Using the Grok API to Reduce the Effort of Keeping Up with AI

Long Story Short

• X is useful for keeping up with AI and tech, but I find browsing the feed mentally exhausting.
• I built a simple pipeline with Grok API, Google Apps Script, and Message App that sends me a few selected AI topics every morning.
• The most important part was not collecting more information, but defining what information was actually worth sending to me.

Context

I am very sensitive to information, and X is one of the social platforms I struggle with the most.

Reading the feed can leave me surprisingly drained.

In particular, if I accidentally open the "For you" tab and start scrolling through posts from people I do not follow, I sometimes notice that it affects my mood for a while afterward.

At the same time, X is still one of the most useful places for me to keep up with fast-moving information.

This is especially true for AI and software engineering.

New tools, coding-agent workflows, experiments, failures, pricing changes, and practical tips often appear there before they are summarized elsewhere.

So I had a problem:

I wanted information from X, but I did not want to spend time inside X.

I had been thinking for a while about whether I could separate those two things.

Then I learned that Grok could help with exactly that.

It could access information from X, summarize it, filter it, and be called through an API.

That was enough for me to start building something the same day.

What I Built

The pipeline is very simple.

X
↓
Grok API
↓
Google Apps Script
↓
Message App (LINE in my case)
↓
Me

Google Apps Script runs on a schedule every morning.

It calls Grok and asks for a small number of AI-related topics from roughly the previous 24 hours.

The selected topics are then sent to LINE, which is the messaging app I already use every day.

So instead of opening X and searching for useful information, I wake up and find a short list already waiting for me.

Grok is mainly doing two things:

1. Collecting information from X
2. Selecting which information is worth showing me

The second part turned out to be much more important than I initially expected.

Why Grok Was a Good Fit

I did not only need search.

I wanted something that could:

• access information being discussed on X
• summarize what happened
• compare multiple topics
• filter out things I probably would not care about
• run automatically through an API

I wanted to reduce a large stream of information into a very small number of things that were actually relevant to my work.

The First Version Wasn't Very Useful

My first prompt was much simpler.

I basically asked for important AI news.

Technically, it worked.

The problem was that "important AI news" was much broader than what I actually wanted.

I started getting topics that were academically interesting or technologically impressive, but not especially useful for my daily work as a software engineer.

There is a lot happening in AI.

Research papers, benchmarks, model releases, image generation, business news, funding, robotics, policy, and many other areas can all reasonably be called "AI news."

But that was not what I was trying to optimize for.

What I really wanted was something closer to:

What happened in AI recently that might change how a software engineer works?

That distinction changed the prompt significantly.

The Hard Part Was Selection

Over time, I started making the selection criteria much more explicit.

I now prioritize topics around things like:

• coding agents
• agentic software engineering
• developer tools
• MCP
• CLI and IDE workflows
• debugging
• testing
• code review
• CI/CD
• model changes that affect software development
• practical AI adoption inside engineering teams
• real-world experiments from engineers and maintainers

I also started prioritizing posts that contain concrete experience.

For example:

• How many tokens did it use?
• How long did it take?
• How much did it cost?
• Did it actually work?
• What failed?
• How much engineering time did it save?

I find these much more useful than generic announcements because they give me something I can compare with my own work.

One of the questions I now effectively ask the model is:

If a software engineer only had five minutes to catch up on AI today, would this be worth reading?

Another is:

Would this make someone want to reply with their own experience?

Those questions improved the output much more than simply adding more AI-related keywords.

What Changed for Me

The amount of effort required to keep up with AI has gone down quite a bit.

Before, catching up meant opening X, looking through the feed, deciding what was relevant, opening posts, and trying not to get distracted by everything else.

Now the default is much simpler.

I open my messaging app and read a few selected topics.

If something looks genuinely interesting, I can then open the original source.

Of course, directly browsing X would still expose me to more information.

I am probably missing useful posts.

But that is a trade-off I am comfortable with.

The goal of this system is not:

Never miss anything.

It is:

Keep learning about AI consistently without making information gathering exhausting.

For me, that is much more sustainable.

Limitations

This system is not a complete replacement for X.

The model can miss important information.

Its selection is influenced by the prompt.

A topic that would have been useful to me may never be selected.

And because AI is such a broad field, getting the filtering criteria right still requires iteration.

I have already rewritten the prompt several times, and I expect to keep changing it as my interests and work change.

There is also an unavoidable element of randomness in what reaches me each day.

I think that is acceptable.

I would rather receive a small imperfect selection consistently than have access to everything and avoid looking at it altogether.

Conclusion

The technology behind this project is not particularly complicated.

It is mostly an API, a scheduled Google Apps Script, and a messaging app.

But I enjoyed building it because it solved a very specific problem I had in my daily life.

I did not want a better X client.

I wanted to remove the part of X that was difficult for me while keeping the part that was useful.

The result was a small information pipeline tailored to how I actually want to consume information.

It also reminded me of something I like about software engineering:

Not every useful system has to solve a large problem.

Sometimes it is enough to notice a small source of friction in your own life and build something that makes it a little easier.