kirdiekirdie

RDF Knowledge Graphs (KGs) have this cool property that each resource has its own URI, so if you put a webserver behind those URIs you can summarize the resources and browse from one to the others.

Normally you need to upload your KG to a SPARQL endpoint and then deploy another browser and a webserver and connect all those components, and it is complicated, error prone and wasteful when you have a small KG.

To solve this I made RickView, a single binary written in Rust that holds a KG in memory and acts as a web server for RDF resources.

Here are some RDF knowledge graphs you can browse that are served by RickView:

- <https://www.snik.eu/ontology/bb>

- <https://hitontology.eu/ontology/>

- <https://annosaxfdm.de/ontology/>

- <https://linkedspending.aksw.org/>

Magic the Gathering (MTG) podcasts could really benefit from a video version where the discussed cards are shown but creating such a video takes hours or even days.

I made a slide show generator which only takes minutes.

Input is lines of text with MTG card names separated by the | character.

Load one of the presets and click "Start Slideshow", then you can screen record and navigate slides with the arrow characters.

RickView is an RDF knowledge graph browser written in Rust with the following design goals:

  - performance: low memory and CPU utilization, fast page load times and high number of handled requests
  - robustness: once it compiles, it runs indefinitely
  - standalone: no dependence on services such as a SPARQL endpoint or a web server
  - adaptability to any small to medium sized knowledge graph via sensible defaults that can be changed in a configuration file and overridden with environment variables
  - containerization: offer a compact container image containing a statically linked binary
  - simplicity: RickView is minimalistic and only offers browsing of static data

Here are some RDF knowledge graphs you can browse that are served by RickView:

- <https://www.snik.eu/ontology/bb>

- <https://hitontology.eu/ontology/>

- <https://annosaxfdm.de/ontology/>

- <https://linkedspending.aksw.org/>

It would suck to be included in the trial and then be randomly selected into the placebo group.

I never understand those articles. I have been doing everything with a normal bike my whole life, including shopping and bringing the kids to school and kindergarden, and I see no reason to let my body atrophy by pointlessly buying an expensive bike with a motor. Then I probably need to spend even more money and time on a bike machine in a fitness studio and more money on doctors after suffering from weight related illnesses. Those articles always assume that car is the god given default state. It's great for the elderly and for my colleague who is suffering from long covid but everyone else I know is doing perfectly fine with a bike without motor including my grandpa until he got around 85 years old.

How do you finetune a dataset on LLaMA2-7b with an A10 with 24 GB? I thought you need to load the dataset 4 times for training, so with 16 bit weights this would be 7 * 10^9 * 2 bytes * 4 = 56 GB GPU RAM. Have you found a way to train with 4 bit weights? And would a single textbook be enough text for a meaningful finetuning? I am worried that a model that is trained on billions of documents will not adapt strongly enough to a comparatively small document.

localGPT can parse PDF into embeddings, see <https://github.com/PromtEngineer/localGPT>.