Every week there’s a new “100 AI Tools You NEED” thread on Twitter. Most of them are demos pretending to be products.
I lead an engineering team doing real-time data processing. Here are the tools that actually survived more than a week in my workflow.
1. Claude Code — Daily Driver for Everything
What it does: Coding assistant that runs in your terminal.
How I actually use it:
- Pre-screen code reviews before I look at PRs
- Generate test cases from specs
- Debug production issues by pasting stack traces
- Write documentation that people actually read
Why it wins: It handles large context windows well. I can paste an entire file and ask specific questions about it. GPT-4 and Gemini are good too, but Claude Code’s terminal integration makes it frictionless.
Cost: Comes with Claude Pro ($20/mo) or use the API.
2. Cursor — When I Need to Write Code Myself
What it does: VS Code fork with AI built in.
How I actually use it:
- Tab-complete that actually understands my codebase
- Inline edits: highlight code → describe what to change
- Chat with codebase context (it indexes your project)
Why I use both Claude Code and Cursor: Claude Code for planning, reviewing, and generating. Cursor for writing and editing. Different modes of work.
3. Notion AI — Meeting Notes and Docs
What it does: AI features inside Notion.
How I actually use it:
- Summarize meeting transcripts into action items
- Draft technical RFCs from bullet points
- “Improve writing” on docs before sharing with stakeholders
Honest take: It’s not revolutionary, but it’s convenient because everything’s already in Notion.
4. GitHub Copilot — The Background Assistant
What it does: Code autocomplete in your editor.
How I actually use it:
- Boilerplate code (API endpoints, data models)
- Writing similar patterns across files
- Unit test generation
Note: I’ve reduced my Copilot usage since getting better with Claude Code. But for quick inline completions, it’s still fast.
5. Perplexity — Research Without 20 Tabs
What it does: AI-powered search with citations.
How I actually use it:
- “What’s the current best practice for X in Python?”
- Researching vendor tools before team decisions
- Quick competitive analysis
Why not just Google? Because I want an answer with sources, not 10 SEO-optimized blog posts saying the same thing.
6. Granola — Meeting Intelligence
What it does: Records meetings and generates structured notes.
How I actually use it:
- Auto-generates action items from 1:1s
- Searchable history of what was discussed and decided
- Catches things I missed during the meeting
Game changer for: Sprint retros and cross-team syncs where there’s a lot of context flying around.
7. Linear + AI — Project Tracking
What it does: Project management with AI features.
How I actually use it:
- Auto-triage incoming bugs
- Generate sub-tasks from epics
- Smart duplicate detection
What I Tried and Dropped
- Jasper — Too marketing-focused. Not useful for technical content.
- Otter.ai — Good transcription but Granola’s structure is better for my workflow.
- Bard/Gemini — Fine for general questions, but Claude and GPT-4 are better for code.
- Devin — Not ready for production codebases. Maybe next year.
The Real Productivity Hack
Tools don’t matter if you don’t have the habit. My actual system:
- Morning: Review PRs with Claude Code pre-screening
- Meetings: Granola running in background
- Coding: Cursor + Copilot
- Research: Perplexity
- Docs: Notion AI for polish
Total time saved: roughly 8-10 hours/week. That’s an entire extra workday.
This post will be updated quarterly as tools evolve. Last updated: February 2026.