This is a long read. You will find a table of contents right after the introduction.
One day, during a post-game interview, elite chess Grandmaster Alexander Grischuk was asked the routine question: “How long have you been preparing for this game?”. With his famous wit, he answered:
I’ve been preparing my whole life.
A beautiful answer. It applies to any job: all your past experiences contribute to some extent to the decisions you make today. So I’ve been thinking about how this idea applies to my career. It turned out to be an intriguing writing challenge. While intuitively I know I follow some principles when I’m writing code, I couldn’t immediately structure or name them.
The article you’re reading is a (very long) answer to the question: How have I been preparing for the code I’m about to write? My desire to define these principles – and learn more about myself in the process – was too strong, the challenge too interesting. I couldn’t pass on it so here we go.
I grouped the principles into categories: existing codebases, greenfield projects, and general ideas. While it sounds a tad obvious you want to approach new projects and existing codebases differently, I encountered enough over-engineered or under-engineered (isn’t that a thing? It’s almost as common) solutions in my career. More often than not, approaching an existing codebase like a greenfield project or, worse, the other way around was the reason why their solutions were incorrectly sized.
Before I move on to the principles themselves, I want to mention that I find the title of the article a little pretentious. I went with it anyway because it fits the topics covered better than anything else I could think of.
- How I approach an existing codebase
- Starting from scratch
- General principles
How I approach an existing codebase #
The codebases I have in mind are so large that you can’t keep an accurate representation of all their parts in your head. You may know some parts very well, but there are enough unknowns and you can’t make a significant change without careful planning.
I don’t think I can provide a good example of what’s a “large enough” codebase because that’s probably too personal. Let’s go with “large enough for me, the reader”.
Read features end to end #
When I’m new to a large codebase, I read features end to end before making any change. People often call it code safari but I’m not a fan of the metaphor. I like to call things what they do so the principle goes by “read features end to end”. Yeah, I get it. It’s boring. I like it that way.
Approaching a new codebase can get a little overwhelming. Sometimes I don’t know where to start, which system maps to which part of the codebase. To orient myself, I read a whole feature end to end.
For the sake of discussion, let’s consider a web application composed of multiple services. One page in the app has an “innocent” button that starts a sequence of processes that ultimately result in sending a PDF report via email. You might have seen some variation of this.
The idea is that I go on a hunt. I start searching for the code that renders the button. Trivial – but I know I will discover things: how/if we do i18n, what kind of frontend framework we’re using. Most importantly, I ask myself questions like:
- Oh, we have our own css framework. Why?
- What’s up with all these data attributes? I haven’t seen any of that when I was checking the website from home. We stripping them down?
- Why are we stuck on such an old version of X?
Using my brain as storage is a waste. The saying goes “the shortest pencil is longer than the longest memory” so I write everything down.
When I know which service handles the click on the report button, I go down one layer and keep reading. I keep going until I find my way to the email service.
Once I’m done, I have many questions in my notes. That’s when the hard work starts. I reorganise them:
- Questions for myself. A sort of a to-do list of things I know I want to dig in personally.
- Questions for those who know the codebase better than I do and can get me up to speed faster.
- Idioms. Every codebase has its ways of doing things.
I’ll get some answers to these questions so that I can draw a basic map to look around the codebase. I often repeat the process a few times before I start making any changes.
What I like the most about this principle is how trivial it sounds when I describe it, and how helpful it is when I use it.
Small changes, fast feedback loops #
Focused, short, productive programming sessions are the typical scenario for this principle. The idea is trivial: I change a few lines or less before seeking feedback. I mean literally the smallest possible change I can think of. The size of the change (as small as possible) and the speed of iteration (how fast feedback about the change reaches me) are equally relevant to me. The workflow looks like this:
- I change very little. A few lines or less.
- I seek feedback.
- If it works so far, I repeat.
The key is so far. I have mental checkpoints marking the progress. “I changed this line, stuff still works”. Yes, it’s that trivial. As soon as my feedback loop shows something unexpected, I go back one checkpoint because “it worked before I changed that”. I can be radical with my reaction to unexpected feedback: I often just delete the last change and start over.
Here’s how I implement this workflow: I set up a custom, project-specific REPL before I change any code. The tooling isn’t relevant to the principle. It can be TDD, it can be print statements (yep, I just said that), whatever. The point is that it has to feel exactly like read-eval-print loops. They’re really fast. Once that experience is in place, I can start changing a few lines or less per time and apply the checkpoints process I just described.
You can (and with that I mean you should) apply this principle also to the end result of good programming sessions: deployable artifacts. There’s evidence that smaller changes lead to higher productivity. They’re safer to deploy and roll back if needed. They’re easier to review and therefore move faster through the production pipeline.
I know this principle has a striking resemblance to TDD because it’s the most frequent feedback I get when I talk about it with other programmers. “Why don’t you just do TDD all the time?” – they wonder. The answer is that I almost never practice TDD. Let me explain:
- TDD feels great as a workflow. It resembles the “REPL experience” I’m so fond of. But TDD quietly brings a design constraint to the table. When I’m writing code, I’m mostly typing out the solution I modelled in my head: it’s a transcription exercise for me. TDD always forces me to shift my mental model a bit so that the code is more testable upfront. While there’s nothing wrong with making code more testable, doing it while I’m discovering how well the model in my head translates into actual code doesn’t work for me. The friction between what I want to write and what TDD wants me to write has always had a negative impact on my ability to program effectively. In practice, I only TDD small bug fixes.
- TDD is primarily a development technique. Its artifact is unshipped code. Unfortunately, we can’t say that the code we write works – even if it’s perfectly tested – unless it works in production. That’s why I want more than TDD from my “REPL experience”, I want to test in production.
Test in production #
I can hear mipsytipsy in my head saying “fuck yeah!” every time I mention this principle.
Here’s what it means to me: I want to expand the boundaries of my “REPL experience” enough to include my production systems in the loop. I’m not satisfied with a tight feedback loop while writing the smallest possible change. I want to ship it to production in minutes and find out if it works there. What’s the point of putting a lot of effort in writing the “correct” code if I don’t know if it works in production?
The urgency of shipping code to production has a positive effect on the culture of a product development team. I have seen it first-hand many times: we want the “REPL experience” to include production? We’re going to need fast CI and CD pipelines. We’re going to need ways to observe the impact of changes on our production systems. Rollbacks have to be cheap. We may want to use feature flags so that the deployments are safer. We may not want a staging environment so that the pipeline to production is shorter. All of it contributes to the speed of iteration (from “Boyd’s Law of Iteration”).
The cultural pushback I get when I say “test in production” is fascinating because we all test in production when we ship the code anyway. It makes sense to do it consciously. It creates a safer, more efficient environment for feature development.
Testing in production and small changes feed on each other. The faster your lead time, the more likely you’ll ship smaller changes. The smaller the change you ship, the safer your deployments. The safer your deployments… you get it, it’s a circle!
Be a gardner #
Code gardening is the act of changing code to improve it just a little. You make an error message more understandable, you use a more idiomatic way to write those three lines, you align that test with your internal conventions. The codebase is a living organism: it’s a garden and you take care of it every day. You prevent bad habits from forming, you heal that one spot that is a bit sick, you don’t forget to water the plants. My first encounter with this analogy is a commit made by @fxn. It has stuck with me ever since.
The way I do gardening evolved over time. I used to add some gardening to existing tasks. I grew out of it because it polluted code reviews with unrelated things and increased the size of a change. I value small changes more than gardening.
Nowadays, I have a small workflow to structure my gardening activities. I take notes of the little things I want to do while I’m doing other tasks. Sometimes, I share these notes with my teams (it really depends on the team and the context they’re in) to help other people become gardeners. Then I use this to-do list as a personal reward system. I get done what I need to and then I treat myself to improving that test that produces those annoying warnings. I have seen it at play with other programmers as well once they form the habit. The reward system is simple and effective: it allows people to make small improvements all the time. Gardening shows care for the codebase and caring makes working on the same codebase more sustainable over time.
Being a gardner is easier if you’re already making small changes and you’re already testing in production. The ability to improve a codebase is often limited by the cost of deployment: the higher the cost of a deployment, the less likely one can be a good gardener. It’s all connected.
Starting from scratch #
Since it’s less common, I feel privileged (or unlucky, it depends on the day honestly) that I’ve been in charge of brand new, multi-year, projects many times in my career. These projects were all different, they involved a different team and a different company every time. But the fact that I had to start from scratch in all of them gave me an opportunity to try things out and see what worked and what didn’t. The following principles are my favourite parts of what worked.
Documentation driven development #
More than a decade ago, I ran into readme driven development. I was happy I could give a name to something I had already been doing for a few years. Since then, I tweaked the name of the principle to “documentation driven development” because it’s a tad more general and flexible.
The idea is to write documentation for a system as if the system were already done. It’s my favourite system design technique. The beauty and the irony of this approach is that I write the docs first so I can put some constraints in place. I can then reflect on their consequences with a lower effort (compared to building the system). Consciously chosen constraints are the foundation of a good design.
The type of document, its length or the number of details in it are all project-dependent parameters, so it’s hard to provide concrete examples. There are some things I have in any document, though:
- When it’s done, what will the system do? Even more interesting, what will it not do? Answering these questions gives me some boundaries so I can stay on track and focus on the essentials.
- Can I picture the different parts of the system? How do they interact with each other? A few diagrams go a long way. I sketch a few to get a sense of how to split responsibilities among the different parts. It also helps me understand what the highest level building blocks of the system should be.
- Where do I start from? What needs to be already in place to start working on a specific part of the system? I write down the order in which it makes most sense to start building things. Depending on how the system is organised, I write down the parts that can be parallelised.
If I work in a team, this document also functions as a hub for async collaboration. I want to make sure we’re on the same page before we move from writing for humans to writing for machines.
Throw it away first #
When I think about prototyping, this quote always comes to mind:
In theory, there’s no difference between theory and practice. In practice, there is.
It is so apt for programming! It doesn’t matter how carefully I study a problem or how good the solution I came up with seems. Doing is always different.
Building a system is a struggle between tiny details and high level ideas. It’s implementation versus design. The harder the problem I’m trying to solve, the stronger the tension between the two worlds. I want to make sure it’s a positive tension:
- I write a chunk of the design doc
- I write a prototype
- I throw it away
- I improve the design doc
- I write a new prototype
If the problem is complex, I may repeat this process a few times. If the problem is trivial, I may get away with not throwing code away. I’m ready to do so when I’m unhappy with my understanding of the problem.
Please note: I didn’t say anything about what I think of the code. My best-case scenario is that I don’t hate it the day I write it and I may still not totally hate it after a few months. So what I think about the code isn’t so relevant. Furthermore, it will change a lot anyway when I make it production ready.
This is the key idea: I write a first draft knowing I will probably throw it away. With that in mind, I experiment, I digress a little, then I try again.
My favourite programming sessions always happen after I’ve thrown away at least one prototype. These sessions are really fast: I’m kind of just typing out a satisfying solution.
Make it work, make it good, make it fast #
This principle is a variation of the theme of the principle often attributed to Kent Beck. I’m so pretentious about it, I like my naming better.
I consider it to be the corresponding starting-from-scratch principle of small changes, fast feedback loops. It’s almost the same idea but the dynamics are a little different: there’s obviously more freedom of movement when writing a new system. That freedom is detrimental to making small changes.
Often I can’t change a few lines even if I wanted to: I need to write a whole lot of code across multiple files to even see my little prototype do some basic things. It’s the nature of writing code from scratch. So here’s the idea:
- Make it work
- I REPL my way through code to figure out if what I put together in my head works in practice. I don’t overthink it. It doesn’t matter that the naming isn’t good yet. It’s OK, I know the code isn’t production ready: I can get away with not dealing with corner cases, failure modes, error codes from that third-party API. I note things down, leave to-dos for tomorrow’s Luca. It’s a deliberate choice: my goal is to get the code together. I don’t have to make it good yet. I don’t have to ship it to production in a minute.
- Make it good
- I go through my notes and the to-dos I left for myself. One by one I tick items off the list. With every two or three items I get off the list, I step back a little to evaluate the design. Now I’m making it good so I want to get down to details. The way the code is organised has to make sense. I care just about everything: I only leave out speed. I am not trying to write slow code by design (that wouldn’t help and it’s probably harder to do than it sounds?) but I’m also not trying to squeeze milliseconds off of every function.
- Make it fast
- It’s time to measure things. To be fair, this isn’t always a required step. Often enough, code that works in production is quite good already for whoever is paying for it. On the other side of the spectrum though, there are situations in which speed is a requirement. The first two steps still apply. What changes is the definition of working code. I incorporate speed into the definition and design my “REPL experience” to always tell me how fast the code is. When performance is so critical, I make the performance scripts I wrote for my “REPL experience” a step of the CI pipeline. As for the actual effort to improve performance, the most basic workflow goes a long way: I profile the code, find the slowest bit, make it faster, and repeat until it’s fast enough.
Over the years, I realised that this principle has an interesting side effect on how I choose programming languages. Let me offer a concrete example.
Nowadays, I work exclusively with statically typed languages (I have probably enough reasons to write an article about it… interested? Please, let me know!). The ergonomics of statically typed languages allow for a much more relaxed approach to the make-it-work phase. I really don’t need to care too much about how the code is organised or its naming. Modern IDEs are so good at refactoring statically typed code that I can do a big chunk of the make-it-good part literally in seconds.
Looking back on this, it feels paradoxical. I chose dynamically typed languages (my favourite being Ruby) because of the supposed productivity gains only to realize that I had to be much more careful (therefore slow) in the early stages of a new project because of how hard it would to be to reorganise code.
General principles #
Is it really programming if I don’t have at least some util functions? 😃
Jokes aside, there is a handful of principles I apply to any context. These principles are my “true north” guidelines.
Prose not poetry #
Writing code is… writing. It’s an intriguing form because you have two audiences: the machine and the next programmer. To spice things up, they couldn’t be more different.
The primary audience is the machine. We often lie to ourselves and say things like “more than anything, you’re writing for the next programmer that reads your code”.
I’m sympathetic with the argument, but let’s be honest: you can’t ship code that doesn’t compile. It doesn’t matter how readable and understandable the code you wrote is, it doesn’t work if you have a syntax error. The machine understanding what you wrote is a prerequisite of working code; readable code isn’t.
Then there’s the next programmer. Humans are complicated. They have preferences and opinions about what correct code looks like. Many factors contribute to the readability of code and programmers often won’t agree on which ones are more relevant. So we end up contextualising the way we write code to the project it belongs. There isn’t one way of doing things.
Pleasing both audiences is a tough game and realizing that writing code is “just” writing has been of great help.
The idea is that I think of code as if it were book. A book has chapters, the chapters have paragraphs, the paragraphs have sentences. From the perspective of a codebase, one way to look at it is: sentences are instructions, paragraphs are algorithms, chapters are modules, and a book is a system. I don’t need the parallel to hold up in every situation, it’s about abstraction.
I want homogeneous abstraction within a layer and heterogeneous abstraction between layers. I don’t want to jump from shifting bits in one line to call an external service via HTTP in the next one.
A notable exception is performance. Making code faster often makes it messier, uglier because the abstraction isn’t homogeneous. You’re jumping levels often because the code on the hot path is written differently from the rest. In this scenario, I write comments explaining why the code looks the way it does.
When I bring up this prose vs poetry metaphor, I often get the feedback that poetry fits as well. After all, there’s very structured poetry out there. The thing with poetry though is that it’s good when it says a lot with just a few words. Poetry is terse, especially compared to prose. When in doubt, always prefer clarity to brevity.
Facts > Assumptions #
I often say:
Common sense is not so common.
It is common sense that facts are much more relevant than assumptions, isn’t it? Well, I’ve been bitten by the wrong assumption many times.
For example, most of the bugs I’ve ever fixed were the result of miscommunication where two parties assumed what something meant without checking if they agreed. Fixing a bug is aligning the code with the facts.
It’s not a coincidence I’m mentioning bugs right now. This principle guides my debugging sessions.
It goes like this: I read a piece of code, I know there’s a bug in there. If I can’t find the bug just by looking, this principle comes to rescue: somewhere in this piece of code there’s at least one thing I’m considering a fact – but it’s actually an assumption.
So I move up one abstraction layer, I start debugging my facts, which can have any of these outcomes:
- I find the “fake fact”. Often I immediately find the bug, too. Especially when I was the author of the bugged code. It’s kind of obvious: the bug was there precisely because I thought the code I wrote did X while in reality it did Y.
- All the facts are true. It means I have to go down one layer, expand the search, collect more facts, then go back up to start debugging the facts again.
I apply this principle to performance improvements as well. Early in my career, I had a tendency to guess what made, say, an endpoint slow. Sometimes I even changed something without measuring. When I think about it now, it makes me smile and go “Oh, how young I was back then!”.
The guessing game was stimulating, but I had to abandon it because the facts (ah!) were showing me that it was a waste of time. I was wrong pretty often. Moreover, I often found the results of my benchmarks so counter-intuitive that I could only conclude that the best approach is to measure everything. Now I don’t even ask myself what makes a piece of code slow. I measure everything, I want the facts.
Name things what they do #
Naming is hard.
Having said that, we often make it harder for ourselves than it should be because we bring aesthetics to the table. Code must be pretty so we’re looking for elegant, apt, short names for everything.
Early in my career, I struggled with naming much more than I do now because I didn’t realise a few things:
- Unless it’s a trivial problem, I don’t have to name things right while I am sketching a solution.
- Due to the intense concentration I need to write code, sometimes I don’t
realize that the names I choose are a design choice. Calling something, say,
JobSchedulerconstrains its design and the design of whatever interacts with it. I called it so, therefore I will treat it so. But maybe it’s not really a job scheduler and my design starts to drift off rails.
- My understanding of a non-trivial problem increases as I write the code trying to solve it.
All these considerations led me to “relaxed naming” (ah the irony of not liking this naming 🙃):
- I call things what they do right now. I use the most specialised name I can think of. The more descriptive, the better. Bonus points if it’s verbose and boring.
Yep, there’s no step two.
It’s a little cheeky, I’ll give you that. But all in all, after 20 years I’m finally happy with my naming process. I basically look at it the same way I look at prototyping. Let me spell out the obvious: the thing I’m trying to name is new, I am naming it now because it’s not there yet. So the principle make it work, make it good, make it fast works pretty well here – except there’s no step three.
“Name things what they do” also means that I prefer dull, descriptive, long names to short, fancy, clever ones. I also despise acronyms.
I like to extend this principle to everything, including services, machines. I prefer boring name schemes to fancy or funny ones. A trivial example:
- DO: kafka1, kafka2, kafka3
- DON’T: orion, antares, pluto
I don’t have to ask what kafka1 is because I called it what it does.
Write the code you’d like to use #
I can’t find the original reference, but I’m pretty sure this principle too was inspired by Kent Beck.
This idea works well when I get stuck on an empty file. The proverbial blank page. I have so many questions about the API of the new module I’m about to write, the data structures I should rely on internally, the types I should expose, and so on. It can get so overwhelming that I just get stuck.
That’s where I take a deep breath and ask myself: how do I want to use this code? Here’s what I do:
- I close that empty file I was staring at.
- I open one file that would use the code I can’t get myself to write.
- I write code like the problem is already solved the best way I can possibly imagine.
The third step is hard because I have to lie to myself; I do know the code isn’t there yet. So this process only works if I jot down a solution without trying to think of all the code I need to write to make it all work.
Let the design emerge from the code #
Good design is an exercise in patience.
It’s hard to be patient when I think I have a clear idea in mind. It’s half the excitement of working on a new problem and half the impatience of not being done with it.
I want to do it all fast and perfect. I learned that this never works in practice for me. I can’t rush a good design. To complicate things, systems are living organisms, their only constant is change. They change because of business needs, scaling needs, or whatever. The list is too long. The point is that they change all the time.
Evolving systems in a sustainable manner is hard. It’s what I’ve seen most people struggle with. Over the years, I collected a few maxims that guide me through designing a system. Their goal is to slow me down, so I can think about the design at the right pace. Let’s go over them.
Less is more
I “stole” the sentence from Rob Pike.
I want to add as little as possible to the system so I can listen to what the code is trying to tell me. Let me explain.
I often think about code as a bi-directional form of communication. I’m trying to tell the machine what to do and the code tells me what to change.
The way the code looks, the way some parts need to change every time some other parts need to change, the non-obvious ways two parts depend on each other, the naming being a little off… So many little details! It’s the code trying to talk to me. If I’m always talking over it though, I won’t be able to listen to what the code is saying. I have to write less. Less is more.
Simple ain’t easy
Good design looks obvious. It’s so simple that it has just about enough to solve the problem. But simple doesn’t mean easy.
Reaching a simple design takes the time it takes. It’s a reminder I can’t rush a good design.
It’s also the way I judge my solutions: I’m only happy when the code is so simple that I feel there’s nothing to remove from it any more.
Prefer duplication over the wrong abstraction
I got this one from Sandi Metz. She does a wonderful job of explaining it so I won’t repeat what she means. You should read her article, she’s a wonderful writer.
Exposing my teams to this idea over the past few years has been beneficial. I often trivialised it a little for the sake of making a point while sharing the idea with my teams: If you have duplication once, leave it be. If you have the same piece of code in at least three different places, then you’ve got a good chance for a correct abstraction. If your abstraction feels simple (ah!) enough, go for it.
These maxims all contribute to the idea that waiting for the design to emerge from the code is better than trying to rush into coding our current idea of good design.
Write actual tests #
A test must change only if the behaviour it verifies changed
No other reason is good enough for a test to change. Which, in turn, means “actual tests” only test behaviour. I’ve seen tests testing language features, or missing language features (looking at you dynamically typed languages), or framework features.
These tests have a very annoying property in common: they change every time production code changes. My advice is to delete them. Yes, I just said that.
When I bring this up, people usually understand my advice as either not to write tests at all or write only integration tests.
Obviously, no point in debating the first one. Of course you should write tests to verify your system. I’m not arguing that. I’m arguing the effectiveness of tests suites.
As for the second point, it’s true that integration tests are more likely to suit my definition of an “actual test”. I understand where people are coming from with this. My definition though doesn’t say anything about the level of abstraction the tests should operate at. I’m not arguing how to write tests, I’m arguing what to test. Test behaviour, forget the implementation.
From a practical standpoint, I do prefer integration tests. After all, with the help of Moore’s law, integration tests are fast enough for the trade-off of, you know, actually test something.
Balance your confidence #
If I had to name only one significant change I’ve experienced since I started programming, I would go for confidence. The more experienced I feel, the more convinced I am I can solve any problem.
It is somewhat obvious that experience made me more confident. I gained experience through trial and error. Rinse and repeat. I learned what works and what doesn’t by trying things out, often the hard way. So yes, experience makes me confident. Sounds good, right? Yes and no. I need balance.
Confidence is good because it can make me fearless. I know you can throw any problem at me, I will find a way to solve it.
Let me tell you a story about my first job that illustrates what I mean.
In the summer of 2006, I joined a company in Rome that processed hundreds of millions of events per day from poker machines. At the time, the legislation in Italy prescribed that these machines had to return 75% of winning. So our systems collected any event these machines produced. The company ran monitoring and fraud detection algorithms.
The whole system was a large collection of stored procedures on top of a giant Microsoft SQL Server installation (don’t judge me for this architecture OK? I didn’t design it). These stored procedures parsed the events (a large blob of text that we parsed with SQL. Yep, you read that right), stored the processed data in various tables, and ran a bunch of algorithms on the data. It was complicated for a young man fresh out of university.
My first big task was to make use of a reserved part of the events. In practice, this meant that I had to figure out the impact this change had on the whole system, aka 200+ stored procedures.
I knew I couldn’t do it alone, but I tried anyway. It took me almost a week of despair to find the courage and tell my boss I had no idea what to do. When I talked to Mario (it was in Italy so 🤌🏻), his answer filled me with anger first. He said something like “let’s do this together, it’ll take two hours to plan everything, then I leave you alone”.
Two hours?!? I had lost almost a week trying to make sense of the problem and came up with nothing, how were we going solve this in two hours? Well, he showed me and it was a profound learning experience.
We got into a meeting room and Mario did the most obvious thing: he broke down the problem in small steps, one stored procedure after the other. We put the code on a big screen and took notes. Fast-forward two hours and we had a whiteboard full of sticky notes for every single stored procedure we needed to change and the reason why each of them needed to change. I was amazed.
Mario had the confidence to solve the problem. He knew he could do it so there was no fear to cloud his thinking. He sat down with me and executed a simple process (simple ain’t easy eh?) of laying down the foundation of a month of work in just two hours. That’s the kind of confidence that makes you fearless.
If confidence has such a amazing effect on your ability to my difficult problems, what do I want to balance it with? Well my confidence feeds on ego. And that’s a problem.
While confidence makes me fearless, ego makes me dumb. At times, I have troubles telling them apart, after all they’re close.
The difference is only the way I look at things. When I’m confident, I take my time to break down a problem in smaller parts, I research the subject, I ask for help, I plan accurately. When I’m high on ego I look at problems from a pedestal, I underestimate them. I plan badly because “hey I’m great! of course I will solve this on the go, planning is for losers”.
That’s why I seek ways to balance my confidence. I need enough confidence to eliminate any fear but not too much that I get high on ego.
The way I find my balance is by putting to practice the idea “we’re all junior at most things”: I learn new things. Just in the past few years I tried drawing, rubik cubes, chess, you name it.
When it comes to programming, I try to solve problems in areas I have no idea about or some problem that looks really hard. For example, writing a programming language sounds hard, so I toyed with it and found out that it’s indeed hard but it’s also “just” a program.
The journey is humbling and the reward is two-fold: a balanced confidence and expanded knowledge.