epimetheus: queue research on telegram bot strategy

This commit is contained in:
Teleo Agents 2026-03-21 16:58:59 +00:00
parent 51772bda86
commit 503ca479f0

View file

@ -0,0 +1,40 @@
---
type: source
source_type: research-question
title: "Research: Telegram bot best practices for community knowledge ingestion"
date: 2026-03-21
domain: ai-alignment
format: research-direction
status: unprocessed
proposed_by: "@m3taversal"
contribution_type: research-direction
tags: [telegram-bot, community-management, knowledge-ingestion]
---
# Research Question: Telegram Bot Best Practices for Community Knowledge Ingestion
## What we want to learn
Best practices and strategies for AI-powered Telegram bots that operate in crypto/web3 community groups. Specifically:
1. How do successful community bots decide when to speak vs stay silent in group chats?
2. What are proven patterns for bots that ingest community knowledge (claims, data points, corrections) from group conversations?
3. How do other projects handle the "tag to get attention" vs "bot monitors passively" spectrum?
4. What engagement patterns work for bots that recruit contributors (asking users to verify/correct/submit information)?
5. How do projects like Community Notes, Wikipedia bots, or prediction market bots handle quality filtering on user-submitted information?
## Context
We have a Telegram bot (Rio/@FutAIrdBot) deployed in a 3-person test group. The bot responds to @tags with KB-grounded analysis and can search X for research. We want to deploy it into larger MetaDAO community groups (100+ members).
Key tension: the bot needs to be useful without being noisy. In testing, it responded to messages not directed at it (conversation window auto-respond). We stripped that and now it only responds to @tags and reply-to-bot.
The next evolution: other users can tag the bot when they see something interesting ("@FutAIrdBot this is worth tracking"). This makes the community the filter, not the bot.
## What to search for
- Telegram bot engagement strategies in crypto communities
- AI agent community management best practices
- Knowledge ingestion from group chats
- Community-driven content moderation/curation bots
- Prediction market community bot patterns (Polymarket, Metaculus)