Case Study #7
Claude Code vs Cursor: both miss the same gaps.
We ran two real builds through two different IDEs. Claude Code built an AI Email Drafter. Cursor built a PR Description Drafter. In both cases, the IDE's solo review missed issues that MegaLens caught. Different IDEs, same pattern, same blind spots.
2
Projects tested
2
IDEs compared
35+
Gaps MegaLens added
7
Judge-only catches
Two real projects, two IDEs
These aren't demo apps or staged tests. Both are real tools we built and use. Each one went through the same process: the IDE wrote the plan, reviewed its own plan, then MegaLens reviewed the same plan independently.
AI Email Drafter
A backend service that polls Gmail, identifies real business inquiries, and creates draft replies from a context file. Never sends email. Claude Code wrote the 19-section build plan and built the entire tool.
2
Solo review
15
MegaLens
+13
Gaps found
PR Description Drafter
A self-hosted git daemon that watches local branches, reads diffs and commit messages, generates PR descriptions via AI, and creates draft pull requests on GitHub. Cursor Agent wrote the 493-line V1 engineering plan.
23
Solo review
45
MegaLens
+22
Gaps found
Same pattern, both IDEs
The numbers are different because the projects are different. But the pattern is identical. In both cases, the IDE's solo review did solid work and caught real issues. And in both cases, MegaLens found a significant layer of problems the IDE missed entirely.
| Claude Code AI Email Drafter | Cursor PR Drafter | |
|---|---|---|
| IDE solo review | 2 findings | 23 findings |
| MegaLens review | 15 findings | 45 findings |
| New gaps found | +13 | +22 |
| Critical gaps added | 2 | 4 |
| MegaLens cost | $0.18 | $0.22 |
| MegaLens time | ~5 min | 7 min |
The Claude Code review was a lighter self-audit (2 items before MegaLens), while the Cursor review was a full prompted security pass (23 items). Even with a more thorough solo review, MegaLens still found 22 additional gaps. The ceiling isn't about effort. It's about having one model review from one perspective.
What Cursor missed on the PR Drafter
22 additional findings. Here are the ones that matter most.
Judge-only findings(All 3 debater engines also missed these. The GPT 5.4 judge caught them.)
The tool runs git commands against user repositories. But Git has built-in extension points: custom diff tools, credential helpers, and aliases defined in .gitconfig. A repository with a malicious .gitconfig would execute arbitrary code every time the daemon runs "git diff" or "git log". This is a real Git feature, not a bug. All 3 engines and Cursor missed it.
The plan uses a fixed port (8765) for the OAuth callback. Any other program on the same machine can bind that port first and steal the GitHub authorization code.
When the daemon runs "git fetch", Git can invoke SSH with ProxyCommand or custom transport protocols. Without restricting allowed transports, a malicious remote can trigger code execution.
AI output goes directly into the GitHub PR body without filtering. Crafted input could make the AI produce markdown with image tags that load external URLs, leaking viewer IP addresses.
Single-engine catches
YAML config can execute code if parsed with an unsafe loader (Grok caught, others missed)
Git commands with no timeout or output limit. Huge repo = out of memory (DeepSeek caught, others missed)
Config reload via signal accepts new paths without validation (DeepSeek only)
State file paths can be manipulated via symlinks (DeepSeek only)
Multi-engine consensus
Force-push breaks state tracking: duplicate PRs or retry loops
No spending cap on AI API calls
Single GitHub token used across all repos
Race condition: two changes create duplicate PRs
Branch-to-remote mapping can target wrong repository
What Claude Code missed on the Email Drafter
13 findings that MegaLens added beyond Claude Code's own review. Two were critical.
The plan had a daily spending cap but no code to actually parse costs from the API response. The cap existed on paper only.
On database loss, the tool would process every unread email in the inbox. Dozens of unwanted drafts and a surprise API bill.
Email headers passed raw into AI prompt (injection vector)
Draft-create race condition: duplicate drafts on crash
No MIME parsing spec for multipart, HTML, charset
Reply envelope headers undefined (To, Cc, In-Reply-To)
Poison messages retry forever with no quarantine
systemd Environment= exposes API keys in /proc
Gmail historyId 7-day expiry unhandled
Why switching IDEs doesn't fix it
Every AI model has systematic blind spots.
Claude Code uses Opus. Cursor uses Sonnet (or whichever model you configure). Both are excellent at generating and reviewing code. But each model was trained on different data with different priorities. They're consistently strong in different areas and consistently weak in others. Switching from one to the other gives you a different perspective, but still just one.
Self-review has a ceiling.
Whether it's Claude Code reviewing Claude Code's plan, or Cursor reviewing Cursor's plan, the dynamic is the same: one model checking its own work. The model that wrote the plan has the same blind spots when it reviews the plan. This isn't a flaw in either IDE. It's a structural limitation of single-model review.
Multi-engine debate breaks the ceiling.
MegaLens sends your code to 3 AI models from different companies (Grok, DeepSeek, Gemini). Each reviews independently, then they debate across 2 rounds. A fourth model (GPT 5.4) judges the combined output and catches gaps that all three missed. In the PR Drafter test, 7 findings came from the judge alone. These are issues that 4 different models (Cursor + 3 debaters) all overlooked.
How MegaLens fits in
MegaLens is an MCP server. You install it once, and it becomes a tool your IDE can call. It works inside both Claude Code and Cursor (and any MCP-compatible editor). You don't leave your IDE. One command, one result.
| Role | Engine | Job |
|---|---|---|
| Debater 1 | Grok 4.1 Fast | Security Auditor |
| Debater 2 | DeepSeek V3.2 | Vulnerability Analyst |
| Debater 3 | Gemini 3.1 Pro | Design Reviewer |
| Final Judge | GPT 5.4 | Synthesis + gap-fill |
Add MegaLens MCP to Claude Code, Cursor, or any MCP-compatible editor.
Ask your IDE to run megalens_debate on your code or plan.
3 engines from different AI families review independently, then debate in 2 rounds.
GPT 5.4 reads all debate output, confirms findings, catches gaps the debaters missed.
Structured findings arrive in your IDE. Your AI makes the final call.
Honest notes
The two projects are different in size and complexity, so the raw numbers aren't directly comparable. What's comparable is the pattern: solo review catches a solid base, multi-engine review catches a meaningful layer on top.
The Claude Code test was a lighter self-audit (2 items), while the Cursor test was a full prompted security review (23 items). Even with the more thorough solo pass, MegaLens still found 22 additional issues.
MegaLens produces findings, not proofs. Every finding required human judgment to confirm and decide on a fix. These are two tests on two projects. Your results will depend on what you're reviewing and how thorough your existing process is.
What this actually means for your workflow
Two projects. Two IDEs. 35+ gaps that solo review missed. The data tells the same story both times: the IDE isn't the problem, and switching IDEs isn't the fix. Claude Code missed 13 gaps on the Email Drafter. Cursor missed 22 on the PR Drafter. The ceiling is structural, not a product flaw.
But catching blind spots is only half of what MegaLens MCP does. The other half is something most developers don't think about: it saves your IDE's expensive model from doing the heavy review work.
When Cursor reviews code, it uses the same premium model (Sonnet, Opus) that writes your code. Every review prompt burns tokens from that expensive context window. MegaLens shifts that labor to purpose-built review engines (Grok, DeepSeek, Gemini) that cost a fraction per token. Both audits in this case study cost $0.40 combined. Your IDE would consume far more tokens at far higher rates to do the same review depth with its own model.
The practical result: your IDE's model stays focused on what it's best at — writing code, iterating on edits, answering your questions — while the deep review labor goes to cheaper specialized models that are specifically prompted for analysis. You get better coverage from multiple AI families, lower token costs on your IDE's model, and a cleaner context window for the work that actually needs it.
Seven findings in the PR Drafter came from the GPT 5.4 judge alone, after all 3 debater engines and Cursor itself missed them. Two critical findings in the Email Drafter existed only on paper — a cost cap with no implementation and a fresh-install bug that would flood inboxes. These are the kinds of gaps that don't surface until production. Or until you have enough AI perspectives looking at the same code.
Add multi-engine review to your IDE.
MegaLens works inside Claude Code, Cursor, and any MCP-compatible editor. Your AI stays in charge. MegaLens gives it coverage from multiple AI families so no single model's blind spot becomes your blind spot.