AI Tools for Web Development: My Current Stack

My AI Journey

I was skeptical when GitHub Copilot launched. Another tool promising to revolutionize development? I'd heard that before. But after two years of daily use, AI tools have genuinely changed how I work. Not in the "AI writes all my code" way the hype suggested, but in practical, time-saving ways.

This isn't a comprehensive review of every AI tool available—it's an honest look at what's actually stuck in my workflow after extensive experimentation. Some tools lived up to the hype. Many didn't.

Code Assistance: What I Use Daily

These tools have earned permanent spots in my workflow:

GitHub Copilot

My primary inline coding assistant. What it does well:

• Autocomplete on steroids: Finishes functions, suggests implementations
• Boilerplate generation: Repetitive code patterns completed instantly
• Context awareness: Understands your codebase and coding style
• Documentation: Generates docblocks and comments

What it doesn't do well:

• Complex architecture decisions
• Understanding business logic nuances
• Code that requires deep domain knowledge
• Security-sensitive implementations (always review these carefully)

Claude (via API and Claude.ai)

My tool for complex problems and code review:

• Code explanation: Understanding unfamiliar codebases
• Debugging assistance: Working through tricky issues
• Architecture discussion: Thinking through design decisions
• Refactoring guidance: Improving existing code
• Learning: Understanding new technologies and patterns

How I Use Them Together

Copilot handles the typing; Claude handles the thinking:

• Start a new feature: Discuss approach with Claude first
• Write the code: Copilot assists with implementation
• Hit a roadblock: Ask Claude to help debug or reconsider approach
• Code review: Claude analyzes for issues I might miss

Image Generation: A Game Changer

This is where AI has most exceeded my expectations for web work:

Hero Images and Blog Graphics

I used to spend significant time (and client budget) on stock photo searches or custom photography. Now:

• Generate unique hero images in minutes
• Create consistent visual style across a site
• Produce images that actually match content (no more "close enough" stock photos)
• Iterate quickly when a concept isn't working

My Current Tools

• Midjourney: Best quality for artistic and photorealistic images
• DALL-E 3: Excellent for concept illustrations and diagrams
• Stable Diffusion (local): For projects requiring data privacy

Practical Applications

• Article hero images (like the one on this post)
• Social media graphics
• Placeholder images during development
• Mockup backgrounds and textures
• Icon and illustration concepts

What Doesn't Work

• Images with specific text (still unreliable)
• Exact brand representations
• Photos of real people (ethical and legal concerns)
• Technical diagrams requiring precision

Writing and Content Assistance

AI writing tools are useful but require more oversight than coding tools:

What Works

• First drafts: Getting words on the page when staring at blank screen
• Outline generation: Structuring thoughts before writing
• Editing assistance: Grammar, clarity, conciseness
• Format conversion: Turning notes into structured content
• Meta descriptions: Generating SEO summaries quickly

What Doesn't Work

• Voice and personality: AI writing sounds generic without heavy editing
• Original insights: It synthesizes existing ideas, doesn't create new ones
• Technical accuracy: Requires verification, especially for code examples
• Publish-ready content: Always needs human review and revision

My Approach

I use AI as a starting point, not an endpoint:

• Generate rough outline based on my topic
• Write key sections myself (the parts requiring expertise)
• Use AI to fill in standard explanations and transitions
• Heavy editing pass to add voice and verify accuracy
• Final human review before publishing

Tools I've Dropped

Not every AI tool survives real-world testing. Here's what didn't stick:

AI Website Builders

Several tools promise to build entire websites from prompts. Reality:

• Output requires significant cleanup
• Customization is often harder than starting fresh
• Generated code quality is questionable
• Doesn't understand real business requirements

Verdict: Useful for quick prototypes, not production sites.

AI-Powered Design Tools

Tools claiming to generate complete UI designs:

• Designs lack coherent visual system
• Don't understand brand guidelines
• Accessibility is usually an afterthought
• Still need designer to make it usable

Verdict: Interesting for inspiration, not ready for production.

Automated Code Review Bots

AI that reviews pull requests automatically:

• Lots of false positives
• Misses context-dependent issues
• Creates noise in PR discussions
• Human review still necessary anyway

Verdict: More hassle than help for my projects.

AI Meeting Transcription (for coding context)

Promised to turn meeting notes into technical specs:

• Transcription quality varies wildly
• Extracting requirements still requires human interpretation
• False confidence in "automated" documentation

Verdict: Regular transcription is useful; AI interpretation isn't reliable enough.

Workflow Integration Tips

Making AI tools productive requires intentional integration:

Start Small

• Add one tool at a time
• Use it consistently for two weeks before judging
• Track actual time saved, not perceived helpfulness
• Be willing to drop tools that don't deliver

Set Boundaries

• Define what you'll use AI for vs. do manually
• Establish review processes for AI-generated content
• Know when to stop prompting and just write the code
• Don't let AI tools become procrastination disguised as productivity

Maintain Skills

• Still write code without AI assistance regularly
• Understand what the AI is generating
• Don't accept code you can't explain
• Keep learning—AI doesn't replace fundamentals

Cost Analysis

Here's what my AI tool stack costs monthly:

• GitHub Copilot: $19/month
• Claude Pro: $20/month
• Midjourney: $30/month (Standard plan)
• Total: ~$70/month

Is It Worth It?

For my workflow, easily. Consider:

• Time saved on coding: 3-5 hours/month minimum
• Stock photo costs avoided: $100-300/month
• Faster project delivery: Better client relationships
• Reduced tedium: Better work satisfaction

At my billing rate, the tools pay for themselves if they save just one hour of work per month. They save considerably more than that.

What's Next

The AI tooling landscape evolves rapidly. What I'm watching:

Improvements I Expect

• Better context awareness across entire projects
• More reliable code generation with fewer errors
• Improved integration between tools
• Better understanding of business requirements

What I'm Testing

• Local LLMs for privacy-sensitive projects
• AI-powered testing tools
• Automated documentation generation
• Voice-to-code interfaces

What I'm Skeptical About

• "AGI" promises from tool vendors
• Claims of fully automated development
• AI replacing developer judgment
• Tools that try to do everything

The Bottom Line

AI tools have earned a place in my development workflow—but not the place the hype suggested. They're productivity enhancers, not productivity replacements. They handle tedious tasks well and assist with complex ones, but they don't eliminate the need for developer expertise and judgment.

The developers who benefit most from AI tools are those who understand what they're building, can evaluate AI output critically, and use these tools to accelerate their existing skills rather than substitute for skills they lack.

My advice: experiment broadly, adopt cautiously, and always verify. The tools that survive your real-world testing are the ones worth keeping.