pascalwhoop

#!/usr/bin/env bash
# check-litellm-compromise.sh
# Checks for compromised litellm versions (1.82.7, 1.82.8) in:
#   - uv cache
#   - all uv.lock files under a given path (default: $HOME)
#
# Usage: ./check-litellm-compromise.sh [search_path]
# Or:    curl -fsSL <gist_raw_url> | bash -s /your/path

set -eo pipefail

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#\!/usr/bin/env bash
# check-litellm-compromise.sh
# Checks for compromised litellm versions (1.82.7, 1.82.8) in:
#   - uv cache
#   - all uv.lock files under a given path (default: $HOME)
#
# Usage: ./check-litellm-compromise.sh [search_path]

set -euo pipefail

pascalwhoop

# NOTE: This file was partially generated using AI assistance.

from kedro.pipeline import node, pipeline


def dummy_node(data=None, *args, **kwargs):
    return data


def create_pipeline(**kwargs):

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#!/bin/bash

# ensure we're in git root
cd "$(git rev-parse --show-toplevel)"

# zip .git folder and store in above folder
zip -r ../git_folder.zip .git

# get uncommitted changes
git diff > ../uncommitted_changes.txt

pascalwhoop

The Architecture of Belonging: An Exploration of Community Dynamics, Well-being, and Thriving Models

I. Introduction: The Vital Role of Community

Community, in its essence, represents more than mere geographic proximity; it signifies a web of relationships, shared identities, and mutual support systems that are fundamental to human experience. It is "one of the major bases for self-definition".1 The drive to form and maintain meaningful interpersonal connections is a pervasive human motivation, often referred to as the "need to belong".2 This need is deeply ingrained, potentially stemming from our evolutionary history as a social species reliant on cooperation for survival and success.4 As social creatures, relationships with family, friends, colleagues, and broader community members are crucial for navigating life's challenges and fostering resilience.5
The significance of community extends profoundly into the realm of emotional and physical well-being. Strong social connections and a sense

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from typing import Dict, Any
import rocksdb
import ray

# Step 1: Initialize RocksDB instance
# note this is mounted in the pod using a high IOPS ReadWriteMany Volume backed by GCP Hyperdisk
db = rocksdb.DB("rocksdb_dir", rocksdb.Options(create_if_missing=True))

# Step 2: Define a Predictor class for inference.
class HuggingFacePredictor:

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#!/usr/bin/python
# requires typer, tqdm, requests, gh cli
import typer
from pathlib import Path
from tqdm import tqdm
import subprocess

app = typer.Typer()

GITHUB_API_URL = "https://api.github.com"

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#!/bin/bash

# install core requirements
NONINTERACTIVE=1 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
sudo apt install -y tmux htop bmon fzf wget curl

# get brew working
curl https://gist.githubusercontent.com/pascalwhoop/4d01bb179132118c565b3db56c4ce68e/raw/fa0ab508cac760efaa399442047256a36578c62e/tmux.conf > ~/.tmux.conf

# fish shell

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{
    "fields": [
        {
            "name": "index",
            "type": "integer"
        },
        {
            "name": "type",
            "type": "string"
        },

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import apache_beam as beam
import logging
import json

from apache_beam.io import ReadFromText
from apache_beam.io import BigQuerySource
from apache_beam.io import BigQuerySink
from apache_beam.io import WriteToText
from apache_beam.io.gcp.bigquery_tools import parse_table_schema_from_json
from apache_beam.io.gcp.internal.clients import bigquery