GOTU is an impact research and technology company. We utilize VOC results to improve our content and technologies (app and AI) we're building.

Current VOC: We re building the 2026 GOTU State of Survivorship Report and we need your help.

Here's what vibe coding actually looked like in practice. What AI handled well ~70% of boilerplate on first pass navigation, state management, notification scheduling. I described the architecture in plain language, got working code back. Content generation, App Store copy drafts, edge case surfacing. That part felt like flow. Where the vibe broke: judgment calls AI suggested a standard onboarding. Generic, functional, wrong. Good UX requires knowing why someone opens an affirmation app at 7am what emotional state they're in, what friction kills the habit before it forms. Scrapped it. Built from scratch. Same with notifications.

https://app.torrify.org/
Getting close to main release but app is back up and working after a short hiatus. Excited to see users start enjoying the product and making things!

I had a weird realization recently.

As developers and makers, we are extremely careful with source code, API keys, internal dashboards, and customer data.

But when it comes to images, a lot of us do something almost automatic:
we drag them into a free online compressor and move on.

Client mockups. Product screenshots. Internal assets. Sometimes even documents, IDs, receipts, or photos that contain more information than we notice at first.

Here's the dirty secret of trading: almost every "edge" you see online is fake. Not intentionally people just don't know how to test properly. They overfit to historical data. They don't correct for multiple testing. They cherry-pick the one timeframe where it worked. They look at in-sample results and think they found something.

I saw this from the inside. I'm a quant I spent years watching even smart traders make the same statistical mistakes over and over. And the tools out there? They let you do it. Pine Script doesn't stop you from overfitting. Python backtesting libraries don't warn you about data snooping. QuantConnect gives you 16 hours of compute per experiment and zero guardrails on methodology.

So we built Varrd.

It's not a backtesting tool. It's a system that an LLM gets placed into where it is physically impossible to test wrong. The AI runs inside a framework with hard guardrails:

Hi, I m Sohail

I work on the India portal side, helping people access and understand important government services online. My focus is on platforms like Bharat Bhumi, Parivahan Sewa, EC (Encumbrance Certificate), and other public service portals.

Many users find government websites confusing or difficult to navigate. My goal is to simplify the process by providing clear information, step-by-step guidance, and updates about these services.

Through my work, I aim to help users:

Hey Product Hunt!

I m Enes, a solo developer, and I m currently building CrunchSave. I m tackling a problem that every SaaS founder hates but often ignores: Passive Churn (Failed Payments).

Most tools out there just send the same boring "Update your card" emails. I m building something different. CrunchSave uses AI Policies to personalize the recovery flow based on customer LTV and the specific reason the payment failed.

The Beta is opening very soon!

Yesterday I shared data from 50,000+ AI answers. Today I'm giving you the exact audit process we use at Rankfender the same one we've run for 500+ brands.

Here's the hard truth: 78% of brands are completely invisible in AI answers. They spend thousands on SEO but never check if ChatGPT, Google SGE, or Perplexity actually mention them.

Don't be one of them.

This 30-minute audit will show you exactly where you stand and exactly what to do next.

Hey everyone,

I don't code for a living, but I genuinely love building tools that save time and fix annoying daily workflows.

I m trying to build a solid foundation before tackling bigger challenges. Right now, I ve put together 4 simple Chrome extensions over at getplugzz.com.