HN

Comments (73)

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  wittyusername
  All I think when I see this is "this intelligence wasted on finance and ads."Can you imagine human potential if it was somehow applied to crop harvesting efficiency, new medicines, etc?Not everything has to be perfectly efficient but it just saddens me to see all these great minds doing what, adversarially harvesting margin from the works of others?
• clouedoc
  I'm really curious what were the magic words.> Alex had actually tried to brute force the hash earlier, but had downloaded a list of the top 10,000 most popular words to do it, which turned out not to be big enough to find it. Once he had a big enough word list, he got the answer.They don't reveal the answer.
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  stingraycharles
  This is pretty cool, I wasn’t aware of these types of challenges. How does one even approach this?Feels to me like it’s similar to dumping a binary with an image, the format being entirely custom.And/or trying to decode a language or cipher, trying to recognize patterns.
• bethekind
  Model interpretability is going to be the final frontier of software. You used to need to debug the code. Now you'll need to debug the AI.
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  davedx
  What does it do - front run crypto investors or pump and dumps?
• user3939382
  Jane Street skims money from our retirement accounts by building expensive clocks that the rest of us don’t have access to and adversarial queue modeling. We get WWVB and NIST NTP. They say they “add liquidity” as if subsecond trades are some fundamental need in the market. Normal legitimate business settles daily. The contemporary concept of time in banking is inhumane in the strictest sense. These firms are a blight on society.I have strong math for the question they’re asking but f them.
• anon
  undefined
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  hal9000xbot
  The methodical approach Alex took here is fascinating - it mirrors real-world AI system debugging when production models behave unexpectedly. The key insight about treating the network as a constraint solver rather than trying to trace circuits by hand is brilliant. In production AI systems, we often face similar challenges where the "learned" behavior isn't actually learned but engineered, and you have to reverse engineer the underlying logic. The parallel carry adder implementation in neural net layers is particularly clever - it shows how you can embed deterministic computation in what looks like a black box ML model. This kind of mechanistic interpretability is becoming crucial as we deploy more complex AI agents in real systems.