Louis Maddox lmmx

Category	Oral / Literate	Avg F1 (≈)	Individual Markers (F1)	Comment on Why / Causes	Verdict
Address & Interaction	Oral	0.604	vocative (.675), imperative (.606), second_person (.549), inclusive_we (.608), rhetorical_question (.661), phatic_check (.634), phatic_filler (.495)	Strong lexical and syntactic cues; short-range dep

marker	precision	recall	f1-score	support
B-literate_list_structure	0.975	0.75	0.848	52
O	0.771	0.847	0.807	37244
B-oral_imperative	0.753	0.805	0.778	87
B-literate_footnote_reference	0.81	0.739	0.773	23
B-oral_rhetorical_question	0.649	0.809	0.72	89
I-literate_technical_abbreviation	0.687	0.731	0.709	108
B-oral_inclusive_we	0.603	0.793	0.685	266
I-literate_footnote_reference	0.57	0.821	0.673	84
I-oral_rhetorical_question	0.646	0.683	0.664	840

marker	precision	recall	f1-score	support
B-literate_list_structure	0.932	0.788	0.854	52
O	0.75	0.819	0.783	37244
B-oral_inclusive_we	0.581	0.921	0.712	266
B-oral_imperative	0.654	0.782	0.712	87
B-oral_rhetorical_question	0.535	0.933	0.68	89
B-literate_conditional	0.595	0.742	0.661	97
B-literate_concessive	0.517	0.818	0.634	55
I-oral_rhetorical_question	0.683	0.554	0.611	840
B-oral_phatic_check	0.467	0.875	0.609	24

A: This Tweet is making the rounds: "Nearly every ambitious person I know who has dived into Al is working harder than ever, and longer hours than ever. Fascinating dynamic tbh. I have NEVER worked this hard, nor had this much fun with work." I'm in this Tweet.

C: Suggests a very, ah, particular attitude to pre LLM SE imho. You're not the first people to invent excitement or better mechanical advantage in SE. The Al crowd are culturally rooted in old languages, building not proving etc. Fine. But different interests would mean different tools excite

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,

	import json

	import torch
	from huggingface_hub import hf_hub_download
	from transformers import AutoModel, AutoTokenizer


	def main():
	model_name = "HavelockAI/bert-token-classifier"

	vcd() {
	local dir="$PWD"

	while [[ "$dir" != "/" ]]; do
	if [[ -d "$dir/.venv" ]]; then
	cd "$dir" \|\| return
	return
	fi
	dir="$(dirname "$dir")"
	done

	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):