First Principles of Building Software

TL;DR

Software isn’t magic. It’s not the “art of shipping fast” or the “science of perfect abstractions.” At its core, building software comes down to a handful of brutally simple principles:

• Understand the problem.
• Break it down.
• Hide complexity until it matters.
• Build something reliable, evolvable, and clear.
• Automate the boring stuff.
• Shorten feedback loops.

That’s it. Everything else is noise.

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Principle 1: Define the Real Problem

Problem > Code

Software dies when it optimizes for output instead of outcome.

• Quibi spent $1.75 billion building slick apps nobody wanted.
• Basecamp (37signals) built a simple project tool with almost no features—and outlived a dozen bloated competitors.

If you don’t know the problem, your code is theater.

Outcome > Output

Most teams confuse motion for progress. They measure story points, velocity, “lines of code shipped.” None of it matters.

What matters: Did the software make life better for someone?

Amazon’s one-click purchase was a tiny feature. Output: one button. Outcome: billions in revenue.

If your team can’t answer “who cares?” in one sentence, stop coding.

Principle 2: Abstract Away Complexity

Abstraction isn't about showing off you understand OOP principles, that causes codebase rot: engineers abstracting too early, or too cleverly. It's about hiding details until they matter.

Think of Stripe’s API: simple charge.create() to move money. Behind it, armies of compliance, fraud detection, and bank reconciliation.

Bad abstraction leaks implementation details everywhere. Good abstraction makes complex things simple.

Good abstraction is invisibility.

Principle 3: Decompose

Big problems paralyze. Smart engineers cut/decompose/modularize them into smaller, independent pieces. Not because it's fashionable, but because it works.

Unix got this right: tiny tools—grep, sort, uniq—that do one thing well, chained together into power.

A modern example: AWS's approach to infrastructure. Instead of forcing one giant stack, they decomposed it into modular services:

• EC2 for compute
• S3 for storage
• CloudFront for distribution
• Lambda for serverless execution

Each piece works independently. You can run S3 without EC2. You can use Lambda without touching CloudFront.

Modularity enables parallel work, easier testing, and actual reuse.

Principle 4: Separate Concerns

Separation of concerns isn’t academic—it’s survival. When code for your data layer also does authentication and sends emails, you’re building spaghetti. And spaghetti doesn’t scale.

There are of course cases where strict “one job per part” is overkill on day 0. Start simple, then pay the tech-debt later when complexity and scale justify it.

Example: CRUD app gaining traction

Day 0 (fine):

          # Django view (day 0)
          def create_order(request):
              form = OrderForm(request.POST)
              if form.is_valid():
                  order = form.save()
                  send_confirmation_email(order)  # inline side-effect (mixing responsibilities: saving data *and* sending email right here)
                  return redirect("orders:show", id=order.id)
          

When traffic grows & workflows multiply (refactor):

• Move email to an async job/queue (Celery/RQ/SQS).
• Extract domain logic into a use-case/service (e.g., CreateOrder).
• Push validation to form/serializer objects; keep the view thin.
• Add an outbox pattern or event bus if other services consume order events.

Result: you shipped fast early, then split responsibilities precisely where the pain shows up. Common sense and telemetry guide the split—not dogma. For a prototype or MVP, it’s fine to let one service handle multiple concerns. If traction comes and complexity grows, that’s when you peel concerns apart. Refactoring later to pay down tech debt is often a smarter trade-off than over-engineering too early.

Principle 5: Correctness

Your code must do what it's supposed to do. Consistently. Correct software is boring software. It does what it should, every time.

TypeScript adoption at Slack reduced production bugs by 40%. Not because TypeScript is magic, but because it catches errors before they reach users.

Rule: First, make it correct. Then, make it fast.

Principle 6: Efficiency

Premature optimization is still the root of all evil in programming. Knuth was right: 97% of the time, don’t bother.

But the other 3% matters. WhatsApp scaled to 400M users with 50 engineers. Their secret? They optimized the right things at the right time. They used Erlang, not because it was trendy, but because it handles massive concurrency naturally.

Amazon optimizes at planetary scale because saving 100ms per request = millions in margin.

Don’t optimize for the hell of it. Optimize when cost, time, or user experience demands it. Get it working. Then make it fast.

Principle 7: Evolvability

Perfect code is a myth. Every business pivots, every feature changes, every system grows old.

Amazon’s infamous “two-pizza teams” weren’t built for elegance. They were built to move, refactor, and replace without ceremony.

Perfection is a religion. Evolvability is survival.

Principle 8: Communication

Code is read 10x more than it's written, so code for humans first. Your compiler doesn’t care if your variable is named x or customerBalance. But your teammate, future-you, and yes, GenAI models, do.

Shopify's codebase has over 3 million lines of Ruby. It's readable because they prioritize clarity over cleverness.

Write code, docs, and commits that explain themselves. That’s leadership.

Principle 9: Automate the Boring Stuff

Manual deployment is fragile.

Testing, deployments, monitoring—if it’s repeatable, script it. GitHub Actions, CircleCI, Terraform. Automation frees your brain for real engineering.

Heroics don’t scale. Consistency does.

Principle 10: Feedback Loop Are Oxygen

The faster you learn, the faster you win.

Agile got this half-right. Standups don’t matter. Tight feedback does.

• Launch MVPs in weeks, not years.
• Ship feature flags to test in prod.
• Gather real customer data, not manager opinions.

Agile is not about Jira tickets. It’s about making the feedback loop tight enough that mistakes are cheap.

Code Example: Simplicity Wins

Which of these will live longer?

          # Over-engineered
          class PaymentProcessorFactory:
              def create(self, method: str):
                  if method == "credit_card":
                      return CreditCardPaymentHandler()
                  elif method == "paypal":
                      return PayPalPaymentHandler()
                  # dozens more...

          # First principles
          def charge_card(card, amount):
              # minimal, direct, clear
              pass
          

The second one. Every time. Because clarity beats abstraction-for-abstraction’s sake.

The Unsexy Truth

Here’s the contrarian truth: building great software is not about genius. It’s about discipline.

• Define problems before you solve them.
• Cut complexity into parts.
• Hide details, separate concerns.
• Prioritize correctness, then efficiency.
• Assume change is constant.
• Communicate like a human.
• Automate everything repeatable.
• Learn fast.

That’s it. Not “AI-first.” Not “10x engineer.” Just first principles, executed with discipline.

The Takeaway

If you strip away the hype, building software is like building a bridge.

• Purpose before concrete.
• Structure before speed.
• Maintenance before monuments.

Your legacy as an engineering leader won’t be “we used the hottest stack.” It’ll be: we solved real problems, clearly and sustainably.

The first principles don’t change. The question is: do you have the courage to follow them when everyone else is chasing trends?