I Use 4 Different AI Coding Tools Every Day — Here’s Exactly When I Reach for Each One

Let’s be honest. The AI coding tool space is a mess right now.

Every week there’s a new agent, a new IDE plugin, a new “Copilot killer.” And everyone’s telling you to use their thing for everything.

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It’s wrong. Actually, it’s lazy.

I’ve spent the last 18 months running a distributed engineering team between San Francisco and Can Tho, Vietnam. We build production systems for fintech and logistics clients. And we’ve learned the hard way that picking the *wrong* AI tool for a specific task doesn’t just waste time — it actively produces worse code.

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Here’s the truth: there’s no single best AI coding tool. There’s only the right tool for the task in front of you.

So let me show you exactly how I decide.

My Daily AI Coding Tool Stack

I’m not some AI tool hoarder. I use four tools, and I use them deliberately:

Tool	Primary Use Case	What It’s Terrible At
Claude Code	Complex refactoring, multi-file changes, architecture decisions	Quick inline edits, small fixes
Cursor	Exploratory coding, rapid prototyping, new file creation	Large-scale refactoring, deep context understanding
GitHub Copilot	Boilerplate, unit tests, repetitive patterns, inline completions	Anything requiring system-level thinking
Local LLM (Ollama + DeepSeek Coder)	Sensitive code, offline work, quick syntax lookups	Complex reasoning, large context windows

I don’t use all four at once. That’d be chaos. But I switch between them throughout the day based on what I’m actually doing.

My Decision Framework (It’s Embarrassingly Simple)

I ask myself three questions before I touch any AI tool:

1. How much context does this task need?

If I need to understand 5+ files and their relationships, I’m reaching for Claude Code in the terminal. Period. Cursor’s context window, even in Composer mode, still loses track of file interdependencies beyond a certain complexity.

2. Am I exploring or executing?

Exploratory work — “Let me see what this library looks like” or “I wonder if this pattern works here” — gets Cursor. It’s fast, visual, and I can iterate quickly. Execution work — “I need to implement this known pattern across 12 files” — gets Claude Code or Copilot depending on complexity.

3. How tight is my feedback loop?

Need an answer in 2 seconds? Copilot inline. Need to wait 15 seconds for a thorough analysis? Claude Code. Don’t want *any* data leaving my machine? Local LLM on Ollama.

You’d be surprised how often developers skip question one. I’ve seen a dev spend 40 minutes in Cursor trying to refactor a service layer that spans 8 files. It was painful to watch. Claude Code finished the same task in 12 minutes.

Real Example: The Payment Gateway Migration

Last month, we needed to migrate a client’s payment gateway from Stripe to Adyen. This wasn’t a simple swap — it involved changing webhook handlers, modifying the subscription logic, updating the event bus contracts, and rewriting the idempotency layer.

Here’s exactly how I sliced the work across tools:

Phase 1: Architecture mapping (Claude Code)

I dropped a Claude Code agent into the codebase with a prompt like: *”Map every file that touches payment processing. Identify the event contracts, the retry logic, and the webhook verification flow. Output a dependency graph.”*

Took 3 minutes. Gave me a clear plan.

Phase 2: Prototype the new webhook handler (Cursor)

I opened a new file in Cursor, described the Adyen webhook verification flow in natural language, and let it generate the initial handler. Then I iterated — tweaked the error handling, adjusted the HMAC verification, fixed the edge case for duplicate notifications.

Cursor is fast here. I got a working handler in about 8 minutes.

Phase 3: Wire up the subscription logic (Copilot + manual)

This was mostly boilerplate — mapping Adyen’s subscription events to our internal models. Copilot inline completions were perfect. I’d type the function signature, and it’d suggest the body. I accepted maybe 70% of suggestions, corrected the rest.

Phase 4: Idempotency layer refactor (Claude Code again)

This was the hardest part. The existing idempotency logic was scattered across 6 files. I needed to understand the flow end-to-end without breaking anything.

I gave Claude Code the full context, asked it to generate a refactoring plan with step-by-step changes, then executed the plan file-by-file using its edit mode. Zero regressions.

Result: What the client estimated as a 3-week migration shipped in 5 days. Our team in Can Tho handled the integration testing and deployment automation in parallel.

The Vietnam Advantage (Yes, I’m Going There)

Here’s something most “AI tooling” blog posts don’t tell you: the tools are only as good as the engineers wielding them.

I’ve worked with developers across four continents. And what I’ve found in the Vietnamese teams I’ve hired through ECOA AI is that they’re exceptionally good at *critical thinking about tool choice*. They don’t blindly trust AI output. They evaluate it.

Every developer on my current team has a clear mental model of “when to use AI and when to write it myself.” That’s rare. I’ve interviewed senior engineers in the US who just dump everything into Copilot and ship whatever it suggests.

So yes, the AI coding tools matter. But the human judgment layer on top matters more.

3 Practical Tips for Your Own Tool Stack

1. Stop using Copilot for architecture decisions

Copilot is an inline completion engine. It’s not a reasoning system. If you’re asking Copilot to refactor your entire authentication flow, you’re using it wrong. Reach for Claude Code or Cursor’s agent mode instead.

2. Learn to write good task descriptions for Claude Code

Here’s a real prompt I used last week:

“I need to add a circuit breaker to the payment service proxy. The circuit breaker should trip after 3 consecutive failures. Use a 30-second cooldown. Log each state transition. Don’t change anything in the logging service or the metrics collector.”

That’s 4 sentences. It gave me exactly what I needed. Vague prompts produce vague results.

3. Keep a local LLM running for sensitive work

We process financial data for some clients. You don’t want that hitting third-party APIs. I keep DeepSeek Coder 14B running on Ollama on my workstation. It’s not as smart as Claude, but it handles 80% of quick lookups and small code generation tasks without sending data anywhere.

Bonus: It costs zero dollars per month. Our team of 12 developers saves roughly $1,200/month by routing simple tasks through local models instead of API-based tools.

When NOT to Use AI Coding Tools

This is important. I’d say about 15% of my coding time is AI-free:

Code review on critical paths. I want my eyes, not a model’s summary.
Security-sensitive logic. Authentication flows, encryption routines, authorization checks. AI models hallucinate edge cases here.
Learning a new concept. If I’m trying to understand something new, I’ll write the code manually first. AI tools steal the learning reps.

Don’t become dependent. Use the tools to amplify your judgment, not replace it.

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Frequently Asked Questions

Which AI coding tool is best for large-scale refactoring?

Claude Code, without question. Its terminal-based agent mode can hold context across dozens of files, understand dependency graphs, and execute multi-step refactoring plans. Cursor’s Composer is a distant second. Copilot can’t handle this at all.

Should I use Cursor or Copilot for day-to-day development?

Depends on your workflow. If you write a lot of new code from scratch (prototyping, new features), Cursor’s agent and Composer modes win. If you spend most of your time editing existing codebases — adding parameters, fixing bugs, writing tests — Copilot’s inline completions are faster and less intrusive.

Is it worth running a local LLM for coding?

Yes, if you work with sensitive code or want predictable costs. A model like DeepSeek Coder 14B or CodeGemma 7B handles boilerplate, syntax lookups, and simple code generation without API latency or data privacy concerns. It won’t replace Claude for complex reasoning, but it saves money and keeps data local.

How do Vietnamese developers compare to US developers when using AI coding tools?

In my experience, Vietnamese developers tend to be more skeptical of AI output and better at validating generated code before shipping. This is partly cultural — the engineering education in Vietnam emphasizes fundamentals and problem-solving over tool proficiency. Our team in Can Tho consistently produces cleaner, more maintainable AI-assisted code than any US-based team I’ve managed.