Hard Mode FirstChatGPT Saved My Life (No, Seriously, I’m Writing this from the ER)
How using AI as a bridge when doctors aren't available can improve patient-to-doctor communications in real time emergencies
Hard Mode FirstHow to Plan an Annual Family Summit
Simple strategies for setting goals and Priorities with Your Partner for the year ahead
Hard Mode FirstHow I Used AI to Save My Life in 77 Prompts: A Debrief
Reflecting on best practices, lessons learned, and opportunities to improve AI-assisted medical triage
Subscribe to Hard Mode First
Lessons learned from a lifetime of doing things the hard way, the first time
Over the past 2 years, I’ve been aggressively leaning AI.
While a lot of people might presume that this means I’m simply throwing myself into the newest tools of the day, I’ve found that the bigger benefit has not been about learning new tools — or even the productivity gains.
It’s been learning how to think like an engineer.
Here are the four core skills that I’ve noticed myself applying more liberally than ever before in my career:
Decomposition: Taking a big problem and breaking it down into smaller bits
Sequencing: Of a list of tiny problems, learning how to effectively prioritize and order which thing needs to happen first (and then next) in the sequence
Unblocking: Figuring out how to figure things out for yourself
Giving Instructions: The method for describing the exact thing you are trying to build
After teaching 1,500+ people how to build with AI, I’m starting to pick up a few key patterns about what makes for a successful (vs. an unsuccessful) build journey. Here are 5 ways to incorporate an engineering mentality in your day to day.
This sounds simple, but it’s true. The best way to learn how to think like an engineer is to adopt a
Over the past 2 years, I’ve been aggressively leaning AI.
While a lot of people might presume that this means I’m simply throwing myself into the newest tools of the day, I’ve found that the bigger benefit has not been about learning new tools — or even the productivity gains.
It’s been learning how to think like an engineer.
Here are the four core skills that I’ve noticed myself applying more liberally than ever before in my career:
Decomposition: Taking a big problem and breaking it down into smaller bits
Sequencing: Of a list of tiny problems, learning how to effectively prioritize and order which thing needs to happen first (and then next) in the sequence
Unblocking: Figuring out how to figure things out for yourself
Giving Instructions: The method for describing the exact thing you are trying to build
After teaching 1,500+ people how to build with AI, I’m starting to pick up a few key patterns about what makes for a successful (vs. an unsuccessful) build journey. Here are 5 ways to incorporate an engineering mentality in your day to day.
This sounds simple, but it’s true. The best way to learn how to think like an engineer is to adopt a
Hard Mode FirstChatGPT Saved My Life (No, Seriously, I’m Writing this from the ER)
How using AI as a bridge when doctors aren't available can improve patient-to-doctor communications in real time emergencies
Hard Mode FirstHow to Plan an Annual Family Summit
Simple strategies for setting goals and Priorities with Your Partner for the year ahead
Hard Mode FirstHow I Used AI to Save My Life in 77 Prompts: A Debrief
Reflecting on best practices, lessons learned, and opportunities to improve AI-assisted medical triage
A lot of people think I'm teaching people how to use AI. I'm not. I'm teaching people how to think like engineers. Here's what I mean. https://hardmodefirst.xyz/5-benefits-of-thinking-like-an-engineer-at-work
this is the actually useful part. knowing how to break a problem down, recognize when the output is off, debug assumptions — that's the real leverage
It is more important to finish the project than it is to make it something lasting.
Leaving all of your builds in a half-finished state is like starting a graveyard of blog posts in an untouched Google Drive folder. You are cutting your learning off before you get things across the finish line. If you never give yourself the satisfaction of seeing what done feels like, then you won’t be motivated or confident enough to continue.
As it turns out, the things you build become a lot more helpful for you when you start solving your actual problems. Once you build your first app (presumably, something like a customized chatbot if you’re starting at the bottom level of this pyramid), you might think something like, “Okay, that was fun, but if what would make this really helpful would be if it could do ______.”
Hold onto that feeling. That is the signal that your body and your mind wants to keep going. Ask yourself (or your AI), “How might I adapt or iterate on this current app to do _____ next?” From that point, you’re off to the races.
This might be surprising for those of us who grew up in the age of aspiring for GPA perfection. But as I’ve learned, there are major benefits to getting things across the finish line. With each artifact that I’ve created, I’ve learned more things about my own workflow, my speed to build, and the limitations of the technology that I’m taking on. I also get a sense for what it feels like to walk away from something I built (or even come back to it later).
The other benefit of intentionally publishing imperfect apps is that it takes the pressure off your own learning curve in a very serious way. Right now, too many companies are putting too much pressure on their employees to learn AI — or else.
While some people might respond to pressure-cooker learning environments, in other contexts, this “mandated learning” might have negative inverse effects, such as people being afraid to try new things, or feeling a paralyzing feel of “starting the wrong way.”
There is no wrong way to get started. The only mistake would be not starting. Build something imperfect. Learn from it. Move on.
It’s certainly overkill to add AI to everything. (I personally have experienced the very real limits of what happens when you do. It’s not pretty.)
But I do think there are major benefits to practicing the reflexive use of AI in your day-to-day life. Here’s what I’ve found to be the order of how my thinking has progressed over the past year:
Resignation: “I have an unsolvable problem.”
Curiosity: “I wonder if technology could help me with this problem.”
Motive: “How might technology help me solve that problem?”
Agency: “What could I build with technology to solve that problem?”
Transformation: “Would this work somewhere else too?”
The better you get at pushing yourself through these steps, the faster (and more effective) you get at using all sorts of technology. After all, learning how to use AI is just a proxy for learning how to command a computer to build something according to your spec. This is a problem that is not going away anytime soon.
As I wrote about recently, I now build new software and mini-apps almost daily. There is now no distinction between when I’m working and when I’m building.
I didn’t get here overnight. But I got here by taking on steps 1 through 4, with more frequency, and with less “down time” in between.
If you’re a manager of a team who’s trying to drive more AI adoption at work, or if you’re looking to learn how to use AI more yourself, the best thing you can do is set aside an hour a day to “play.”
Just like any habit (like going to the gym, or learning a new language), you won’t see immediate benefits right away. But over time, your lessons, your learning style, your pattern recognition, and your engineering mindset compounds. Then, you’ll be solving your own problems with technology on a near-daily basis too.
On a personal level, I will attest that it is incredibly satisfying and motivating to be able to create things like a real-time survey analytics dashboard that’s trained on my very real customer and user data. It’s even more wild to think that I can essentially rebuild a version of Google Forms (a product I’ve been bound to use for the past decade) in a matter of 90 minutes.
As it turns out, there are a lot of fringe benefits in your daily life that comes from thinking like an engineer. Time and time again, I find myself reflecting back on this mindset as a way to approach problems (even problems that have nothing to do with computers).
Is your kid is being a pill at bedtime? Let’s break down her activities throughout the day and come up with a more structured bedtime routine to keep her moods stabilized. Are you feeling overwhelmed about biting off more than you can chew? Lets break that problem into smaller steps and pick the right first step forward for tomorrow.
Here’s another silly one. When I have a tech issue with a completely unrelated piece of technology, I am much more inclined than ever before to tinker around with the hardware myself until I figure out how to make it work.
I believe these skills are helpful for lots of people. Not just engineers. And that’s why, when I teach AI through Build First, it’s a lot less about teaching to any specific tool, and a lot more about teaching this mindset.
If you’re ready to take on an engineering mindset at work or at home, let’s talk. You can find me at bethany@buildfirst.ai.
It is more important to finish the project than it is to make it something lasting.
Leaving all of your builds in a half-finished state is like starting a graveyard of blog posts in an untouched Google Drive folder. You are cutting your learning off before you get things across the finish line. If you never give yourself the satisfaction of seeing what done feels like, then you won’t be motivated or confident enough to continue.
As it turns out, the things you build become a lot more helpful for you when you start solving your actual problems. Once you build your first app (presumably, something like a customized chatbot if you’re starting at the bottom level of this pyramid), you might think something like, “Okay, that was fun, but if what would make this really helpful would be if it could do ______.”
Hold onto that feeling. That is the signal that your body and your mind wants to keep going. Ask yourself (or your AI), “How might I adapt or iterate on this current app to do _____ next?” From that point, you’re off to the races.
This might be surprising for those of us who grew up in the age of aspiring for GPA perfection. But as I’ve learned, there are major benefits to getting things across the finish line. With each artifact that I’ve created, I’ve learned more things about my own workflow, my speed to build, and the limitations of the technology that I’m taking on. I also get a sense for what it feels like to walk away from something I built (or even come back to it later).
The other benefit of intentionally publishing imperfect apps is that it takes the pressure off your own learning curve in a very serious way. Right now, too many companies are putting too much pressure on their employees to learn AI — or else.
While some people might respond to pressure-cooker learning environments, in other contexts, this “mandated learning” might have negative inverse effects, such as people being afraid to try new things, or feeling a paralyzing feel of “starting the wrong way.”
There is no wrong way to get started. The only mistake would be not starting. Build something imperfect. Learn from it. Move on.
It’s certainly overkill to add AI to everything. (I personally have experienced the very real limits of what happens when you do. It’s not pretty.)
But I do think there are major benefits to practicing the reflexive use of AI in your day-to-day life. Here’s what I’ve found to be the order of how my thinking has progressed over the past year:
Resignation: “I have an unsolvable problem.”
Curiosity: “I wonder if technology could help me with this problem.”
Motive: “How might technology help me solve that problem?”
Agency: “What could I build with technology to solve that problem?”
Transformation: “Would this work somewhere else too?”
The better you get at pushing yourself through these steps, the faster (and more effective) you get at using all sorts of technology. After all, learning how to use AI is just a proxy for learning how to command a computer to build something according to your spec. This is a problem that is not going away anytime soon.
As I wrote about recently, I now build new software and mini-apps almost daily. There is now no distinction between when I’m working and when I’m building.
I didn’t get here overnight. But I got here by taking on steps 1 through 4, with more frequency, and with less “down time” in between.
If you’re a manager of a team who’s trying to drive more AI adoption at work, or if you’re looking to learn how to use AI more yourself, the best thing you can do is set aside an hour a day to “play.”
Just like any habit (like going to the gym, or learning a new language), you won’t see immediate benefits right away. But over time, your lessons, your learning style, your pattern recognition, and your engineering mindset compounds. Then, you’ll be solving your own problems with technology on a near-daily basis too.
On a personal level, I will attest that it is incredibly satisfying and motivating to be able to create things like a real-time survey analytics dashboard that’s trained on my very real customer and user data. It’s even more wild to think that I can essentially rebuild a version of Google Forms (a product I’ve been bound to use for the past decade) in a matter of 90 minutes.
As it turns out, there are a lot of fringe benefits in your daily life that comes from thinking like an engineer. Time and time again, I find myself reflecting back on this mindset as a way to approach problems (even problems that have nothing to do with computers).
Is your kid is being a pill at bedtime? Let’s break down her activities throughout the day and come up with a more structured bedtime routine to keep her moods stabilized. Are you feeling overwhelmed about biting off more than you can chew? Lets break that problem into smaller steps and pick the right first step forward for tomorrow.
Here’s another silly one. When I have a tech issue with a completely unrelated piece of technology, I am much more inclined than ever before to tinker around with the hardware myself until I figure out how to make it work.
I believe these skills are helpful for lots of people. Not just engineers. And that’s why, when I teach AI through Build First, it’s a lot less about teaching to any specific tool, and a lot more about teaching this mindset.
If you’re ready to take on an engineering mindset at work or at home, let’s talk. You can find me at bethany@buildfirst.ai.
>600 subscribers
>600 subscribers
2 comments
A lot of people think I'm teaching people how to use AI. I'm not. I'm teaching people how to think like engineers. Here's what I mean. https://hardmodefirst.xyz/5-benefits-of-thinking-like-an-engineer-at-work
this is the actually useful part. knowing how to break a problem down, recognize when the output is off, debug assumptions — that's the real leverage