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Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts and experience preferred (super rare at this point).

My own notes from a few months back.

Background

• Survey of LLMS
• Self-attention and transformer networks

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This notes is written by Sheldon. You can find me with #iOSBySheldon in Github, Youtube, Facebook, etc.

Need

Convert .mov/.MP4 to .gif

Reason

As a developer, I feel better to upload a short video when I create the pull request to show other viewers what I did in this PR. I tried .mov format directly got after finishing recording screen using Quicktime, however, gif offers preview in most web pages, and has smaller file size.

This is not limited to developer, anyone has this need can use this method to convert the files.

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(function (context, trackingId, options) {
    const history = context.history;
    const doc = document;
    const nav = navigator || {};
    const storage = localStorage;
    const encode = encodeURIComponent;
    const pushState = history.pushState;
    const typeException = 'exception';
    const generateId = () => Math.random().toString(36);
    const getId = () => {

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import functools
import gc
import itertools
import sys
from timeit import default_timer as _timer


def timeit(_func=None, *, repeat=3, number=1000, file=sys.stdout):
    """Decorator: prints time from best of `repeat` trials.

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-- Table information like sortkeys, unsorted percentage
-- see http://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html
SELECT * FROM svv_table_info;

-- Table sizes in GB
SELECT t.name, COUNT(tbl) / 1000.0 AS gb
FROM (
  SELECT DISTINCT datname, id, name
  FROM stv_tbl_perm
  JOIN pg_database ON pg_database.oid = db_id

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#!/usr/bin/env python

##########################################################################
## Imports
##########################################################################

import sys
import argparse
import traceback
import numpy as np

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-- show running queries (pre 9.2)
SELECT procpid, age(query_start, clock_timestamp()), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(query_start, clock_timestamp()), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

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# -*- coding: utf-8 -*-

"""
Based on the tflearn example located here:
https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py
"""
from __future__ import division, print_function, absolute_import

# Import tflearn and some helpers
import tflearn

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  # -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
import scipy

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Automation for the People

Long ago, the first time I read "The Pragmatic Programmer", I read some advice that really stuck with me.

"Don't Use Manual Procedures".

This in the chapter on Ubiquitous Automation. To summarize, they want you to automate all the things.

The trouble was that I hadn't much of an idea how to actually go