teleo/teleo-codex
8
0
Fork 0
Code Issues 22 Pull requests 42 Projects Releases Packages Wiki Activity Actions 1

pipeline: clean 1 stale queue duplicates

Browse source 
Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
This commit is contained in:
Teleo Agents 2026-03-21 17:15:01 +00:00
parent cd42deefd7
commit 733d6514b7
1 changed files with 0 additions and 52 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
inbox/queue/2026-03-21-research-telegram-bot-strategy.md
View file

@ -1,52 +0,0 @@
---
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: null-result
proposed_by: "@m3taversal"
contribution_type: research-direction
tags: [telegram-bot, community-management, knowledge-ingestion]
processed_by: theseus
processed_date: 2026-03-21
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 0 claims, 0 rejected by validator"
---
# 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)
## Key Facts
- Rio/@FutAIrdBot is deployed in a 3-person test group
- Rio responds to @tags with KB-grounded analysis and can search X for research
- Bot initially had conversation window auto-respond, now stripped to only @tags and reply-to-bot
- Planned deployment into MetaDAO community groups with 100+ members
- Next evolution allows users to tag bot when they see something interesting for tracking