Here’s the answer it gave me — surprisingly practical.
- AI coding assistant
Tools like GitHub Copilot → https://github.com/features/copilot
or Cursor IDE → https://cursor.com
Writes boilerplate, fixes bugs, explains code.
At this point it’s baseline.
GitHub Copilot alone is used directly inside your editor to suggest entire functions and workflows ()
- Local + cloud LLM setup
Something like Ollama → https://github.com/ollama/ollama
APIs like OpenAI API → https://platform.openai.com
Local for speed/privacy, cloud for power.
- A better terminal
Warp Terminal → https://warp.dev
Modern terminal built for AI workflows and automation, used by hundreds of thousands of developers ()
- AI usage / cost tracking
This is where most devs get blindsided.
If you’re building with APIs, you need to see:
how many tokens you’re using
what each session is costing
where spikes are happening
Tools like TokenBar → https://tokenbar.site
It just sits in your menu bar and shows usage live.
You catch runaway prompts early instead of after the bill hits.
- API testing workflow
Postman → https://postman.com
or Insomnia → https://insomnia.rest
Faster iteration, easier debugging.
- Fast deployment
Vercel → https://vercel.com
/ Railway → https://railway.app
Ship instantly instead of overthinking infra.
- Knowledge capture
Docs, notes, saved snippets.
You forget more than you think.
- Focus tooling
Anything that blocks distractions while you work.
Direct impact on output.
Final thought
The stack is shifting from:
editor + framework + cloud
to:
AI + infra + visibility
Most people upgraded the first two.
Very few upgraded the last one.
Curious what others are using in 2026.
