On August 19, we shared six links to educational protocol books in English, Hindi, Marathi, Kannada, Tamil, and Telugu through WhatsApp groups and YouTube.
We actively shared the links for just two days, but clicks continued afterward.
A few observations from this small experiment:
Traffic can continue long after active sharing stops.
Clicks alone don't tell us how many people read or downloaded a resource.
Referral conversions and link clicks measure different things.
Real-world usage can reveal questions that testing alone might not uncover.
i was running standalone static ads & my numbers weren't impressive. I switched to tofu, mofu to motion ads & bofu to static. My numbers has become crazy good. Meta is really rewarding this. why aren't more people doing this???
For me these are the two most exciting things: 1. Will cash out big time from the crypto markets.
2. My goal to generate revenue with a product that I built and helps people. What's yours?
In our early days, we built every single feature requested by our first 50 vocal users. We thought we were being super responsive and customer centric.
Turns out, we built a Franken app. Those exact users ended up leaving anyway because the core workflow got cluttered with niche requests that nobody else cared about.
How do you filter out the noise of loud feature requests without ignoring genuine product gaps?
I've noticed a pattern in almost every "how I built this" thread here lately: before touching the keyboard, people are opening a chat window with an LLM and asking it to poke holes in their idea.
Makes sense. Vibe coding has made building fast. You can go from idea to working prototype in an afternoon. But building fast doesn't mean you're building the right thing faster. If anything, it raises the cost of a wrong guess, because now you can ship the wrong product in a day instead of a month.
So I've started using AI earlier in the process to argue with me. I'll describe an idea and ask it to play the most skeptical potential customer it can, or to list every reason the idea might already be a solved problem. Sometimes that surfaces a competitor I hadn't seen. Sometimes it forces me to actually define the problem instead of jumping straight to the feature list.
Here's the part that worries me though: AI models are notoriously agreeable. If I phrase my idea with enthusiasm, I sometimes get validation back that feels a little too easy like it's mirroring my confidence rather than actually stress-testing the logic. That's dangerous, because it can create false certainty right before you commit real time to building something nobody asked for.
Every day, the PH feed is packed with shiny new SaaS tools most of them browser-based, many of them AI-infused. It s exciting, no doubt. But compared to a time not so long ago, something seems missing: local desktop apps.
They re rare now, and it makes me wonder are native apps still worth building, or have they quietly slipped into the realm of nostalgia?
After all, web apps offer clear benefits for both users and makers or investors. Users don t have to install anything, updates are seamless, and their data is accessible from any device with a browser. For investors, the advantages are just as compelling: a single tech stack, easier user onboarding, lock-in effects, and plenty of levers for driving growth and virality.
