I’m curious about how other developers are integrating AI into their everyday web development work. I’ve been coding for several years now and there’s always something new to master, especially with all these AI assistants popping up everywhere.
Lately I’ve been experimenting with AI for creating template code, troubleshooting strange CSS bugs, and improving my HTML structure. Sometimes the suggestions are spot on, other times they’re hilariously wrong but still helpful for brainstorming.
Are you mainly using these tools to work faster, pick up new libraries, or just as a second opinion when you’re stuck? I have to admit though, sometimes the AI gives really bad advice instead of useful solutions.
Does anyone else wonder if these tools can actually keep pace with how quickly web technologies evolve?
Claude and Cursor are game-changers for my workflow - especially when I’m debugging messy state management or refactoring old codebases. The biggest win? I can paste confusing code and get a breakdown of what it actually does. Saves me tons of time when jumping between projects or dealing with third-party libraries. I’ve also started using AI for unit tests (used to hate writing those). The tests aren’t perfect, but they give me a solid starting point. That said, I don’t trust AI for security stuff or performance tweaks - those still need human eyes and proper testing.
AI’s been a game-changer for regex patterns and complex CSS selectors. I used to hate those, but now I just paste them in and get a clear breakdown. I also use it constantly for converting between JS frameworks - like turning jQuery code into vanilla JS. It’s not perfect, but beats spending hours on Google trying to figure it out myself.
I use AI mainly for diving into unfamiliar codebases and understanding architectural decisions. When I inherit a project or join a new team, I’ll dump code chunks into AI and ask it to explain the patterns and design choices. It’s like having a senior dev walk you through everything, even if they occasionally make stuff up.
I also use it as a rubber duck for system design. Before building major features, I’ll describe my approach and ask what could go wrong or if there’s a better way. It’s pretty good at catching scaling issues or suggesting cleaner ways to separate concerns.
AI’s great for cross-language translation when migrating between tech stacks. I recently moved a Python API to Node.js and AI helped translate syntax and idiomatic patterns between both ecosystems. Still had to double-check everything, but it sped up the migration a lot.
The training data lag sucks, but AI’s still useful for fundamentals that don’t change much. Database optimization, algorithm selection, general architecture principles - that stuff works well even when the specific framework syntax is outdated.
Been using GitHub Copilot for eight months now and it’s completely changed how I handle repetitive tasks. The autocomplete is surprisingly solid for boilerplate and API integrations, but it falls apart with newer frameworks that weren’t in its training data. What really surprised me was using AI for code reviews. I’ll dump problematic functions into ChatGPT and ask it to spot issues or suggest optimizations. It’s not always right, but it catches stuff I miss after staring at code for hours. The fast pace of web dev is definitely a problem though. AI keeps suggesting outdated React patterns or deprecated JavaScript methods constantly. I always double-check against current docs anyway, which kills the speed benefits sometimes.
AI’s been a game-changer for database optimization and API design. When I’m stuck with slow queries or complex SQL joins, I dump the execution plan and ask for help. It’s crazy good at catching missing indexes and suggesting better structures. One surprise win - using it for accessibility audits. I feed it my HTML and ask about ARIA labels, semantic structure, keyboard nav issues. It finds stuff automated tools miss and explains why certain patterns break screen readers. The biggest breakthrough? Technical debt assessment. I throw messy legacy code at it and ask for maintainability ratings or refactoring priorities. Perfect for making the business case since it explains risks in terms non-tech people actually get. For the outdated training data problem - I’ve learned to get specific with versions. Don’t ask about React hooks, ask about React 18 hooks with concurrent features. Forces it to be more careful about what it suggests.
My biggest win? Using AI for documentation and getting new devs up to speed. We’ve got this massive legacy codebase that’s been around forever - onboarding juniors used to be a nightmare.
Now I dump our ugliest functions into Claude or GPT for plain English breakdowns. Quick review and corrections on my end. What used to be 3-hour explanation marathons? Down to 30-minute walkthroughs.
I flip the typical AI workflow too. Instead of asking it to write code, I paste my working solutions and ask “what edge cases am I missing?” or “how’s this gonna break in prod?” It’s scary good at catching potential memory leaks or race conditions I totally missed.
The stale training data problem is real though. I don’t trust AI with recent framework stuff anymore. But core JavaScript, debugging logic, architecture brainstorming? Rock solid.
Weird use case - I have AI rewrite my code comments to actually make sense. Turns out my 2am comments are garbage, but AI can turn “this fixes the weird thing” into documentation that doesn’t make me want to cry six months later.
I’ve moved past using AI for basic coding suggestions. The real game-changer is automating your entire dev workflow.
Why manually feed code to ChatGPT or wait for Copilot? I built pipelines that handle everything - code generation, testing, deployment. Client wants changes? My system generates boilerplate, runs tests, and updates docs automatically.
Connect your AI tools with your whole stack. My workflows monitor repos, refactor code when dependencies update, and generate unit tests for new functions. No more copy-pasting into Claude or fixing deprecated patterns by hand.
For debugging, I automated error analysis that catches issues before production. It analyzes stack traces, suggests fixes, and creates pull requests with solutions.
This fixes your problem with AI lagging behind web tech. Don’t rely on outdated training data - build workflows that pull from current docs and adapt to new frameworks automatically.
Time savings are insane. Hours of back-and-forth with AI assistants now take minutes without touching anything.
I’ve been exploring similar ideas and it’s interesting to see how much AI is starting to influence web development workflows. Like you, I’ve tested AI assistants for generating quick starter code, fixing CSS quirks, and even refining layouts. Sometimes the output speeds things up a lot, but other times it introduces odd suggestions that require more debugging than they save.
I’m curious, how are you balancing AI’s role in your development process? Do you treat it as just a time-saver, a teaching tool for learning new frameworks, or more of a brainstorming partner?
While experimenting with these approaches, I’ve also been working on projects around website design, and it makes me wonder how other developers see AI fitting into their daily workflow.