Logge LoggeL

Video-Analyse: "Kurzfilm - Infected"

YouTube: https://youtu.be/6iNdfsbZWHs
Kanal: Logge (@LoggeL)
Genre: Post-Apokalypse / Zombie-Pandemie / Kurzfilm

1) Visuelle Analyse (Thumbnail & Branding)

Matthew Berman OpenClaw Use Cases — Detailanalyse

Video: OpenClaw Use Cases that are actually helpful... Prompts: mberman84/065631c62d6d8f30ecb14748c00fc6d9

Alle 11 Use Cases

#	Feature	Was genau	Nützlich	Machbar
1	Personal CRM	Gmail/Calendar auto-scannen, AI-Filtering (Spam/Newsletter raus), Contact Scoring, Embeddings für Semantic Search ("Wer war das von Firma X?")	⭐⭐⭐⭐⭐	✅ Easy — SQLite + Gmail API + Cron

🗳️ Wahl-O-Mat Bundestagswahl 2025 — Pragmatischer RLP-Bürger

Methodik

Alle 38 Thesen beantwortet aus Sicht eines pragmatischen, nicht-ideologischen Bürgers von Rheinland-Pfalz. Fokus: Was bringt dieses Bundesland langfristig voran?

RLP-Kontext: Grenzregion (LU/FR/BE), BASF Ludwigshafen, Ramstein, Weinbau, ländliche Struktur, Fachkräftemangel, Infrastrukturdefizite.

Alle 38 Thesen

🍕 Pizzeria Bella Italia — Hettenleidelheim

Hauptstraße 44, 67310 Hettenleidelheim · Seit 1993
☎ 06351/37526 · 06351/122874
Montag Ruhetag
★ 4.8/5 (335 Bewertungen)

Liefergebühren

| Gebiet | Gebühr |

Aurora Alpha — Hard Benchmark Report

Model: openrouter/aurora-alpha
Date: 2026-02-10
Benchmark: Hard questions (30 questions across 6 categories) + 26 knowledge cutoff probes
Evaluator: Automated analysis with manual verification + web-validated fact-checking

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>InnovaVento — Session Cost Analysis</title>
	<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
	<style>
	:root {
	--bg: #0d1117;

	def wer(ref, hyp ,debug=True):
	r = ref.split()
	h = hyp.split()
	#costs will holds the costs, like in the Levenshtein distance algorithm
	costs = [[0 for inner in range(len(h)+1)] for outer in range(len(r)+1)]
	# backtrace will hold the operations we've done.
	# so we could later backtrace, like the WER algorithm requires us to.
	backtrace = [[0 for inner in range(len(h)+1)] for outer in range(len(r)+1)]

	OP_OK = 0

	[
	[
	[
	"charname",
	"AllChampions"
	],
	[
	"spell_name",
	"SummonerSnowball"
	],

	const puppeteer = require('puppeteer')
	const fs = require('fs');
	// Hardcoded for 200 shapes
	(async () => {
	const browser = await puppeteer.launch({ headless: true })
	const page = await browser.newPage()
	await page.goto('https://ondras.github.io/primitive.js/', { waitUntil: 'networkidle0' })

	const files = fs.readdirSync('img') // Input Folder
	for (let i = 0; i < files.length; i++) {