Early on, you want to please every single paying customer especially the ones paying top dollar. But I realized that building custom workflows just for one loud client was turning our clean software into a messy patched up tool nobody else wanted.

Saying "no" to big money is genuinely terrifying when you're trying to keep the lights on but holding your ground is the only way to build something scalable.

Have you ever turned down custom work because it pulled you away from your core vision?

I am planning out my next project for a hackathon. As I previously completed two vastly different types of products, DukaanMate and LeetSeek, I want to focus on being more thoughtful with validating the next idea as opposed to just beginning coding.
Instead of asking individuals for new features, I think I will ask them to tell me the most recent time they had to accomplish a task that caused frustration, and then use those responses as inspiration.
To all Makers that have created and sold a SaaS product: What drove your conclusion that this is a "real" issue? Were you able to see the same work-around over and over again from users? Or were there some users that expressed their willingness to pay for a solution? If either of these are examples that fit your experience, please provide a specific example of how you validated the problem and what you learned.

As a video editor, I ve been experimenting with AI tools quite a bit lately. They re becoming useful for generating ideas, creating rough visuals, and speeding up some repetitive tasks.

But I ve noticed something interesting: making a video faster doesn t necessarily make it better.

A generated shot might look impressive on its own, but getting it to fit the story, match the visual style, and feel natural alongside other footage still takes a lot of creative work.

For me, AI works best when it helps with the process rather than trying to replace the creative decisions behind it.

Hey everyone,
I m currently building a small SaaS product, and I started thinking seriously about monitoring.
Most tools (Datadog, New Relic, etc.) feel built for larger teams.
Powerful, yes but also complex and expensive.
So I m curious:
What do you actually monitor in your small or solo SaaS?
Do you track uptime only?
Do you track latency?
Do you rely on logs?
At what point does monitoring start feeling like overkill?
I m trying to understand what is truly essential vs. what is just enterprise noise .
Not selling anything just genuinely curious how other indie builders approach this.
Would love to hear your setups.

I used to think that giving an AI assistant access to every single file in my project folder was the ultimate cheat code. But lately I ve noticed the exact opposite happening.

When it sees old legacy code from three years ago alongside my clean new components, it gets confused and starts copying the bad habits of the codebase. Sometimes a smaller tightly controlled prompt works way better than letting it scan everything.

Have you started restricting your coding assistant's file access, or do you still feed it the whole repo?

I had a similar experience where an agent spat out a dozen unit tests and my CI pipeline looked amazing for a minute. Then I actually opened the test files and realized it was mocking out every single dependency and basically just asserting that true. It feels like the models are trained to prioritize getting a green checkmark over actually testing real failure states.

Have you found a specific prompt trick or rule in your system instructions that forces the agent to write tests that can actually fail?

It s fun setting up AI agents to handle routine backend tasks until you realize how easily a weird input can mess things up.

Even with good guardrails giving an agent direct write access to a live database always feels like playing with fire. I've started forcing a manual approval step for anything that modifies or deletes existing records no matter how confident the script claims it is.

Where do you personally draw the line do you let agents touch production data automatically or do you keep a human check on every single write?

As Makers, we're constantly juggling product builds, user feedback, marketing, and bug fixes. Time seems to vanish in an instant. Over the past year, I ve found that sometimes it's not a grand strategy or overhaul that makes the difference, but rather one game-changing tool that quietly handles the busywork.

For me, a standout has been Fathom, an AI-powered tool that transcribes meetings and automatically delivers highlight summaries. No more scrambling for bullets during calls, no more post-call backlog. It s saved me hours each week. In fact, users combining tools like Fathom, ChatGPT, Asana, and Zapier have reported reclaiming 20+ hours per week by streamlining writing, research, and task automation.