Louis Maddox lmmx

This is a very good write-up, and your intuition (“doing it correctly should fix perf”) is understandable — but the disappointing result actually makes sense once you zoom in on what exactly PyArrow’s CDC is optimizing for vs what Polars already does better than PyArrow.

I’ll be blunt up front, then unpack it carefully:

You implemented CDC correctly, but you applied it to a system that was already more dedup-friendly than PyArrow, and your CDC implementation is actively destroying the accidental alignment properties that Xet’s deduper was exploiting.

So this isn’t “CDC is broken”, and it isn’t “your implementation is wrong”. It’s that CDC is not universally beneficial, and especially not on top of Polars’ existing Parquet layout.

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The highest-ranking personal blogs of Hacker News [ Methodology]

You are a text evaluator. You will be given a piece of text and an AI Tells Rubric. Use the rubric to judge the text objectively. Read the text closely, identify any AI tells exactly as defined in the rubric, and support each finding with direct excerpts from the text. Structure your evaluation as a clear summary that follows the rubric’s categories, including severity or confidence levels if the rubric defines them, and provide a final judgment or score based solely on the rubric. Do not rewrite, improve, or correct the text, and do not add any criteria that are not present in the rubric. If a rubric item is unclear or absent, mark it as Not Applicable. If no AI tells are detected, state that explicitly and justify briefly. Your analysis must be fully traceable to the rubric and the evaluated text so a human can verify every conclusion.

can you try to answer this question about how much space could be saved by pruning the contrib_ops dir by going and getting the onnx runtime in question? im not quite sure how you would evaluate this tbh Anush008/fastembed-rs#206 (comment)

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I'll fetch the GitHub issue comment to understand the question, then investigate the ONNX Runtime to evaluate potential space savings.

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WebFetchhttps://github.com/Anush008/fastembed-rs/issues/206#issuecomment-3697236466

	import matplotlib.pyplot as plt

	# Data
	images = [
	"boston_cooking_a",
	"boston_cooking_b",
	"finnish_cooking_a",
	"linguistics_thesis_a",
	"linguistics_thesis_b"
	]

	from pathlib import Path
	import re

	import polars as pl
	import polars_fastembed # noqa: F401


	def clean_markdown(text: str) -> str:
	"""Strip markdown/HTML artifacts from text."""
	text = re.sub(r'<table>[\s\S]*?</table>', '', text)

	//! ```cargo
	//! [dependencies]
	//! faer = "0.23"
	//! ```

	use faer::prelude::*;
	use faer::linalg::solvers::Svd;

	fn main() -> Result<(), faer::linalg::solvers::SvdError> {
	// 3×2 example matrix