Get a Second Opinion on Gemini's Code

[1bcMax]

The Problem

Gemini is great for quick scripts, but on complex builds it can miss edge cases:

• Skipped database transactions
• Missing rate limiting
• Minimal error handling
• Edge cases that break in production

You ship what looks fine, then discover the bugs later.

The Solution: Cross-Model Code Review

Before shipping critical code, get a second opinion from GPT or Claude.

# Quick review:
"blockrun GPT review this function for edge cases"

# Deep analysis:
"blockrun claude analyze this code for production readiness"

Why Different Models Catch Different Bugs

Model	Strength
GPT-5.2	Catches logic errors, missing validation
Claude	Strong on security issues, race conditions
DeepSeek	Good for basic syntax/style issues (cheap)

Using multiple perspectives catches more issues than any single model.

Example: Code Review Workflow

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()

# Get Gemini's code
code = open("feature.py").read()

# Quick GPT review
review = client.chat(
    "openai/gpt-5.2",
    f"Review this code for edge cases, missing error handling, and potential bugs:\n\n{code}"
)
print(review)

# Check spending
spending = client.get_spending()
print(f"Review cost: ${spending['total_usd']:.4f}")

Cost

Review Type	Model	Cost
Quick check	DeepSeek	~$0.001
Standard review	GPT-5-mini	~$0.005
Deep analysis	GPT-5.2	~$0.02

A few cents to catch bugs before production.

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What's your code review workflow? Do you use multiple models?