An AI Bot For Homelessness and Substance Abuse

An encampment in San Bernardino, California

Substance Abuse and Homelessness are inextricably linked. Often times, the sad reality around homelessness involves heartache within families and individuals whose addictions have clouded their better judgment. This state of mind happens to also inhibit the ability to manage their housing situation with self esteem, hygiene and other basic experiences of an individual neglected in the pursuit of an addictive drug.

The Future Of Homelessness As Result Of Reduced Social Net

This upcoming year will bring many positive developments, like a more manageable pandemic, more measured consumption patterns and a bit more resilience on a population that has collectively gone through so many unprecedented events.

However, we should be mindful that 2023 will be the first truly unsubsidized year for many Americans since November 2019 began to disrupt business as usual. In 2023, Covid relief will be non-existent within many communities. Tax relief, which is often a type of savings for many Americans, will be more limited because of the removal of tax credits for childcare, electric car purchases or other somewhat work-related items that ease economic pain.

This implies that homelessness will continue its upward trend which in 2021, 2022 appeared uninfluenced by massive state and federal intervention.

Doing Our Part – Homeless Chatline

We’ve opened up a hotline using the Twilio Service. The number is My collaborators, Natalia Rueda and Quetzalli Solis, have developed dialogues for attending to the potential needs of people experiencing homelessness.

Certain cities in Riverside County have a compassionate but also not enabling policy towards homelessness.

We used the following prompt in Chat GPT-3 and used the Google Cloud suite of tools to deploy our chatbot: The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.

In this hotline, a person can ask basic questions about homelessness rules and resources available to them. Yes, homelessness is a state of affairs that is governed by laws/rules. People would do well to abide by these laws because they can receive help in a better manner through these rules.

The chatbot number is (951)-404-1863. You can text this bot basic questions about homelessness and chat bot.

Text the bot basic questions. There are some specific Riverside County recommendations.

Hand Wringing In The IT Space Needs Company By Company Analysis

Today’s mixed report that the US labor market added more than a quarter million jobs but had unemployment rise should indicate that some sectors will maintain historical demand, while others will suffer downsizing driven by market conditions. In my view, the 18 month long stint for easy debt financing environment is the real culprit for specific types of layoffs. These layoffs involve companies that overestimated demand peaks lasting well into next year (or needed demand to extend that far into the future) and over hired to avoid an opportunity cost in a historically hot consumer market.

IT professionals should be as cautious as any other group about their respective sector and subsectors strength. But, no more nervous than other groups and, perhaps, a bit more optimistic even.

Despite the recent push for RTO, my suspicion is that everyone whose worth a decent salary still has leverage in their own way provided that they know the inner workings of their industry.

Analyzing Value Internally

Whose going to churn that data into a workable language model?

Whose going to automate those Jira tickets?

These tasks will certainly not fall on the 250k Data Scientist whose position may be more questionable than the 70 to130k professional quietly providing value in a mostly low-key way.

For instance, those individuals that work on products that involve business to business transactions, like Salesforce, could be in a better position than any product that is discretionary, like a Peloton bike or a website linked to travel. For some, it would be tempting to bucket an iPhone purchase in the discretionary category, but this purchase is on par with a house or car payment, which makes for a mutually reinforcing relationship between Apple and Samsung with all sorts of Telecom providers. In other words, those employees are fine since they basically work for a utility indirectly.

We have reason to be calm when we note where the layoffs are taking place. Remember, the IT sector, which is enormous, intersects with many industries but suffers from too much of an obsession with FAANG and FAANG-like companies. This skews our reasoning. Start-ups, like publicly traded companies, are very much subject to debt market conditions dictated by high or low fed interest rates.

How I Think About Companies? After Step 1, Add 1 For Max Risk Score Of 6 For Layoffs:

Amazon Layoffs

In a non-exhaustive way, I think we can look at the most recent high profile layoffs with a grain of salt. With regards to Amazon, both IT services and deliveries had extreme usage entirely linked to discretionary funds made available through WFH. The same holds for all FAANG entities.

Meta Hiring Freeze

Meta is currently caught up in what amounts to investor fraud over usage numbers. Metaverse offerings have flopped because the market/social conditions no longer favor remote activities which also makes for even lower tolerance for the brittle technologies underlying them. In context, whereas Meta was once aligned with corporate ambitions to know everything about consumers, now they’re not aligned with a corporate pushback on remote work. We’re regressing back to 2019, essentially. Put another way, Meta’s ambition to have everyone stuck on a home-based VR set doesn’t advance corporate interests like its invasive advertising did a decade ago.

Never Profitable Start-Ups

As for Lyft, Uber and Twitter, they were all suffering from years of debt financed growth with no real clarity on future revenue or profitability. If anything, Twitter investors were basically subsidized by the recent Elon Musk purchase. Put another way, those positions were subsidized by investors who could not cash out. Now they have and those individuals must be let go from someone whose recovering acquiring an overprized asset.

Golden Parachutes

An overvalued C-Suite presence is always a fixture in start-ups, big and small. In 2 of those 3 companies, their C-Suite was constantly sued over sociopathic behavior. Wage theft. Liability. You name it, they got sued over it.

The business case for Lyft, Uber – despite their ubiquity – is weak since neither has ever accomplished the scale of user adoption needed to justify the investor expense. Therefore, in a financing environment that is levels more expensive than just a few months ago, these two entities would logically need to cut expenses just to service existing debt. The same applies for dozens of over-leveraged publicly traded companies. Too many IT companies are financed on the promise, not evidence, of profitability. Their visibility is just function of funny money too.

My point is specifics matter. Look under the hood because no one is going to do it for you. Personally, I’m tired of companies acting broke when they’re not and rich when they’re in debt.

The Hoover Dam – Windy, Dry and Hot As The Dam Looks

My family and I visited the Hoover Dam recently this windy month of October 2022. I was surprised by how windy Las Vegas can get, though, I have known about those legendary winds for some time. I suspect one must live through experiences to really believe them.

For instance, we drove from Las Vegas to Boulder City and we found that the car moved drastically with debris presenting itself everywhere. Las Vegas drivers truly are nutty. Many wouldn’t stop driving below 70, but certain areas, like the 15 highway, did resort to one lane traffic in order to ensure a slowdown.

Anyways, here’s the Hoover Dam:

The Biggest Fight In Boxing

Canelo Alvarez and Triple G have gone on a worldwide tour promoting the third installment of their rivalry. The boxing world awaits the best fight of the year on September 17, 2022.

Already, folks are clamoring for this fight with dozens of fan videos dedicated to both fighters describing the boxers. Below, for example, reviews Gennady Golovkin’s career whose start with Abel Sanchez led to his intelligent but high pressure style.

The Fed Is Committed To Lowering Inflation At Job Market Expense

While CNBC is not necessarily known as the most non-partisan entity, Marc Sumerlin is not prone to hyperbole.

In his view, Fed Chairman Powell is committed to breaking the trend of an expensive labor market. Unemployment numbers must rise in order to reduce the amount of discretionary funds available in the US economy. These two figures could be the necessary factor for reducing demand for all kinds of goods throughout the US economy.

Marc Sumerlin – Evenflow Macro Managing

GDP & Unemployment – Recession

A cutback on spending is thought to reduce the amount of inflationary pressure on the overall economy. One of the primary drivers could be general consumer sentiment. Still, a decent explanation besides the fact that the Fed has acted as a guarantor for equities is simply the fact that there has been a lot of stimulus to both remove hesitant workers from the labor pool while also simultaneously providing them with spending subsidies via unemployment.

Why a 2022 Recession Would Be Unlike Any Other | WSJ

Chicano Culture Update

Mentions Cultural events in the news, on peoples minds within the Twitter space.

Updates consist of 20ish most recent or available tweets that were deemed newsworthy for the Chicano community. Lots of discussions about skin tones, reactions against hollywood and other

[xyz-ips snippet=”chicanoculture”]

Explicit Content Related To Mexicans – Please Review

For cultural news, please see here: Chicano Culture.

In this page, we review possibly objectionable content related to Mexicans. We have stored these tweets in a database. Many people make statements on Twitter ‘with a pinch of salt’. However, therein lies a powerful question: who gets to define what is simply a cheeky reference and what crosses the line as cemented or fomenting detrimental worldviews? A few simple questions will help the reader of the tweets figure out what to potentially report to Twitter:

  • Can this person make this statement in front of the demographic mentioned?
  • Is this a member of the community referenced?
  • Can this person make this statement at work without an HR consult afterwards or some other kind of censure?

If the answer to any of the above mentioned is “no”, then this tweet is likely objectionable and worth passing along to Twitter.

The social media representation of communities is important. While Freedom of Speech is important too and we should not seek to prevent statements from being uttered/tweeted, we can check their propagation; a racially biased or offensive view must always be countered by a concerted rebuttal.

[xyz-ips snippet=”offensive”]

Chicano Chatter On Twitter

Check out the latest chatter from people using the word ‘Chicano’ on twitter.

In an effort to highlight more content, we developed a few database queries to routinely retrieve uncontroversial tweets. Some of these contain frivolous references or insightful comments. Unfortunately, in many social media platforms, some of the least informed content often gets more elevated in the general public’s conscience. This page is an effort to add visibility to the reactions, concerns and ideas of the less prominent (“unliked”, less indexed) voices on Twitter, which are equally valid.

In this page, you can monitor content that contains the keyword ‘Chicano’ without any explicit content. For that more flagrant content, please visit this link. This relatively neutral content should be easy enough to follow along. I sort this list of tweets programmatically; using the Twitter Search API, I am able to amass a daily sampling of tweets on the concepts most.

At any rate, these pages allow one to observe what topics are on the mind of the more vocal members of the community. Feel free to report to twitter any objectionable content. The tweets are shown in their entirety and the views expressed their do not express my own or those of my employer.

The content is refreshed roughly every 24 hours. You will either get today’s results or the day before.

[xyz-ips snippet=”ChicanoNeutral”]

US Employee Pensions Finance PEGASUS Software; University of California, CALPERS Among Group

This article is reshared with permission from La Cartita. Originally published in that platform 12/16/2017.

La Cartita — (6/30/2017) — PEGASUS is the worlds most advanced spyware, a special type of software designed to spy on cellular phones and computers without the user’s permission. The software is most often used to target a victim’s phone camera and microphone. The audio and video are recorded and then leveraged against the victim in some way. PEGASUS is designed by the NSO Group, a team of former and current Israeli soldiers from UNIT 8200, a signals intelligence unit from the Israeli army (Israeli Defense Forces or IDF). The company was (and may still be) subsidized by the Israeli government. All of the funds that develop the Israeli’s espionage capacity is ultimately from the large military aid package provided by the US government.

Francisco Partners LP, the real owners of PEGASUS

PEGASUS was recently the subject of a highly circulated article from the NY Times detailing how the NSO Group’s software was found to have been used by the Mexican government against activist lawyers and journalists. The NY Times article was based primarily on a report from Citizen’s Lab group in Toronto. NSO Group works exclusively with governments. The first documented use of the software was against Ahmed Mansoor, a respected legal scholar who speaks out against torture.

Unfortunately, NSO Group does not operate independently of private capital. NSO Group was acquired by a private equity firm: Francisco Partners LP. The firm has several technology holdings, for instance, a software unit from Dell Computers that was spun off to Francisco Partners LP.

II. CALPERS puts 100 Million on Pegasus’ Owner; UC Regents 25 million
CALPERS funds Francisco Partners LP, owners of the NSO Group

Francisco Partners LP has two publicly listed locations that function as their corporate offices. There is 1 Letterman Drive, C Suite 410, San Francisco, California and another office in London. Their holdings are valued at 8 billion dollars. Ironically, they are increasingly in a better position to exploit commercial software since they own increasingly ubiquitous software and hardward platforms to which the NSO Group can presumably gain privileged access.

Francisco Partners LP has many government contacts. At least, one can assume this to be case with the high number of public pension funds that have invested in the company. Most notably for some of our readers, CALPERS has paid into a 100,000,000 dollars into a Francisco Partners LP fund. The following is a cursory review of the amount invested in Francisco Partners LP’s funds from US public pensions.

The Following Public Pensions Pay Into Francisco Partners LP Fund: *** How To Interpret Figures: The amount invested is to the right. The rightmost section contains the latest known investment made from the Public Pension funds to the Franscico Partners LP funds that finance company operations, e.g. capitalization, providing loan collateral, operating costs. etc.

    California Public Employees’ Retirement System USD 100,000,000 9/30/2016
    Oregon Public Employees Retirement System USD 100,000,000 12/31/2016
    University of Texas Investment Management Co/The USD 75,000,000 5/31/2016
    California State Teachers’ Retirement System USD 75,000,000 9/30/2016
    Florida Retirement System USD 75,000,000 9/30/2016
    New York City Fire Pension Fund USD 75,000,000 6/30/2016
    Colorado Public Employees’ Retirement Association USD 50,000,000 12/31/2015
    School Employees Retirement System of Ohio USD 40,000,000 12/31/2016
    Regents of the University of California/The USD 25,000,000 9/30/2014
    West Midlands Pension Fund USD 30,008,541 3/31/2016
    University of Michigan USD 20,000,000 9/17/2009
    Pennsylvania State Employees’ Retirement System USD 20,000,000 12/31/2015
    Ohio Police & Fire Pension Fund USD 15,000,000 6/30/2014

III. The profit model for NSO Group: Hack More, Pay Less: Realizing Scale

Documents leaked to the NY Times revealed the NSO Group’s external clients and their fee structure. The NSO group charges USD 500,000 dollars to a client state that wishes to install their software in some piece of hardware. An additional USD 650,000 dollars is assessed to intercept/hack 10 I-Phones or 10 Androids. Finally, a client may be charged USD 800,000 dollars more to hack 100 phones of any make or model. This pricing model reflects a disposition to hack more in order for a government to ‘get its money’s worth’.

IV. Government of Mexico: Ayotzinapa Hacks

The Government of Mexico – even before it had a massive fiasco in its hands with the Ayotzinapa case of 2014 – has, at least, 80 million dollars invested in projects with the NSO group since 2013. That figure could only have gone up since the EPN administration struggles to maintain power.

The Ayotzinapa case involves many dozens of lawyers and activist groups. A rough estimate from the Inter American Commission on Human Rights claims that at least 196 people were affected on the night of September 26, 2014. These people and their extended families should presume themselves to be subjects of surveillance in one shape or another because of their legal connection and right to claim restitution. At the time of writing, many of the direct family member’s of the disappeared 43 have phones that exhibit strange behavior.

WebScraping As Sourcing Technique For NLP


In this post, we provide a series of web scraping examples and reference for people looking to bootstrap text for a language model. The advantage is that a greater number of spoken speech domains could be covered. Newer vocabulary or possibly very common slang is picked up through this method since most corporate language managers do not often interact with this type of speech.

Most people would not consider Spanish necessarily under resourced. However, considering the word error rate in products like the Speech Recognition feature on a Hyundai, Mercedes Benz or text classification generally on social media platforms, which is skewed towards English centric content, there seems to certainly be a performance gap between contemporary #Spanish speech in the US and products developed for that demographic of speakers.

Excellent example of a ML model struggling because of lack of an exclusion list.

Lyrics are a great reference point for spoken #speech. This contrasts greatly with long form news articles, which are almost academic in tone. Read speech also carries a certain intonation, which does not reflect the short, abbreviated or ellipses patterning common to spoken speech. As such, knowing how to parse the pages may be a good idea for those refining and expanding language models with “real world speech”.


  • Point to
  • Retrieve Artist
  • Retrieve Artist Songs
  • Generate individual texts for songs until complete.
  • Repeat until all artists in artists file are retrieved.

The above steps are very abbreviated and even the description below perhaps too short. If you’re a beginner, feel free to reach out to I’d rather deal with the beginner more directly; experienced python programmers should have no issue with the present documentation or modifying the basic script and idea to their liking.


In NLP, the number one issue will never be a lack of innovative techniques, community or documentation for commonly used libraries. The number one issue is and will continue to be a proper sourcing and development of training data.

Many practitioners have found that the lack of accurate, use case specific data are better than a generalized solution, like BERT or other large language models. These issues are most evident in languages, like Spanish, that do not have as high of a presence in the resources that compose BERT, like Wikipedia and Reddit.

Song Lyrics As Useful Test Case

At a high level, we created a list of relevant artists: Artists then looped through the list to search in whether they had any songs for them. Once we found that the request yielded a result, we looped through the individual songs for each artists.

Lyrics are a great reference point for spoken speech. This contrasts greatly with long form news articles, which are almost academic in tone. Read speech also carries a certain intonation, which does not reflect the short form, abbreviated or ellipsis that characterizes spoken speech. As such, knowing how to parse the resource may be a good idea for those refining and expanding language models with “real world speech”.

Requests, BS4

The proper acquisition of data can be accomplished with BeautifulSoup. The library has been around for over 10 years and it offers an easy way to process HTML or XML parse trees in python; you can think of BS as a way to acquire the useful content of an html page – everything bounded by tags. The requests library is also important as it is the way to reach out to a webpage and extract the entirety of the html page.

# -*- coding: utf-8 -*-
Created on Sat Oct 16 22:36:11 2021
import requests
artist = requests.get("").text

The line `’requests.get(“”).text` does what the attribute ‘text’ implies; the call obtains the HTML files content and makes it available within the python program. Adding a function definition helps group this useful content together.

Functions For WebScraping

Creating a bs4 object is easy enough. Add the link reference as a first argument, then parse each one of these lyric pages on DIV. In this case, link=”” is the argument to pass along for the function. The function lyrics_url returns all the div tags with a particular class value. That is the text that contains the artists landing page, which itself can be parsed for available lyrics.

def lyrics_url(web_link):
    This helps create a BS4 object. 
    Args: web_link containing references. 
    return: text with content. 
    artist = requests.get(web_link).text
    check_soup = BeautifulSoup(artist, 'html.parser')
    return check_soup.find_all('div', class_='cnt-letra p402_premium') the highlight portion is contained within <div> tag.

The image above shows the content within a potential argument for lyrics_url “”. See the github repository for more details.

Organizing Content

Drilling down to a specific artist requires basic knowledge of how is set-up for organizing songs into a artists home page. The method artists_songs_url involves parsing through the entirety of a given artists song lists and drilling down further into the specific title.

In the main statement, we can call all these functions to loop through and iterate through the artists page and song functions to generate unique files, names for each song and its lyrics. The function generate_text will write into each individual one set of lyrics. Later, for Gensim, we can turn each lyrics file into a single coherent gensim list.

def artist_songs_url(web_link):
    This helps land into the URL's of the songs for an artist.'
    Args: web link is the 
    Return songs from;/
    artist = requests.get(web_link).text
    print("Status Code", requests.get(web_link).status_code)
    check_soup = BeautifulSoup(artist, 'html.parser') 
    songs = check_soup.find_all('li', class_='cnt-list-row -song')
    return songs
#@ div class="cnt-letra p402_premium

def generate_text(url):
    import uuid 
    songs = artist_songs_url(url)
    for a in songs:
        song_lyrics = lyrics_url(a['data-shareurl'])
        print (a['data-shareurl'])
        new_file = open(str(uuid.uuid1()) +'results.txt', 'w', encoding='utf-8')
        print (song_lyrics)
    return print ('we have completed the download for ', url )

def main():
    artistas = open('artistas', 'r', encoding='utf-8').read().splitlines()
    url = ''
    for a in artistas : 
        generate_text(url + a +"/")
        print ('done')
#once complete, run copy *results output.txt to consolidate lyrics into a single page. 

if __name__ == '__main__':
    sys.exit(main())  #