aadyachinubhai

3 ms·

1.
by aadyachinubhai 5d ago
For LLM generated Python code, pydantic's monty is awesome: https://github.com/pydantic/monty Plus, pydantic is funded so it will be well maintained.
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by aadyachinubhai 10d ago
The title of this post is super intriguing
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by aadyachinubhai 10d ago
cool idea, how helpful is this in practice?
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by aadyachinubhai 11d ago
maybe unrelated by NumPy and Cython have made it incredibly easy to write vectorized code.
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by aadyachinubhai 12d ago
https://github.com/aadya940/scikit-verify Trace Python+NumPy programs into SymPy formulas and check them symbolically, across all branches
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by aadyachinubhai 14d ago
The reality is most of the pandas audience don't care about performance. Whenever performance is in the question people have always used polars, duckdb, dask etc. These users are usually software engineers and not data analysts. Agreed
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by aadyachinubhai 14d ago
great, save the next generation from cognitive decline
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by aadyachinubhai 15d ago
It is speculated that there was context leak from a NYU Professor's chat I think? I contribute to SciPy heavily and I've seen AI shit the bed a few times now. So 13 million lines of lean? Hell no dude!
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by aadyachinubhai 15d ago
One of the first things I learned in ML is that the model can only learn from the information in X. If X doesn't contain enough information to determine y, no amount of compute can fully recover it. That's why I'm skeptical o
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by aadyachinubhai 16d ago
I don't like grandiose claims about AI.
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by aadyachinubhai 16d ago
That is why I like to use __slots__ when defining a class. Unlike dicts, using __slots__ is a tuple so using it to store class attributes is much faster.
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by aadyachinubhai 16d ago
Am I the only one who still believes that these resignations are not a big deal?
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by aadyachinubhai 16d ago
Awesome, at 5 km/h it feels like I'm in a black hole. Time stops at the speed up light if I'm not wrong!
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by aadyachinubhai 17d ago
The `.c.src` files with the @arch@ like dispatching logic is specific to numpy. Also, they are currently making efforst to shift to C++ templates using Google's Highway project. I worked on porting `np.negative` to it!
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by aadyachinubhai 17d ago
LLMs can't contribute good code to some of the good OSS math libraries, How is it even solving these problems?
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by aadyachinubhai 5mo ago
Each node in the graph is a plain English instruction. An AI agent executes them in order inside a Docker container with a full browser and desktop. Because each node is independent, the agent stays on task, it doesn't drift or halluci
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Show HN: n8n like workflows for AI agents that control a real VM(github.com)
6 points by aadyachinubhai 5mo ago|1 comments
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by aadyachinubhai 6mo ago
Fair, mature frameworks do a lot more. Orbit isn't trying to out-feature them. It's a thinner abstraction specifically for computer-use, where the bottleneck isn't orchestration complexity, it's controllability at the st
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by aadyachinubhai 6mo ago
Most agents are prompt + a bunch of tool calls. Writing everything in one prompt is like writing your entire app in one function. It works until it doesn't, and when it doesn't, you have no idea where it broke. Orbit gives you ste
20.
Show HN: Structured Python control over AI computer use agents(github.com)
1 points by aadyachinubhai 6mo ago|4 comments
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ChainoPy: A Python Library for Discrete Time Markov Chains(github.com)
1 points by aadyachinubhai 1y ago|1 comments
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by aadyachinubhai 1y ago
Why ChainoPy? Covers most of the fundamental agorithms for Markov Chain Analysis Memory efficient Model saving Faster than other libraries (eg: 5x Faster than PyDTMC) First Package to contain functions to build equivalent Markov Chain Neura
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Show HN: Autoresume – OpenSource, Resume Builder and TeX Editor with AI Features(github.com)
3 points by aadyachinubhai 1y ago|0 comments