2025 was the year AI tools stopped being “nice to have” and became part of the default developer workflow.
Not because they’re perfect.
Not because they replaced thinking.
But because — when used intentionally — they genuinely save time and mental energy.
This is not a hype list.
No affiliate links.
No “Top 50 tools you’ll never use.”
These are AI tools I actually used in real projects, under real deadlines, with real consequences.
Some helped a lot.
Some surprised me.
Some almost caused problems.
Here’s the honest breakdown.
1️⃣ ChatGPT — Still the Thinking Partner
Yes, it’s obvious.
And yes, it still deserves the top spot.
Where it shines
- breaking down unclear problems
- exploring architectural options
- refactoring ideas
- explaining legacy code
- writing first drafts of docs or tests
I don’t trust it blindly — but as a thinking partner, it’s unmatched.
Where it fails
- confidently hallucinating APIs
- missing project-specific constraints
- sounding right while being wrong
Rule I learned in 2025:
If you can’t clearly explain the problem, ChatGPT won’t magically fix it for you.
2️⃣ GitHub Copilot — Quiet, Constant Productivity
Copilot isn’t exciting anymore — and that’s a good thing.
It doesn’t try to replace you.
It just removes friction.
Best use cases
- repetitive boilerplate
- predictable patterns
- test scaffolding
- small utility functions
It works best when:
- you already know what you’re building
- the codebase is consistent
Important caveat
Copilot amplifies existing patterns.
If your codebase is messy — it will happily generate more mess.
3️⃣ Sourcegraph Cody — The Underrated Codebase Navigator
This one surprised me.
Cody is especially useful in:
- large, unfamiliar codebases
- legacy systems
- onboarding scenarios
Why it stands out
- understands your actual repository
- answers questions like:
- “Where is this logic used?”
- “What depends on this service?”
- “Why does this exist?”
This is one of those tools that doesn’t feel flashy —
but quietly saves hours.
4️⃣ AI for Documentation — A Silent Win
AI didn’t make me love it —
but it made it bearable.
What worked well
- drafting READMEs
- summarizing changes
- explaining decisions after the fact
What didn’t
- final wording
- tone
- accuracy
AI writes the first 60%.
You still own the last 40%.
That’s a trade-off I’m happy with.
5️⃣ Hidden Gem: AI as a Debugging Rubber Duck
One unexpected habit I developed this year:
I explain bugs to AI before fixing them.
Not for the solution —
but for the clarity.
By the time I finish explaining the problem clearly,
I often already know what’s wrong.
The AI response is secondary.
The thinking process is the real value.
6️⃣ Experiments That Didn’t Stick
Not everything worked.
Things I tried — and dropped:
- full component generation
- large-scale refactors via AI
- AI-written business logic
Why?
- too risky
- too context-heavy
- too hard to validate
AI is great at assisting decisions.
It’s still bad at owning them.
7️⃣ The Biggest Lesson of 2025
The most valuable insight wasn’t about tools.
It was this:
AI doesn’t make you faster by writing code.
It makes you faster by reducing hesitation.
When used intentionally, AI:
- lowers the cost of exploration
- shortens feedback loops
- helps you move forward with more confidence
But only if you stay in control.
Final Thoughts
AI tools didn’t replace my job in 2025.
They reshaped how I work.
The best ones:
- stay quiet
- remove friction
- respect human judgment
Going into 2026, I’m not looking for “smarter AI”.
I’m looking for tools that make me think better.
👋
Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.
If you enjoyed this year-end special, follow me here on dev.to
and join me on X where I share shorter thoughts, experiments, and behind-the-scenes ideas.
Let’s keep building — thoughtfully. 🚀
Top comments (0)