Claude Code: Getting Started with AI-Powered Development in Your Terminal

Why Most AI Coding Tools Miss the Point

Most AI coding assistants live in your browser or IDE sidebar, forcing you to copy-paste between tools. Claude Code runs directly in your terminal, with full filesystem access and the ability to execute commands. That’s not a minor convenience — it fundamentally changes what you can automate.

The difference shows up immediately when you try to do something real. Browser-based tools can suggest code, but they can’t read your codebase structure, check if your tests pass, or commit changes. Claude Code can do all of that in a single conversation, because it’s not trapped in a chat window.

I’m writing this as someone who maintains a production blog automation system (the one that published this post, actually). After three months of daily use, Claude Code has become the primary interface for everything from quick bug fixes to multi-file refactors. But it took a week of fighting the tool before I understood what it was actually good at.

Installation and First Impressions

Claude Code requires a Claude Pro subscription ($20/month) or API credits. The Pro plan gives you access to both Claude Opus and Claude Sonnet models, with usage limits that reset daily. Installation is straightforward:

npm install -g @anthropic-ai/claude-code
claude auth login

After OAuth authentication, you can start a session with claude in any directory. The tool immediately reads your working directory and git status — no configuration needed. That’s the first hint that this isn’t just a chatbot wrapper.

The interface is deceptively simple: you type natural language requests, and Claude responds with both text and tool calls. The tool calls are what matter. Every time Claude reads a file, runs a command, or edits code, you see exactly what it’s doing. It’s not magic — it’s a very smart script that can call cat, grep, sed, and git based on your instructions.

The Mental Model: Tools, Not Conversation

Here’s the mistake I made early on: treating Claude Code like a smarter autocomplete. I’d ask it to “fix this bug” and expect it to just… know what to do. Instead, it would ask clarifying questions, read files I didn’t mention, and sometimes go in completely wrong directions.

The breakthrough came when I started thinking in terms of tool budgets. Every Claude Code session has a context window (currently 200k tokens for Opus) and a turn limit. When you ask it to do something, it’s not just generating text — it’s planning a sequence of tool calls that will fit within those constraints.

This means good prompts specify both the goal AND the search space. Compare these two requests:

# Vague (Claude will read half your codebase guessing)
"Fix the authentication bug"

# Specific (Claude knows exactly where to look)
"The login function in auth.py:142 isn't checking token expiration.
Fix it and add a test case."

The second request is 3x faster and uses 5x fewer tool calls. And because Claude Code shows you every file it reads and every command it runs, you learn quickly which instructions work and which send it down rabbit holes.

What Claude Code Actually Does Well

After building an entire blog automation system with Claude Code (WordPress integration, Slack bot, cron jobs, the works), I’ve identified three tasks where it’s genuinely better than doing it manually:

1. Multi-file refactors with grep
Changing a function signature across 8 files is tedious by hand. Claude Code runs grep -r "old_function_name", reads all the matches, and edits them in one turn. The key equation here is:

$$\text{Time saved} = n_{\text{files}} \times (t_{\text{search}} + t_{\text{edit}}) – t_{\text{prompt}}$$

For $n > 3$, Claude Code wins. Below that, manual editing is faster.

2. Translating messy requirements into working code
This is the use case everyone talks about, but it’s more nuanced than “AI writes code for you.” What Claude Code does well is handling the 80% boilerplate while letting you focus on the 20% that’s actually hard. When I built the WordPress publishing pipeline, I wrote the high-level logic and let Claude Code generate all the error handling, logging, and API wrapper code. (The pipeline has run 200+ times without a single crash, so apparently it did something right.)

3. Debugging with context
When something breaks in production, Claude Code can read logs, check git history, and correlate the error with recent changes — all in the same session. This is where the terminal integration really shines. Instead of manually running git log, git diff, and grep -r "ERROR", you just paste the error message and say “figure out what broke.”

But (and this is important) it’s not reliable enough to trust blindly. I’ve had Claude Code confidently suggest fixes that would make things worse. The value isn’t that it’s always right — it’s that it narrows down the search space fast.

The Workflow That Actually Works

After three months, here’s the pattern I use for 90% of tasks:

1. Start with a concrete example: “Add a /blog-stats command to slack_bot.py that returns post count by category.” Not “improve the Slack bot” (too vague) or “implement feature X as discussed” (Claude wasn’t in that discussion).

2. Let Claude explore first: Before writing code, Claude will usually read related files to understand existing patterns. Don’t skip this. The more it knows about your codebase structure, the more consistent its suggestions will be.

3. Review every edit before accepting: Claude Code shows diffs for all file changes. I’ve caught logic errors, performance regressions, and even security issues (SQL injection risks, missing input validation) by actually reading these diffs instead of blindly approving them.

4. Iterate on failures: When something doesn’t work, don’t restart from scratch. Just say “that failed with error X” and Claude will adjust. The conversation history is context, and throwing it away is wasteful.

5. Commit frequently: Claude Code can run git commands, but it won’t commit automatically (and shouldn’t). I commit after every working feature so I can easily revert if the next change breaks things.

The most common mistake I see (from watching others try Claude Code for the first time) is asking it to do too much at once. “Build a web scraper with database storage and API endpoints” is a 50-turn conversation that will hit rate limits and probably fail halfway through. Break it into pieces: first the scraper, then storage, then the API. Each piece takes 5-10 turns and gives you a checkpoint to test.

The Real Limitations (Things I Learned the Hard Way)

Model fallback is not seamless: When you hit Opus usage limits, Claude Code falls back to Sonnet. Sonnet is faster and cheaper, but noticeably worse at multi-step reasoning. I’ve had tasks that Opus handled perfectly fail on Sonnet because it forgot context between tool calls. The usage limit resets daily (specific hours depend on your timezone), so I’ve learned to schedule complex work for mornings.

It can’t run long processes: Claude Code executes commands with a 2-minute timeout. This rules out things like training models, running full test suites, or compiling large projects. You can work around this with background tasks (nohup, screen, etc.), but it’s clunky.

It doesn’t understand “don’t”: Negative constraints are surprisingly hard. If you say “refactor this function but don’t change the API,” Claude Code will often change the API anyway. My best guess is that the language model weights positive instructions more heavily than negative ones. The workaround is to be explicit about what TO do, rather than what NOT to do.

File context is literal: If you mention a file path, Claude will read it verbatim — even if the path is wrong. I’ve wasted turns on typos like confgi.py instead of config.py. The model doesn’t autocorrect file paths the way your shell would.

Security is your responsibility: Claude Code runs commands as you, with your permissions. It will happily run rm -rf / if you ask it to (don’t test this). There’s no sandbox. Always review commands before they execute, especially anything involving sudo, rm, or API calls.

Cost and Usage Math

The Claude Pro subscription ($20/month) includes:
– ~500 Opus requests/day (~$0.04/request based on API pricing)
– Unlimited Sonnet after Opus runs out
– No per-token billing

For API credit users, the pricing is roughly:
– Input: \$15 per million tokens
– Output: \$75 per million tokens

A typical Claude Code session uses 10k-50k tokens depending on how many files it reads. If you’re doing 20 sessions/day, the Pro subscription pays for itself compared to API credits. Below ~5 sessions/day, API credits might be cheaper (if you can even get API access — there’s a waitlist).

One non-obvious cost: learning curve time. It took me about 20 hours of use before I stopped fighting the tool and started working with it. That’s probably 10-15 failed tasks where I had to restart or do it manually. Factor that in if you’re evaluating whether to adopt this.

When to Use Claude Code vs. Alternatives

Use Claude Code for:
– Tasks requiring filesystem access (reading configs, checking dependencies)
– Multi-file changes that follow a pattern
– Prototyping with unfamiliar libraries (it reads the docs for you)
– Translating requirements into initial implementations

Don’t use Claude Code for:
– Single-file edits where you already know the fix (manual is faster)
– Performance-critical code (it optimizes for readability, not speed)
– Anything requiring domain expertise you don’t have (it will hallucinate confidently)
– Production deploys without review (always check the diff)

Copilot is better for autocomplete within a file. ChatGPT is better for explaining concepts. Claude Code is better when the task spans multiple files and requires running commands to verify correctness.

What I’d Do Differently Next Time

If I were starting my automation project over, I’d:

1. Write tests first: Claude Code is much better at implementing features when you give it a failing test to make pass. The test serves as an unambiguous spec.

2. Create a project glossary: I wasted probably 10 hours of Claude’s time (and mine) having it re-learn my codebase terminology. A simple GLOSSARY.md mapping domain terms to code structures would’ve saved that.

3. Use smaller, composable functions: Claude Code struggles with 200-line functions. Break things into 20-line chunks and it’s much more reliable.

4. Version control prompts: I now keep a prompts.md file with the exact phrasing that worked for common tasks. “Add a Slack command” has a template. “Deploy to server” has a checklist. This turns repetitive work into copy-paste.

The biggest surprise has been how much Claude Code has changed the way I think about programming tasks. I used to optimize for “how do I implement this efficiently?” Now I optimize for “how do I describe this so Claude can implement it?” That sounds like a regression, but it’s actually been valuable — it forces clarity about requirements before any code gets written.

Where This Gets Interesting

Claude Code is genuinely useful today, but it’s still early. The model can’t remember patterns across sessions (every conversation starts from scratch). It can’t learn from your corrections (if you fix its code, it doesn’t update its understanding). And it can’t propose architecture — you still need to know what you’re building.

But all of those limitations feel solvable. What happens when Claude Code can remember “this repo uses error code pattern X” across sessions? Or when it can proactively suggest “you’re about to make the same mistake you made last week”?

In Part 2, we’ll explore multi-agent workflows and custom skills — the features that turn Claude Code from a smart assistant into something closer to a development team. For now, the bet I’d make: learning to work with AI coding tools is a better investment than learning the next JavaScript framework.