We're Forgetting How to Sharpen Our Tools

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Over the past two months, I stopped writing code professionally. Now I only code as a hobby, using it as a way to keep learning and stay sharp. Communication skills, writing better prompts, managing context — when combined with years of hands-on experience — have suddenly become more valuable than ever.

Recently, we completed a project that would normally take a month. It required integrating with external APIs across 2,000+ pages of documentation, building tests, services, applications, and internal integrations. We finished it in three days. How did we get here?

The Lazy Developer and the Brain’s Optimization #

Someone once told me, “The lazy developer is the best developer.” I immediately understood what they meant: write less code, achieve more results. I’ve met developers who tried to achieve this through abstraction; these people still exist, but that’s a story for another time. What I want to emphasize is this: that saying aligns perfectly with how our brains work.

Our brains map every action and its outcomes. They trigger the same neurons to reproduce the hormonal rewards we’ve experienced before. While this pattern is simple, it’s directly tied to habits, gains, and effort.

In other words, “less effort, more results” isn’t just laziness — it’s our brain’s natural optimization strategy.

A New Pattern: From Notepad to IDE #

You might wonder why I’m diving into this. It’s simple: we’re facing a new pattern.

Back when you wrote code in a text editor, you had to memorize everything — every function, every package, what each one did. Then IDEs started showing you this information with a simple hover. You gradually stopped memorizing. You’d type list. and think, “Let me see what methods are available,” scrolling through the options and clicking to select. You might have even stopped typing altogether.

Then came the shortcuts: “replace all,” “rename in all occurrences.” These features made your work easier and satisfied you. “Wow, this would’ve taken me so much longer if I did it manually,” you’d say. And just like that, you lost the ability to write from memory.

This didn’t make you incompetent or less of a developer. You still wrote the code — you just had some help. You moved from a text editor to an IDE.

But this transition wasn’t just a tool change. It was the first erosion of your mental muscle memory . Typing a dot and waiting for autocomplete became faster than writing from memory, but you also outsourced the trial-and-error process.

From IDE to Agentic Development: Operating at the Semantic Level #

Some of us moved from IDEs with Copilot to agentic development . We started experiencing the same satisfaction, the same shortcuts. “Replace all” turned into “replace the entire logic.” “Find all” became “find the logic.” We stopped searching for keywords and started searching for meaning.

This is where LLMs began to transform our lives. This revolution is so profound that you can now explain logic, debate logic, analyze results, iterate on logic, and get exactly what you want — without writing a single line of code.

We’re no longer operating at the keyword level. We’re operating at the semantic level.

The Vibe Coder: The Developer Who Writes No Code #

Here it is: “The lazy developer is the best developer” has evolved into “The developer who writes no code is the best developer.” If you found the original saying reasonable, then the vibe coder should feel more significant to you than you might think.

We are no longer inside the code — we’re above it. The moment you jump off this cliff, you find yourself in an infinite loop, especially when you stop using LLMs as assistants and start acting as their manager. Here’s what I mean: you can suddenly define requirements and specifications, delegate all the work to agents, send tests to other agents, development to another, project management to yet another — and transform yourself into an entire team, all on your own.

But you’re no longer inside the code. You’re above it.

Theory Over Practice #

Notice we still haven’t discussed “better code” or “more secure code.” That’s because we’re losing this ability. We’re prioritizing theory and abandoning practice.

This trajectory will push us toward conversational analysis: “Is this code memory-efficient enough?” “Does this code consume too many CPU cycles?” “Is there an unnecessary roundtrip here?” We won’t even say, “This variable allocated X times” or “This function used Y cycles.” We’re not writing the code anymore. We’re not developing. We don’t know why it was written. We only know what we’re told. We only know the results. We’re not even part of the process.

The game is changing, and so are we. But if we don’t control this change, we’ll just be dragged along.

Practical Knowledge Is Disappearing #

At the end of the day, we’re going to lose a lot. Remember patterns?

You can’t make a developer who finishes a month’s work in a day write code anymore. In today’s world, as long as the output meets requirements and cash flow is maintained, no one cares about the details. That’s where the real problem begins.

Our hard-earned practical knowledge is disappearing. We’re specializing in soft skills, architecture, and theory while offloading the labor. The downside? We’re becoming — or training — expert developers who can’t write code. We won’t know the differences in the development processes of REST APIs versus GraphQL; we’ll only know “which one gives us what result.”

Ouroboros: The Snake Eating Its Own Tail #

If I had told this story five years ago, I would’ve struggled to believe it myself. What will matter? How will we hold on? What will we have left?

What’s left is a Ouroboros .

LLMs were trained on code written by human developers. Now LLMs are writing code. The next generation of LLMs will be trained on that code. Then we’ll use those new models to generate even more code. The loop continues.

In this cycle, shortcuts, security vulnerabilities, and technical debt will accumulate. Packages updated with low-quality, poorly controlled tests will lead to serious performance degradation, more data breaches, and more damage.

In the short term, this will generate serious profit. But in the medium term, it will lead to collapse. What will we say in five years? What will happen that we can’t even believe today?

What Should We Do? #

Right now, every developer needs to focus on preserving their skills. We’re losing them.

Use LLMs to accelerate your thinking process, not to get answers. Don’t ask for direct results; ask, “How should I think about this?”
Use LLMs as assistants in your research, not as managers. Make prototyping and architectural decisions yourself. Gain experience — don’t lose it.
Don’t lose your ability to write code. Don’t lose yourself in the ruthless, fast-paced business cycle. Keep your skills sharp on platforms like Codewars and LeetCode.
When learning something new, read documentation and source code. Let the models summarize for you, but write the proof of concept yourself.
Prioritize cybersecurity. Code generated by LLMs has a high likelihood of security vulnerabilities.
Focus on analyzing output. You need to be able to perform observability and performance analysis.

We’re learning how to forget to sharpen our tools. But we must not lose the muscle memory of sharpening.