Brian Ray brianray

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))

Scenarios

Jailbreaking for Data Leaked from Hacker Actor: the hacker gets data out of the model by pure prompt manipulation,
Intrusion from Hacker Actor: The malicious instructions pass through the application and can instruct the extension services to do bad things.
Data poisoning for Training data: The source data used to train the LLM has malicious content before it is trained.
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		...

	{
	"version": "0.2.0",
	"configurations": [
	{
	"name": "Python: Cloud Function Emulate",
	"type": "python",
	"request": "launch",
	"module": "functions_framework",
	"justMyCode": false,
	"args": [

	prophet_model = Prophet(
	growth='linear',
	changepoints=None,
	n_changepoints=25,
	changepoint_range=0.8,
	yearly_seasonality=10, # Fourier
	weekly_seasonality=3, # Fourier
	daily_seasonality='auto',
	seasonality_mode='additive',
	seasonality_prior_scale=10.0,

	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]
	out.append(dij)

	function sentiment_analysis(content) {
	var URL = "https://us-central1-mw-automl-first-look.cloudfunctions.net/analyze"
	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 google.cloud import language
	from google.cloud.language import enums
	from google.cloud.language import types

	def analyze(request):
	request_json = request.get_json()
	client = language.LanguageServiceClient()
	sentance = request_json['content']
	document = types.Document(
	content=sentance,