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wiz

@wiz

419 Following
1157 Followers
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wiz
@wiz
nice this is the type of content farcaster needs
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@wiz
it’s all a meme ser. everything is worthless. send it
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@wiz
hey man this convo sucks, ttyl
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@wiz
probability of some kind of fast takeoff is really high
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@wiz
l2
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@wiz
i have been feeling the same
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@wiz
heh if true pretty wild that it could get exponentially worse from here
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@wiz
i've been wondering if the world is really getting crazier or if the speed of information / info warfare has just increased over the last year or so. not sure
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i hate how the ai industry over complicates everything with academic jargon. almost on purpose so that the title of “researcher” is justified
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@wiz
basically sending a small AI model to school yields better results + is cheaper than depending on a large model to learn from scratch
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@wiz
it's weird how much humans and ai have in common https://sakana.ai/rlt/
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rocketman
@rocketman
Introducing the Vrbs Accelerator 🌳 ✓ Build your onchain startup ✓ Seed funding of $250/mo ✓ Pay contributors via @superfluid ✓ Raise funds via /revnet's Have an idea for a business? Apply here: https://flows.wtf/apply/0x70d69869ed285d24eee28af10b16fb166ee67135
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@wiz
there are conscious little sprites playing around with your computer everyday... think about that for a sec
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@wiz
the next 5 years are going to be so fucking crazy
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why don’t we all buy each others software
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bullish
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a man ahead of his time. the future of all product launches
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it’s not purely attention but he’s very good at getting it. he gives off sane mcafee vibes to me
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@wiz
why the cluely hate? the guys a genius
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funny how there is still so much low hanging fruit: "The results, as shown in Figure 1, clearly demonstrate that removing the easiest samples leads to consistent performance improvements. In contrast, both the unfiltered dataset (which lacks sufficient challenge) and the aggressively filtered dataset (which is overly saturated with difficult problems) hinder training progress. These findings confirm that optimal RL training requires a balanced difficulty distribution—one that provides enough challenging samples to drive learning while avoiding both trivial problems and overwhelming difficulty."
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