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Cake day: February 10th, 2025

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  • CFD = Computational Fluid Dynamics.

    It is kind of what they said, you’re right. I was more pointing how how it could be that they could ‘sense the vibes’ of a CFD result to determine if it is accurate or if the model decided to do something weird. Since it’s a chaotic process and also an artificial one, the starting conditions can yield results that are impossible/not based on reality.

    If you look at enough of them you start to notice the kinds of things that go wrong. They would also have a pretty good idea about how their design should perform and if the simulation shows different they’d first want to troubleshoot the simulation before attempting to re-design whatever system they’re creating.




  • Anfinsen won the Nobel in 1972 for showing that the amino acid sequence is what is responsible for the 3D structure of proteins.

    Since then we’ve been able to take images of protein’s structures using xray crystallography but that is a painstaking process. The ability to accurately predict a protein’s structure from an amino acid sequence has been an unsolved problem until very recently.

    It wasn’t until 2024 that Hassabis, Jumper and Baker won the Nobel for their work in predicting protein structure (using an AI called AlphaFold) and computationally designing new proteins.

    The ability to create arbitrary proteins is new and will revolutionize some fields of medicine (like cancer treatment) and, to me, is a much more impressive use of AI.

    LLMs are interesting but they are incredibly over-hyped as far as ‘changing the world’ goes, imo.


  • Those kinds of simulations are inherently chaotic, tiny changes to the initial conditions can have wildly different outcomes sometimes to the point of being nonsensical. Also, since they’re simulating a limited volume the boundary conditions can cause weird artifacts in some cases.

    If you run a simulation of air over an aircraft wing and the end result is a mess of turbulence instead of smooth flow then you can assume that simulation was acting weird and not that your wing design is suddenly breaking the rule of physics. When the simulation breaks it usually does so in ways that are obvious due to previous testing with physical models.


  • I’m failing to see why the creative writing machine is better than a simulation set to ‘rough’.

    The problem is that you saw AI and thought LLM.

    Machine Learning is a big field, AI/Neural Networks are a subset of that field and LLMs are only a single application of a specific type of LLM (Transformer model) to a specific task (next token prediction).

    The only reason that LLMs and Image generation models are the most visible is that training neural network requires a large amount of data and the largest repository of public data, the Internet, is primarily text and images. So, text and image models were the first large models to be trained.

    The most exciting and potentially impactful uses of AI are not LLMs. Things like protein folding and robotics will have more of an impact on the world than chatbots.

    In this case, generating fast approximations for physical modeling can save a ton of compute time for engineering work.












  • Yeah, setup a pi-hole container/server to do DHCP and disable it on your router. The documentation should cover it, but you have to use network_mode: host in order for it to do DHCP.

    You can then add an A record entry for your Immich server’s domain name pointing to the LAN IP and so any device on your LAN will resolve its domain to the LAN IP.

    You also get pi-hole DNS filtering/adblock and, probably, a larger DNS cache than what the router provides.