111–120 of 227 posts
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The phrase embarrassingly parallel has a history in computer science. Many computer science paper titles allude to past titles in other CS papers. Calling it “cringe worthy” is unnecessarily mean. There is context and history you don’t understand.
"Embarrassingly" considered harmful?
It’s so ironic that Apple still publishes AI research and OpenAI does not.
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I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind. I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.
LLMs are orders of magnitude simpler than brains, and we literally designed them from scratch. Also, we have full control over their operation and we can trace every signal. Are you surprised we understand them better than brains?
Haven't read the paper yet, but it is interesting how seemingly simple many breakthroughs in ML are. Even transformers are like that. Maybe it's hindsight bias. I suppose we just don't have a deeper underlying theory to lean on and help us 'design' anything.
A lot of discoveries are like that. In fact, simplicity is often the hallmark of correctness, and complexity is often a sign that our understanding is incomplete and we’re still stumbling towards the right model. Not always, but often. It’s been a good rule of thumb in my programming career.
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I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind. I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.
LLMs are orders of magnitude simpler than brains, and we literally designed them from scratch. Also, we have full control over their operation and we can trace every signal. Are you surprised we understand them better than brains?
After TurboQuant and Gemma 4, came across the following video[0] running Gemma on local machine at 50 token/second. That already looks like Sonnet 3x and 4 level capabilities to me where the model in question (Gemma 4) set ups whole python project with a UI and installs python libraries using uv etc. Add this Simple Self Distillation to the picture and by 2028 I see cheaper coding model providers with much more gener…
> power users would be mostly running their own models ...with a fair amount of supervision, while frontier models would be running circles around them using project-specific memory and on-demand training (or whatever we would have by then).
Haven't read the paper yet, but it is interesting how seemingly simple many breakthroughs in ML are. Even transformers are like that. Maybe it's hindsight bias. I suppose we just don't have a deeper underlying theory to lean on and help us 'design' anything.
A lot of discoveries are like that. In fact, simplicity is often the hallmark of correctness, and complexity is often a sign that our understanding is incomplete and we’re still stumbling towards the right model. Not always, but often. It’s been a good rule of thumb in my programming career.
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I've always thought that it is kinda weird that we spend exactly the same amount of compute to calculate both "fork" tokens and "lock" tokens. I think that with grammar-aware sampling / constrained decoding [0][1] it is possible to sometimes skip calling the model altogether if only one token is allowed by grammar and just insert it, but I don't think that any of the current, widely used combinations of models/harnes…
Give coding agents access to intellisense and syntax highlighting. Making coding agents spit out syntactically correct code token by token is like asking a human to code on a whiteboard.
I do think there is some merit in a tool that dumps all namespaces and reachable symbols so the agent can do its own autocomplete without a round-trip.
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Maybe, but you couldn't tell that these days, casually scrolling this or any other tech-oriented discussion board.
I mean... You could? AI comes in all kinds of forms. It's been around practically since Eliza. What is (not) here to stay are the techbros who think every problem can be solved with LLMs. I imagine that once the bubble bursts and the LLM hype is gone, AI will go back to exactly what it was before ChatGPT came along. After all, IMO it's quite true that the AIs nobody talks about are the AIs that are actually doing goo…
(Not fine tuning, but interesting none the less. If a model can so easily find a more elegant solution, why didn't it pick that in the first place?)