Can we just pause for a second and talk about how completely weird it is to be a developer right now? Seriously, three years ago, if you told me my text editor would be finishing my sentences better than I could, I'd have thought you were talking about a sci-fi movie.
But here we are. AI—specifically these big-brained Large Language Models (LLMs) and coding assistants—has gone from being a cool gimmick to being the most important tool in my IDE. It hasn't replaced me (yet, ha!), but it has fundamentally changed how I approach a typical workday. It's like I finally got that legendary pair-programming partner who knows every library and never gets tired.
I wanted to share what my daily workflow actually looks like now, because it's probably way different than it was even a year ago.
1. The Code Completion Revolution: From Autocomplete to Auto-Function
The first and most obvious change is the coding itself. We used to have autocomplete that suggested variable names. Now, I have an assistant that writes entire functions based on my context.
I use a coding assistant—let's call it 'Copilot-esque' for simplicity—and it’s wild.
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Boilerplate is History: Need to set up a basic Express route handler? Start typing
app.get('/api/users', (req, res) => {and the whole thing fills in. Need a standard Redux selector? It nails it every time. I spend zero brainpower on the stuff I've written a thousand times before. -
The Comment Prompt: My favorite trick is writing a detailed comment first, hitting enter, and watching the AI spit out the code. For example:
// Function to calculate the Fibonacci sequence up to 'n' and cache results // in a map to prevent redundant calculations.BAM. Functional, memoized code appears. I still review it (always, always review it!), but getting a 90% complete, working draft in two seconds? That’s not a productivity boost; that’s a time warp.
The best part is that it keeps me in the flow. I'm not context-switching to Stack Overflow for a syntax I forgot or a common pattern. It's just there, inside my editor, keeping my focus locked on the high-level business logic.
2. Debugging and Explaining: My 24/7 Rubber Duck
Debugging used to be this painful, solitary process. Staring at an error message, copying it into a search engine, clicking through ten forum threads. Now, my process is a little simpler: I ask the AI.
I treat the general-purpose LLM (like ChatGPT or Claude) as my ultimate Rubber Duck Debugger.
- Error Analysis: I copy the stack trace, paste it in, and ask, "Why is this crashing my service?" The AI doesn't just give me the top Google result; it analyzes the trace, points to the likely file/line, and offers 3 plausible scenarios for the bug. Sometimes it suggests fixes that involve a library I didn't even know existed.
- Code Explanation: Diving into a complex, legacy chunk of code written by someone who left the company six months ago? I highlight the entire block, paste it into the AI chat, and ask, "Explain this code in simple terms." It summarizes the intent, identifies the inputs/outputs, and points out any potential "code smells" or confusing logic. This saves me hours of manual tracing. It’s like getting a dedicated code commentary on the spot.
This dramatically reduces the "time-to-understanding," which is one of the biggest silent killers of dev productivity.
3. Documentation: The Chore That Doesn't Exist Anymore
Let’s be honest: who loves writing JSDoc comments or updating internal technical docs? Nobody. It’s a necessary chore that usually falls to the bottom of the priority list, leading to an undocumented mess six months later.
AI has made me a good citizen of my codebase, not because I want to, but because it's effortless.
- Instant Docs: I write a function, and then I prompt my IDE assistant: "Generate docstrings for this function." It instantly produces beautiful, accurate, formatted comments detailing parameters, return values, and a short description.
- Commit Message Power-Ups: I use tools that analyze my staged changes and draft a detailed commit message for me. I usually have to tweak a word or two for tone, but going from zero to a perfectly formatted, informative commit message in a single click feels like cheating. This makes our Git history infinitely cleaner.
4. Architectural Brainstorming and Learning New Tech
This is where the power of the LLM shines beyond just code generation. It’s a strategic thinking partner.
I often jump between different tech stacks—React, Python/Django, some AWS Lambda stuff. The overhead of remembering all the specific syntax and best practices for each one used to be huge.
Now, I use AI to bridge the knowledge gap instantly.
- "How do I correctly set up a CORS policy for a GraphQL endpoint in a Django Rest Framework project?"
- "Write a simple Tailwind CSS utility class for a vertically centered flex container and explain the utility classes used."
It serves as a dynamic, personalized textbook. I'm not just copying the answer; I'm asking for the why and the best practice in the context of my current project. This speeds up my ability to jump into an unfamiliar task from days to hours.
The Catch: It’s an Assistant, Not a Boss
Despite all this, AI hasn't made coding boring or obsolete. It's just elevated the game.
Here's the critical human factor that remains:
- The Oversight: AI code can be wrong, inefficient, or downright insecure. If I treat it as gospel and don't review it, the bugs it introduces will be way harder to find because I assumed the tool was right. My job is still quality control and architectural design.
- The Context: The AI is only as good as the context it has. My senior-level skill is providing the right prompt and the right architectural constraints.
- The Creativity: It can’t define the truly new, innovative, and creative solution that solves a business problem in an entirely novel way. That still requires human insight, empathy, and abstract thinking.
In the end, AI has automated the drudgery of coding. It’s taken the "low-value" repetition off my plate and freed up my brain for the "high-value" problem-solving and architectural thinking.
My daily work hasn't gotten easier; it's gotten smarter and more focused on the hard problems. And honestly, that makes being a developer way more fun.
What about you? How has AI changed your daily dev life? Let me know!
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