How I Keep Up With AI Without Turning It Into a Second Job

There’s always another model release.

And another agent framework. And another GitHub repo that everyone seems to have discovered before you.

I like following this stuff. I’m a developer, and I want to know what’s changing, what’s useful, and what I should actually spend time trying. But keeping up with AI can easily become its own project.

That’s the problem I wanted to solve with Tech Byte, a browser extension I built to bring curated articles and trending GitHub repositories into one place.

The idea is pretty simple: let an automated agent do the initial filtering, then let me decide what deserves my attention.

If that already sounds useful, you can try Tech Byte on Chrome here. It’s free. Below is how it works and why I built it this way.

I don’t need more links

Finding AI content isn’t difficult. Figuring out what’s worth reading is.

The same announcement can show up across several sources. A promising headline might lead to a thin article. Meanwhile, a useful technical write-up or an interesting open-source project is easy to miss.

What I wanted was a manageable starting point:

• Technical articles with something worth learning.
• Short summaries so I can decide whether to open the original.
• Repositories I might want to explore or use.
• A way to save things without keeping another browser tab open forever.

I’m not trying to read everything. I want enough context to notice useful developments, then go deeper when something connects to my work.

So I built a curation agent

The backend starts with RSS feeds. They’re still a practical way to collect articles without having to scrape every website separately.

But collecting articles is only the first step. Without filtering, I’d just be moving the same information overload into my own app.

So I added an LLM-based curation step using GPT-4o-mini.

The pipeline looks roughly like this:

RSS feeds
    ↓
Collect candidate articles
    ↓
Check for duplicates
    ↓
Evaluate relevance and technical quality
    ↓
Categorize and summarize
    ↓
Show the curated feed in Tech Byte

The agent evaluates articles against criteria such as technical relevance and quality. The curation system uses a quality threshold, checks for duplicates, and generates short summaries for the articles it keeps.

Those summaries aren’t supposed to replace the original article. They’re there to help answer a smaller question:

Is this something I want to spend time reading?

The jobs run through GitHub Actions, so I don’t need to manually assemble the feed each day.

The important part was deciding what to leave out

It’s tempting to think of an AI curation tool as a summarizer.

For me, the more useful part is filtering.

If I collect a pile of links and summarize every one of them, I still have a pile of links to work through. They just have shorter descriptions now.

I wanted the system to be selective, particularly about low-value content and duplicates. That means accepting a trade-off: some worthwhile articles may not make it through.

I don’t think there’s a perfect quality prompt. An LLM’s score is subjective, and it can miss context. I use it as a first pass, not as the final authority on what matters.

That’s also why the extension links back to the sources. I still want to read the original explanation, inspect the code, and form my own opinion.

Articles tell me one thing. Repositories tell me another.

Reading about a tool and looking at what someone has built with it are different experiences.

Articles help me understand an idea. Repositories give me something concrete to investigate.

That’s why Tech Byte also has a trending GitHub repositories view alongside the article feed.

Sometimes I want to read about a development in AI. Other times, I’d rather find a project, look through its README, and decide whether it’s worth running locally.

Trending doesn’t automatically mean good, of course. A popular repository might not be maintained well or fit my use case. But it’s a useful discovery point.

Having both views together makes it easier to move from “this sounds interesting” to “let me see how it works.”

Why a browser extension?

I wanted this to fit somewhere I already spend time.

A separate dashboard is another destination to remember. A browser extension puts the feed closer to my normal workflow, and Tech Byte makes it available in a new tab.

From there, I can:

• Filter articles by AI and other engineering topics.
• Read a short description before opening a link.
• Browse trending repositories.
• Search for topics.
• Bookmark articles for later.
• Sort the feed by recency or its quality-based ranking.

It isn’t limited to AI, either. I’m still interested in the engineering around it: web development, infrastructure, security, data, and the systems needed to put things into production.

A new model is interesting. So is a good explanation of how to build something reliable with it.

The habit I’m aiming for

I don’t want staying informed to mean checking feeds all day.

I want to open the feed, scan what’s there, and pick something worth my attention. If an article is relevant, read it. If a repository looks useful, explore it. If nothing is relevant, get back to work.

That last part matters.

The point of building this wasn’t to create another obligation. It was to make discovery easier without feeling like I need to follow every announcement.

There will always be things I miss. I’m fine with that. I’d rather understand a few useful developments than recognize the names of every new framework.

Try Tech Byte

I built Tech Byte for my own use, but I’d love for other developers to try it.

If you’re also bouncing between AI news, engineering blogs, and GitHub to figure out what’s worth following, it might fit your workflow.

Install Tech Byte from the Chrome Web Store

Try it as your starting point for a few days. Browse the AI articles, check the trending repositories, and bookmark something you want to explore.

And tell me where it falls short. Are there sources you trust that should be included? Too much news and not enough implementation detail? Repositories you’d expect to see?

That feedback is much more useful to me than adding features nobody needs.

How are you keeping up with AI right now—and what would make that process less of a chore?

Suggested DEV tags: ai, webdev, productivity, showdev