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benob
Joined 297 karma

  1. Is there a pip installable version?
  2. What, no reference to quantum or crypto?
  3. Will there ever be a llama12? Is it going to go the yolo route?
  4. How was the attack detected in the first place?
  5. What does it mean nowadays to start from scratch? At least in the open scene, most of the post-training data is generated by other LLMs.
  6. If caching is bounded in time, can't you use other fingerprinting methods to seal the gaps?
  7. Why doesn't this apply to any kind of cached content?
  8. Google's move is very good for the web. By pushing app makers away from walled platforms, you turn them to standardized, open ones such as the web.
  9. > Bring Your Own Language

    Few-shot new languages is going to be a game changer for linguists

  10. Not tested on that particular model, but the idea has been flying around for some time: https://arxiv.org/abs/2509.04166v1
  11. It comes with a basic interpreter, but the thriving community develops plenty of stuff: python, lua, forth, lisp... have nice ports which you can play with on device. There also is a library of software developed for rp2040 which have been ported, such as a mac classic emulator. If you feel that a micro controller is too low power, you can plug in a luckfox lyra which runs a proper linux with 128M of RAM.
  12. I have been having a lot of fun with PicoCalc. It's not targeted at end users but is fun for developers alike who want a taste of developing things from first principles. More than anything it can live independently from your other devices.
  13. This is very lenient French: "fetchez le dico"
  14. Audio tokenization consumes at least 4x tokens versus text. So there is an efficiency problem to start with. Then is there enough audio data to train a LLM from scratch?
  15. Is there anything preventing them from using heterogeneous memory chips, like 1/4 GDDR7 and 3/4 LPDDR? It could enable new MEO-like architectures with finer-grained performance tuning for long contexts.
  16. This work is a good argument against memorization of information seen less than 250 times during training.
  17. I wonder if one could store only the binary representation at training and sample a floating point representation (both weights and gradient) during backprop.
  18. There was a time when people would estimate n-gram probabilities with feed-forward neural networks [1,2]. We just improved that with the (multilayer) attention mechanism which allows for better factoring over individual tokens. It also allowed for much larger n.

    [1] https://jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

    [2] https://www.sciencedirect.com/science/article/abs/pii/S08852...

  19. Like it or not, LLMs are effectively high-order Markov chains
  20. These techniques are limited to structures that can be checked with bounded history or bounded memory (that can be checked with a grammar or FSA). What about more complex structures that don't factor easily?
  21. My understanding was that the Alpaca data was a distillation from text-davinci-003
  22. a good benchmark for video understanding in IA
  23. It's funny because automation is the only thing you can expect from AI
  24. > YouTube views seem to have fallen off a cliff recently

    So they started discounting AI data collection bots?

  25. Stop thinking about text being the data. There are so many other sources, even some that you can generate.

    https://arxiv.org/pdf/2506.20057

  26. Even if it's not on topic, that post is quite interesting.
  27. How is it different from mere syntactic sugar over the same programming concepts? What does it bring that C++ cannot do?

    Isn't it just a way of controlling the language vs using normative bodies?

  28. Are there efforts to include the neccessary context in compilers to autovectorize?
  29. I wonder how difficult it would be to bias a model so that it subtly corrupts election results when performing OCR.

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