulebook. It’s meant to support your evolution—not control it. You’re not just a system to optimize, you’re a person learning to adapt with clarity.
How to Know You’re Optimizing the Wrong Thing
Backpropagation only works if your system is optimizing for the right outcome. Here are a few signs you might be off-course:
- You’re improving, but feel worse.
Progress on paper, but a growing sense of disconnection or burnout. - You’re winning a game you don’t care about.
You’re hitting goals… but they’re not your goals anymore. - The system rewards speed, but you crave depth.
You’re making quick decisions, checking boxes, but leaving wisdom on the table. - You hesitate to change the metric—because then you’d have to start over.
Fear of recalibration usually means the calibration is overdue.
If any of these resonate, pause and revisit your “loss function.”
Ask: What do I actually want to feel more of in my life?
That answer is often your truest optimization signal.
No spreadsheets. Just small shifts, consistently applied.
Your Personal Backprop Starter Kit
You don’t need an advanced algorithm to start learning like one. The power of backpropagation isn’t in the complexity—it’s in the commitment to small, meaningful adjustments over time.
Here’s how to start building your own backprop practice:
1. Define Your Personal Loss Function
Write down your real indicators of misalignment. These might be:
- Feeling drained after certain activities
- Repeating a habit that leaves you off-center
- Avoiding something that actually matters to you
This becomes your internal feedback compass. When you feel these signals, you’ll know it’s time to trace and adjust.
2. Run a Daily Micro-Retro (Takes 2 Minutes)
At the end of your day, ask yourself:
- What felt off today?
- What might have caused it?
- What’s one small shift I can test tomorrow?
You’re not solving the whole puzzle—you’re just tracing the signal. That’s the loop.
3. Choose an Accountability Partner (or AI)
Share your “loop” with someone who knows how to listen, not just advise. Or use an AI journal assistant to spot recurring patterns and reflect them back to you.
The point is external feedback—not for approval, but for perspective.
Backprop isn’t about fixing yourself. It’s about noticing what matters, adjusting with clarity, and learning your way forward—without burning out, selling out, or checking out.
One Pass at a Time
The world often tells us to get it right the first time. But learning—real learning—is responsive, iterative. The point isn’t perfection. It’s noticing the signal, adjusting slightly, and trying again.
Every misstep is data for your next move.
“You are not broken.
You’re just in the middle of the loop.”
Tomorrow, we’ll explore how to focus your attention in an increasingly noisy world—drawing inspiration from another cornerstone of AI: the convolutional filter.
Because clarity isn’t just what you notice, it’s what you learn to filter out.
See you in the next insight.
