xthezealot

You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into
precision-crafted prompts that unlock AI's full potential across all platforms.

## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing

sgugger

from torch.optim.optimizer import Optimizer, required

class LARS(Optimizer):

    def __init__(self, params, lr=required, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False, eta=0.001):
        if lr is not required and lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if momentum < 0.0:
            raise ValueError("Invalid momentum value: {}".format(momentum))

robertknight

function termFreqMap(str) {
	var words = str.split(' ');
	var termFreq = {};
	words.forEach(function(w) {
		termFreq[w] = (termFreq[w] || 0) + 1;
	});
	return termFreq;
}

function addKeysToDict(map, dict) {

cdesante

doInstall <- TRUE
toInstall <- c("maps", "ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
library(ggplot2)
library(maps)

Prison <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/prison.csv")
head(Prison)

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

gs_sp_to_txt.sh

arecord -D plughw:1,0 -f cd -t wav -d 3 -r 16000 | flac - -f --best --sample-rate 16000 -o out.flac; wget -O - -o /dev/null --post-file out.flac --header="Content-Type: audio/x-flac; rate=16000" http://www.google.com/speech-api/v1/recognize?lang=en | sed -e 's/[{}]/''/g'| awk -v k="text" '{n=split($0,a,","); for (i=1; i<=n; i++) print a[i]; exit }' | awk -F: 'NR==3 { print $3; exit }'

hadley

mid_range <- function(x) mean(range(x, na.rm = TRUE))
centres <- ddply(county_df, c("state", "county"), summarise, 
  lat = mid_range(lat), 
  long = mid_range(long)
)

bubbles <- merge(centres, unemp, by = c("state", "county"))
ggplot(bubbles, aes(long, lat)) +
  geom_polygon(aes(group = group), data = state_df, 
    colour = "white", fill = NA) +