Key Takeaways
- Automated research agents that deliver to your existing communication channels remove the 'remembering to check' problem entirely.
- The self-improvement mechanism — learning from what gets ignored — is what separates a static newsletter from an agent that improves signal-to-noise over time.
- Multi-platform delivery (Discord, Slack, Notion, email, Obsidian) means research surfaces in whichever context is most relevant.
The Problem: Staying Current in a Fast-Moving Field
Staying current with AI and agent developments is a full-time job in itself. The space moves fast enough that skipping a week means missing significant shifts. But manually reading everything is unsustainable, and curated newsletters lag by days or weeks.
“watches the AI/agent space, picks out useful signals”
What the Agent Does Each Day
The daily research loop: monitor configured sources, extract signals that meet relevance criteria, draft a structured brief with key developments and content angle suggestions, and deliver it across the configured channels simultaneously.
- Monitor: curated sources across Twitter, GitHub, research blogs, and newsletters
- Extract: surface items meeting relevance and novelty thresholds
- Write: structured brief with context and content angle suggestions
- Deliver: simultaneously to Discord, Slack, Notion, email, Obsidian, and markdown
- Learn: track which items prompted follow-up vs were skipped
The Self-Improvement Loop
The most valuable part of this setup isn't the brief itself — it's the feedback loop. Items that get clicked and explored signal high relevance. Items consistently ignored signal low relevance for this particular reader. Over time, the agent's signal selection tightens without manual reconfiguration.
Story sourced from the official Nous Research Hermes user-stories page. Original author: @gkisokay.