Some software is naturally transactional people use it on Friday month ends, or during specific project sprints. But then they churn because they didn't get enough daily value.
If your product isn't a daily-active messaging or project board tool trying to force daily usage feels unnatural and forced. Yet, investors and metric dashboards always push for higher engagement frequency.
If your product isn't meant to be opened every single day, how do you engineer sticky loops or meaningful checkpoints so users don't forget you exist between use cases?
Today, the productivity domain in tech is very well developed - there are tools for almost any need!
But at the same time, there s always a feeling that there might be something else, something better. All the time.
What I like about this space is that once people start using tools like Miro, Notion, Trello, ClickUp, etc., they tend to keep testing new things and experimenting with different tools.
When you re trying to cover multiple platforms with your brand and also cover other platforms for your business account, things can get a little out of hand.
How do you manage your communities and everything that comes with it?
Our Product Hunt mobile team has been working diligently behind the scenes to build a brand new app experience from the ground up, and we re thrilled to finally debut it. For those that missed it, we launched Product Hunt mobile apps for iOS and Android earlier this week. Download the app to browse Product Hunt from anywhere.
Here are just a few things to look forward to:
Native features and designs Faster, more reliable, and platform-specific, to deliver a top-notch mobile experience.
I m designing a content-safety check for an app that generates personal feedback text, and I m stuck on one tradeoff.
Option A: one call. The model returns JSON with the label as the first key, then the generated text. This saves a round trip and one failure point. The cost is that the classification and the generation share one prompt, so a long or adversarial user message could hurt both at once.
Option B: a separate call. It s cleaner, and you can run it on a smaller, stricter prompt. The cost is added latency and a second thing that can time out.
I have been thinking about how much time we spend explaining the same things to AI over & over again. Our projects, preferences, day to day work & past decisions, sometimes it feels like conversations are same & starting it from scratch. As I'm working on this problem, i'm interested how others here handle it.
Some questions like Do you keep your notes to AI, feed them every information? & just rely on built in AI Memory? Do you have any better way to keep your context organized?
When someone says a product feels confusing, that feedback is useful but hard to act on without more context. I think asking them to walk through what they were trying to do could reveal more than asking which feature they want.
For those who talk to users regularly, what follow-up question helps you get from this is confusing to something you can actually improve?
