I can't believe the number of products posted here "anonymously."

Almost every product here is asking its users to provide some data. Even if it's just an email address. Which is obviously normal.

Hey everyone,

I m currently building a platform focused on helping people turn hardware ideas into actual working prototypes (connecting them with engineers, fabricators, etc).

But I ve hit a wall I didn t expect.

I m finding it surprisingly hard to locate people who are actively trying to build physical products. Not people interested in hardware, but people who are actually in the process and need help.

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?

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.

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?