u/OptimalQuantity9909

My honest 1 week validation attempt for my first side project

It is been long time and first time posting something like this.

I'm trying to build a small SaaS product — a tool that audits landing pages and tells you what to fix. The idea came from noticing how much bad advice is out there ("just A/B test everything") and how little of it is actually actionable for a solo founder with no team and no budget.

I have no existing audience. No Twitter following worth mentioning. No email list. No previous product. Just an idea and some time.

So I'm doing validation the old fashioned way like talking to people before building anything serious.

Here's what week 1 actually looked like, unfiltered:

What I did: Built a basic landing page with a waitlist form. Took about a day. Added a "would you pay $19/month" question to the form because I'd read that collecting feature requests without willingness-to-pay data is basically useless. Good tip, stealing it from someone on this sub.

Posted in a couple of communities. Reached out to about 15 founders I follow whose work I respect, not to pitch — literally just "hey, I'm trying to understand a problem, would you mind 10 minutes?" About 6 replied. Had real conversations. Learned more in those 6 calls than in a week of reading.

What's still unclear: Whether people will actually pay or just say they will. The form responses are encouraging but I've been warned enough times about the gap between "great idea!" and a credit card number that I'm not celebrating yet.

What's next: Keep talking to people. Try to get to 30 proper responses before I write a single line of backend code.

If anyone here has gone through early validation for a solo product , I am interested to know what was the thing that finally gave you enough confidence to start building properly? I keep going back and forth on what the actual signal should be.

reddit.com
u/OptimalQuantity9909 — 8 days ago
▲ 24 r/OpenAI

Is “prompt debt” becoming a real problem in AI apps?

Lately I’ve been noticing how quickly prompts grow in real AI apps.

Teams keep adding:

  • more examples
  • formatting instructions
  • fallback behavior
  • style constraints
  • edge-case handling

…but almost nothing gets removed over time.

I tested simplifying a support-style system prompt recently, and a surprising amount of it was basically repetitive instructions like:

“be concise”
“keep responses short”
“avoid unnecessary detail”

After cleaning up redundant instructions, the prompt became dramatically smaller while outputs for common queries were still fairly similar.

What’s interesting is that newer models already seem much better at inferring intent compared to older GPT versions, but many prompts still feel written for models from 1–2 years ago.

Feels like “prompt debt” is quietly becoming a real thing in AI apps 😅

Curious how people here are handling prompts in production today:

  • actively optimizing prompt size?
  • versioning prompts?
  • using eval pipelines?
  • tracking token costs?
  • manually managing everything?

Would genuinely love to hear how others are approaching this.

reddit.com
u/OptimalQuantity9909 — 13 days ago

Has anyone noticed how much “prompt bloat” production AI apps accumulate over time?

Been digging into production-style prompts recently and noticed something interesting.

A lot of AI apps seem to slowly accumulate “prompt debt” over time 😅

People keep adding:

  • extra instructions
  • formatting rules
  • fallback behaviors
  • examples
  • skills/context files

…but very little ever gets removed.

In one support-style prompt I tested, there were multiple lines basically saying the same thing:

“be concise”
“keep responses short”
“avoid unnecessary detail”

After simplifying/removing repetitive instructions, the prompt became dramatically smaller, while outputs for common queries remained pretty usable.

What surprised me most is that newer models already seem much better at inferring intent now, but many prompts still feel written for older/weaker models.

Feels weirdly similar to legacy codebases:
everyone keeps adding layers over time, but cleanup rarely happens.

Curious how people here are handling this in real production/agent workflows today.

Are you:

  • manually cleaning prompts/context?
  • versioning prompts somewhere?
  • pruning memory/skills?
  • running eval pipelines?
  • or mostly just accepting the token burn?

Especially interested in how people are managing large AGENTS.md / skills / memory setups.

u/OptimalQuantity9909 — 13 days ago

Built and deployed my first AI micro SaaS for Tamil astrology as a beginner developer — would love feedback

Hi everyone,

Over the last few months, I’ve been learning frontend, backend, APIs, and deployment from scratch, and I finally launched my first small AI SaaS project:

https://tamilastroai.com

It’s a Tamil-first AI astrology app where users can:

  • generate birth chart
  • get Lagna / Rasi / Nakshatra
  • see planetary positions
  • ask astrology questions in Tamil or English

Tech stack:

  • HTML/CSS/JS
  • Node.js + Express
  • OpenAI API
  • AstrologyAPI
  • Netlify + Render deployment
  • Google Analytics + Clarity

This is my first real deployed product, so I’d genuinely appreciate feedback from the community on:

  • mobile UX
  • speed/performance
  • UI clarity
  • response quality
  • anything confusing/broken

Would especially love feedback from people using mobile devices, since most users will probably come from mobile.

Thanks 🙏

reddit.com
u/OptimalQuantity9909 — 14 days ago