The AI Curation Experiment: Beyond Automation
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
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
- 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?