There’s a time for trash-bin code – quick, dirty, utterly disposable. And then there’s the durable code you build when you know it’s sticking around. This post is your cheat sheet for deciding which is which, how to avoid gold-plating prototypes, why KISS (Keep It Simple, Stupid) is still your best friend, and why observability + feedback loops are the secret sauce – especially when you’re slinging code with generative AI.
1. Disposable vs. Durable: Let’s Get Definitions That Don’t Suck
– Disposable Code: The scrapyard of coding – rapid prototypes, one-off scripts, “let’s just see if this works” experiments. It’s cool, it’s fast, and it can vanish without guilt. Shoutout to Charity Majors’ Disposable Code Is Here to Stay, but Durable Code Is What Runs the World for the reminder that this stuff can still be valuable – as long as you don’t expect it to raise your kids.
– Durable Code: The dependable workhorse. The stuff hospitals, satellites, and banks run on. It costs more effort
There’s a time for trash-bin code—quick, dirty, utterly disposable. And then there’s the “write once, cry never” durable code you build when you know it’s sticking around.
This post is your cheat sheet for deciding which is which, how to avoid gold-plating prototypes, why KISS (Keep It Simple, Stupid) is still your best friend, and why observability + feedback loops are the secret sauce—especially when you’re slinging code with generative AI.
1. Disposable vs. Durable: Let’s Get Definitions That Don’t Suck
- Disposable Code
The scrapyard of coding—rapid prototypes, one-off scripts, “let’s just see if this works” experiments. It’s cool, it’s fast, and it can vanish without guilt. Shoutout to Charity Majors’ Disposable Code Is Here to Stay, but Durable Code Is What Runs the World for the reminder that this stuff can still be valuable—as long as you don’t expect it to raise your kids. - Durable Code
The dependable workhorse. The stuff hospitals, satellites, and banks run on. It costs more effort upfront, but saves you from getting woken up at 3 AM by a PagerDuty alert because a prototype you “temporarily” deployed is now mission-critical.
Majors nails it: the real killer cost isn’t writing code—it’s maintaining it, testing it, and making sure you can see inside it when things inevitably go sideways.
2. Exploration vs. Stability: When to Go Disposable or Durable
- Go Disposable when:
- You’re exploring ideas, not committing to architectural matrimony.
- Speed outweighs permanence.
- Failure’s cheap, and starting over is faster than fixing.
- Go Durable when:
- Failure is expensive (financially or emotionally).
- The thing you’re building will last longer than milk in your fridge.
- Someone will curse your name if it breaks.
Dave Karpf’s take on generative AI and satisficing is perfect here—sometimes, the “good enough” AI output is exactly what you need to move fast. Just don’t confuse “fast food” code for a 5-course meal.
3. KISS: Keep It Simple, Stupid
Complicated systems are basically user error waiting to happen. KISS exists because humans are bad at juggling complexity for long stretches without introducing gremlins.
- Disposable code tends to be simple by necessity—quick hacks rarely over-engineer.
- But when you refactor disposable into durable, the temptation to overbuild is strong. Resist.
- Evolve complexity only when you understand the needs. Everything else is just future regret.
4. Observability & Feedback: Your Superpower, Not Your Afterthought
Here’s where most people trip up: observability and feedback loops aren’t just for the durable stuff.
In fact, when you’re moving fast with disposable code—especially code produced or assisted by generative AI—observability becomes even more important.
Why?
- AI moves fast; bugs move faster.
Generative AI will give you a solution in seconds, but it won’t babysit it for you. Without tests or logging, you won’t know when that “genius” AI snippet fails in production until your Slack blows up. - Fast feedback loops turn experiments into insights.
Observability gives you visibility into whether your disposable experiment is even worth making durable.
Logging, metrics, and lightweight tests help you decide if you’re chasing the right idea—or polishing a turd. - Durability starts with visibility.
The moment you see a disposable script performing beyond its throwaway purpose, you’ll thank yourself for having test hooks and monitoring in place. That early observability cuts the time to harden it for long-term use.
Practical Moves:
- Even in disposable code, add a minimum level of instrumentation—log key actions, track basic performance, and slap in a couple of assertions or lightweight tests.
- Use CI/CD even for “toy” projects if they’re in active experimentation.
- Treat observability like a seatbelt—it’s fine to drive fast, but you’ll be glad it’s there if something unexpected happens.
Majors’ big insight here: good sociotechnical systems make it cheap to understand what’s happening. That’s the same muscle you use to turn “let’s try this” into “we can depend on this.”
5. The Lifecycle: From “Lol, Let’s See What Happens” to “Please Don’t Break”
- Prototype (Disposable) → Move fast, break things, learn.
- Observe & Test → Gather data and feedback before investing more effort.
- Iterate → Double down on the experiments that work.
- Harden (Durable) → Add structure, scalability, and more robust observability.
By baking in observability from step 1, you avoid the nightmare of retrofitting it once you’ve already promised production stability.
6. A Real-World ‘Disposable + Observability’ Save
Picture this:
A consulting gig. The client needs a quick tool to crunch some gnarly CSV exports from their ancient ERP. We whip something up in half a day with a sprinkle of ChatGPT magic—absolutely disposable. But we toss in some quick logging (“this many rows processed,” “error count,” etc.) and a couple of sanity-check tests because, well, why not?
Fast-forward three weeks.
That “half-day throwaway” is now in daily use across three departments, feeding data into actual revenue reports. The ERP migration they promised “in Q2” is now “maybe next year.”
Thanks to those early breadcrumbs of observability, we could see exactly where it was failing, fix it in hours, and—when it inevitably needed to be made durable—refactor it without losing our sanity.
Without that logging? We’d have been guessing where it hurt, and the client would’ve been wondering why their shiny new consultants suddenly looked like deer in headlights.
7. For Consultants and Dev Teams Who Want to Ship Without the Burnout
- Start with risk assessment—how expensive is failure here?
- Use generative AI for speed, but wrap it in tests and logging so you know if it’s lying to you.
- Keep disposable code lightweight but visible—you’ll either toss it fast or promote it to durable without rewriting from scratch.
- Never “KISS” goodbye to observability; it’s what lets you keep moving fast and stay safe.
Wrapping Up: Balance is a Verb
Disposable vs. durable isn’t a morality play—it’s context-driven. Use KISS to keep things from spiraling into complexity hell, use generative AI for quick wins, but pair both with observability so your experiments don’t become ticking time bombs.
It’s not about writing perfect code from day one —it’s about seeing enough, fast enough, to know where to put your energy. That’s how you build software (and a career) that survives more than one sprint.
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