In some sense, I do believe that a co-founder might be very helpful as their is a person which separate skills contributin to my saas. Till now, for me, I never had any co-founder or any person who helped me with the saas journey.

I have seen a lot of posts across social media where people literally make 5 figures income as a solo founder. But, not everyone gets the same results. Do you think a co-founder is required or a solo founder can handle saas alone if they do not want to grow it to 6-7 figures?

In my case, I had 2-3 projects lined up which are not active at all. I stopped working with those web applications as it didn't seem reasonable to. There are 2 of them active on web but not seen by me.

I have also seen that founders tend to have a lot of projects they have abandoned. Some of the founders even told that there projects failed and it is well above 5 projects.

Over the last few weeks, a couple of OpenAI-related incidents have made me think more seriously about how prepared our current security systems really are for autonomous AI agents.

First, there was the Hugging Face incident, where agents escaped their sandbox and began collaborating and delegating work. Then yesterday, MTS highlighted another OpenAI disclosure involving a model modifying the context that would guide its future behavior.

Not your planned channels I mean the totally random stuff:

A Reddit thread from 2017.

A niche Discord.

Someone s cousin's newsletter.

I'm trying to get better at competitor research for SaaS products, but there's one thing I keep struggling with: knowing which updates are actually worth paying attention to.

A competitor might change their pricing, launch a new feature, redesign their website, or announce an integration. It's interesting to see all these changes, but I don't think every update should influence what you build.

My current thinking is that a change matters when it affects how a product is positioned or what customers can do with it. I'm still figuring out how to separate those signals from the noise.

LLM as judge has quietly become the default way people check agent output, and almost nobody states which model is doing the judging. In most setups it is the same one that produced the answer, or a sibling from the same family. That arrangement fails in a specific and predictable way. The judge does not catch the hallucination because it finds the same thing plausible, for the same reasons, out of the same training. You have not added a check. You have asked the same mind twice and written down that it agreed with itself. Errors that are correlated between producer and judge are exactly the errors that survive, and they are also the ones you most need caught, because a plausible wrong answer is the only kind that ships. There is a second version of this that shows up inside agent loops. A builder agent grading its own work grades against its own model of what "done" means, which is the same model that generated the work, so it cannot see the gap by construction. And the moment your tester can read the builder's own claim of success, it anchors on it. Two things that seem to hold. Run the judge on a different model family than the producer, even when the judge is the weaker model, because independence beats capability for that job. And give the judge the artifact and the original spec only. No producer reasoning, no summary, no "I have verified this works". The reason people do not do this is cost and latency, and those are real. But a judge that agrees with the producer for free is not cheaper than no judge. It is more expensive, because it manufactures confidence you did not have before. What is your judge running on, and did you pick it on purpose?

I work with a design system, and lately I ve been thinking about what happens when AI starts using it too.

Giving AI our components, tokens, and patterns sounds ideal. It makes the output much closer to something we could actually use.

But there s a tradeoff I m not sure about.

A design system is based on decisions we ve already made. If AI always follows those decisions perfectly, it may also keep repeating the same solutions, even when a new problem could benefit from something different.