The AI landscape moves at breakneck speed. New models drop weekly, research papers flood arXiv regularly, and the most insightful discussions often happen in the trenches of Reddit communities. But staying on top all of it everyday is a challenge!
So I did what AI enabled me to do :D Built an agent that monitors AI discussions across Reddit, identifies the most important topics, and delivers curated insights straight to my inbox, twice a day.
Reddit hosts some of the most valuable real-time discussions about AI developments. Communities like r/OpenAI, r/ClaudeAI, r/LocalLLaMA, and r/MachineLearning are where practitioners share breakthroughs, debate limitations, and surface emerging trends before they hit mainstream tech media.
But manually monitoring these communities is unsustainable:
I needed an agent that could filter, categorize, and summarize these discussions while preserving the nuanced insights that make Reddit valuable.
The agent operates on a simple but powerful principle: combine Reddit’s engagement metrics with AI’s analytical capabilities to surface what actually matters.
The script is deployed via AWS Lambda and EventBridge for twice-daily execution. Here’s how it works:
Data Collection: The system fetches posts from both “hot” and “new” feeds to ensure comprehensive coverage. This dual-feed strategy prevents missing important discussions in less active communities while prioritizing engagement-driven content.
Engagement Preservation: Before sending data to AI, the system captures Reddit’s native engagement metrics—upvotes, comment counts, and calculated engagement scores. This ensures the final output reflects what the community actually found valuable.
AI Analysis: Google’s Gemini model processes the content, identifying the key topics per subreddit and generating concise summaries. The AI receives not just post titles but full content plus the top 15 comments for context.
Intelligent Ranking: The system matches AI-identified topics back to original engagement data, creating a TL;DR section featuring the five most engaging discussions across all communities.
Production systems need comprehensive monitoring. The agent integrates with Langfuse for end-to-end observability:
The system includes extensive error handling and fallback mechanisms to ensure reliable twice-daily delivery, even when individual APIs experience issues.
Building this agent taught me several valuable lessons about production AI systems:
Reddit’s engagement metrics provide crucial signal about content quality. Preserving and leveraging this data ensures AI summaries reflect community consensus rather than just algorithmic preferences.
Including top comments dramatically improved summary quality. The AI could understand not just what was posted, but how the community responded—capturing nuance that titles alone miss.
Langfuse integration proved invaluable for understanding system behavior, optimizing prompts, and debugging issues. Production AI systems need comprehensive monitoring.
The system’s reliability comes from straightforward architecture, extensive error handling, and fallback mechanisms rather than complex optimization.
Deploying this agent via Lambda function was a learning experience by itself.
The first version ran on a VM on GCP, triggered via cron twice a day. The VM got shutdown for a silly reason which made me realize I needed a better system.
I moved to AWS Lambda (a first time for me) and it was quite a learning experience. Different packaging requirements, EventBridge for triggers instead of cron, and managing dependencies in a serverless environment.
The current system runs reliably though I’ve not figured out automatic deployment yet and with comprehensive monitoring.
The system surfaces important discussions I would have missed and delivers them in a digestible format.
Each email includes:
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The agent has surfaced numerous important developments: