r/SystemsAndSignals

I have a small setup and I have been trying out different AI tools over the last year to help me out.

It felt like a game changer at first. Emails, content creation, even handling small things got faster.

But after a while I began to notice something. There are still so many tools where I need to hop between multiple apps, repeat the same instructions and connect things together manually. It’s like the work is faster but not truly automated.

Take something as basic as dealing with incoming leads. There are still several steps involved checking messages, writing responses, organizing info, following up later.

I thought AI would take away more of this kind of work than it actually does.

Now I am trying to figure out if I am just using these tools wrong or this is where more advanced setups like workflows or agents start to make a difference.

I want to know what everybody else is doing.

Are you really able to automate end-to-end or are people still doing a lot of manual work underneath?

Hello guys! As my brain itches for information, I am focusing mainly on what people/targeted audience would want to have an application on..

Could be anything that pops into mind.. any form of information or different perspectives would help me alot to understand how deep we can get when it comes to these subjects!!

Thank you!

I am observing a pattern where the AI models wants you to keep interacting with them until you hit the limit. Especially, with claude, it tries to give long answers and redundant information and doesn’t follow the exact context! GPT 5.5 seems to be more efficient in answering but still far from one-shot answers! Also, the typical prompt practices like defining the role, giving examples, etc doesn’t lead to good results in one go! The models tend to keep the interaction alive, expand context or over contextualize. Are there any proven prompting techniques to break such patterns?

A lesson we learned the hard way: if customer feedback lives in five different places, your roadmap will drift fast.

For a while, we were treating feature requests like loose notes from calls, support messages, random Slack threads, and a few emails. Every request sounded important in the moment, but when it came time to choose what to build next, we had no real way to compare them.

The biggest issue was not lack of ideas. It was that we had no consistent signal. A loud request from one customer could look bigger than a quieter problem shared by many others. That made prioritization feel more like politics than product work.

Once we started collecting feedback in one place and scoring it by pattern instead of volume, the roadmap got a lot clearer. We stopped debating every request from scratch and started asking better questions like: how many customers need this, how often does it come up, and what business problem does it actually solve?

I am curious how other founders handle this. Do you have a system for turning scattered feedback into something actionable, or do you still rely mostly on intuition and a few strong customer voices?

I am observing a pattern where advanced reasoning models try to over hypothesize, explore too many edge cases, and infer hidden intent, which generates very long chains of logic. If the advanced reasoning model doesn't know something, it tries to interpolate and come up with a coherent explanation, even if it is not fully correct. Additionally, for a retrieval-based task, the models start reasoning instead, leading to hallucinations. This usually happens when the prompts are too ambitious and the context window is too large. Curious to see if others are observing similar patterns