Louis Maddox lmmx

SKILL: Document Transcription Pipeline

This guide describes how to transcribe a PDF document (book or paper) into a hierarchical modular directory tree of markdown files. Follow each step in order.

Overview

The pipeline produces:

Split PDFs - one per top-level group (chapter/section), extracted with qpdf
Transcript files - page-level markdown files with YAML frontmatter, named by page number

Analysis: How AI Stupid Meter Detects LLM Degradation

Source Repository: https://github.com/StudioPlatforms/aistupidmeter-api

This is a comprehensive code review with specific file and line references for verification.

Repository Structure Overview

Modulating Text Toward Literate Register: A Practical Guide

Theoretical Foundation

The distinction between oral and literate modes of expression, as elaborated in Walter J. Ong's Orality and Literacy: The Technologizing of the Word (1982), concerns not merely the medium of transmission but the underlying cognitive and syntactic structures that organise thought. Oral discourse—shaped by the constraints of memory and real-time performance—exhibits characteristic markers: additive syntax, formulaic aggregation, redundancy, participatory engagement,

I wrote this blog https://cog.spin.systems/future-paths-template-strings it describes how to use t-strings and pathlib Paths together to do symbolic path manipulation. I want to take the idea further though - see the code https://gist.github.com/lmmx/f5d1b07d266f160f9a431c1f6bdc8a17 - I want to use pyo3 to optimise the path operations like polars does with its queries and to avoid allocations - right now it is not genuinely deferred, only symbolic (deferred resolution). Polars builds a lazy logical plan, optimizes it, then executes. You can do the same: separate the expression tree (what you have now) from a compiled plan that's cheap to execute repeatedly. take a look under the hood in the polars source code and write me a report on the specific analogues (as in analogies) between the code bases and how i would design it. write an extensive report and make sure you cite the sources for factual claims, dont just imply something is true without giving me the ability to verify it. write your report to markdown

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.

REFACTORING ENGLISH Author Sample Chapters Blog Tools Services HN Popularity Contest

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.

	from pathlib import Path
	from typing import Annotated

	from pydantic import BaseModel, BeforeValidator, Field, FilePath

	PathA = Annotated[FilePath, BeforeValidator(lambda _: Path("a.txt"))]
	PathB = Annotated[FilePath, BeforeValidator(lambda _: Path("b.txt"))]


	class MyModel(BaseModel):

	from __future__ import annotations

	from dataclasses import dataclass
	from pathlib import Path
	from string.templatelib import Interpolation, Template
	from typing import Any

	# ============================================================
	# Layer 1: Parameters (late binding slots)
	# ============================================================

	import polars as pl
	from pathlib import Path

	claims_file = Path("results/claims/chunk_0-00400-of-00546.parquet")

	lf = pl.scan_parquet(claims_file)

	base = (
	lf.select(pl.col("claims").explode().struct.unnest())
	.drop("key")