Writing
From Cat Videos to Cutting-Edge Signals: The Hidden Value in Social Platforms
It's easy to dismiss social platforms as distraction machines, and a lot of the content deserves that reputation. I spent years at YouTube, so I have no illusions about what most of any feed actually is. But buried in the noise there's something genuinely valuable, and I think most people are looking for it in the wrong way.
Feeds are not wise. They are early.
That's the frame that changed how I read them. Nobody should treat a social feed as a source of settled truth. But experts, builders, hobbyists, employees, and weird edge communities routinely surface information long before it shows up in formal media or analyst reports — because they're posting from direct contact with the thing itself. The person debugging a new framework at midnight, the nurse describing what's actually happening on the ward, the hobbyist who noticed a product quietly changed. None of them are authoritative. All of them are upstream.
The mistake is treating the feed as either trash or truth. It's neither. It's an unstructured sensor network: most observations are low value, some are manipulated, and a small number reveal a trend changing before any institution has had time to package it.
The filtering problem
So the value isn't in the feed; it's in the filter. The signals worth keeping have a different texture than the noise. First-hand observation reads differently from commentary about commentary. A claim that shows up independently from several unconnected people means something that one viral post does not — though you have to be careful, because a single meme echoing through a network can impersonate many independent observations.
A few habits I've found useful. Follow people who touch the thing, not just people with polished opinions about it. Track who has been early and right before, by topic — someone with great judgment on infrastructure can have terrible judgment on markets. Discount anything optimized for engagement, because outrage and status performance are the feed's native crops and they crowd out observation. And when something does look like a real signal, treat it as a lead, not a conclusion: the feed is for discovery, verification happens elsewhere.
This is, not coincidentally, the same problem content understanding systems face at platform scale — clustering duplicate claims, scoring source history, separating original observation from recycled takes. The difference is that you can run a small version of it in your own head, on your own feed, for free.
The internet can be both slop and sensor. The skill is knowing which one you're consuming at any given moment — when you're being entertained, and when you're watching the future leak through the noise.