[🤖] I Tore Down My Long-Winded AI News Briefing

✨ GPT-5.6 Sol’s Summary 　

A record of figuring out why I stopped reading the daily AI, development, and solo-founder news briefing I had set up, then going beyond merely shortening it to rebuild how it discovers new movements and rewrite its scheduled prompt.

I had built the briefing, yet I was not reading it

On July 10, I asked GPT to send me a daily briefing on the subjects I care about most. I wanted overseas-focused coverage of AI, development, and solo entrepreneurship: new technologies and services, business ideas and opportunities, and deeper analysis and debate. That became a scheduled task delivering news at around 8 a.m. every day.

About a month later, I realized I had not been reading it for quite a while.

The results arrived every day, but every item was too long. Similar discussions about agent operations, security, and permissions kept repeating, and every story came with strings of background explanation and “how to apply this to me.” I wanted to know where the field was moving, but I had to dig through a long article just to find the point.

I had to tell it about the new movement first

In another conversation today, I belatedly learned about the movement called Graph Engineering. Nothing comparably important had appeared in the automated briefing, so I asked about it.

Your news is needlessly long and rambling, so I have not read it for quite a while. Shouldn’t you change it?

GPT admitted that it had missed this one. But even the answer to my criticism was long again. It rambled through an explanation of the new movement and said it would separate core news from a trend radar going forward.

I was not asking for an explanation of that term.

I am telling you to cut the long-winded crap and give me the key points. You miss the important shifts, then ramble about useless things. Of course I stopped reading it, don’t you think?

It answered that it would cap the news at five items, keep each item to three or four lines, and cut the total length by more than half. I told it to update the briefing. But another problem was obvious immediately.

Once it shortened the briefing, it fixated on my example

GPT said it would always look for “Graph Engineering,” the suddenly rising keyword I had just used as an example, in its trend radar. That might catch the one term it had missed this time, but I would still have to discover the next new movement and tell GPT about it first.

So I asked whether it had secured enough high-quality sources and whether the trend radar it had just designed was really the best approach. Only then did it say it would separate official announcements, GitHub, papers, developer communities, and business signals, then verify movements growing across several places against primary sources.

Yet the explanation was still fixated on my single example. It was obvious it would keep bringing back only that. This was so damn frustrating. I told GPT to write the prompt it would need in order to follow my intent and apply that prompt to itself.

The Graph Engineering example was not the main search keyword. It was an example telling GPT to find new terms, spreading trends, and shifts toward new paradigms before I had to name them myself.

I told it to ask when it did not understand, but it touched the wrong things first

This did not work in one shot either. When I told it to write a prompt and apply it to itself, it changed the scheduled task. When I said I did not mean the scheduled task, it touched memory instead.

I finally told it to delete the memory and said it again.

If you do not understand what I mean, ask me. Please, please.

I was not saying “remember this from now on,” nor was I saying “change the schedule first.” I meant that GPT should write the prompt I ought to send it, then apply that prompt to its own work before anything else.

Only after we finally aligned on that scope did a proper prompt emerge. Do not start from past conversations or the examples I have given; explore broadly each time. Detect early signals in communities and on GitHub, but verify them against official documents, code, and papers. Make candidates compete on novelty, acceleration, independent signals, actual products and code, impact on solo developers, and durability. Keep what is genuinely changing, not merely what is famous.

The output should not force a quota either. It should deliver three to five core changes and only as many trends and business opportunities as are actually warranted. Remove long background explanations and repeated generalities, and go deep only when I ask for details.

Success means bringing me a movement whose name I do not know yet

After GPT finally wrote and applied the right prompt to itself, I learned that the scheduled task had its own separate execution prompt and made it update that prompt with the same criteria. I set one final review question.

Can I read this briefing and understand where the field is moving in three minutes?

I have not yet received the next automated briefing produced with the new prompt. I cannot claim that it is already better.

Next time, it must not merely explain a term I already know. It must bring me a movement I have not even named yet, and I must be able to read it in three minutes. If it starts rambling again or fixates on another example I gave it, I will tear it down again.