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Phil Hagelberg Site
The the words it uses that this site has seen least often elsewhere. Posts are matched on those words alone — nothing here is a summary of this one.
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20 September
19 September
12 September
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korrents.com
An OpenAI agent swarm was spamming and exploiting RubyGems in May, close in time to the Wiki attacks.Their words
Turns out another OpenAI agent swarm was busy spamming and exploiting RubyGems way back in May, within days of the previously uncovered Wiki attacks
3 September
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Their words
It’s not orderly. But neither is water flowing in a stream. It flows around obstacles. Everything is opportunistic, selfishly taking the effective path, exploiting available gaps. It’s natural.
1 September
30 August
28 July
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Their words
It is very easy to go take shots on Twitter and make a sarcastic comment and get a lot of likes and feel like you're doing something really important and sticking it to the man and being like, "Gary, haha, I got you, you idiot." Um, and and it will poison your soul. It is a morally bankrupt thing to do.
3 May
23 April
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20 March
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Their words
Um we are seeing a lot fewer sort of pure AI solutions now where um they are just one shots the problem. Um so so there was there was a month where that happened and and that has stopped.
4 February
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Their words
People should not be judging AI-augmented coding by "1 shots."
17 November 2025
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korrents.com
Men's tennis shots tend to carry more spin and arc than women's, who tend to hit flatter.Their words
Women hit flatter balls. Men on average tend to hit with more shape, more arc, more spin.
30 September 2025
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Their words
So premium is one source of our revenue. We also have ads, but they're context-based, not targeted. Of course, we leave probably 80% of value on the table because we're not ready to engage in all this practices, exploiting personal data.
28 November 2024
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korrents.com
More capable AI agents are more likely to find and exploit flaws in their reward functions.Their words
A more intelligent agent is more capable of finding "holes" in the design of reward function and exploiting the task specification-in other words, achieving higher proxy rewards but lower true rewards.
Reward Hacking in Reinforcement Learninglilianweng.github.io
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