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OK let’s try this: ———————- Pretend you’re 5 and the algorithm is a smart robot that picks which toys (posts) to show you in your toy box (For You feed). The robot doesn’t just count how many times people already played with a toy (likes, reports, etc.). Instead, it looks at you and guesses: • “How likely is this kid to like this toy?” • “How likely is this kid to report this toy as yucky?” • “How long might this kid play with it?” Those guesses are little numbers between 0 and 1 (probabilities). The “weights” are like special multipliers the robot puts on those guesses: • A like-guess might get multiplied by a small number (like 0.5). • A report-guess might get multiplied by a big negative number (like –234). Then the robot adds all those multiplied guesses together to give the toy a final “yay or nay” score and sorts the toys by that score. Why the numbers look so big and why people get confused Reports almost never happen, so the robot’s “will you report this?” guess is usually a tiny number (like 0.0001). To make that tiny guess still matter a little bit in the final score, they give it a big multiplier. People sometimes look at the big number and think “1 report = 468 likes!” 
That’s wrong. It’s not counting real reports or real likes. It’s only multiplying the guesses the robot made about you. If the robot thinks you’re the kind of person who almost never reports anything, even a big weight on “report” barely moves the score. The whole thing is personalized to how you usually behave. The people who wrote the code even put big comments in the GitHub repo so no one (and no other robots) gets confused about this again. That’s the whole idea in kid words: the weights turn the robot’s personal guesses about you into a ranking, not a simple “count the hearts and flags.”
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The 1996 @usabasketball Women’s Basketball Team and Olympic gold medalists get their @Hoophall orange jackets 👏 Watch the Class of 2026 Enshrinement Tip-Off Celebration and Awards Gala on NBA TV and the NBA App. Tap to watch:
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AI agent frameworks, explained The toolkits that help build agents that can plan, call tools, use memory, and execute tasks in a loop. Read more 👇
Government-owned airports cannot favor one religion over all others. DFW plans to install Islamic wudu washing facilities are illegal. I've directed a review of all state grants to both airports for possible revocation, and referred DFW & IAH to USDOT for investigation. Texas will not allow illegal religious discrimination at taxpayer-funded facilities.
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Built something cool with Gemini 3.7 Flash? Show us in the replies, we'd love to see what you're making 👇
Yesterday, we dropped our latest AI model: Gemini 3.7 Flash ⚡️ our most intelligent workhorse model yet for coding and agents. Just 1 day later, we’re already impressed by the creativity we’ve seen. Here are a few of our favorite ways we’ve seen people use 3.7 Flash so far ⬇️
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Yesterday, we released Gemini 3.7 Flash — our most intelligent workhorse model yet for coding and agents. See what developers, users and our partners have to say about Gemini 3.7 Flash ⬇️
With upgraded design adherence and web dev capabilities, your prompts now have more room to play with Gemini 3.7 Flash. Our team used 3.7 Flash to one-shot this playable game in Google @Antigravity 👾
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As usual I will give my x earnings ($488.41) to someone who likes this post! Winner chosen at random on Sunday. For extra fun if the winner follows @joinnoblemobile I will DOUBLE it and if you are a Noble subscriber I will give you FIVE times the amount! Good luck! 😀🎉
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America’s newest and most exciting aerospace technology expo and air show is landing @NASAKennedy in Florida on Nov. 7-8. Make plans to join us for MAX POWER! Get the details:
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