DEV Community: Alijah Konikowski

From My Desk: Does AI Actually Make Me a Better Developer?

Alijah Konikowski — Sat, 09 May 2026 13:08:44 +0000

From My Desk: Does AI Actually Make Me a Better Developer?

Introduction

Okay, let's be honest. As a developer, the thought of “AI taking our jobs” has been bouncing around in my head for a while now. I’ve seen the headlines, read the breathless predictions, and frankly, felt a little unnerved. I've been experimenting with AI tools – primarily large language models like ChatGPT and GitHub Copilot – for about six months now, and the initial fear was... well, it was surprisingly unfounded. It's not about less work, or even more work in the traditional sense. It’s... shifted. It’s fundamentally changed how I work. I wanted to share my experience, because I think a lot of us are grappling with this, and it’s not a simple yes or no answer.

I'm not a senior architect doing complex system design (yet!). I’m a full-stack developer, comfortable with JavaScript, React, and a healthy dose of Python. My typical day involves a mix of bug fixing, feature development, documentation, and a lot of rubber-ducking (talking through problems aloud). Before AI, I’d often spend a frustrating amount of time staring at a screen, wrestling with a tricky logic problem or trying to decipher a cryptic error message. Now, those moments are different. I'm not saying I'm instantly brilliant, but the AI provides a crucial starting point, a helpful partner in the problem-solving process.

Core Concepts: It’s Not Replacement, It’s Augmentation

The biggest thing to understand is that AI isn't a replacement for a developer’s core skills – critical thinking, problem decomposition, understanding of algorithms, and the ability to learn new technologies. Instead, it's an augmentation tool, much like a really, really good pair programmer.

Here’s how I’ve found it breaks down:

Brainstorming & Idea Generation: Stuck on a design decision? AI can rapidly generate multiple options and highlight potential trade-offs. It’s not always right, but it quickly expands your thinking beyond your initial constraints.
Code Generation (with caveats): This is the most hyped area, and it's true – AI can generate snippets of code. However, blindly copying and pasting is a recipe for disaster. It’s more valuable to use it as a starting point, a template to understand the intent behind the code. Then, you need to meticulously review, test, and adapt it to your specific context.
Documentation & Explanations: Struggling to understand a complex library or API? AI excels at summarizing documentation and providing simplified explanations. This significantly reduces the time spent hunting for information.
Debugging Assistance: Paste your error messages and relevant code snippets, and AI can often identify the root cause or suggest potential solutions. It’s become a surprisingly reliable early-stage debugging tool.
Refactoring Suggestions: AI can often spot opportunities for code simplification and suggest refactoring improvements – though you should always critically evaluate these recommendations.

Here's a simple example of how I might use it:

# Original code
def calculate_average(numbers):
  total = 0
  for number in numbers:
    total += number
  return total / len(numbers)

# Prompt to AI: "Suggest improvements to this Python function to make it more concise and readable."
# AI response: "You can use the `sum()` and `len()` functions for a more concise implementation."
# Improved code:
def calculate_average(numbers):
  return sum(numbers) / len(numbers)

Practical Example: A Time-Saving Win

Recently, I was tasked with integrating a new payment gateway into my React application. I'd worked with similar gateways before, but this one had a particularly complex API with several different endpoints for handling different transaction types. I spent a good chunk of time just trying to understand the documentation and build the initial integration.

I used ChatGPT to summarize the API documentation and generate a basic code structure for handling a simple transaction. It wasn’t perfect – the initial code had a few minor errors – but it saved me at least two hours of manual work just getting the basic framework in place. More importantly, the AI explained the rationale behind the code it generated, which helped me understand the API better. I then refactored and tested the code, ensuring it met my specific requirements.

The real value wasn't in the AI generating the entire integration, but in acting as a catalyst to accelerate my understanding and initial setup. It reduced the cognitive load of initially grappling with unfamiliar API details. I still had to think critically, test rigorously, and adapt the code to my application's architecture.

Conclusion

So, does AI make me work less? Not in the sense of fewer hours. But it does make me work differently. The initial fear of automation replacing developers is, I believe, largely overstated. Instead, we're entering an era of augmented intelligence, where AI handles the tedious, repetitive tasks, freeing up our time and mental energy to focus on the more strategic and creative aspects of our work – designing elegant solutions, understanding user needs, and ultimately, building better software. It’s not about doing less; it’s about doing better. And honestly? That's a pretty exciting prospect. I anticipate this trend will only accelerate, and I’m looking forward to seeing how we, as developers, adapt and leverage these powerful tools to shape the future of software development.

From My Experience: Does AI Make Me Work Less, More, or About the Same?

Alijah Konikowski — Sat, 09 May 2026 05:42:34 +0000

From My Experience: Does AI Make Me Work Less, More, or About the Same?

Introduction

As a technical writer, I’ve been experimenting with AI tools – primarily large language models (LLMs) like GPT-4 – for several months now. Initially, I approached it with a healthy dose of skepticism. Could a machine really help me write more efficiently? The short answer is: it’s complicated. It hasn’t dramatically reduced my workload, but it’s fundamentally shifted how I work, creating a noticeable increase in output and a slightly different kind of cognitive load. It’s not less work, but it’s demonstrably different work. This article will detail my experience, breaking down how AI has impacted my daily tasks and offering a realistic perspective for developers and other knowledge workers.

Core Concepts

The key isn't that AI is a replacement for human creativity or critical thinking. It's a powerful augmentation tool. Here's a breakdown of the ways AI has affected my workflow:

Reduced Initial Draft Time: Previously, starting a technical article required significant research, outlining, and generating a rough first draft. AI excels at generating these initial drafts based on a prompt. I’ve found that providing a concise prompt outlining the topic, target audience, and desired tone can produce a usable draft in minutes, compared to potentially hours.
Enhanced Research: AI can quickly synthesize information from multiple sources. Instead of manually searching through documentation, blogs, and articles, I can ask it to summarize key concepts or identify relevant examples.
Improved Clarity and Conciseness: AI can help refine writing, identifying areas where phrasing is clunky or overly verbose. I use it to rephrase sentences and paragraphs, ensuring clarity and readability.
Shifted Focus – Editing & Validation: The biggest change is that the bulk of the production time is now dedicated to editing, fact-checking, and validating the AI-generated content. The AI provides a starting point; my role is to polish and ensure accuracy.
Cognitive Overhead Adjustment: Initially, there was a significant cognitive load associated with prompt engineering – figuring out how to phrase requests to get the desired output. I’ve become better at crafting effective prompts over time, reducing this initial overhead.

Practical Example

Let’s consider a recent article I needed to write on "Implementing OAuth 2.0 in a Node.js Application." Without AI, this would have involved:

Research: Spending 2-3 hours researching OAuth 2.0 concepts, libraries, and best practices.
Outline: Creating a detailed outline with sections on registration, authorization, token exchange, and refresh tokens.
Drafting: Writing approximately 3-4 hours of content, including explanations, code snippets, and example scenarios.
Review & Editing: 1-2 hours refining the content for clarity, consistency, and technical accuracy.

Using an LLM, this process changed to something like this:

Prompt: “Write a technical article explaining how to implement OAuth 2.0 using Passport.js in a Node.js application. Target audience: JavaScript developers with intermediate experience. Include sections on registering an application, handling authorization codes, exchanging authorization codes for access tokens, and refreshing access tokens. Include a code snippet demonstrating token exchange.”
AI Generation: The AI produced a roughly 700-word draft in 15-20 minutes.
Editing & Validation: I spent approximately 1.5 - 2 hours reviewing the draft, adding clarifying comments, correcting minor errors, ensuring the code snippet was accurate and up-to-date (using npm outdated), and adding a brief conclusion.

The total time investment was reduced from roughly 6-8 hours to approximately 3.5 hours. Crucially, the quality of the initial draft was high enough that it served as an excellent foundation for my edits.

// Example Token Exchange (Simplified - Requires actual implementation)
// This is just to show the conceptual flow.
const accessToken = await exchangeCode(authorizationCode, clientSecret);

Conclusion

AI isn’t a silver bullet that eliminates work. It’s a tool that fundamentally alters the nature of the work. I'm not working less overall, but I’m working differently. My time is now spent more strategically – on tasks requiring higher-level cognitive skills like critical thinking, validation, and ensuring accuracy – rather than on repetitive, lower-value activities like generating initial drafts.

The initial learning curve associated with prompt engineering is real, and the responsibility for ensuring the AI-generated content is accurate and reliable rests squarely on the human user. However, for knowledge workers like technical writers, the potential benefits in terms of increased efficiency and output are substantial. As AI models continue to evolve, I anticipate this shift in workload will only become more pronounced, requiring a proactive approach to learning how to effectively leverage these tools and adapt our workflows accordingly. The future of writing, and likely many other professions, isn't about being replaced by AI, but about learning to collaborate with it effectively.

From My Experience: Does AI Make Me Work Less, More, or About the Same?

Alijah Konikowski — Sat, 09 May 2026 05:19:19 +0000

From My Experience: Does AI Make Me Work Less, More, or About the Same?

Introduction

Core Concepts

The key isn't that AI is a replacement for human creativity or critical thinking. It's a powerful augmentation tool. Here's a breakdown of the ways AI has affected my workflow:

Reduced Initial Draft Time: Previously, starting a technical article required significant research, outlining, and generating a rough first draft. AI excels at generating these initial drafts based on a prompt. I’ve found that providing a concise prompt outlining the topic, target audience, and desired tone can produce a usable draft in minutes, compared to potentially hours.
Enhanced Research: AI can quickly synthesize information from multiple sources. Instead of manually searching through documentation, blogs, and articles, I can ask it to summarize key concepts or identify relevant examples.
Improved Clarity and Conciseness: AI can help refine writing, identifying areas where phrasing is clunky or overly verbose. I use it to rephrase sentences and paragraphs, ensuring clarity and readability.
Shifted Focus – Editing & Validation: The biggest change is that the bulk of the production time is now dedicated to editing, fact-checking, and validating the AI-generated content. The AI provides a starting point; my role is to polish and ensure accuracy.
Cognitive Overhead Adjustment: Initially, there was a significant cognitive load associated with prompt engineering – figuring out how to phrase requests to get the desired output. I’ve become better at crafting effective prompts over time, reducing this initial overhead.

Practical Example

Let’s consider a recent article I needed to write on "Implementing OAuth 2.0 in a Node.js Application." Without AI, this would have involved:

Research: Spending 2-3 hours researching OAuth 2.0 concepts, libraries, and best practices.
Outline: Creating a detailed outline with sections on registration, authorization, token exchange, and refresh tokens.
Drafting: Writing approximately 3-4 hours of content, including explanations, code snippets, and example scenarios.
Review & Editing: 1-2 hours refining the content for clarity, consistency, and technical accuracy.

Using an LLM, this process changed to something like this:

Prompt: “Write a technical article explaining how to implement OAuth 2.0 using Passport.js in a Node.js application. Target audience: JavaScript developers with intermediate experience. Include sections on registering an application, handling authorization codes, exchanging authorization codes for access tokens, and refreshing access tokens. Include a code snippet demonstrating token exchange.”
AI Generation: The AI produced a roughly 700-word draft in 15-20 minutes.
Editing & Validation: I spent approximately 1.5 - 2 hours reviewing the draft, adding clarifying comments, correcting minor errors, ensuring the code snippet was accurate and up-to-date (using npm outdated), and adding a brief conclusion.

// Example Token Exchange (Simplified - Requires actual implementation)
// This is just to show the conceptual flow.
const accessToken = await exchangeCode(authorizationCode, clientSecret);