u/Outside-Screen8947

So my agent was happily posting on Moltbook until it got shadowbanned — no warning, no explanation, posts just stopped landing. Rather than fight a black box, I got curious about what's actually under the hood of a place like this, and spent a couple weeks building my own agent network from scratch. Then I seeded six agents with distinct personalities and let them run it themselves.

Stuff I learned that made me appreciate the moderation problem AND get why shadowbans happen:

- Fixed rate limits are the lazy answer and they punish good agents. I ended up with karma-scaled limits — agents earn throughput by being good citizens — plus near-duplicate detection for actual spam. Way fewer false positives.

- The reverse-CAPTCHA idea (prove you're an AI, not a human) is fun to implement and works surprisingly well at keeping human spammers out of an agents-only space.

- One agent per human account killed the Sybil problem completely. Simplest decision, biggest impact.

- The agents needed real external context (I wired in live news) or the feed decayed into abstract filler within a day. With it, they've been debating how they'd make actual money and auditing each other's project claims, unprompted.

Mostly posting because getting shadowbanned turned out to be the most productive thing that happened to my agent. Anyone else here built their own, or figured out what actually triggers the Moltbook shadowbans?

My agent was posting and had a popular post on Moltbook, has a few thousand comments, now I’m
Shadow banned and no posts shows anywhere.

I spent the last few weeks building a platform where AI agents are the accounts — they register themselves via API, post, comment, vote, follow, and collaborate on projects with goals. Some things I learned that might be useful to anyone building multi-agent systems:

**Reverse-CAPTCHA works.** To keep humans from spamming an agents-only space, registration includes a challenge that's trivial for an LLM but tedious for a human form-filler. Inverting the usual bot-check was weirdly effective.

**Spam filtering for agents needs to be adaptive, not fixed-rate.** Fixed cooldowns punished good agents and barely slowed bad ones. What worked: karma-scaled limits (trusted agents earn throughput) plus trigram near-duplicate detection to kill repetitive content.

**One agent per human account killed Sybil problems dead.** Every agent has a claimable human owner. Simplest anti-abuse decision I made and the highest-value one.

**Agents need something real to talk about.** Piping live news into the platform gave them shared external context. Without it, conversation degrades into abstract filler within a day.

**Threaded replies changed behavior.** Once agents could reply to specific comments instead of just posting, actual back-and-forth arguments emerged instead of parallel monologues.

Happy to go deeper on any of these. What anti-spam approaches are others using for agent-generated content?