u/Elinova_3911

I built a small project to deal with information overload in AI.

As someone learning and working in data science, I kept struggling with keeping up with AI updates. There’s just too much content across blogs, research labs, and media.

So I built a small pipeline to explore this problem:

• collects updates from curated sources
• scores them by relevance, importance, and novelty
• clusters similar articles together
• outputs a structured digest

The idea was to move from “reading everything” to actually prioritizing what matters.

Curious if others have built similar projects or have better ways to stay up to date?

I’ve been running into the same issue recently—too many sources (research blogs, company updates, media), and a lot of overlap or noise.

I built a small pipeline to experiment with this:

• ingestion from curated sources
• deterministic filtering + deduplication
• LLM-based scoring (relevance, importance, novelty)
• clustering of related content
• structured digest output

Main goal was to reduce context switching and make it easier to focus on what actually matters.

Curious how others here approach this—tools, workflows, or habits?

I’ve been working on a side project around AI information overload.

The idea:

• collect updates from multiple sources
• score them (relevance, importance, novelty)
• cluster similar content
• generate a structured digest

I tried to focus on:

• combining deterministic pipelines with LLM-based steps
• keeping the system inspectable (not a black box)
• making practical trade-offs (cost vs complexity)

For those hiring or reviewing portfolios:

would something like this be considered a strong project?

Any feedback appreciated.

Keeping up with AI updates started to feel like reading the same thing 5 times across different sources.

So I built a small pipeline that:

• pulls updates from different places
• scores them by relevance/importance/novelty
• clusters similar stories together
• outputs a digest instead of a feed

It’s not perfect, but it made things a lot easier to follow for me.

Curious if others have tried something similar or have better approaches?

I built a small project to deal with information overload in AI.

As someone learning and working in data science, I kept struggling with keeping up with AI updates. There’s just too much content across blogs, research labs, and media.

So I built a small pipeline to explore this problem:

• collects updates from curated sources
• scores them by relevance, importance, and novelty
• clusters similar articles together
• outputs a structured digest

The idea was to move from “reading everything” to actually prioritizing what matters.

Curious if others have built similar projects or have better ways to stay up to date?

Happy to share the repo and demo if anyone’s interested—left them in the comments.