Rahul Ravishankar rahul1906

Goal: find a Linux alternative to FancyZones for Windows

Name	Recommended	Type	Supports main colum	Supports layouts	Multiple windows in same tile	Windows can span multiple zones	Notes
Tiling Shell	👍👍👍	Gnome extension	yes	yes	yes	yes	Explicitly inspired by Windows tiling, works great out of the box
gSnap	👍👍	Gnome extension	yes	yes	yes	yes	Can be configured almost just like FancyZones; in the settings: disable Show tabs enable Hold CTRL to snap windows
gTile		Gnome extension			no?
Tiling Assistant ([Now bundled with Ubuntu](https://www.omgubuntu.co.uk/2023/06/

The initial source comes from sdcuike/issueBlog#4

https://github.com/PacktPublishing free to download books code by Packet

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	import asyncio
	import base64
	import json
	import os
	import pyaudio
	from websockets.asyncio.client import connect


	class SimpleGeminiVoice:
	def __init__(self):

	# -----------------------------------------------------------------------------
	# AI-powered Git Commit Function
	# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
	# 1) gets the current staged changed diff
	# 2) sends them to an LLM to write the git commit message
	# 3) allows you to easily accept, edit, regenerate, cancel
	# But - just read and edit the code however you like
	# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

	gcm() {

	# Setup:
	# conda create -n whisper python=3.9
	# conda activate whisper
	# https://github.com/openai/whisper
	# pip install git+https://github.com/openai/whisper.git

	# Usage:
	# python whisper-audio-to-text.py --audio_dir my_files --out_dir texts

	import argparse

	# Sebastian Raschka 09/24/2022
	# Create a new conda environment and packages
	# conda create -n whisper python=3.9
	# conda activate whisper
	# conda install mlxtend -c conda-forge

	# Install ffmpeg
	# macOS & homebrew
	# brew install ffmpeg
	# Ubuntu

	def get_classification_report(y_test, y_pred):
	'''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''
	from sklearn import metrics
	report = metrics.classification_report(y_test, y_pred, output_dict=True)
	df_classification_report = pd.DataFrame(report).transpose()
	df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False)
	return df_classification_report