An enlightening conversation with my eldest cousin sister currently living abroad who used to work for Infosys 15 years ago:
Me: "Ya so Infosys is still doing mass hiring in Indian colleges, so is Wipro."
Her: "Oh okay, what's the package these days?"
Me: "3-6 LPA CTC."
Her: "What?? This is what WE were paid!"
My other cousin sister worked for Wipro. She said the exact same.
And then Narayana Murthy, Billionaire, Founder of Infosys, says that Indians should have a 72-hour work week, i.e, 12 hours for 6 days.
This is the current state of India's tech sector. Freshers, experienced developers, everyone is struggling to find a job in this market, having to settle for absolute outdated minimum wage stipends. The country's premier engineering institutes are an export sector for global tech companies contributing to the GDP of first world countries, all while said companies setup shop in Bengaluru, Pune, Hyderabad, Gurgaon and outsource majority of their work to cheap Indian labor.
But by all means, lets purchase AI tokens and further add to the problem.

does this happen to anyone else?

you get an idea and it feels genius. you can't stop thinking about it, you start building right away. the first weeks are pure energy.

then somewhere along the way the doubts creep in. is this actually useful? would anyone pay for it? didn't someone
already do this better?

and the weird part is it gets worse the closer you get to finishing. right when there's not much left to do, I start seriously thinking about dropping it and moving on to the next shiny idea.

When I started out, I kept trying to post new threads right away thinking it would drive traffic but almost none of them got through moderation.

After spending time engaging on other founders' launches and building up actual profile activity the approval process felt completely different. It seems like the system prioritizes known participants over raw post volume.

Did you notice a specific turning point where your discussion posts started getting approved consistently?

I spent an entire afternoon trying to get an LLM to write API documentation for a payment gateway. The first three outputs were full of summaries and fake endpoint URLs that looked professional but were completely useless.

The breakthrough happened when I stopped asking for text and fed the model raw JSON payloads along with three specific error response codes. Giving it constraints forced it to write accurate reference guides instead of high-level marketing fluff.

Documentation turnaround time went from four hours down to twenty minutes per release. The secret is treating the model like a technical editor rather, than an open-ended creative writer.

How do you structure your input prompts to get technical output from generative AI models?

I found that my automated coding assistants kept resetting my main environment template file every time I asked them to update dependencies. They acted like my config files were scratchpads erasing custom keys I needed for production. That was a headache.

The fix was simple: I set file-level permissions and added a clear exclusion list in the root instruction file. I made sure to tell the AI exactly which files were, off-limits. After that unintended changes stopped completely.

It saved me hours of debugging and made my local test runs consistent again. Now every time I use an agent the prompt includes a reminder to check file boundaries before running any script.

What method do you use to keep your coding tools from making unauthorized edits to sensitive system files?

One thing I've learned while building software is that getting a feature to work and making it reliable are two different challenges.

A feature might work perfectly during testing but behave differently when real users interact with it.

For example:

• A form works, but doesn't handle unexpected input properly.

• An API responds correctly, but fails when an external service becomes unavailable.

• A dashboard displays accurate information, but doesn't distinguish between current and outdated data.

• An AI feature produces a convincing answer, but doesn't have enough context to support its conclusion.

The hardest thing to get right was getting people successfully through sign-up.

Initially I started with one time links, had a few successful sign ups and inbetween them would be a couple 'unauthenticated' emails. I would reach out via email to try find out why they didn't complete the process, but get no response. I eventually found out that some email providers burn the one time link when they scan the email.

Hi everyone!

We always hear about the big, popular productivity tools but I m more interested in the quiet game-changers. The tools that aren t in every headline but made a huge difference in how you work or build.