AI Developer Tools
Most engineers know some AI tools. Few know where each fits in a real build flow.
I mapped the tools, from research → UI → backend → prototypes → deployment. The goal wasn’t to list every product, but to show where AI helps you ship real software, not just write code snippets.
Here’s the thinking that shaped it -
🔎 Answer & research tools help you explore unfamiliar tech, compare patterns, and avoid bad paths early.
🎨 Design tools accelerate screen design, not just mockups, but layouts ready for dev handoff.
⚡ Prototyping tools turn text prompts into clickable UIs and working backend logic fast.
👩 AI coding assistants live in your editor and keep you productive without context switching.
🔄 Automation tools stitch systems together without glue code.
📄 Docs/knowledge tools let you use your private codebase as a searchable source.
🚀 Deployment tools get prototypes live quickly, no complicated CI required.
Why this matters now:
AI isn’t a sidekick anymore, it's core to how teams build, iterate, and ship. But chaos sets in fast if we treat every new tool as equally valid.
This stack helps you:
✔ Know when to use which tool
✔ Avoid redundancy in your workflow
✔ Make AI part of a predictable build process
Example Project - Tools Used
Goal: Build + deploy a simple AI-powered task management web app - CRUD tasks + login + analytics
1. Research & Planning
Tools: ChatGPT, Claude, Perplexity, Gemini
How they’re used:
- Use Perplexity to research best practices for task-management UX and current SaaS patterns.
- Use ChatGPT / Claude to:
- Clarify requirements
- Compare tech stack options
- Draft a high-level system architecture
- Use Gemini to summarize trade-offs between authentication approaches (JWT vs session-based).
Outcome:
A clear scope, tech stack decision (React + Supabase + Vercel), and feature list.
2. UI & Design
Tools: Figma, Uizard, Builder.io
How they’re used:
- Use Uizard to generate initial screens from text prompts (“Create a task dashboard with sidebar navigation”).
- Refine layouts and components in Figma.
- Optionally sync designs to Builder.io for faster UI iteration.
Outcome:
Wireframes and responsive UI designs ready for development.
3. Frontend Development
Tools: Webflow, v0
How they’re used:
- Use v0 to generate React components directly from UI descriptions.
- Use Webflow (if applicable) for static marketing pages (landing page, pricing).
Outcome:
Functional frontend components with routing and basic client logic.
4. Backend Development
Tools: Supabase, Firebase, Baserow
How they’re used:
- Use Supabase for:
- Database tables (tasks, users)
- Authentication (email/password)
- API access
- Optionally use Baserow for quick internal admin views.
Outcome:
Secure backend with auth, APIs, and persistent data.
5. Prototyping & Iteration
Tools: Replit, Bolt, Lovable
How they’re used:
- Use Replit to spin up a quick sandbox for testing logic.
- Use Lovable to generate a functional prototype from prompts.
- Validate flows before full deployment.
Outcome:
Rapid iteration with minimal setup friction.
6. Core Development (Coding)
Tools: Cursor, Windsurf, GitHub Copilot
How they’re used:
- Cursor for AI-assisted coding directly inside the editor.
- GitHub Copilot for autocomplete, refactoring, and boilerplate.
- Windsurf for context-aware suggestions across files.
Outcome:
Faster, cleaner code with reduced manual overhead.
7. Automation & Workflows
Tools: n8n, Make, CrewAI
How they’re used:
- Use n8n to automate:
- Daily email summaries of completed tasks
- Webhooks when tasks are created or updated
- Use Make to connect third-party tools (Slack, email).
- Optionally use CrewAI for multi-agent task planning.
Outcome:
Automated workflows without writing custom glue code.
8. Documentation
Tools: Claude Projects, Flowise
How they’re used:
- Use Claude Projects to:
- Generate README files
- Create API documentation
- Use Flowise to create an internal knowledge chatbot trained on the project docs.
Outcome:
Clear, maintainable documentation for users and developers.
9. Deployment
Tools: Vercel, Netlify, Railway
How they’re used:
- Deploy frontend to Vercel.
- Host backend services or workers on Railway.
- Use Netlify for static assets if needed.
Outcome:
Live, production-ready application accessible via URL.
10. Analytics & Improvement
Tools: (Not shown but implied, e.g., PostHog)
How they’re used:
- Track user behavior
- Identify feature drop-off
- Guide iteration based on real usage
Outcome:
Data-driven improvements instead of guesswork.
SUMMARY:
A software engineer uses these AI tools to move seamlessly from idea → design → code → deploy → automate → optimize, dramatically reducing development time while increasing quality and scalability.
🟰🟰 UX | UI 🟰🟰
UX (User Experience) and UI (User Interface) design are distinct but tightly interconnected disciplines, each addressing a different layer of how users interact with a product.
UX design focuses on the overall experience a user has with a product—how intuitive it is, how efficiently users can accomplish tasks, and how the product solves real problems. It involves research, user journeys, information architecture, usability testing, and iterative refinement to ensure the experience is logical, frictionless, and aligned with user needs.
UI design focuses on the visual and interactive layer—the layout, typography, color, buttons, icons, and visual feedback that users directly interact with. It ensures the interface is clear, aesthetically consistent, and usable across devices.
The tie between UX and UI
UX defines what the product should do and how users should move through it; UI defines how that experience is presented and felt. Strong UX without good UI feels confusing or unfinished, while strong UI without solid UX looks polished but fails in real-world use. Together, they ensure a product is both functionally effective and visually intuitive, turning user needs into clear, engaging, and usable digital experiences.
