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I Built a Multi-Agent Code Review System That's 4x Faster (Thanks to Zero-Copy Database Forks!)

Simran Shaikh on October 28, 2025

"I didn't know you could do that!" - My reaction when I discovered Tiger Cloud's zero-copy forks 🤔 The Problem I Wanted to Solve Cod...
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Varshith V Hegde

Really cool approach! 🚀 I love how each agent works in its own fork to avoid conflicts. Quick question — how do you handle cases where agents give conflicting recommendations? Do you have a priority system or some kind of consensus mechanism planned? 🤔

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Simran Shaikh

Thanks! Great question! 🙌
Right now, I use a weighted scoring approach:

Security gets the highest weight (35%) - can't compromise on security!
Quality and Performance get 25% each
Documentation gets 15%

The approval logic is simple: Security must score ≥80 AND all others ≥60. This way, a critical security issue will always block approval even if everything else looks good.
For actual conflicts (like one agent says "good" while another says "bad" for the same code), I honestly don't have a sophisticated mechanism yet. Right now, all findings are just displayed together, and the weighted score helps decide overall approval.
I'd love to add a consensus mechanism in the future - maybe flagging when agents disagree on the same line, or having confidence scores. The "agent debate" idea sounds really cool too!

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Varshith V Hegde

A great project and awesome explanation ... Loved it !!!

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Simran Shaikh

Thanks you 😊

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Pravesh Sudha

Great Work!

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Simran Shaikh

Thank you Pravesh

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Divya

This is super awesome 🔥

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Simran Shaikh

Thank you Divya 😊

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Ali Farhat

Amazing!!!

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Simran Shaikh • Edited

Thank you Ali Farhat

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Wayne Workman

Database forking, what a concept. I wonder how it works under the hood...

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Simran Shaikh

Original DB (10GB) → Full Copy (10GB)
Time: 5-10 minutes
Storage: 20GB total

Tiger Cloud's Zero-Copy Fork:
Original DB (10GB) → Lightweight reference
Time: 2-3 seconds
Storage: 10GB + only changes
How it actually works:**

  1. Initial Fork: Creates a new database that points to the same underlying data blocks as the parent
  2. Shared Reads: All forks read from the same physical storage
  3. Copy-on-Write: When a fork modifies data, only that specific block gets copied
  4. Isolation: Each fork sees its own version, but shares unchanged data