brian piercy bjpcjp

## LLM benchmarks.md

      
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                bjpcjp
                / LLM benchmarks.md
            
            
              Created
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Group
Benchmark
Summary Explanation
Link


English
MMLU (EM)
Measures multi-task learning across diverse knowledge domains to evaluate language models' general academic proficiency.
MMLU Benchmark


English
MMLU-Redux (EM)
A reduced version of MMLU focusing on key topics or subsets of academic questions.
No dedicated link available.


## linux compgen cmnd options.md

      
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                bjpcjp
                / linux compgen cmnd options.md
            
            
              Created
              January 19, 2025 20:39
            
          
    The compgen command in Linux is a shell built-in used primarily for generating possible completions for commands, functions, files, or other shell elements. It is part of the Bash shell and is often utilized in scripts for tab-completion or command suggestion mechanisms.
Syntax:

compgen [option] [word]
Key Features:


## 10_oneliners.py
# https://www.kdnuggets.com/10-python-one-liners-change-coding-game

# 1. Lambda Functions
price_after_discount = lambda price: price*0.9


# 2. Map Operations on Lists
discounted_prices = list(map(price_after_discount, prices))


## d3js-bullet.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
margin: auto;
padding-top: 40px;
position: relative;
width: 800px;
}

## shell-best-practices.sh
#!/usr/bin/env bash

# source: https://sharats.me/posts/shell-script-best-practices/

set -o errexit
set -o nounset
set -o pipefail
if [[ "${TRACE-0}" == "1" ]]; then
    set -o xtrace
fi

## my first svg
<svg width="10" height="10">
  <rect x="0" y="0" width="10" height="10" fill="blue" />
</svg>

## pocket_json_prodmgmt_50_20220607
{"status"=>1, "complete"=>1, "list"=>{"3492948982"=>{"item_id"=>"3492948982", "resolved_id"=>"3492948982", "given_url"=>"https://www.practicalecommerce.com/tag/video-tools", "given_title"=>"", "favorite"=>"0", "status"=>"0", "time_added"=>"1638225235", "time_updated"=>"1654562695", "time_read"=>"0", "time_favorited"=>"0", "sort_id"=>0, "resolved_title"=>"Video Tools", "resolved_url"=>"https://www.practicalecommerce.com/tag/video-tools", "excerpt"=>"Video offers merchants a range of ways to engage customers and prospects. Here is a list of new and updated tools from video platforms and social media applications. There are tools for promoting products and producing shoppable videos and as well as for editing, live-streaming, and monetizing.", "is_article"=>"1", "is_index"=>"0", "has_video"=>"0", "has_image"=>"0", "word_count"=>"55", "lang"=>"en", "tags"=>{"ecommerce"=>{"item_id"=>"3492948982", "tag"=>"ecommerce"}, "prodmgmt"=>{"item_id"=>"3492948982", "tag"=>"prodmgmt"}, "tools"=>{"item_id"=>"3492948982", "tag"

## trading-investment-metrics.py
from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
import numpy as np
from math import sqrt


def cagr(start_value: float, end_value: float, years: int):
    return (end_value / start_value) ** (1.0 / years) - 1

def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
    diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array

## approximate_nearest_neighbors.py
# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
import time
import sys

try:
    import annoy
except ImportError:
    print("The package 'annoy' is required to run this example.")

## prodmgmt-art-of-profitability-booknotes.md

      
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                / prodmgmt-art-of-profitability-booknotes.md
            
            
              Last active
              May 14, 2019 21:07
            
          
Original notes by James Clear


Do the math yourself. Too many people take numbers from unreliable sources.


There are 4 levels of learning: Awareness, Awkwardness, Application, Assimilation


Customer-Solution Profit: Know your customers incredibly well and create a solution specifically for them.
Group	Benchmark	Summary Explanation	Link
English	MMLU (EM)	Measures multi-task learning across diverse knowledge domains to evaluate language models' general academic proficiency.	MMLU Benchmark
English	MMLU-Redux (EM)	A reduced version of MMLU focusing on key topics or subsets of academic questions.	No dedicated link available.
	# https://www.kdnuggets.com/10-python-one-liners-change-coding-game

	# 1. Lambda Functions
	price_after_discount = lambda price: price*0.9


	# 2. Map Operations on Lists
	discounted_prices = list(map(price_after_discount, prices))
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>
	body {
	font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
	margin: auto;
	padding-top: 40px;
	position: relative;
	width: 800px;
	}
	#!/usr/bin/env bash

	# source: https://sharats.me/posts/shell-script-best-practices/

	set -o errexit
	set -o nounset
	set -o pipefail
	if [[ "${TRACE-0}" == "1" ]]; then
	set -o xtrace
	fi
	<svg width="10" height="10">
	<rect x="0" y="0" width="10" height="10" fill="blue" />
	</svg>
	from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
	import numpy as np
	from math import sqrt


	def cagr(start_value: float, end_value: float, years: int):
	return (end_value / start_value) ** (1.0 / years) - 1

	def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
	diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array
	# Author: Tom Dupre la Tour
	#
	# License: BSD 3 clause
	import time
	import sys

	try:
	import annoy
	except ImportError:
	print("The package 'annoy' is required to run this example.")