August 16, 2024 — We have migrated to Go! The biggest thing to know is that **the previous installation instructions in the various Fabric videos out there will no longer work** because they were for the legacy (Python) version. Check the new [install instructions](Installation) below.
> [!NOTE]
August 16, 2024 — We have cleaned up the Pull Requests and Issues in the following ways as part of the Go release: 1) We incorporated all Pattern submissions in the new version. 2) We closed all Issues related to Python/Code because we we moved to Go. If your issue still persists, just resubmit and we'll get on it. 3) We did the same with Question issues because most of them were related to Python. 4) We left the Enhancement issues because those tend to not relate as much to Python vs. Go, and we'll be working through those.
Since the start of 2023 and GenAI we've seen a massive number of AI applications for accomplishing tasks. It's powerful, but _it's not easy to integrate this functionality into our lives._
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<h4>In other words, AI doesn't have a capabilities problem—it has an <em>integration</em> problem.</h4>
</div>
Fabric was created to address this by enabling everyone to granularly apply AI to everyday challenges.
## Philosophy
> AI isn't a thing; it's a _magnifier_ of a thing. And that thing is **human creativity**.
We believe the purpose of technology is to help humans flourish, so when we talk about AI we start with the **human** problems we want to solve.
### Breaking problems into components
Our approach is to break problems into individual pieces (see below) and then apply AI to them one at a time. See below for some examples.
Prompts are good for this, but the biggest challenge I faced in 2023——which still exists today—is **the sheer number of AI prompts out there**. We all have prompts that are useful, but it's hard to discover new ones, know if they are good or not, _and manage different versions of the ones we like_.
One of <code>fabric</code>'s primary features is helping people collect and integrate prompts, which we call _Patterns_, into various parts of their lives.
Fabric has Patterns for all sorts of life and work activities, including:
If you have the Legacy (Python) version installed and want to migrate to the Go version, here's how you do it. It's basically two steps: 1) uninstall the Python version, and 2) install the Go version.
- **First, we use `Markdown` to help ensure maximum readability and editability**. This not only helps the creator make a good one, but also anyone who wants to deeply understand what it does. _Importantly, this also includes the AI you're sending it to!_
- **And finally, we tend to use the System section of the prompt almost exclusively**. In over a year of being heads-down with this stuff, we've just seen more efficacy from doing that. If that changes, or we're shown data that says otherwise, we will adjust.
3. Run the `extract_wisdom` Pattern with the `--stream` option to get immediate and streaming results from any Youtube video (much like in the original introduction video).
If you're not looking to do anything fancy, and you just want a lot of great prompts, you can navigate to the [`/patterns`](https://github.com/danielmiessler/fabric/tree/main/patterns) directory and start exploring!
We hope that if you used nothing else from Fabric, the Patterns by themselves will make the project useful.
You can use any of the Patterns you see there in any AI application that you have, whether that's ChatGPT or some other app or website. Our plan and prediction is that people will soon be sharing many more than those we've published, and they will be way better than ours.
You may want to use Fabric to create your own custom Patterns—but not share them with others. No problem!
Just make a directory in `~/.config/custompatterns/` (or wherever) and put your `.md` files in there.
When you're ready to use them, copy them into:
```
~/.config/fabric/patterns/
```
You can then use them like any other Patterns, but they won't be public unless you explicitly submit them as Pull Requests to the Fabric project. So don't worry—they're private to you.
The content features a conversation between two individuals discussing various topics, including the decline of Western culture, the importance of beauty and subtlety in life, the impact of technology and AI, the resonance of Rilke's poetry, the value of deep reading and revisiting texts, the captivating nature of Ayn Rand's writing, the role of philosophy in understanding the world, and the influence of drugs on society. They also touch upon creativity, attention spans, and the importance of introspection.
## IDEAS:
1. Western culture is perceived to be declining due to a loss of values and an embrace of mediocrity.
2. Mass media and technology have contributed to shorter attention spans and a need for constant stimulation.
3. Rilke's poetry resonates due to its focus on beauty and ecstasy in everyday objects.
4. Subtlety is often overlooked in modern society due to sensory overload.
5. The role of technology in shaping music and performance art is significant.
6. Reading habits have shifted from deep, repetitive reading to consuming large quantities of new material.
7. Revisiting influential books as one ages can lead to new insights based on accumulated wisdom and experiences.
8. Fiction can vividly illustrate philosophical concepts through characters and narratives.
9. Many influential thinkers have backgrounds in philosophy, highlighting its importance in shaping reasoning skills.
10. Philosophy is seen as a bridge between theology and science, asking questions that both fields seek to answer.
## QUOTES:
1. "You can't necessarily think yourself into the answers. You have to create space for the answers to come to you."
- _Jonathan Dunn_ for being the absolute MVP dev on the project, including spearheading the new Go version, as well as the GUI! All this while also being a full-time medical doctor!
- _Jason Haddix_ for the idea of a stitch (chained Pattern) to filter content using a local model before sending on to a cloud model, i.e., cleaning customer data using `llama2` before sending on to `gpt-4` for analysis.
