Fastfetch: High-Performance Alternative to Neofetch for System Information Display

Yesterday I wrote about Neofetch which is a tool that I have used in the past on Linux systems I owned. It was an easy way to provide a good snapshot of the distribution I was running and some other pertinent information about my computing environment. One of my readers replied to let me know that the project was no longer being maintained. It was last updated in August 2020. The commenter suggested that I check out Fastfetch. I thanked the reader and followed the link he provided to the Github repository for Fastfetch.

The project maintains that it is, “An actively maintained, feature-rich and performance oriented, neofetch like system information tool.” It is easy to install and provides much of the same information that was provided by Neofetch. However, it does supply your IP address but the project maintains that presents no privacy risk. The installation for Fedora and RPM based distributions is familiar by entering the following command.

$ sudo dnf install fastfetch

If you are a Ubuntu based distribution like my Linux Mint daily driver then the installation requires the download of the appropriate .deb file. Once the package was installed on my system I decided to try it.

Screen picture by Don Watkins CC by SA 4.00

Fastfetch can be easily installed on a MacOS with Homebrew. I decided to try it on my MacBook.

Screen picture by Don Watkins CC by SA 4.0
% brew install fastfetch

Fastfetch is written in C with 132 contributors. It is open source with an MIT license. In addition to Linux and MacOS systems you can install Fastfetch on Windows with Chocolatey. The project states that Fastfetch is faster than Neofetch and it is actively maintained. Fastfetch has a greater number of features than it’s predecessor and if you want to see them all enter the following command. For more information and examples be sure to visit the project wiki

Exploring Hollama: A Minimalist Web Interface for Ollama

I’ve been continuing the large language model learning experience with my introduction to Hollama. Until now my experience with locally hosted Ollama had been querying models with snippets of Python code, using it in REPL mode and customizing it with text model files. Last week that changed when I listened to a talk about using Hollama.

Hollama is a minimal web user interface for talking to Ollama servers. Like Ollama itself Hollama is open source with an MIT license. Developed initially by Fernando Maclen who is a Miami based designer and software developer. Hollama has nine contributors currently working on the project. It is written in TypeScript and Svelte. The project has documentation on how you can contribute too.

Hollama features large prompt fields, Markdown rendering with syntax highlighting, code editor features, customizable system prompts, multi-language interface along with light and dark themes. You can check out the live demo or download releases for your operating system. You can also self-host with Docker. I decided to download it on the M2 MacBook Air and my Linux computer.

On Linux you download the tar.gz file to your computer and extract it. This opened a directory bearing the name of the compressed file, “Hollama 0.17.4-linux-x64”. I chose to rename the directory Hollama for ease of use. I changed my directory to Hollama and then executed the program.

$ ./holllama 

The program quickly launches and I was presented with the user interface which is intuitive to an extent.

Screen picture by Don Watkins CC by SA 4.0

At the bottom of the main menu and not visible in this picture is the toggle for light and dark mode. On the left of the main menu there are four choices. First is ‘Session’ where you will enter your query for the model. The second selection is “Knowledge” where you can develop your model file. Third selection is ‘Settings’ where you will select the model(s) you will use. There is a checkoff for automatic updates. There is a link to browse all the current Ollama models. The final menu selection is ‘Motd’ or message of the day where updates of the project and other news are posted.

Model creation and customization is made much easier using Hollama. In Hollama I complete this model creation in the ‘Knowledge’ tab of the menu. Here I have created a simple ‘Coding’ model as a Python expert.

Screen picture by Don Watkins CC by SA 4.0

In ‘Settings’ I specify which model I am going to use. I can download additional models and/or select from the models I already have installed on my computer. Here I have set the model to ‘gemma2:latest’. I have the settings so that my software can check for updates. I also can choose which language the model will use. I have a choice of English, Spanish, Turkish, and Japanese

Screen picture by Don Waktins CC by SA 4.0

Now that I have selected the ‘Knowledge’ I am going to use and the model I will use I am ready to use the ‘Session’ section of the menu and create a new session. I selected ‘New Session’ at the top and all my othe parameters are set correctly.

Screen pictire by Don Watkins CC by SA 4.0

At the bottom right of the ‘Session’ menu is a box for me to enter the prompt I am going to use.

Screen picture by Don Watkins CC by SA 4.0

You can see the output below that is easily accessible.

Screen picture by Don Watkins CC by SA 4.0

The output is separated into a code block and a Markdown block so that it is easy to copy the code into a code editor and the Markdown into an editor. Hollama has made working with Ollama much easier for me. Once again demonstrating the versatility and power of open source.

Neofetch: The Universal System Info Display Tool

Neofetch, hosted on the reputable and active project homepage at the Github repository, is designed to create system configuration screenshots on various platforms. The primary difference between Neofetch and ScreenFetch lies in its broader support; it extends beyond Fedora, RHEL, or CentOS and provides compatibility with almost 150 different operating systems, including lesser-known ones like Minix and AIX!

The Neofetch installation procedure is equally straightforward:

Debian and Ubuntu users use the following command:

$ sudo apt install neofetch

For Fedora and other RPM-based distributions use the following command:

$ sudo dnf install neofetch
Screen picture by Don Watkins CC by SA 4.0

You can also install neofetch on other operating systems including MacOS.

$ brew install neofetch
Screen picture by Don Watkins CC by SA 4.0

Once installed, Neofetch provides a standard system info display that can be further modified for your specific preference: image files, ASCII art, or even wallpaper, to name a few; all these customizations are stored in the .config/neofetch/ directory of the user’s home folder.

Discovering New Passions: Writing, Linux, and Sharing Open Source Stories

Our children gifted with a subscription to Storyworth for Father’s Day this year and each week a new writing prompt arrives in my email inbox. This week the prompt was what are some hobbies you have pursued or want to pursue in your retirement. It took me a while to think about that topic. I am not a guy to put together model planes and I don’t have a train set. I don’t play golf.

I walk, tinker with computers and write. I didn’t think of writing as a hobby until this week and maybe it’s not exaclty a hobby in the traditional sense but it’s a way for me to share my thoughts and journey with the wider world. I have been blogging frequently since early 2006 and have written over nineteen hundred articles for my own blog. In addition I have written hundreds of articles that have been published on a variety of sites including Both.org where I am a regular contributor. I also write for Allthingsopen.org and TechnicallyWeWrite.com.

I have created most of my content this year for Both.org, where we focus on Linux and open source. We are seeking individuals who would like to share their Linux and open source journey with our audience. Our website has been attracting more and more visitors. If you have an open source story to share, we encourage you to join us. Later this month, I’ll be traveling to Raleigh, NC to attend All Things Open. This will be my tenth ATO, and I am excited to learn from the people I will meet.

Write for us! We have a style sheet with guidelines and we’d love for you to share your open source journey with us.

Empowering Creators with Open Source Software

As we welcome another academic year, the integration of creative arts within school curriculums remains vital. Among open source resources that empower students to engage with digital soundscapes is Audacity, a free and versatile audio recording and editing software cherished by educators for its simplicity and power in the classroom setting. Audacity’s capacity has only grown, making it an indispensable tool not just today but as we look ahead to 2025 with ever-evolving educational needs:

Podcasting Platform of Choice: Connectivity through Sound
Teachers and students alike have adopted Audacity for crafting podcasts, serving an array of purposes from explaining classroom procedures directly within lessons to delivering language learning content. This interactive form has become a cornerstone in modern pedagogy by facilitating out-of-class communication that supplement the traditional teaching experience and offering students additional access points into course material through auditory means, which can enhance comprehension for many learners.

Language Acquisition with Audio Engagement: Learning Languages Through Listening
For language education, Audacity has been transformative by providing a platform where foreign language pupils record their spoken lessons and listen to them repeatedly—all within the safety net of open source software that champions accessibility for all students. This feature nurtures self-directed learning as well as peer interaction in multi-language classrooms, setting up an immersive auditory environment akin to real-world conversational scenarios.

Creative Expression Through Sound: Student Audio Projects Evolving with Time and Technology
Students’ love for sound extends beyond passive listening; they are creators in their own right using Audacity to produce unique audio projects such as bird songs, oceanic ambient tracks, or even creating custom narrations over chosen background music. This engagement stimulates imagination while providing a practical understanding of digital tools and copyright laws through exploring resources from Creative Commons and Wikimedia sound collections—a learning process that teaches respect for intellectual property alongside technical skills in audio manipulation.

Interviews as Interactive Learning: Engaging with Experts Through Sound Waves
Audacity allows students to conduct interviews, integrating them into their educational activities by adding layers of personal experience and expert insight directly through the auditory channel—a method that not only humanizes learning but also bridges generations within a classroom setting as older family members share experiences with younger ones. This formative approach promotes active listening skills while fostering familial bonds, an essential lesson beyond academics alone.

From Capture to Share: Effective Audio File Management for the Modern Classroom Stage and Beyond (2024 Edition)
Education today is not just about content but also delivery methodologies—therefore Audacity’s importance as a tool in helping students understand how different audio file formats serve various platforms. From .aup files that facilitate ongoing educational collaboration, to MP3 and WAV for final projects suitable for wider sharing via streaming web servers or digital portfolios, the software prepares young minds not only with technical skills but also industry standards they will encounter in professional spheres such as podcasting careers.

Open Source Software: A Lesson on Rights (2024 Update) and Legacy of Ubuntu’s Free Audio Education Toolkit
With its GNU GPLv2 license, Audacity is more than a mere software—it’s an educational journey itself with room for dialogue about copyright laws. This invites students into the world of intellectual property rights discussions that are increasingly relevant in our digital age and offers Linux users straightforward installation processes through standard repositories:

$ sudo apt-get install audacity
or with Fedora
$sudo dnf install audacity 

The software continues to stand its ground against the backdrop of continually developing technology with instructions provided for Mac OS X and Windows users ensuring no one is left behind in leveraging this educational powerhouse. Audacity is also available for Linux users as a Flatpak.

According to Wikipedia, “Audacity is the most popular download at FossHub,] with over 114.2 million downloads since March 2015.” Thus as we advance into 2025 and beyond, Audacity remains at the forefront of integrating creativity with digital sound technologies to enrich our classrooms while providing essential open source knowledge sharing that prepares students for a connected world where audio artistry goes hand-in-hand alongside academic excellence.

This article was adapted and rewritten using Ollama and the Phi3.5 model. Text was taken from an article originally published for Opensource.com in 2016.

Taking a look at financial data with Ollama

Several weeks ago a person asked me to assist her with organizing her financial records to take them to a tax professional. This person does not use a financial program like GnuCash which could make that project much easier. Instead we downloaded a csv file from her bank and then she used Microsoft Excel to add a category to each expense. This was a tedious process. I used a pivot table to further organize and display her data which she took to the tax preparer.

Recently while working on other projects with Ollama I wondered if it might be possible to use a local large language model to accomplish the same task. It is easy to download Ollama. If you are a LInux user like I am you can enter the following command in a terminal.

curl -fsSL https://ollama.com/install.sh | sh

I experimented with phi3.5 and Llama3.2 and found the latter to work better for me. It is easy to pull the model down to your computer with the following command:

$ ollama pull Llama3.2

Once the model was downloaded to my computer I wanted to make my own custom model to analyze my financial data set which was a csv file from the bank. I created a model file which I called financial using nano. Here is the text of the modelfile I created for this activity:

FROM llama3.2

# set the temperature to 1 [higher is more creative, lower is more coherent]

PARAMETER temperature .6

# set the system message

SYSTEM “””

You are a financial analyst. Analyze the financial information I supply.

“””

I used the model file to to create the custom model for this financial analysis. I set the temperature PARAMETER to .6 to make the work more accurate. I entered the following command in the terminal:

$ ollama create financial -f financial

This created the unique LLM based on Llama3.2 to perform the financial analysis. I made sure that the csv file from my financial institution was in the same directory as I was currently operating. This is important and then entered the following command to pull the csv file into the custom LLM.

ollama run financial:latest "$(cat data.csv)", Summarize my transactions. 

This gave me a complete summary of the debits and credits that were included in the small csv file. I have encountered some errors and I plan to keep working with the model and reading. I’m encoueraged by the results.

In search of the right GPU

I rely on my powerful Intel NUC with an i7 processor and 64 GB of RAM for my daily computing needs. However, it lacks a GPU, which makes it unsuitable for the experimentation I’ve been conducting with locally hosted large language models. To address this, I use an M2 MacBook Air, which has the necessary power for some of these tasks.

I had helped some local folks purchase a refurbished Dell computer from a refurbisher. They began to experience difficulty with it in a couple of months and when they did it was beyond the ninety day warranty. Rather than see them lose their money I wrote them a check for the original purchase price.

I believe that when you do good things that you will be rewarded in some fashion. I helped these folks purchase a new Dell Inspiron desktop which has a full factory warranty and when I was about to leave their home they asked me if I wanted to take the defective computer. I thought I might be able to fix it or use it for parts. I removed the cover and discovered that this Optiplex 5060 with an i5 CPU didn’t have a traditional hard drive like I had thought but instead was equipped with a Western Digital SN 270 NVME drive. I also discovered that the only thing wrong with the unit was a bad external power switch. Once I removed the front bezel I was easily able to power the device on.

Karma was working once again in my favor as I have found it does when you do for others as youu would have them do for you. I erased the Windows 11 install and installed Linux Mint 22 in it’s place. This unit also had two open low profile expansion slots and I wondered if I could find a graphics card with a GPU that would allow me to experiment with Ollama and other LLMs. I did some research and decided to purchase a XFX Speedster SWFT105 Radeon RX 6400 Gaming Graphics Card with 4GB from Amazon. The card came a couple days later and I installed it in one of the expansion slots.

After installing the card I placed the cover back on the machine, connected a spare Sceptre 27 inch display and an ethernet cable to it and downloaded Ollama and the Phi3 model. I downloaded and installed the ROCm modules which are helped Ollama to recognize the GPU. Ollama states that it recognizes the GPU when it finished installing the software. I think Ollama and the Phi3 module run faster with this unit. But maybe that’s wishful thinking. I also wanted to try Stable Diffusion on this computer and used Easy Diffusion which I have installed on the NUC before. I was frustrated to discover that my RX6400 card and GPU don’t work with EasyDiffusion. Am I missing something? Is there a fix?

I hope that if you’re reading this and you know of a fix for this issue that you would share it. I’d love to find and answer. Nonetheless, doing good for others always results in good coming back to you.

Pandoc: The Ultimate Document Conversion Tool

Pandoc is a versatile command-line tool that facilitates seamless file conversions between different markup formats. It supports an extensive range of input and output formats, making it indispensable for writers, researchers, and developers. I have found it particularly useful when converting output from LLMs to to HTML or more common word processing formats.

Pandoc’s strength lies in its support for various input formats, including Markdown, HTML, LaTeX, Open Document, and Microsoft Word. It can convert those documents to PDF, HTML, EPUB, and even PowerPoint presentations. This flexibility makes Pandoc an invaluable tool for individuals working with documents across different platforms and tools.

Here are some specific examples that may fit your use case.

1. Converting Markdown to HTML:

Markdown, known for its simplicity and readability, is widely used for creating content for the web. With Pandoc, you can effortlessly convert Markdown files to HTML, enabling seamless web content publishing. For instance, the following command can be used to convert a Markdown file named “example. md” to HTML:

$ pandoc example.md -o example.html

2. Generating PDF from LaTeX:

LaTeX, renowned for its powerful typesetting capabilities, is favored for academic and technical documents. Pandoc seamlessly converts LaTeX files to PDF, producing high-quality documents suitable for printing or digital distribution. Consider the following command to convert a LaTeX file named “paper.tex” to PDF:

$ pandoc paper.tex -o paper.pdf

3. Transforming Word documents to Markdown:

Many writers and researchers prefer working with Markdown due to its simplicity and portability. With Pandoc, you can convert Microsoft Word documents to Markdown, allowing editing and collaboration using lightweight, text-based tools. Use the following command to convert a Word document named “report.docx” to Markdown:

$ pandoc report.docx -o report.md

4. Creating EPUB from HTML:

EPUB, a popular e-book format compatible with a wide range of e-readers and mobile devices, is a common choice for digital content distribution. If you have content in HTML format, Pandoc can assist in converting it to EPUB for convenient distribution and reading. Here’s an example command to convert an HTML file named “book.html” to EPUB:

$ pandoc book.html -o book.epub

5. Convert Markdown file to a PowerPoint presentation using Pandoc, you can use the command

$ pandoc myslides.md -o myslides.pptx

You can open the resulting .pptx file in PowerPoint

In addition to these examples, Pandoc offers extensive customization options for fine-tuning the output of document conversions. Users can specify styling, metadata, and other parameters to ensure the converted files meet their specific requirements.

In conclusion, Pandoc stands as a robust and versatile tool for document conversion, offering support for a wide array of input and output formats. Pandoc can help streamline your workflow and enhance your document management capabilities, whether you’re a writer, researcher, or developer.

Seamlessly Convert Your Markdown Docs to Office Formats Using Pandoc

I have been experimenting a lot with Ollama and other artificial intelligence tools and the anwers to my prompts always are rendered in Markdown. I have Marktext on my Linux computer and MacDown on my MacBook Air so I can easily copy and paste the output into either of those editors and save it as a Markdown file on my computer. However, when I want to share those files with colleagues who are unfamiliar with Markdown I need a way to convert those files into a format that’s easily accessible for them. My Markdown editors can only export the Markdown files as HTML or PDF.

That problem is easily solved with Pandoc which is a great tool that anyone can install on Linux, MacOS or Windows that easily converts Markdown into any number of different formats. Easily install Pandoc on Linux with the following commands:

$ sudo dnf install pandoc
or 
$ sudo apt install pandoc

On MacOS use Homebrew to easily install Pandoc by opening a terminal and entering the following command.

$ brew install pandoc

You can install Pandoc on Microsoft Windows using Chocolatey with the following command:

choco install pandoc

Once the application is installed it works very well from the command line. The project mainains great documentation. I needed to convert a Markdown document to .docx so my Windows using colleagues could easily read the output from the LLM I had been using. I used the documentation and the programs man page on my Linux system to enter the following command.

$ pandoc -o ProposedLease.docx -f markdown -t docx LeaseReview.md

The conversion was flawless and occured in a second or two. Your experience may vary based on CPU, RAM and length of the document converted. The Markdown file of our lease review was converted to “ProposedLease.docx” which I could easily share with my colleagues who were using Microsoft Word.

If you are a person who is uncomfortable installing software on your computer or you are an unpriviledged user in a corporate or educational setting you can use Pandoc online. Pandooc is open source with a GPL v2 license. For more information about Pandoc be sure to consult their website and examine all of the options that the program provides you.

Evaluating writing using open source artificial intelligence

In today’s digital age, writers seek tools that enhance their craft and provide real-time feedback and assistance. Enter Ollama – an open-source machine learning system designed to democratize accessibility for natural language processing tasks across a wide range of languages and scripts with ease. Coupled with the Phi3 model, this powerful duo promises unparalleled benefits in refining your writing style through sophisticated evaluations beyond grammar checking. This article will explore how Ollama, integrated with phi3’s innovative language comprehension and assessment approach, revolutionizes the writer’s journey toward excellence. So buckle up as we embark on a transformative exploration of your writing potential using these advanced AI tools!

I have been experimenting with Ollama and various models designed to work with it. In this article, I wanted to use Ollama and Phi3 to demonstrate the power of open source to revolutionize and evaluate writing. Both Ollama and Phi3 are open source, both have MIT licenses, and they work well together. You need to have Ollama installed on your computer. My daily driver is an Intel NUC 11 with an i7 processor running Linux Mint Cinnamon. Though this is a powerful computer, running a large language model takes more than a great CPU. A powerful GPU would make the process a little quicker.

First, I needed to make sure that Ollama was installed. If you are on Linux, you do that by executing the following code in a terminal.

$ curl -fsSL https://ollama.com/install.sh | sh

The installation is very straightforward and then you will need to download the Phi3 model. Enter the following command into your terminal.

$ ollama pull phi3

This pulls the Phi3 model down to your computer. You can check to make sure it is there by entering the following code.

$ ollama list

You should see a list of all the models loaded on your computer.

NAME            	ID          	SIZE  	MODIFIED       
phi3:latest     	4f2222927938	2.2 GB	56 minutes ago	
cooking:latest  	59c8496c62aa	4.7 GB	7 days ago    	
llama3.1:latest 	42182419e950	4.7 GB	7 days ago    	

Now I am ready to begin. I wanted Ollama and the Phi3 model to develop a rubric to evaluate my writing sample. I need to enter two commands. First, I need to get the LLM running, which will open a REPL (Read Only Print Loop) prompt very similar to a Python REPL.

ollama run phi3:latest
>>> create a rubric to score college level writing.  

The model quickly created a rubric to score my writing some of which I have copied and pasted here:

“College Level Writing Rubric: Essay (20 points)

I. Introduction and Thesis Statement (4/20 = 2 points):

A student’s introduction must be clear, concise, engaging, and
well-structured. A strong thesis statement should identify the topic of
discussion clearly while presenting an argument or perspective that sets
up a logical progression throughout the essay. Students will lose one
point for each instance where these two components fail to establish
context effectively (e.g., unclear writing style, unconvincing tone).

1 Point – Clear and engaging introduction with well-structured paragraphs setting the stage of discussion or argumentation


2 Points – Introduction is structured clearly but fails in some areas such as clarity of language use or lacking an enticing hook to captivate readers’ interest. (Lose one point)


1 Point – Thesis statement presented, but lacks precision and clear articulation about the topic discussed; does not establish a logical thread for essay progression

2 Points – Clearly stated thesis that presents argument or perspective while setting up an effective structure to follow throughout. (Lose one point)

0 PointS – Introduction is unclear, disjointed language use fails in
providing context and lacks persuasive tone necessary; no clear indication
of what the essay will discuss

The rubric, over seven hundred words long, was generated in a few minutes. All text output from Ollama is in MarkDown. The rubric will be rendered much quicker if you are a developer using an Apple Silicon MacBook and an M2 processor. Though exceptionally powerful, the NUC 11 with the i7 lacks a GPU, which eases the strain on the CPU.

Now that the rubric has been created, I entered the following command in the Ollama REPL to evaluate an article I wrote for Opensource.com nearly ten years ago.

>>>Use the rubric to evaluate this article https://opensource.com/education/15
... /12/my-open-source-story-don-watkins

The process took a few minutes and provided an in-depth review of my article. The evaluation was very detailed and over nine hundred words long. I have pasted the first part of the review here.

“2 Points – Introduction engages with opening hook; however, lacks clarity in setting the stage and doesn’t align well with Watkins’ thesis statement (lose up to three points) 1 Point – Thesis statement present but vague or imprecise about what readers should expect throughout this article. Lacks clear alignment between I and II components of essay-like structure; no explicit roadmap provided for reader follow along (lose two maximum points); fails in captivating the audience right from introduction


0 PointS – Introduction lacks coherence, disjointed language use provides little context or interest to readers about open source contributions. No engaging hook presented nor clear alignment between I and II components of essay-like structure; does not provide explicit roadmap for reader follow along (lose one point)…”

Using large language models to assess writing could offer the subtlety writers require to enhance their writing. Are there potential issues? Will artificial intelligence models replace copywriters? What other implications might they have that change how we write and re-write?