Yes yes, I know, I partook in yet another side quest to getting nag.bot shipped ( slacker). But this side quest helps me do the thing I like least, marketing. I don’t have a big following, so simple tweeting into the void isn’t very effective. I have to go get users. I figured out a pretty good workflow for outreach, but it was super tedious. Lots of manual searching through X to find prospects for engagement. But, I figured out a way to speed it up a lot using AI. The following bookmarklet app was the result.

It’s quick and dirty, but I’m getting some results! I’m finding and engaging with more users I might be able to help, faster. I’m sure this app will see some refinement as I use it. But, here it is…. I call it Coldpost, named after cold calling. I hope you like it!

ColdPost (d4b7ba)

• Dragthis link (bookmarklet app) to your bookmarks bar.
• Go to X.com
• Click the bookmarklet you just installed
• Go to the Config tab.
• Configure your Objective, Context, Reply Directions, and Keywords.
• Go to the Run tab.
• Choose the configuration to run, if it’s not already selected.
• Specify how many articles it should scrape, default 100.
• Click, Start Scraping.
• When complete, you’ll be taken to Grok.
• Paste your clipboard into Grok’s input.
• Copy the output result into the Results tab.
• Engage with a curated list of prospective customers!
• Click Next Keyword and do it again.

Key Features Implemented

1. Authentication and Profile Setup: Early in August, foundational work was laid out with the implementation of user authentication. This was a critical step to ensure that users can securely access and manage their accounts. Following the authentication setup, basic profile management features were introduced, allowing users to personalize their experience within the app.
2. Chat Functionality: A major focus has been on building a robust chat system, which is central to the user experience in Nag.bot. The chat now supports functional communication, with a server-side prompt system that enables AI-driven responses. Additionally, the frontend has been optimized to handle real-time conversations, ensuring a smooth and responsive user experience.
3. Error Notifications and Markdown Support: To enhance usability, an error notification system was integrated into the chat. This helps users quickly identify and address issues during their interactions with the app. The chat also supports markdown, allowing for formatted text, which is particularly useful for displaying structured information and links within conversations.
4. UI/UX Enhancements: Significant attention has been given to refining the user interface. The chat now includes a list styling feature, making it easier for users to navigate through messages. Additionally, the text input area has been improved to dynamically adjust as users type, providing a more intuitive and user-friendly experience. A standard button component has been added to maintain consistency across different parts of the app.
5. Conversation Initiation from Home Screen: One of the more recent updates allows users to start a conversation directly from the home screen. This update is aimed at making it easier for users to dive into the chat with the AI, streamlining the process of getting the support they need right from the start. The starter tile on the home screen now forwards users to the chat with pre-seeded prompts, creating a seamless transition into the conversation.

Looking Ahead

With the core functionalities of authentication, chat, and error handling in place, the focus will now shift to refining these features based on user feedback. Additionally, there are plans to further enhance the AI’s capabilities, enabling it to provide more personalized and insightful responses to users’ queries and challenges.

Stay tuned for more updates as Nag.bot continues to evolve into a powerful tool for personal accountability and goal management.

Time is a precious resource, we should spend it as wisely as we spend our money. That is what this app will do. It will discuss your projects with you and help you break your goals or ideas down into actionable tasks. Then, it will check in from time to time and see if you’re working towards those goals. If not, Nag with NAG you to get to work!

Running into issues with it though. I was hoping I could depend on ALL CAPS and other identifiers to separate sections out. In a few cases, it’s difficult to decipher between section titles and body. In the PDF, I might be able to use italics and other variations in the font to figure out what’s what. This increases the complexity of sorting out the different sections of the document into something with a usable structure.

Goal 1: Read and parse the document

Goal 2: Extract sections.

Read and parse the document

What are some readily available open source projects that I can use to parse PDFs into text?

1. Poppler Utils: Poppler is a PDF rendering library that includes a variety of utilities for extracting text, images, and other content from PDFs. The pdftotext command is part of this suite and is widely used for converting PDF documents into plain text.
2. Xpdf: Similar to Poppler, Xpdf is a toolset that includes the pdftotext command. It’s an older project but still effective for extracting text from PDF files.
3. PDFMiner: PDFMiner is a tool written in Python specifically for extracting text, images, and metadata from PDF files. It’s more flexible than pdftotext and allows for more detailed analysis of the PDF structure, making it suitable for more complex extraction tasks.
4. mutool: Part of the MuPDF suite, mutool can extract text and images from PDF files. MuPDF is known for its speed and the quality of its rendering.
5. Apache PDFBox: Although primarily a Java library for working with PDF documents, PDFBox comes with a command-line utility that can be used to extract text from PDFs. It’s useful for those who prefer a Java-based solution.
6. Tesseract OCR: For PDFs that contain mostly images of text (like scanned documents), Tesseract OCR can be a powerful tool. It’s an optical character recognition (OCR) engine that can convert images into text, and with the right preprocessing, it can be used to extract text from image-based PDFs.

Let’s try some of these out and see how the result varies. I’m most interested in #1 poppler and #3 pdfminer

Using the poppler option I found provides a good starting point for text cleanup.

pdftotext -layout -enc UTF-8 WDI39597.pdf poppler.txt

PDFMiner has more options in term of formats (text | XML | HTML). The first thing I noticed though, it’s significantly slower to execute. Annnd. the output is far less usable. I was hopeful for the HTML or XML output. The most ridiculous output was XML. There was literally tags around every letter.

pdf2txt.py -o pdfminer.txt -t text -A WDI39597.pdf 
pdf2txt.py -o pdfminer.html -t html -A WDI39597.pdf 
pdf2txt.py -o pdfminer.xml -t xml -A WDI39597.pdf 

POPPLER WINS! It creates a usable output and is WAY faster in terms of execution, not that that is a huge factor.

Now we have something that looks like this: https://snovak.com/wp-content/uploads/2024/03/poppler.txt

Now, Let’s strip out some garbage and format this a bit more.

I’m using a Python script to do this part.
First it detects page number and formats that appropriately.
Then, it gets rid of leading whitespace.
Then, ditch the date, and the line under that, which has some crazy special characters….
Then, ditch any lines that end in ‘SEN. APPRO’

Now we have something that looks like this… https://snovak.com/wp-content/uploads/2024/03/WDI39597.txt

I’ve preserved the page numbers and line numbers for citation purposes. So, if I want to recall where appropriations were made in the bill, I can cite “Page 36 Line 22” for example.

I’ll have to get to extracting the sections tomorrow…

This week another Uniparty Omnibus spending bill was passed without much a fuss. I was thinking Speaker Johnson was going to be a force to stand up to the machine and reduce spending. I thought he was going change things. I may have been mistaken. We need to get inflation under control, its like a brush fire that could consume the country. Meanwhile the money printing machine is in overdrive. Instead of whining about it on X, why not do something that’ll bring some visibility and comprehensibility to these massive bills?

Many years back, I’ve registered a domain politipal.com, which I had grandiose plans for. Naturally, I’ve done nothing with it. It’s time to change that too.

If you haven’t seen one before, these bills are published in the most unusable format possible. A super lengthy document, that no one can easily read and/or understand. Example

No way to compare to previous years, no way to visualize using common graph paradigms. Hopefully, this project will fix that.

How does a project like this make money? I have not f’ing clue, but I’m tired of doing nothing and watching the shit show carry on uninterrupted.

The first step is a POC. Can I parse this bill text into usable data with readily available open source scripts, programs, etc?

Automated Workflow:

1. Read and parse the document, extracting sections.
2. For each section, extract relevant details.
3. Format those details into a JSON object.
4. Insert the JSON object into Database.

I recently watched “Leave the World Behind“. It’s a message, a clear and terrifying message from our adversary. It’s a message about what happens when we resist they/them. When I refer to “they/them”, I’m not referring to the confused millennial non-binary they/them sorts. I’m talking about “The Machine”…. you know, one that “Rage Against the Machine” raged about before the band by that name was corrupted, consumed, and assimilated into the very machine they raged against. I’m talking about The Machine that has largely had a monopoly on influence and power for the last century or so. I’m supposing the 1913 creation of the Federal Reserve is a good marker for that level of influence and power, and the global elite class that wields it.

I internalized the message they wanted to deliver in the movie. They want people to duck and cover, to hide in the basement, with a cache of food, and a box set of “Friends” DVDs to keep our little minds occupied while the world tears itself apart. They want us out of the way, while their carefully choreographed chaos unravels the fabric of society.

After watching the video below, I’m thinking THAT, would be taking their bait. THAT is playing into their hands. THAT is exactly what they want. Instead, in the video below, Bret Weinstein, an extremely brilliant man/scientist/educator advocates for forming coalitions. Getting people together to share ideas and combining the power of the multitudes who stand against The Machine. He suggests that Goliath (as Weinstein refers to The Machine), has lost the first onslaught in their war for power. He speculates that the heros, who have emerged through the first wave largely fit the description of lone wolves. The Machine is learning and leveling up. So we, “The Resistance”, need to learn and level up as well. Those lone wolves need a pack.

With the first wave behind us, the confusion persists. Goliath is looking for a rematch. Some of they/them have been exposed. Presidents of the last decades are all on the list, mix in a bit of Jeffrey Epstein and Hunter Biden, and you have a hot bowl corruption soup. It seems, their exposure only reveals more questions than answers. These revelations now float on the surface, but this bowl runs deep.

I listened to Weinstein’s Dark Horse Podcast throughout the COVID crisis. He and Dr Robert Malone were a bastion of common sense and inquisitive curiosity about the confusion that didn’t fit then. His insight earned my respect and trust. During the below interview, I can see he has a sort of existential alarm about him. He has left the rage behind, and gone to “war with the machine”. I will pray for him, as I will pray for us too.

This is a first run at the UI, basically I need it to add, edit, and remove a nested hierarchy of topics. Only admin users will see this, so it doesn’t have to be pretty, just functional.

Each topic has a color, which I suppose should trickle down to it’s child topics. This way there will be some visual separation among the different lessons.

Here I have some initial topics, generated by ChatGPT, these will certainly change. I may use KhanAcademy as a template. They have certainly put careful consideration into their taxonomy.

Also, pay no attention to the branding. I’m still undecided between, “Homeschool Link (homeschool.ink)” or “Learnalot.net”.

I would love your opinon @ x.com

I’ve been running Nextcloud version 22 for the last couple years. As you can see from https://nextcloud.com/changelog/ , there have been many updates and upgrades since my original installation and I’ve been quite negligent with my sys admin duties. Today, I’m trying to remedy that situation.

I have Nextcloud running in docker. I use docker-compose to set up the environment, so I need to also upgrade through each version of Nextcloud using docker-compose, one major version at a time.

I use this app DAILY so, I don’t want any surprises, which often happen during upgrades. So first, I’ll replicate all the data from my server to my PC. This way I have a sandbox where I can make all my changes while my production environment remains untouched. If something goes wrong.. No problemo.

The PC is a Windows machine, so I’ll spin up an Ubuntu image to do all the transfers.

docker run -it -v "$(pwd):/volume" ubuntu /bin/bash

Next I’ll get the image equipped with the tools that I need to rysnc my way to a mirrored environment.

cd /volume && \ 
apt update  && \
apt install ssh rsync && \
rsync -rav --stats --progress admin@sourceIP:/path/to/nextcloud /volume -e "ssh -o StrictHostKeyChecking=no"

Good, the transfer is ~80Gigs in my case, so that took a min.

This is my existing docker-compose.yml You’ll also notice that I’ve specified mariadb:10.7 as that is what is currently running in the production env. I’ll upgrade that as needed.

version: '3'
services:
  nextcloud:
    container_name: nextcloud
    image: "nextcloud:22"
    ports:
      - 8000:80
    restart: always
    volumes:
      - ./html:/var/www/html
      - ./logs:/var/log/apache2
    env_file:
      - ./db.env
    networks:
      - proxy
      - internal_network

  mariadb:
    container_name: mariadb
    image: "mariadb:10.7"
    command: "--transaction-isolation=READ-COMMITTED --binlog-format=ROW --innodb-file-per-table=1 --skip-innodb-read-only-compressed"
    restart: always
    volumes:
      - ./db:/var/lib/mysql
    env_file:
      - ./db.env
    networks:
      - internal_network

  phpmyadmin:
    container_name: phpmyadmin
    image: phpmyadmin/phpmyadmin
    links:
    - mariadb:mysql
    ports:
      - 8001:80
    env_file:
      - ./db.env
    environment:
      PMA_HOST: mariadb
      UPLOAD_LIMIT: 300M
    networks:
      - proxy
      - internal_network

networks:
  internal_network:
    internal: true
  proxy:  
    external: true

Now that I have a sandbox to start running these upgrades, let’s just run everything once through to make sure the app is running “as-is”.

docker-compose up -d

The logs reported a minor upgrade, but other than that, we’re up and running.

Let’s upgrade to the next major version now. To do that, I just increment the number in docker-compose.yml from image: "nextcloud:22" to image: "nextcloud:23" then run:

docker-compose down
docker-compose up --force-recreate --build -d

Then I’ll watch my logs docker logs nextcloud to see when everything is done upgrading. You should see something like

docker logs --tail 1000 -f daecd812fefe464712b9b6717cb6e2a3d842260e0c64c63ec88ea22e2edb9623 

Initializing nextcloud 25.0.3.2 ...
Upgrading nextcloud from 24.0.9.2 ...

… but with the versions you’re currently updating. The update between 22 and 23 just worked.

Be sure to update all the apps to the new version in between each upgrade with php ./occ app:update --all or through the web UI.

It was between 23 and 24 where I needed to upgrade mariadb as well. In this case, I’m now using mariadb:latest. Then attach a shell into that container and run mysql_upgrade --user=root --password=rootpassword

If you catch a snag at any point, your best bet is to attach a shell into the nextcloud container and run php ./occ upgrade. If you are dealing with file permission issues, try attaching to the shell as the owner with: docker exec -it -u 33 nextcloud bash where 33 is the user #.

I’m continually impressed by ChatGPT. This morning I thought it would be really nice to be able to track my health statistics on Nextcloud, my private cloud that I have running just behind me in my closet. What a cool little project to give to ChatGPT and see how quickly we can get something up and running. It’s 8am on a Tuesday morning, I’m back to work on my day job, but I have about an hour to fiddle around with it. Let’s see how quickly ChatGPT can get this started….

A little background, I’ve been tracking some health parameters for a while with iHealth, mainly because they’ve made it easy to do so. I have a bluetooth bloodpressure cuff, every time I take my BP, it’s logged to the cloud. It has a nice UI. But, I’m not very happy with giving my health information away anymore. So I’ve been looking for a new home for my health data. Lately, I’ve been using “Waistline”, an open source app found on F-Droid. It works, but not nearly as nicely as iHealth. The data is siloed, and I’m not really sure how to get it out of the app. So, passively I’m still looking. That’s were we pick up the story for this idea.

Here is the chat in it’s entirety. I basically walk the bot through the process of coding the entire plugin for me.

At this point, I have yet to test it out, but as you can see, it’s an amazing start. I’ve got a plugin templated out, an API, and directions to get the frontend started as well. It’s 9am now, so I need to get to my day job. But, wow. Just WOW.

More to come as time permits.