Back to BlogHere's What I Learned Competing Against Funded Startups.

Here's What I Learned Competing Against Funded Startups.

Ferit·

A developer's honest look at the AI code review landscape, from the inside.

I'll start with the uncomfortable part: I'm the developer behind Octopus Review, an open source AI code review tool. So everything I say here comes with that bias baked in.

But I've also spent the last year obsessively studying every competitor in this space. Not just reading their landing pages, but actually using them on production codebases. And I think the things I've learned are worth sharing, even if you never touch my tool.

The Moment That Started Everything

It was 2 AM on a Tuesday. I was reviewing a pull request from a teammate. The diff looked fine. Clean code, good naming, reasonable logic. I approved it.

The next morning, we had a production incident.

The PR introduced a function that did almost the exact same thing as an existing utility in our codebase, but with a subtle difference in how it handled edge cases. Two functions, nearly identical, now behaving differently in different parts of the app. A classic duplication bug.

No AI review tool would have caught it. Because every tool I tried only looked at the diff. They saw the new function in isolation and said "looks good." None of them knew the other function existed.

That's when I started building Octopus Review.

The Core Idea (and Why It's Harder Than It Sounds)

The concept is straightforward: before reviewing a PR, index the entire codebase into a vector database. Use RAG (Retrieval Augmented Generation) to pull relevant context when analyzing changes. So when someone adds a new function, the AI can say "hey, this looks similar to utils/formatDate.ts on line 47, are you sure you need a new one?"

Simple in theory. In practice, I ran into problems I didn't anticipate:

Indexing is slow on large repos. Chunking and embedding a 200k-line codebase takes time. I optimized it a lot, but first-time setup on big projects is still not instant. Most competitors skip this step entirely, which is why they're faster to set up.

RAG retrieval is imperfect. Sometimes the vector search pulls in irrelevant context, and the AI confidently tells you about code that has nothing to do with your PR. It's better than having no context, but it's not magic. Tuning the retrieval quality is an ongoing battle.

"Full codebase understanding" is a strong claim. In reality, it's "better codebase awareness than diff-only tools." I try to be careful with the marketing language, but the gap between the promise and the reality is something I think about constantly.

What I Found Testing the Competition

I tested six other tools on a real TypeScript monorepo. Here's what I took away from each one.

CodeRabbit Taught Me About UX

CodeRabbit's inline PR comments are beautifully formatted. Clear, well-structured, easy to act on. When I saw their review output next to mine, I immediately felt embarrassed about Octopus's comment formatting.

Their reviews are scoped to the diff only, which means they miss the codebase-wide context that I built Octopus to solve. But for many teams, that's fine. If your codebase is small enough or your team is disciplined enough, you might not need full-repo context. And CodeRabbit's polish makes the experience genuinely pleasant.

I've been slowly improving Octopus's comment quality because of what I saw in CodeRabbit. Competition makes everyone better.

Panto and Aikido Made Me Accept My Limitations

Both tools are serious about security. Panto combines static analysis, secrets detection, dependency scanning, and compliance reporting (SOC 2, ISO, PCI-DSS) into one platform. Aikido focuses on reducing false positives in security scanning with smart context-aware filtering.

When I looked at what they offer, I had a choice: try to build all of that into Octopus, or accept that security scanning is not my tool's strength.

I chose acceptance. Octopus does basic code quality and pattern detection. It's not a security tool. If you need vulnerability scanning and compliance reports, use Panto or Aikido. Trying to be everything to everyone is how you end up being mediocre at everything.

This was one of the hardest product decisions I've made. The temptation to add "security scanning" to the feature list is real. But shipping a half-baked security feature is worse than not having one.

Devlo.ai Showed Me What "Deep" Actually Means

Devlo focuses on logic-level analysis. Not "you forgot a semicolon" but "this function doesn't handle the case where the input array is empty, and your test suite doesn't cover it either."

That's a different kind of intelligence than what Octopus provides. Octopus knows your codebase and can spot patterns. Devlo understands program logic and can reason about edge cases. They're complementary approaches, and honestly, Devlo's logic analysis is ahead of anything I've built.

It made me rethink what "AI code review" even means. There are multiple dimensions: style, patterns, logic, security, architecture. No single tool does all of them well.

Semgrep Reminded Me That AI Isn't Always the Answer

Semgrep is not an LLM-powered tool. It's rule-based static analysis. You write rules, it applies them. Deterministic, predictable, fast.

And you know what? For a lot of use cases, that's better.

When I use Octopus, the reviews are helpful but occasionally surprising. The AI might flag something unexpected, or miss something obvious. LLMs are probabilistic. Semgrep is not. If you write a rule that says "never use eval()", Semgrep will catch every single eval() call. Every time. Guaranteed.

There's a place for both approaches. But testing Semgrep was a humbling reminder that "AI-powered" is not automatically better than "rule-based." Sometimes you want creativity and context. Sometimes you want certainty.

Codacy Showed Me the Long Game

Codacy's strength is not in individual PR reviews. It's in showing you how your code quality changes over weeks and months. Dashboards, trend lines, technical debt scores.

Octopus reviews PRs. It doesn't tell you whether your codebase is getting better or worse over time. Codacy does. For engineering managers and tech leads who need to justify refactoring time to stakeholders, those dashboards are worth more than any individual review comment.

It's a feature I want to build eventually, but I'm being honest with myself about how far away it is.

What I'd Tell Someone Choosing a Tool Right Now

Don't start with the tool. Start with the problem.

  • "Our PRs sit in review for days because senior engineers are overwhelmed." You need something that handles the first pass automatically. CodeRabbit or Octopus can take the initial look, so humans only need to focus on the non-obvious stuff.

  • "We keep shipping security vulnerabilities." You need Panto or Aikido. Or Semgrep with well-written rules. Code review AI is not a replacement for security tooling.

  • "We have inconsistent patterns across the codebase." This is where Octopus's RAG approach shines. Because it knows the existing codebase, it can flag when new code deviates from established patterns. But CodeRabbit with custom rules can also help here.

  • "We need to show leadership that code quality is improving." Codacy. The dashboards exist for exactly this conversation.

  • "Our tests don't catch edge cases." Devlo. It thinks about logic and test coverage in a way other tools don't.

  • "We want one tool that does everything." It doesn't exist. Anyone who tells you otherwise is selling something.

The Uncomfortable Truth About This Market

Most AI code review tools (including mine) are built on the same foundation: send code to an LLM, get feedback back. The differentiation is in what context you send alongside the code, how you present the results, and what additional analysis you layer on top.

This means the moat is thin. What Octopus does with RAG today, someone else could build tomorrow. What CodeRabbit does with inline comments, I could replicate next month. The tools in this space are converging, and in two years, the feature differences between them might be marginal.

What won't converge: open source vs. closed source. Self-hostable vs. cloud-only. Free vs. paid. These are philosophical choices, not feature gaps. That's why I chose to make Octopus open source and free. Not because I've figured out every aspect of the business model (that's a problem for another day), but because I believe the right tool for reviewing your private code should be something you can run on your own infrastructure if you want to.

Give It a Try

If any of this resonated, you can try Octopus Review at octopus-review.ai. Connect your GitHub repos and it starts reviewing PRs automatically. It's free, it's MIT licensed, and if the reviews are off, open an issue. I read every single one.

But also try the other tools I mentioned. Use CodeRabbit for a week. Set up Semgrep rules. Look at Codacy's dashboards. The best setup might be two or three tools working together, each doing what it's best at.

The AI code review space is still figuring itself out. I'm building in public, learning from competitors, and trying to be honest about what works and what doesn't. That's the best I can offer.

Tags: ai, code-review, open-source, devtools, startup, competition, lessons-learned