A while back, I came across the AI Fabric system created by Daniel Miessler. I liked its approach of providing command-line tools for filtering text using artificial idiocy services, allowing stepwise operations to be applied to a piece of text. The output of one operation can then serve as the input for another—in other words, Unix pipeline processing powered by large language models. I do no longer remember exactly how I discovered it, but suspect it was via Matthew Berman's video "How To Install Fabric - Open-Source AI Framework That Can Automate Your Life".
While the idea and concept behind AI Fabric appealed to me, its implementation has continued to rub me the wrong way. It started off as a Python project that I could only get running by downloading random programs from the internet using Poetry. I tried to assess how much work it would take to package all its missing dependencies for Debian. However, before I got very far, the project shifted away from Python and over to Go. This new implementation also relied on a build system that seemed to encourage users to run arbitrary code downloaded from the internet to get software working, and further moved to a language I do not master as well as Python. The change bothered me enough that I set my effort to set up a working command line LLM tool in Debian aside for several months.
By chance, I came across a simple Python recipe in January demonstrating how to communicate with a llama.cpp API server. I had already been working on packaging llama.cpp for Debian together with the rest of Debian's AI team, and was fortunate enough to own a working instance with a 24 GiB VRAM GPU from AMD, allowing me to run useful models. Until that point, I had only used the basic web client provided by the Debian package, lacking the spare time to explore what else could be done. Then, I found this simple 50 line Python script demonstrating how to interact with llama.cpp's OpenAI-compatible API. I decided to revive the AI Fabric concept, and implement the Unix pipeline filter tool with as few dependencies as possible. It is now operational and working very well, relying solely on standard Python features. The tool include a copy of the LLM recipes from the AI Fabric project (called "patterns"), enabling easy access to request summaries, translations, code review and other useful tasks. Several hundred patterns are included, though I have only tested about ten so far.
The LLM API server can be specified in ~/.config/hraesvelgr/config.ini like this:
[server] url=https://some.llm.example.com:8080/v1/ model=Qwen/Qwen3.6-27B-FP8
With this configuration in place (you can also specify these values directly on the command line), you can specify a pattern and a file to process like this:
% bin/hraesvelgr --pattern explain_code bin/hraesvelgr
EXPLANATION:
This Python script is a client tool for interacting with an AI
service (likely a local LLM server) to process text using prompts
defined in the "AI Fabric" repository. It reads system and user
prompts from markdown files, sends them along with input text to a
chat completion API endpoint, and prints the generated response.
Key components:
1. It uses argparse for command-line argument parsing
2. The `send_chat_completion_request` function formats messages
(system, user, query) into JSON and sends them via HTTP POST to
an AI service endpoint
3. `read_file` function reads markdown files, replacing placeholders
like {{lang_code}} with actual values from arguments
4. In main():
- Parses command-line arguments for input file, API base URL,
pattern type, language code, and debug flag
- Ensures the base URL ends with a slash
- Reads system prompt from data/patterns/{pattern}/system.md
- Optionally reads user prompt from data/patterns/{pattern}/user.md
- Reads input text either from stdin (when "-" is passed) or a file
- Handles encoding fallback to ISO-8859-1 if UTF-8 fails
- Sends the formatted request to the AI service and prints the response
The script assumes it's running in a directory containing a git
clone of https://github.com/danielmiessler/fabric/, which contains
the necessary prompt files.
This tool is designed to interface with local LLM servers that
support OpenAI-compatible chat completion APIs.
%
The list of available patterns can be viewed by running
bin/hraesvelgr --list-patterns. I have found the
summarize, translate,
improve_writing, review_code, and
explain_terms_and_conditions patterns particularly
useful. For example using the latter combined with a text based web
browser capable of dumping a page as plain text, can be done like this
(originally formatted in markdown, I converted to HTML using pandoc
for easier readability):
% w3m -dump https://runbox.com/about/terms-service/ | \ hraesvelgr --pattern explain_terms_and_conditions Executive SummaryThis is a transparent, privacy-focused contract from a Norwegian provider that generally respects user data rights and operates under strict EU/EEA standards. However, it carries strict liability limitations and an aggressive data-deletion policy upon cancellation. The vibe is “Professional & Privacy-First,” but you must manage your own backups and understand that the company heavily shields itself from financial responsibility during technical failures.
Key Takeaways
- 🛡 Your Data Stays Yours: Section 10.2 explicitly states Runbox will never use your transmitted or stored data for commercial purposes. This is a major privacy win.
[... trimmed output, as it is not the focus of this blog post ...]
If you sign:
- 🔒 Set up automated backups immediately. Use IMAP sync to a local drive or a secondary email provider before storing any critical documents or emails. Do not rely on Runbox as your only archive.
- 📅 Mark your calendar for the 30-day trial end date. Miss the payment window, and access closes instantly with no recovery period.
- 💰 Monitor price changes at renewal. Since they can adjust fees anytime, check their pricing page a few days before your subscription renews to avoid unexpected charges.
NO FORCED ARBITRATION CLAUSE FOUND.
REFUND POLICY IS STRICTLY CONDITIONAL (see Sections 4.2–4.5).
As you might have already noticed, I name my project after the Norse God of Wind. I found a nice description of the origin of the name on Wikipedia:
In Vafþrúðnismál (The Lay of Vafþrúðnir), Odin questions the wise jötunn Vafþrúðnir about the origin of the wind, and the jötunn answers:
He is called Hræsvelg, who sits at heaven’s end, a giant, in the shape of an eagle; from his wings they say the wind comes over all people.(translated by John Lindow in Norse Mythology: A Guide to Gods, Heroes, Rituals, and Beliefs 2002)
The latest version of the code can be found at https://codeberg.org/pere/hraesvelgr/. Perhaps you will find it as useful as I did?
As usual, if you use Bitcoin and wish to show your support of my activities, please send Bitcoin donations to my address 15oWEoG9dUPovwmUL9KWAnYRtNJEkP1u1b.
