Discover gists

List of NFL API Endpoints

This page has been updated a lot in the past 3 years. Older revisions you might like more than this one:

They should work. Works for all cores of your host system. Also you can download ESXi from here.

This page has been updated a lot in the past 3 years. Older revisions you might like more than this one:

If your device is X306F (Wi-Fi Only), follow the standard process on UT's website: https://devices.ubuntu-touch.io/device/amar-row-wifi/

Install adb and fastboot version 30.0
Unlock Bootloader and Enable USB debugging
Download the latest firmware of Android 11 and extract the zip.
Boot your device in fastboot mode by holding volume down and power keys together.
Run fastboot devices to confirm if the device is being detected or not.
Open a terminal in unzipped directory and run the following commands

	<html ng-app="ionicApp">
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="initial-scale=1, maximum-scale=1, user-scalable=no, width=device-width">

	<title>Ionic Fullscreen Image</title>

	<link href="//code.ionicframework.com/1.0.0-rc.1/css/ionic.min.css" rel="stylesheet">
	<script src="//code.ionicframework.com/1.0.0-rc.1/js/ionic.bundle.js"></script>
	</head>

	#!/usr/bin/env python

	"""
	This script processes SAM (Sequence Alignment/Map format) inputs from standard
	input and extracts alignment information that is then provided in a tab-separated
	table. The following fields are produced: query, target, query_length, query_start,
	query_end, target_start, target_end, alignment_length, alignment_identity.

	This script was designed for use with SAM files produced by minimap2. However,
	it will work with any SAM data that:

	# the "verifiers" repository is a clean implementation of templated GRPO reinforcement learning training environments
	# this is a generic set of "install from scratch" commands complete with a deepspeed z3 config that i have been using when i spin up nodes
	# it will run on the gsm8k example w/ default batch size & generation size (8), and the 8th GPU is used for vllm generations
	# qwen 14b full finetuning will run on this configuration too without LoRA or CUDA OOM, at least for the gsm8k task's context sizes + generation lengths
	# hyperparameters are controlled by `verifiers/utils/config_utils.py`; i have been preferring extreme grad clipping (between 0.001 and 0.01) and low beta (under 0.01)

	# NOTE FEB 27: examples have moved into `verifiers/examples` not `/examples`

	cd /root
	mkdir boom

	#!/usr/bin/env bash

	# checck if pidof exists
	PIDOF="$(which pidof)"
	# and if not - install it
	(test "${PIDOF}" && test -f "${PIDOF}") \|\| brew install pidof

	# find app in default paths
	CO_PWD=~/Applications/CrossOver.app/Contents/MacOS
	test -d "${CO_PWD}" \|\| CO_PWD=/Applications/CrossOver.app/Contents/MacOS