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Brian Ray brianray

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brianray / launch.json
Last active December 27, 2023 22:21
debug GCP Cloud Functions locally with VSCode
"version": "0.2.0",
"configurations": [
"name": "Python: Cloud Function Emulate",
"type": "python",
"request": "launch",
"module": "functions_framework",
"justMyCode": false,
"args": [
graph LR
  A((Start))--take Survey-->B((Goal Setting))
  B((Goal Setting))--1 on 1 coach-->C((Working On Concentation))
  C((Working On Concentation))--1 on 1 coach weekly-->C((Working On Concentation))


  1. Jailbreaking for Data Leaked from Hacker Actor: the hacker gets data out of the model by pure prompt manipulation,
  2. Intrusion from Hacker Actor: The malicious instructions pass through the application and can instruct the extension services to do bad things.
  3. Data poisoning for Training data: The source data used to train the LLM has malicious content before it is trained.
  4. Prompt Poising: hidden content or injected content unintentional from an innocent bystander.
[name] By: [by]
Data Access ...
Runtime and Cost ...
Namespaces ...
Advanced Types ...
Scheduling ...
Streaming ...
Feature Transform ...
Time Sensitivity ...
Databricks Feature Store By: Databricks
Data Access ...
Runtime and Cost ...
Namespaces ...
Advanced Types ...
Scheduling ...
Streaming ...
Feature Transform ...
Time Sensitivity ...
prophet_model = Prophet(
yearly_seasonality=10, # Fourier
weekly_seasonality=3, # Fourier
import scipy.signal as sg
def decimate_rolling(df, size=100):
out = []
for name in names[:n_samples]:
inds = np.arange(0, len(df), 8000) ## TODO
for i, j in zip(inds[:-1], inds[1:]):
dij = d[i:j]
dij = sg.decimate(dij, 2, ftype="iir")[::2]
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function sentiment_analysis(content) {
var URL = ""
var options = {
'method' : 'post',
'contentType': 'application/json',
// Convert the JavaScript object to a JSON string.
'payload' : JSON.stringify({"content": content})
return UrlFetchApp.fetch(URL, options).getContentText();
from import language
from import enums
from import types
def analyze(request):
request_json = request.get_json()
client = language.LanguageServiceClient()
sentance = request_json['content']
document = types.Document(