~mostafa_al_mahmud

Weekend Agent Challenge: Dev Community Pulse Agent

A weekend-built serverless AI agent that wake up at 6 AM, scans developer communities/news/posts, filters what matters with Amazon Bedrock, and delivers a curated digest by email.

#aws#ai#agents#amazon-bedrock#serverless
Dev Community Pulse Agent architecture and schedule overview

Original publication

This post is adapted from the original AWS Builder Center article: Weekend Agent Challenge: Dev Community Pulse Agent

Why I built it

The core problem was simple: every morning, I was spending too much time scanning Hacker News, Dev.to, and Reddit to figure out what actually mattered.

The goal was to turn that manual routine into an always-on agent that could fetch, filter, summarize, and email me a focused digest without needing any interaction.

What the agent does

The Dev Community Pulse Agent runs on a schedule and does four things automatically:

  • Pulls posts from Hacker News, Dev.to, r/aws, and r/programming.
  • Filters content based on configured interests.
  • Summarizes relevant items with Amazon Bedrock.
  • Sends a clean HTML digest to email through Amazon SES.
Dev Community Pulse Agent scheduled workflow and digest concept
Scheduled agent workflow: fetch, filter, summarize, and deliver.

How it was kept lean

A key design decision was to keep the build intentionally small:

  1. One Lambda instead of multiple services.
  2. Python standard library HTTP calls instead of extra dependencies.
  3. AWS SAM for fast deployment.
  4. Static JSON configuration instead of a database.

That made the whole project easier to ship quickly and cheaper to run continuously.

Challenges and fixes

A few practical issues came up during implementation:

  • Reddit rate limits required a custom User-Agent.
  • Bedrock responses needed defensive parsing.
  • Hacker News fetches had to be constrained to stay within Lambda runtime limits.
  • SES sandbox mode required verified sender and recipient emails.

Those fixes mattered more than the AI model itself because they made the agent dependable.

Architecture takeaway

This project is a good example of what an effective agent looks like in practice: small, scheduled, stateless, and focused on a single daily job.

Instead of trying to build a generic assistant, it solves one workflow well and does it consistently.

What I learned

  • AI agents are most useful when they remove repetitive context switching.
  • Retrieval and summarization are often more valuable than broad general-purpose chat.
  • Serverless and scheduled workflows are a strong fit for low-frequency automation.
  • Simplicity beats cleverness when you want something reliable enough to run every day.

Read more

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