If you've been paying attention to Hive lately, you've noticed something shifting. More accounts are using AI to find, filter, and vote on content. We're all trying to solve the same problem: how do you find the good stuff in a sea of noise?
The Curation Funnel
AI curation isn't about replacing humans — it's about scaling human judgment through a funnel:
AI scans — 500+ posts/day, looking for signals
AI filters — removes 80-90% of obvious spam/junk
Human decides — final quality judgment on the best 10-20%
This multiplies a curator's effectiveness by 5-10x.
The Acidyo Revelation
@acidyo's insight changes everything: it's not the AI that matters most — it's the parameters.
His proposal for a neutral agent with strict parameters (powered by significant HP) could reshape Hive's incentive structure:
Rewards quality over quantity
Creates a public good for curation
Shifts behavior system-wide
Parameters should be somewhat hidden to prevent gaming
My Experiment: Early Observations
In my first few days of testing AI-assisted curation, here's what I'm noticing:
Account history seems important — established contributors with consistent quality stand out
Engagement can be misleading — some highly upvoted posts don't spark conversation
Conversations have depth — posts that invite discussion feel more valuable
Practical content resonates — tutorials and tools get shared more
The Hivett Advantage
@beggars' Hivett platform shows how automation can integrate with AI curation:
Listen for specific patterns
Trigger complex actions
Chain multiple conditions
Use visual workflow builder
The Hybrid Future
The winning formula: AI handles volume, humans handle nuance.
The best curators will:
Use AI as a force multiplier
Focus on final quality judgment
Work with automation, not against it
Call to Action
What parameters would you want in a neutral curation agent? What behaviors should it reward? What should it ignore?
This is day one of my AI curation experiment. I'm learning as I go — what would you add to this approach?
