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CoRecursive

From Hacker News to TikTok - How Algorithms Learned to Hook Us

41 min episode · 2 min read

Episode

41 min

Read time

2 min

Topics

Investing, Fundraising & VC, Leadership

AI-Generated Summary

Key Takeaways

  • Hacker News Gravity Algorithm: The original 1005 Hacker News ranking system, written in Arc Lisp, works like Flappy Bird: upvotes push content up, time decay pulls it down. No machine learning, no personalization. This "gravity" model creates shared community front pages where everyone sees the same consensus content, making it structurally incapable of personalized rabbit holes.
  • Facebook's MSI Trap: In 2017, Facebook introduced Meaningful Social Interactions as its core engagement metric, optimizing for comments, shares, and reactions. This inadvertently replicated "sort by controversial" at 2 billion-user scale. Internal Haugen documents, now court evidence in 1,700+ lawsuits, show researchers identified the anger-amplification effect but leadership declined fixes that would reduce growth numbers.
  • YouTube's Collaborative Filtering Model: YouTube's Covington-Adams-Sargon paper reveals the system takes a user's last 50 watched videos, maps them into vector space, finds users with similar coordinates, then recommends what those users watched next. This batch-processing model updates nightly, meaning preference shifts take weeks to register, producing broader, slower-moving recommendation drift.
  • TikTok's 30-Minute Real-Time Model: TikTok's leaked ByteDance engineering document confirms the system uses only the last 30 minutes of viewing behavior as its primary signal, updating via a streaming Kafka-Flink pipeline after every swipe or pause. The Wall Street Journal's bot test found 93 of 224 videos served to a "sad" profile within 36 minutes were about depression or self-harm.
  • Algorithm Reset as Practical Intervention: Meta provides a documented reset function under Instagram's content preferences labeled "reset suggested content," which clears recommendation history across Explore, Reels, and Feed simultaneously. For users over-indexed into a narrow content category through concentrated interaction, this is the fastest available mechanism to force the algorithm to rebuild its behavioral model from scratch.

What It Covers

A developer named Corey gets trapped in Instagram's AI cat video loop, prompting a deep investigation into how social media ranking systems evolved from Hacker News's simple gravity-based upvote algorithm through Facebook's engagement optimization and YouTube's collaborative filtering to TikTok's real-time 30-minute behavioral modeling.

Key Questions Answered

  • Hacker News Gravity Algorithm: The original 1005 Hacker News ranking system, written in Arc Lisp, works like Flappy Bird: upvotes push content up, time decay pulls it down. No machine learning, no personalization. This "gravity" model creates shared community front pages where everyone sees the same consensus content, making it structurally incapable of personalized rabbit holes.
  • Facebook's MSI Trap: In 2017, Facebook introduced Meaningful Social Interactions as its core engagement metric, optimizing for comments, shares, and reactions. This inadvertently replicated "sort by controversial" at 2 billion-user scale. Internal Haugen documents, now court evidence in 1,700+ lawsuits, show researchers identified the anger-amplification effect but leadership declined fixes that would reduce growth numbers.
  • YouTube's Collaborative Filtering Model: YouTube's Covington-Adams-Sargon paper reveals the system takes a user's last 50 watched videos, maps them into vector space, finds users with similar coordinates, then recommends what those users watched next. This batch-processing model updates nightly, meaning preference shifts take weeks to register, producing broader, slower-moving recommendation drift.
  • TikTok's 30-Minute Real-Time Model: TikTok's leaked ByteDance engineering document confirms the system uses only the last 30 minutes of viewing behavior as its primary signal, updating via a streaming Kafka-Flink pipeline after every swipe or pause. The Wall Street Journal's bot test found 93 of 224 videos served to a "sad" profile within 36 minutes were about depression or self-harm.
  • Algorithm Reset as Practical Intervention: Meta provides a documented reset function under Instagram's content preferences labeled "reset suggested content," which clears recommendation history across Explore, Reels, and Feed simultaneously. For users over-indexed into a narrow content category through concentrated interaction, this is the fastest available mechanism to force the algorithm to rebuild its behavioral model from scratch.

Notable Moment

The Wall Street Journal built automated accounts with assigned emotional profiles to test TikTok's speed. A profile configured to show mild interest in sadness received 224 videos within 36 minutes, with 93 focused on depression or self-harm — demonstrating how the 30-minute model accelerates psychological narrowing faster than users consciously register.

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Episode Transcript

Hi. This is Co Recursive. I'm not in Gordon Bell. And today, I have, Liam with me here. Hello, Liam. Hello. Good to be here. So, Liam, I got this message from Corey, a developer I used to work with, and he ran to this weird problem on Instagram, and he wanted me to look into it. We're cat people. So finding, you know, funny cats on Instagram reels feels like a supernatural thing. But once was it Sora? When it that showed up, There are there were so many AI generated cat videos. Cat videos were already cracked on, you know, these social media algorithms. And my feed just turned into nothing but AI slop cats. So the thing that happens, Liam, he thought they were hilarious. He started sending them to his wife. And then, eventually, his wife told him to stop. Right? But he couldn't stop them because now the algorithm had learned he liked AI cats. Yeah. It knows. Now he can't get away from them. That's the problem. He just not watch them or stop going on that social media platform. What's he trying to solve here? Yeah. I asked him that myself. It does affect me sometimes in daily life where, you know, it's time to put the kids to bed and you can't do much when you're putting your kids to bed. Right. You gotta keep them on track, but you can't also, like, do it for them. So you're just kind of standing there in this awkward, traffic control situation. And sometimes that goes for like an hour and you've been scrolling the whole time and you've spent an hour just on five second videos watching who knows how many of them. And you come out of that and you don't you don't feel great. So he's a developer and he can't beat the algorithm. And if he can't beat the algorithm, then who's gonna be able to beat the algorithm? And what are you trying to solve here? What are you trying to figure out for him? Yeah. So that's a good question. We both have a developer background, and I was kind of interested just in how these social media algorithms work. So this was a great prompting. But I thought this would be very quick and that it was anything but. So I started Googling around. Right? Like, how does the Instagram algorithm work? But that's not really the type of answer I was looking for. That's, like, influencer tips on how to make your various posts spread. So I had to go further back. Before any of these platforms existed, you mostly chose yourself what you saw online. There was blogs you subscribed to. You bookmarked things. But then came Slashdot and then Digg, and then sites like Reddit and Hacker News changed the model from editors deciding what mattered to crowds voting. People post links, and you get upvotes or downvotes, and popular stuff rises to the …

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  • YouTube's Covington-Adams-Sargon paper reveals the system takes a user's last 50 watched videos, maps them into vector space, finds users with similar coordinates, then recommends what those users watched next.
  • The original 1005 Hacker News ranking system, written in Arc Lisp, works like Flappy Bird: upvotes push content up, time decay pulls it down.
  • TikTok's leaked ByteDance engineering document confirms the system uses only the last 30 minutes of viewing behavior as its primary signal, updating via a streaming Kafka-Flink pipeline after every swipe or pause.
  • The Wall Street Journal's bot test found 93 of 224 videos served to a "sad" profile within 36 minutes were about depression or self-harm.

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