Category: AI & AI Tooling

Staying in Software Dev? Best be able to just do things!

I sat down recently and started reading through some articles. Of all the articles of the 20 or so I was reading, one that stood out from the bunch was something Geoffrey Huntley wrote. In the article “The future belongs to people who can just do things” he brings up some points that I – and I think a LOT of people out there like Geoffrey and I – have been thinking in the preceding months. Let’s delve into a few of those thoughts, paraphrased, and elaborated on.

  • First and foremost, for those coders that have been making a living writing those artisanal, bespoke, hand crafted, single lines of thought out code – your time is nigh.
  • Second, if you’re one of those coders that churns out code, but you don’t care or don’t think about the bigger picture of the product you’re working on, you’re also in for a rude awakening.
  • Third, if you have your environment or your stack that you build with, and don’t explore much beyond that stack (i.e. you’re specialized in .NET or Java or iOS) and rarely touch anything outside of that singular stack, you’re running straight at a brick wall.
  • Fourth, if you’re pedantic about every line of code, every single feature, in a way that doesn’t further progress product but you love to climb up on that hill to die arguing about the state of the code base, you’re going to be left up on the hill to starve to death.

Beyond the developers. If you’re a technical product manager and can’t implement, or coder and can’t product, if you don’t understand the overlap then I’d bet you’ll run into some very serious issues in the coming years. If you’re a manager get ready to mitigate all of the above, and above all get ready to deal with a lot of upheaval. If you still focus on hiring the above focused and inflexible engineers, you’re likely to be falling on that sword for your team. Needless to say, there are very rough waters ahead.

That paints the picture of where the industry is right now and who is at greatest risk. Cast out and unable to realign and move forward with the industry – nay – the world in the coming weeks, months, and years. I’m not even going to mention why, what for, or how we got here. That’s a whole other article. I’m just going to focus on the now and the future.

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AI Prompt Engineering: Mastering Language Constructs

In the spirit of expanding upon the ideas laid out in Precision in Words, Precision in Code: The Power of Writing in Modern Development, I delve further into how the precision (where precise that is) of English. By extension I continue with the nuances of other language constructs which serves as a powerful tool when crafting prompts for AI systems. My exploration here, which is a few of the things I’ve discovered through deduction and some trial and error underscores the importance of choosing words with care. It also illuminates how language patterns can trigger distinct model behaviors.

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Precision in Words, Precision in Code: The Power of Writing in Modern Development

I’ve always found it advantageous to enjoy writing—yes, even documentation writing. It’s one of those foundational skills that doesn’t just make you a better developer; it makes you a better communicator, collaborator, and problem solver. In my experience, being able to articulate ideas, requirements, or solutions in written form isn’t just a nice-to-have, it’s a superpower.

Fast forward to today, and the value of this skill has grown exponentially. Why? Because the world of software development is no longer confined to our IDEs or command lines. With the advent of tools like Claude, ChatGPT, Copilot, TabNine, and Replit Ghostwriter, we’ve entered an era where your ability to write clearly and concisely isn’t just a productivity hack—it’s a core part of how we develop software.

Writing as the Bridge Between Ideas and Implementation

Every time you use ChatGPT to brainstorm API designs, or lean on Copilot to generate boilerplate code, you’re engaging in a collaborative act that requires clear articulation of your goals. Tools like these don’t “read your mind” (at least, not yet, and seem prudent they don’t!). They depend on your ability to communicate requirements effectively—whether that’s describing a function’s purpose, outlining a system’s architecture, or debugging a thorny issue.

Let’s break it down:

  • Problem Definition: Writing forces you to clarify what you’re solving. “Write me a REST API that does X” only works when X is well-defined. If you’re vague, the AI’s output will be vague too.
  • Code Refinement: With tools like Copilot, you can guide its suggestions by writing meaningful comments or even inline documentation. The better you describe your intention, the more useful its suggestions.
  • Collaboration: When sharing prompts, workflows, or generated code with teammates, clear writing makes the difference between helpful and confusing.

This isn’t just about documentation anymore; it’s about precision and intent.

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