Google Maps for Codebases: I Pasted My Own Repo URL and Got a Little Scared

Gitingest, Repomix, CodeViz — last week, a bunch of tools hit my radar all promising the same thing: paste a GitHub URL and you can chat with the code, map it, understand its architecture in seconds. The community is discovering them with the usual excitement. I tried them too. And the most interesting experiment wasn't analyzing some famous framework's repo.

It was analyzing mine.

There's something mildly narcissistic about pasting your own project's URL into an analysis tool. And something mildly terrifying about what comes back.

Codebase Visualization with AI: What Are We Actually Talking About?

Before I get into what I found, it's worth being clear about what "AI-powered codebase visualization" actually means.

It's not just a dependency graph. Those existed years ago and nobody used them because a Node.js dependency graph looks like the Tokyo subway map after an earthquake.

What changed is the natural language layer on top. Tools like Gitingest convert the entire repo into a format that an LLM can ingest. Then you can ask questions in plain English: "where are the performance bottlenecks?", "which components are most tightly coupled?", "are there inconsistent patterns in error handling?"

Repomix does something similar but focused more on generating a compressed context file. The idea is you feed that file to Claude or GPT-4 as context and ask whatever you want.

What these tools produce isn't magic — it's massive context delivered efficiently. The analysis is done by the LLM. The tool is the preprocessor.

# Basic Repomix installation
npx repomix

# Or pointing directly at a remote repo
npx repomix --remote juanchi-dev/juanchi.dev

# Generates a repomix-output.xml file with all the code
# compressed and ready to pass to an LLM

Nothing groundbreaking so far. The interesting part is what happens when the code being analyzed is yours — code you know by heart, or at least thought you did.

What a LLM Found in juanchi.dev That I Couldn't See

juanchi.dev is my public project. I built it with Next.js 15, React 19, Tailwind v4, deployed on Railway. I've written about the stack in detail. I figured I knew it well.

I ran the repo through Repomix, generated the context file, and loaded it into Claude with a simple prompt: "Analyze this codebase as if you were a senior developer doing a code review. Be honest. Don't pat me on the back."

What came back made me open three files I hadn't touched in weeks.

Finding 1: Inconsistent Error Handling

My Server Components and Route Handlers were handling errors differently. In some places I used try/catch with explicit logging. In others, I let Next.js silently absorb the error. There was no unified strategy.

// How I handled errors in some Server Components
async function getBlogPost(slug: string) {
  try {
    const post = await db.query(/* ... */)
    return post
  } catch (error) {
    // Explicit logging, controlled re-throw
    console.error(`Error fetching post ${slug}:`, error)
    throw new Error('Post not found')
  }
}

// How I handled errors in OTHER places (the problem)
async function getProjects() {
  // No try/catch. If it fails, it fails silently or explodes upstream
  const projects = await db.query(/* ... */)
  return projects
}

The LLM flagged it as "lack of consistent error handling strategy." It was right. It wasn't a bug — it was technical debt accumulated from building the project across multiple sessions with no defined standard.

Finding 2: Components With Too Many Responsibilities

There was a component the analysis identified as a "God Component" — it was doing fetching, data formatting, AND rendering all in one place. I'd built it that way because at the time it was faster. It worked. But it wasn't right.

// The problematic component (simplified)
// Does too much: fetch + transform + render
export async function BlogPostCard({ slug }: { slug: string }) {
  // Fetching logic that should be separated out
  const post = await fetch(`/api/posts/${slug}`).then(r => r.json())
  
  // Transformation that should live in a utility
  const formattedDate = new Intl.DateTimeFormat('en-US', {
    year: 'numeric',
    month: 'long',
    day: 'numeric'
  }).format(new Date(post.date))
  
  // Only this should be here
  return (
    <article>
      <h2>{post.title}</h2>
      <time>{formattedDate}</time>
    </article>
  )
}

Finding 3: The One That Actually Stung

I had duplicated business logic in two different places. The same data transformation written twice, slightly different each time. The kind of thing you'd reject on the first comment in any code review.

I hadn't seen it because when you're inside the code, you navigate it functionally — you open the file you need, make the change, close it. You don't have the bird's-eye view.

That's exactly what happened when I optimized the site's performance to 300ms: I went file by file, function by function. Efficient for that goal. Completely blind to the big picture.

The Antipatterns You Can't See Because You're the One Who Wrote Them

There's an epistemological problem with reviewing your own code: you know too much.

You know why you made every decision. You know the context. You know what you were trying to do. And that knowledge acts as a rationalization layer that filters out problems before you even register them.

When an outsider — human or LLM — looks at the code, they don't have that context. They see only what's written. And sometimes what's written doesn't reflect what you had in mind.

This reminds me of something I learned long before I was writing TypeScript. When I was studying for my CCNA back in 2009, I'd practice network configs in Packet Tracer until I had them memorized. But when I sat down for practice exams, I'd get exactly the things I "knew" wrong. Implicit knowledge doesn't always survive a context switch.

An LLM operating on my code is that forced context switch.

That said — it's not magic, and it has clear limits.

What the analysis did NOT catch:

• Why certain architectural decisions are intentional (tradeoffs that I know about)
• The evolutionary context of the project (code that looks legacy but has a reason for existing)
• Performance problems that only show up at runtime — for that I had to instrument things manually
• Specific integrations with external services where the "inconsistency" is actually necessary

Combined with what it did find, the map is useful. Alone. Not sufficient.

Common Mistakes When Using These Tools

Mistake 1: Taking everything as absolute truth

The LLM doesn't know if that "inconsistency" in error handling is technical debt or a deliberate decision. You do. Filter accordingly.

Mistake 2: Running it on giant repos without scoping the context

Repomix on a 500k-line monorepo is going to generate a context file no LLM can process well. The analysis degrades. Better to scope it: pass only the relevant directories.

# Better to scope the analysis to what matters
npx repomix --include "src/components/**,src/lib/**"

# Instead of dumping the whole repo with node_modules and noise
npx repomix  # no filters = a lot of noise

Mistake 3: Expecting it to replace human code review

The AI analysis found three real problems in my code. A senior developer with business context would have found those three plus five more that the LLM rationalized away or couldn't evaluate. This is the same thing I learned with coding agents: AI accelerates, it doesn't replace judgment.

Mistake 4: Not iterating the prompt

My first analysis request was generic. The useful one came when I refined it: "Focus specifically on data fetching patterns and how errors propagate to the client. Ignore styling and config." Specificity matters. A lot.

Mistake 5: Using it only once

The real value is using it as a periodic checkpoint. A snapshot of your code's state every month, with the same questions, shows you whether your technical debt is growing or shrinking.

This connects to something I noticed when I embedded a small LLM directly in a Next.js app: the quality of the output depends brutally on the quality of the context you give it. Garbage in, garbage out — but also noise in, diluted analysis out.

FAQ: Common Questions About AI Codebase Visualization

What's the best tool for analyzing a GitHub repo with AI?

Depends on your goal. For free-form conversation about the code, Gitingest (gitingest.com) is the most direct — paste the URL and ask questions in plain English. For generating context to feed into your LLM of choice, Repomix is more flexible and configurable. For visual dependency graphs, CodeViz or Mermaid generated by the LLM both work well. There's no silver bullet.

Is it safe to paste a private repo URL into these tools?

Depends on the tool and your threat model. Repomix installed locally never leaves your machine — the code doesn't go to any external server. Web tools like Gitingest process the code on their servers. For private repos with sensitive code, the local option is always safer. For public repos, there's no difference.

How accurate are the LLM's analyses?

In my experience: accurate at detecting pattern inconsistencies, imprecise at evaluating architectural decisions without context. The LLM sees the code as written. It doesn't see why it's written that way. False positives exist — it will flag something as a problem that's actually an intentional decision. The human filter is mandatory.

Does it work well with large repos (100k+ lines)?

Poorly, in general. LLMs have finite context windows. Repomix compresses the code to maximize what fits, but with very large repos you need to scope the analysis to subdirectories or specific modules. Focused analysis beats superficial analysis of everything.

Can it replace a human code review?

No. Complement it, yes. AI analysis is fast, egoless, and context-free — those three things are simultaneously its strength and its limit. It finds what a human might miss from fatigue or familiarity. It can't evaluate whether a decision is right for the business, the team, or the project's history. They're different tools for different layers of the problem.

Is it worth using on your own code if you already know it well?

Especially on your own code. That's the paradox. The more you know the code, the more you need the external perspective. The implicit knowledge you carry acts as a filter — it prevents you from seeing problems because you've already rationalized them away. An LLM without context sees only what's written. Sometimes that's exactly what you need.

Useful Map or Digital Anxiolytic?

The question I asked myself before writing this: did I actually change anything after the analysis?

Yes. I unified the error handling. I broke the God Component into three pieces. I eliminated the duplicated logic. Three concrete changes in code that's running in production today.

But I also asked myself whether the exercise was partly an anxiolytic — the feeling that you "audited" your code without actually confronting the deeper problem, which is having more discipline from the start.

The honest answer is: probably both. And that's fine.

What I've learned in 30 years with technology — from diagnosing network outages at 11pm in an internet café to deploying on Railway — is that tools that give you perspective are valuable even when they're imperfect. The CCNA didn't teach me to manage real networks. It gave me the vocabulary to understand what I was looking at. Claude Code doesn't write the code for me. It accelerates the time I spend on what I already know how to do.

These visualization tools do something similar: they give you back your own code through fresh eyes. What you do with that is up to you.

Paste your repo URL. Give yourself a productive scare.