jexp

Shouldn’t it be possible since forever to put machine readable source information into PDF metadata. It’s more a problem of the tools and programs generating the PDFs.

We spend millions turning structured information into PDFs and billions to extract the same data from a printer rendering language

Imported the graph json into Neo4j

Have fun

https://gist.github.com/jexp/8d991d1e543c5a576a3f1ee70132ce7...

Catchall for 25 years :) (on domainfactory - df.eu) each company/service gets their own email prefix, so I can determine spam and also filter unsolicited emails.

Thanks for the praise for APOC-ML, happy that it's useful.

Did you see the two blog posts that Tomaz Bratanic did on the topic?

For the ingestion: https://neo4j.com/developer-blog/global-graphrag-neo4j-langc... For the retrievers: https://neo4j.com/developer-blog/microsoft-graphrag-neo4j/

My general point on GraphRAG is that it extracts and compresses the horizontal topic-clustering across many documents and makes that available for retrieval.

And that by creating the semantic network of entities, you can use patterns in the graph structure to answer questions that rely on information coming together from different documents. Think the detectives board connecting facts with strings from many different sources.

Feel free to ping me for a deeper discussion: michael at neo4j

RenTec was covered in much depth at the Acquired podcast. Basically algorithms from signal processing applied to huge volumes of historical and current data to determine buy and sell signals. Originally developed for national defense.

Very secretive all external partners were bought out. Only hundred or so people benefited in the billions per person. Including Robert Mercer of Trump campaign financing and Cambridge Analytica fame.

Very interesting but also disheartening episode about smart people only caring about getting richer.

https://www.acquired.fm/episodes/renaissance-technologies

Many advanced RAG patterns are easier with a graphdb and you can pull the relevant context starting with the vector search results.

You can also construct graphs with an llm out of text. See here. https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/

There was a really awesome deep history of TSMC on the Acquired podcast

https://www.acquired.fm/episodes/tsmc

Quick 5 minute video on downloading and running Hugging Face language models in GGUF format (quantized by TheBloke) with Ollama on your local machine and checking GPU consumption with asitop (Apple Silicon Mac Top).

God wrote in Lisp, Bob Kanefsky performed by Julia Ecklar. My favorite song.

https://www.prometheus-music.com/audio/eternalflame.mp3

Refrain (full lyrics): http://www.songworm.com/lyrics/songworm-parody/EternalFlame....

For God wrote in Lisp code

When he filled the leaves with green.

The fractal flowers and recursive roots:

The most lovely hack I’ve seen.

And when I ponder snowflakes, never finding two the same,

I know God likes a language with its own four-letter name.

Unbelievable that they completely missed to mention the equivalent in size but failed airship company Cargolifter in Germany.

They operated in the early 2000s and went bankrupt.

They construction dome is as high as the Statue of Liberty and as long as the Eiffel Tower. Now used as indoor tropical resort.

https://en.m.wikipedia.org/wiki/CargoLifter

The article is factually incorrect on many fronts. There were only 3 reactors running at last. Nuclear was never profitable, the country shouldered most of its costs (esp for radioactive waste storage)

The renewable energy transition was blocked and delayed by the same Merkel govt. Germany was once leading in wind and PV energy and all those industries were destroyed and almost 100k jobs lost. 60bn subsidies per year go to fossil fuel companies including coal mining and burning. Often under the disguise of job retention. All through industrial lobbying and strong unions in the fossil fuel industries.

Luna Display for wirelessly extending screen to ipad and teleprompter.

I used to do Lego Time Tracking when I was still consulting, as none of the other approaches worked for me (ADHD).

It even made it on LifeHacker back then: https://lifehacker.com/track-your-time-with-lego-bricks-3997...

The article is only on the internet archive these days https://web.archive.org/web/20081206084136/http://jexp.de/bl...

Not really, they don't support graph algorithms.

I'm not impressed. Same in Neo4j's

MATCH (e:Employee) RETURN e { .* } as employee;

MATCH (e:Employee) RETURN collect(e { .* }) as employees;

MATCH (e:Employee)-[:IN]->(d:Department) WITH d, collect(e {.*}) as employees

Not sure if you saw our graphql integration, that takes typedefs and converts a graphql query into a single cypher query, which can then be executed directly. https://neo4j.com/product/graphql-library/ has links to docs and api scaffolding tools.

When I started back then in 2016 with it, it was pretty cool how directly graphql mapped to the graph model in the db.

That's what you can do in Neo pretty easily. The DS library offers a bunch (50+) algorithms to run on the graph data directly or projected, e.g. PR on 117M wikipedia links runs in 20s.

Not sure why, but the blog post in question (same URL), has been sneakily replaced with one replacing "graph database" with "GraphQL" in the title.

And changing the tagline on relational database from "Relational databases: a software engineering fail" to "Relational databases: engineering success is not always the best software engineering fit"

https://dgraph.io/blog/post/graphdb-for-your-next-app/

Fortunately internet archive still has the previous version, that the comments here were discussing. https://web.archive.org/web/20200709192510/https://dgraph.io...