I’ve been using AI tools professionally for over two years now. In that time, I’ve tried literally everything — every IDE integration, every API, every no-code builder, every agent framework. Most were hype. A few were genuinely life-changing.
Here’s my actual daily toolkit as of March 2026, with honest assessments of each tool’s strengths and limitations.
1. Claude Code — The Main Workhorse
What I use it for: Feature development, bug fixing, code review, refactoring, documentation
Time spent: 4-6 hours/day
Claude Code has become my primary development tool. Not a supplementary tool — the primary one. Here’s a typical workflow:
- I describe the feature I want to build
- Claude Code analyzes the existing codebase
- It proposes an architecture
- I approve (or modify) the plan
- It implements, writes tests, and creates a PR
What it does best: Complex multi-file changes, understanding large codebases, following project conventions (via CLAUDE.md), writing comprehensive tests
Limitation: Can occasionally over-engineer simple things. Sometimes you need to say “just write a simple function, don’t create an entire abstraction layer.”
2. GitHub Copilot — The Inline Assistant
What I use it for: Autocomplete, small code completions, repetitive patterns
Time spent: Always on in VS Code
Copilot doesn’t do the heavy thinking. But for typing speed and pattern completion, it’s unbeatable. When I’m writing boilerplate — imports, type definitions, repetitive test cases — Copilot predicts what I want before I finish typing.
What it does best: Fast inline completions, pattern matching from context, reducing keystrokes on repetitive code
Limitation: Suggestions are sometimes confidently wrong. You need to read what it suggests, not just Tab-accept blindly.
3. Gemini (Google AI Studio) — The Free Research Tool
What I use it for: API research, documentation lookup, exploring new libraries, prototyping ideas
Time spent: 30-60 minutes/day
Gemini’s killer feature for developers is the grounding with Google Search. When I’m researching a new library or trying to find the right API endpoint, Gemini searches the web and provides answers with citations. This is faster than manually searching documentation.
What it does best: Finding current documentation, researching libraries, explaining unfamiliar code, free unlimited API for prototyping
Limitation: Code generation quality is below Claude. I use it for research, not implementation.
4. Cursor — The Visual AI Editor
What I use it for: Quick edits, visual code navigation, pair programming on specific files
Time spent: 1-2 hours/day
When I need to make targeted changes to specific files and want to see the diff visually before applying, Cursor is excellent. Its Cmd+K inline editing and the Composer feature for multi-file edits are smooth and intuitive.
What it does best: Visual diff review, targeted file edits, quick refactors within a single file
Limitation: For large-scale changes across many files, Claude Code is more powerful. Cursor works best for focused, file-level edits.
5. ChatGPT — The Quick Question Box
What I use it for: Quick questions, explaining error messages, brainstorming approaches, rubber duck debugging
Time spent: 15-30 minutes/day
ChatGPT is my quick-answer tool. When I get a cryptic error message, I paste it into ChatGPT and get an explanation in seconds. When I need to brainstorm three different approaches to a problem, ChatGPT gives quick options without the overhead of setting up a Claude Code session.
What it does best: Fast answers, explaining errors, brainstorming, general programming knowledge
Limitation: For actual code implementation, Claude is significantly better. ChatGPT is for thinking, not building.
6. n8n — The Automation Engine
What I use it for: Workflow automation, webhook processing, scheduled tasks, integrating services
Time spent: 2-3 hours/week (mostly maintenance)
n8n runs my automations — deployment notifications, error alerting, data sync between services, and scheduled reports. It’s self-hosted, which means no vendor lock-in and no per-execution pricing.
What it does best: Complex multi-step automations, AI-powered workflows (integrates with all major AI APIs), self-hosted reliability
Limitation: Steeper learning curve than Zapier. Worth it for power users, overkill for simple automations.
7. Playwright + AI — Testing Automation
What I use it for: End-to-end testing, visual regression testing, automated QA
Time spent: 2-3 hours/week
I use Playwright for automated testing, often driven by Claude Code via the MCP Playwright server. Claude writes the tests, Playwright runs them, and I review the results. This combination has cut my QA time by 70%.
My Typical Day Using These Tools
- 9:00 AM: Open Claude Code, review yesterday’s PR feedback, make adjustments
- 9:30 AM: Start new feature — describe to Claude Code, review architecture
- 10:00 AM – 12:00 PM: Implementation loop — Claude Code builds, I review and guide
- 12:00 PM: Quick ChatGPT session to brainstorm solutions for a tricky problem
- 1:00 PM: Cursor for targeted edits and visual review of changes
- 2:00 PM: Gemini for researching a new library we’re considering
- 3:00 PM – 5:00 PM: More Claude Code implementation + PR creation
- 5:00 PM: Review automated test results from Playwright
People Also Ask
How much does this AI toolkit cost?
Claude Pro: $20/month. Cursor Pro: $20/month. GitHub Copilot: $10/month. Gemini: Free. ChatGPT Plus: $20/month. n8n: Free (self-hosted). Total: about $70/month — less than one hour of my billable time.
Can junior developers use these tools?
Absolutely, but with a caveat: you need enough knowledge to evaluate AI-generated code. Blindly accepting AI suggestions without understanding them is dangerous. Use AI to accelerate learning, not to skip it.
Do these tools actually save time?
Conservatively, I estimate 2-3x productivity improvement. Tasks that used to take a day take a few hours. The biggest savings come from Claude Code handling boilerplate and Copilot reducing typing time.
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