Every day, the PH feed is packed with shiny new SaaS tools most of them browser-based, many of them AI-infused. It s exciting, no doubt. But compared to a time not so long ago, something seems missing: local desktop apps.

They re rare now, and it makes me wonder are native apps still worth building, or have they quietly slipped into the realm of nostalgia?

After all, web apps offer clear benefits for both users and makers or investors. Users don t have to install anything, updates are seamless, and their data is accessible from any device with a browser. For investors, the advantages are just as compelling: a single tech stack, easier user onboarding, lock-in effects, and plenty of levers for driving growth and virality.

In yesterday's discussion by @aaronoleary, there were a few thoughts about using robots at home.

In this context, several questions occurred to me.

For example, what will happen to the future of humans if we delegate most of the manual and mental work to machines? How will we handle our free time? How will people be rewarded?

Lately, my DMs have been full of this question.
While I can t say exactly what changed after Product Hunt s Aug 25 update (since the featured criteria seem a bit mysterious now), here are a few patterns I ve noticed:

1. Version updates: New versions or re-launches (like Product v2.0) often don t get featured anymore.

2. No freemium or free trial : Launches without a free plan or trial seem to be struggling to get featured.

3. Hunter Featured : Having a well-known hunter doesn t guarantee a feature. In fact, recently, the two seem almost mutually exclusive.

4. Look & feel: Launches that use the same template design or layout as others often get skipped.

Curious to hear your thoughts - have you noticed similar trends?

Everyone s been talking about AI Agents over the past year.

But the real leverage doesn t come from the agent itself. It comes from the context you provide:

It is a question of choosing between two evils for us now. Neither option is completely free of flaws.

Human: Recruiters with "gut feelings" who harbor unconscious bias. they reject excellent candidates who just didn't go to the "right" school or didn't just "click." Inconsistent, unfair, and un-auditable.

AI: Algorithms whose training datasets are themselves replete with historical biases. They increase the scale of discrimination at light speed, becoming so-called black boxes that end up rejecting qualified candidates for reasons that humans cannot even fathom.

We are truly deciding to exchange messy, subjective human prejudice for cold, ruthlessly efficient algorithmic prejudice. Is that really an upgrade?

I spend a lot more time on PH at the moment to see what indepedent makers are spending their time on. I've noticed some patterns and also want to share a little bit about my journey at South Park Commons. Most startup stories begin at zero when there s already a team, an idea, maybe even a prototype. But at South Park Commons (SPC), the philosophy is different: people gather in the -1 to 0 stage. That liminal space where you don t yet know what you re building or even if you should build at all. It s a place for exploration, experimentation, and being brutally honest about what s working and what s not.

A hallmark of SPC is how often industry leaders drop by to share what they ve learned in the wild. Recently, I was in a small chat with Tyler Payne former Google and LinkedIn AI lead, startup builder, who has spent the last decade helping teams actually ship real-world ML systems. We're always talking about what's being launched at SPC.