alankarmisra

4 ms·

1.
by alankarmisra 1y ago
They’d presumably do worse. LLMs have no intrinsic sense of programming logic. They are merely pattern matching against a large training set. If you invent a new language that doesn’t have sufficient training examples for a variety of codin
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by alankarmisra 1y ago
Same. I use Apple Notes. I have a few notes pinned (regular work, creative work, self-education, travel, chores). I write tasks. Break them up into small tasks with indents. Pick a task from the pool and execute. "Regular Work" ta
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by alankarmisra 1y ago
Genuinely curious; while I understand why we would want a language to be open-source (there's plenty of good reasons), do you have anecdotes where the open-sourceness helped you solve a problem?
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by alankarmisra 1y ago
It's like the secret beaches in every south-east asian nook and crany. They're so secret there's signs pointing to them every where and they are overrun with tourists.
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by alankarmisra 1y ago
I see the value in showcasing that LLMs can run locally on laptops — it’s an important milestone, especially given how difficult that was before smaller models became viable. That said, for something like this, I’d probably get more out of
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by alankarmisra 1y ago
My thinking is threaded. I maintain lists (in a simple txt file and more recently, in Notes on the Mac) and add the tasks to it. Subtasks go into an indent. I have different notes for regular work/pet project/blog/learning&#x
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by alankarmisra 1y ago
I would argue that framework isn't the winning component, the people are. A lot of people can say similar things for framework <<X>> and they'd be right given their own experience but I think they give themseleves too
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by alankarmisra 1y ago
I would guard against "arguing from the extremes". I would think "on average" compact is more helpful. There are definitely situations where compactness can lead to obfuscation but where the line is depends on the litera
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by alankarmisra 2y ago
In your specific example, time of day, weather (foggy, sunny, over-cast) along with images of cars with different colors, models, makes, from different angles will all be training parameters to begin with so the neural net can do this on it
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by alankarmisra 2y ago
I'm wondering if this is a limitation though. If it can be learnt from training data, would it not be part of the neural network training data? I imagine we use Scallop to bridge the gap where we can't readily learn certain rules
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by alankarmisra 2y ago
I read the paper on Lobster a little bit. Scallop does its reasoning on the CPU - whereas Lobster is an attempt to move that reasoning logic to the GPU. That way the entire neurosymbolic pipeline stays on the GPU and the whole thing runs m
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by alankarmisra 2y ago
It's a combination of neural networks and symbolic reasoning. You can use a neurosymbolic approach by combining deep learning and logical reasoning: A neural network (PyTorch) detects objects and actions in the image, recognizing &quot
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by alankarmisra 2y ago
This. I'm trying to set up a personal developer blog and I have a very specific set of requirements. Tried several static blogging frameworks. Apart from the software bloat, I found myself spending a gratituous amount of time trying to
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by alankarmisra 2y ago
This paper suggests that LLMs can be trained to handle multi-stage questioning by automatically optimizing prompts using feedback-based methods, improving their ability to process complex, multi-step interactions.
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by alankarmisra 2y ago
Same. I was writing my own language compiler with MLIR/C++ and GPT was ok-ish to dive into the space initially but ran out of steam pretty quickly and the recommendations were so off at one point (invented MLIR features, invented libra