Hey Product Hunt community!

I ve been a gamer and a dev for years, but recently, I hit a wall of frustration. We have incredible 4K graphics and ray-tracing, but the "brains" of our games still feel like they re from 2005.

Hey PH Community! I m Samir Rashed,

This week, I m stepping into the hunter s seat for something I truly believe in: @Mindverse a secure, all-in-one AI ecosystem crafted to boost creativity and productivity.

Why Mindverse?

• Used and loved by 500,000+ users

• Adapts to your brand voice, personas, and content style

• Built for marketers, creators, teams, and ambitious minds

Interior designers still spend too much time rebuilding drawings. You receive 2D floor plans and Blueprints. You trace, you model, you clean up. Only then can you start real design work.

Foursite by VirtualSpaces attacks that bottleneck. It is built for teams who want to go from 2D to 3D in minutes and then live entirely in an AI visualization and AI 3D visualization workflow.

Hi all! I'm Melanie, founder of Verve, a video-only dating app launching exclusively in DC to start.

Some of my friends are in the divorce season of life, and this idea was born out of so much frustration with endless texting and traditional dating apps' inability to give you a true sense of a person.

Recruitment technology is a hot topic these days thanks to AI (duh) - but it has been there for sometime.

If you have built & launched products in this space would love to hear them. Me too just started in this space and even though we are in a niche I see bunch of similar products coming out weekly. How can you build you unfair advantage here?

Also here is what we are building: https://www.producthunt.com/post... (launching next week)

We open-sourced optimize_anything, an API that optimizes any text artifact. You provide a starting artifact (or just describe what you want) and an evaluator it handles the search.

import gepa.optimize_anything as oa result = oa.optimize_anything( seed_candidate="<your artifact>", evaluator=evaluate, # returns score + diagnostics
)

It extends GEPA (our state of the art prompt optimizer) to code, agent architectures, scheduling policies, and more. Two key ideas:
(1) diagnostic feedback (stack traces, rendered images, profiler output) is a first-class API concept the LLM proposer reads to make targeted fixes, and
(2) Pareto-efficient search across metrics preserves specialized strengths instead of

averaging them away.

I've finally published my Nox app on the Play Store! For its official launch, I need 12 testers to keep the app for 14 days.

Here are the 3 steps:

• Join the testing group: Click on this link with your Gmail address to be authorized to view the app: https://groups.google.com/g/nox-...

• Download the app via this link: https://play.google.com/store/ap...

We built Nuwtonic , industry's first Fully Agentic solution for SEO , GEO , AIO , AEO under one platform having 120+ SEO and AIO capabilities and several Multi agentic pipelines , which does not Only report data and issues , It goes beyond and Fix it for your brand and bring a ton of traffic .
Most founders don't lose traffic because they stopped doing SEO.

They lose it because nothing connects. The execution layer is broken.

After spending a lot of time deep in Google Search and AI answer engines, the same patterns kept showing up:

Traffic tanks after a core update, even when the content was genuinely good. Pages rank fine but never get cited in AI answers. Impressions look healthy but CTR is weak. Topical authority gets watered down across scattered articles.

Had a thought, and I must put it out here:
Over the past year, building with LLMs, I ve noticed that the big change is not necessarily that AI gives us better answers. Change your perspective and think like this: we, humans, are starting to ask better questions. And here's why:
When search engines dominated, we asked:
What is X?
How do I do Y?
Best tools for Z?
With LLMs, the questions are different:
Help me think through this decision.
Challenge my assumptions.
What am I not seeing?
Act as a product strategist and critique this.
Clarify my messy thinking.
That s not getting the information, that s cognitive augmentation.
I feel we re moving from:
Finding knowledge to extending reasoning
And this raises bigger questions for builders:
1. Are we designing AI tools that optimize for answers
or for better thinking?
2. Is the real advantage having a stronger model...
or asking better questions?
3. In a world where everyone has access to GPT-level intelligence,
does leverage come from prompting skill, system design, or taste?
4. What new human skills become valuable when execution becomes cheap?
It feels like we re entering a phase where:
Curiosity > credentials
Clarity > speed
Systems thinking > single outputs
If you re building in AI right now:
What types of questions are your users asking that they couldn t (or wouldn t) ask before?
And are you building for answers or for thinking?
Would love to hear what patterns you're seeing.
______________________
www.zackapp.space
Download Zack from the App Store and soon from Google Play: https://apps.apple.com/us/app/za...

The idea is pretty simple: take the vault-based note-taking you'd get from Obsidian (folder structure, markdown, ![[media.png]] embeds, all that) and combine it with the local AI chat experience of LM Studio in one app.

So you can:

- Organize notes, images, audio, and video in a local vault