🚀 5 AI Tools That Saved Me 20+ Hours Last Month

(And Why I’ll Keep Using Them in My Dev Workflow)

Let’s be real — there are way too many “must-try AI tools” out there.
But as a developer juggling content, side projects, freelance, and shipping code — I care about speed and sanity.

These are 5 AI tools that actually earned a spot in my daily workflow — not just because they’re cool, but because they saved me time.

1. 🧠 ChatGPT (GPT-4o)
I use it to:

Write clean README files

Refactor code snippets

Translate logic to English (for clients)

Brainstorm product copy

💡 Bonus tip: Create custom GPTs for code reviews or bug-hunting patterns. Surprisingly useful.

2. ✍️ Notion AI
What I use it for:

Documenting systems

Writing user flows for clients

Summarizing 40-minute meetings in seconds

⚡ It helps me explain technical things to non-technical people — and that’s priceless.

3. 🎬 Descript
Video-editing via text. Literally.

Cut “uhs” and “ums” in one click

Edit code demo voiceovers

Auto-captions for shorts

🎥 If you’re recording code walkthroughs or tutorials, this tool is a must.

4. ✨ Midjourney + CapCut AI
Visuals are key to building in public.
I use these to:

Create product launch images

Edit mobile-friendly short videos

Share my workflow visually

📈 Great for landing page visuals and demo GIFs too.

5. 🛠️ GitHub Copilot
Still the gold standard for:

Autocompleting functions

Explaining unknown libraries

Writing boilerplate tests

🔍 I paired it with TabNine for a while but Copilot still wins in long-term dev sessions.

🤔 Is AI Magic? No.
But it's a massive leverage tool when used well.

Here’s the key:
🔁 Don’t chase tools. Pick 3–5 and make them your team.

🔥 What tools saved you hours this month?
Drop your go-to AI stack in the comments 👇
I’m building a new roundup from real dev feedback.

🧲 Optional CTA:
If you want me to write a follow-up comparing AI-powered code editors, or showing how I edited a full YouTube video using just AI tools — let me know below. 💬

🧠 Final Thoughts
You don’t need more tools.
You need a workflow that works for you.

Use AI like a smart junior dev — fast, focused, and never tired.
And never forget: you still write the logic.

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✌️ Thanks for reading. You can follow me here for weekly dev tips.

Comments

[Paul Towers]

Have you used Claude Code? I'd be interested on any comparison you have done with Claude Code and Github Copilot specifically for writing tests. I have found AI generally poor at writing tests compared to actually writing code. Any insights would be greatly appreciated.

Paul

[theorie CBR]

Hi Paul! 👋

Great question — and I agree with you: AI is generally better at writing implementation code than tests, which often require more context and intention.

That said, I’ve tried both Claude (Anthropic) and GitHub Copilot for test writing, and here’s what I noticed:

⚖️ Claude Code:
Pros: Claude tends to write more thoughtful test cases when given proper context (like a full function or class). It explains its reasoning better and is great when you're doing TDD-style prompting.

Cons: It’s slower in IDE integration (you usually have to copy-paste), and not ideal for fast-paced workflows.

⚙️ GitHub Copilot:
Pros: Much faster inside the IDE. It autocompletes unit tests as you write, especially if you're using popular frameworks like Jest, PyTest, or Mocha. It recognizes naming patterns and fills in stubs pretty well.

Cons: Often writes shallow tests unless you explicitly guide it. Sometimes skips edge cases or mocks incorrectly.

💡 What works best for me:
I usually let Copilot generate the basic structure, then I tweak or improve it using Claude or ChatGPT (especially for edge cases or integration tests).

Totally agree — testing with AI still needs a human in the loop. But with the right prompt and setup, it saves me a lot of time.

Let me know if you’d like to see a side-by-side example! And subscribe if you found my article helpful.