lmmx

import json

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


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

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lmmx

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

lmmx

vcd() {
  local dir="$PWD"

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

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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):

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:

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

lmmx

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

lmmx

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,

lmmx

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