In about 17 days (I hope I m counting correctly), I ll be re-launching the mobile app, and now I m wondering how much the Product Hunt community will try it out.

I spend 100% of my time on a desktop on this platform.

But the majority of the population is mobile-only.

Trace tends to show up where workflows get complex. The parts of work that involve reasoning, coordination, and follow ups instead of simple triggers.

If you are using Trace, we want to hear how. What kind of workflow is it handling for you? What problem finally felt manageable once Trace was in the mix?

Wordware tends to appeal to people who want more control over how AI logic flows through their work. Less magic, more intention.

If Wordware is something you rely on, share how you are using it. What workflow did you build that felt worth keeping?

Airtop often shows up when workflows need to interact with real interfaces, not just APIs. The kind of automation that feels closer to how humans actually work.

If Airtop is doing real work for you, tell us what that looks like. What task did it finally take off your plate?

Relay shows up when workflows start to feel brittle and you want something more intentional than a chain of rules. It is often about coordination, not just automation.

If Relay is part of how your work moves forward, tell us what it is responsible for. What does it orchestrate? What used to fall through the cracks?

Gumloop often replaces the custom but fragile scripts people were maintaining themselves. It gives structure to workflows that used to live half in code and half in someone s head.

If you are using Gumloop, share what it is doing for you now. What workflow did you finally stop babysitting?

Lindy usually earns its place by handling the kind of work that is repetitive, interrupt driven, and easy to forget. The stuff that eats time quietly.

If Lindy is part of your workflow, tell us how. What does it handle for you automatically? What do you no longer have to remember?

There are countless products and services out there, and I ll admit I sign up for more than I probably should. But I usually stop using them for a few common reasons:

1. It doesn t actually fit my needs

2. The company feels unreliable or opaque

3. The value doesn t justify the cost

After spending my career in enterprise software, I ve noticed that many of these issues aren t just product problems, they re relationship problems.

When companies show a bit of intention, clarity, and care, trust goes up. When they don t, everything feels disposable, even good tools.

I came across Deutsche Bank s latest report on AI, and it sparked an interesting thought experiment: how likely is it that we ll see AGI (AI that thinks and learns like a human) within the next five years?

The report highlights a fascinating divergence: the view from money vs. the view from science.

• Money: the probability inferred from trillions poured into data centers, Nvidia chips, and servers. Investors seem to be betting that AGI is inevitable.

• Science: the probability inferred from research papers and AI development models. Experts are far more cautious, suggesting the realistic probability is only 20%.

At the beginning, my reason was very simple: I needed a job and I genuinely liked the product.

I graduated with a Marketing degree, but I never felt like I belonged in agencies or similar environments. It just wasn t for me. At the same time, I didn t have much experience in tech either. So I took a leap of faith and applied for a Customer Support role, almost blindly.

The early days were tough. I had no technical background, no real understanding of how apps were built, and everything felt overwhelming. But the product itself became my motivation. I started from the most basic things: learning simple technical terms, understanding how an app is structured, and slowly exploring how everything works behind the scenes.

Dear Product Hunt community,

If you re reading this and you ve launched something - or you're close to launching - you are already incredibly special.

In a notable shift in the AI landscape, Apple and Google have announced a multi-year collaboration under which Apple s next generation of Apple Foundation Models will be based on Google s Gemini models and cloud technology.

According to the joint statement, these models will help power upcoming Apple Intelligence features, including a more personalized Siri, expected later this year.

Guys, a couple of hours ago, the author of this problem, which he published on ProblemHunt about four months ago, wrote. Here is the essence of his message:

 Hello! We've partnered with one of the market leaders in our segment. We had negotiations today we're integrating into their ecosystem for mutual growth and development. Soon our solution will be available to their audience. We did it! 

This isn't a unicorn story. Rather, it's an example of a sustainable micro-SaaS for a niche audience and a local market. Although who knows?

Today, we declare the death to Search.

For too long, we've let Search dictate the way we work.

First day back with my co-founder Charlie, and we showed up ready to build!
Feels a bit like the first day back at school: excited, a little nervous, ready to dive in .

Building a team or want to join a startup? Let's kick 2026 off to a great start.

Founders, teams, and startups drop a comment if you're hiring.

We ve been growing really fast (30%+ MoM ARR) at @Basedash since launching last year. Most of that growth has been the result of hard work, but we ve also had a secret weapon: an AI agent that acts as both a data analyst and a PM, working 24/7 to optimize our product s activation and conversion rates.

For decades, companies have been making product decisions based on intuition and manual data analysis. We wanted to see what would happen if AI could take the wheel completely.

Slowly but surely, the Christmas holidays are approaching, which usually means more time at home with family and more movie binge-watching.

Yesterday I watched TV for the first time in a while, and they were playing Bezos: The Beginning (2023). A decent movie overall, even though it could ve been longer or had a sequel.

For a lot of people, Zapier is the quiet backbone. The thing connecting tools you do not want to think about connecting yourself.

If Zapier has been doing invisible work for you, this is the moment to surface it. What automations are still running months later? What would be annoying to rebuild from scratch?

For teams using Taskade, it often becomes the place where planning turns into action. Tasks, automations, and AI features all living in the same loop instead of scattered across tools.

If Taskade is part of how you run work, tell us how. What workflow did you set up that stuck? What changed once it was in place?

Many of you sometimes write to me in DMs asking how to position yourself on Product Hunt.

From the question, I always get the feeling that people want to speed up the process, publish something quickly, get a high position in the ranking of launched products and a badge. But this is a long-term game.

Hey all,

15 years ago I wrote an article about the rise of a more social web for Huff Post.

I'm building My Financ , which is a tool that allows you to understand your finances, and plan for the future.

I launched on PH in September, got some users and have been iterating.

Whenever I browse product launches, I somehow subconsciously judge not only the product itself and its quality, but also the quality that is reflected in the effort the makers put into preparing it.

It may sound insignificant, but in my case, these things also make a significant difference:

• Icon GIF at the launch it enlivens the overall impression and is dynamic

• Quality graphics and video

• First, a properly filled-out comment

• Photos in the makers' profiles (it's less trustworthy for me when there's only the letter "J" or something similar)

• Whether any of my contacts or acquaintances on the platform reacted to the launch

We often see launch posts, milestones, and success stories.
What we don t see as much are honest breakdowns of products that quietly stalled or failed.

I feel there s a lot of learning hidden there about timing, assumptions, and trade-offs.