You can use this to navigate wikidata graph (90m node, 750m edges)
Wrote a parser with a rust compatible pattern matching syntax here:
https://github.com/py2many/spy-ast
Should help both JIT and static translation.
Latentpedia starts from wikidata, not Wikipedia.
https://github.com/py2many/py2many
https://github.com/adsharma/fquery/blob/main/tests/django_example_model.pyThe extremists are the ones who believe that Wikipedia is working well and quote Poe's law as described on the page, which Google will cite literally.
* one source of truth. Not useful for controversial topics
* On topics like graph databases (where I've spent 10+ years), the content doesn't reflect best practices (e.g. VLDB papers)
* Someone with sufficient resources can remove content in the name of "clean up"
* Google <-> wikipedia effect that others discuss in the thread.
That is why we are building latentpedia.org.
An entire dept exists to make it work. Atomicity and security are other arguments. The larger the company, the stronger the incentive.
It's not something average software developers working elsewhere care about much.
Amazon seems to be the only big company bucking this trend.
uv pip install nx-arrowThere is a Q about how much RAM these phones will need and the average selling price.
People are seriously addicted to doom scrolling. But what are they willing to pay for a phone?
Is all over LinkedIn and Twitter
Improvements welcome!
It should be possible to transpile datalog or another query language that is interesting.
Implementing storage and secondary indicies is the hard part.
Won't comment on rewriting the DB in Rust beyond what's in GitHub discussions.
Most databases have a notion of logical clock to implement MVCC/transactions. So this is just a mapping. Some in the spanner family actually use a drift limited physical clock.
The second part is using an index to answer queries about facts in the past. Like what was the capital of India in 1800?
There are existing embedded graph databases which do this.
Disclosure: I maintain one.
Existing cypher based databases which support typed properties (including timestamps) on relationship tables can handle this use case just fine.
https://github.com/networkx/networkx/discussions/8933
Writes will be slower though. That's the trade-off for all columnar tech in general. You can enjoy the savings for a read-only network and switch to classic networkx when you need to write.
HN has other biases that mirror the biases of the avg tech worker in the valley and those of the people called out in the article.
But they don't neatly fit into "far left" and "far right" buckets. People who use those terms usually are professional agitators.
SQLite was mentioned as a placeholder to make it a queryable database. Genome as an analogy.
We need to develop new type of databases and connect them to ML.
But connect them all together...
Training data is treated as IP. Distillation is seen as an attack.
Open data, open training based systems such an Marin are just getting started. Explainability is not a priority there.
The ones who do discuss these ideas are confrontational about LLMs and not effective spokespeople.
Training data is the database. Model is a lossy compressed index.
All I'm saying is: if you have a choice between two systems with equal power of discovery and one is more understandable than the other, we choose the more understandable one.
Limits of Human comprehension and quest for power are two different motivations that could lead to black box systems that are marketed as semi-explainable.
We need to verify that human comprehension is actually limiting progress before allowing such things and even when we do, do it responsibly on an explainable foundation.
There is little evidence that the RSI we are discussing is capable of inventing the theory of relativity (or the more advanced equivalent). All we have seen is pattern matching in a much larger space than humans can, with some human provided verification tech.
I would argue that human-AI collaboration with explainable tech has a better chance. Continuous learning can be done in a way that doesn't violate IP or privacy.
It helps both the bad guys and good guys. Like responsible disclosure in cyber security, we need to have a conversation around it.
https://research.google/pubs/the-case-for-learned-index-stru...
In 2017 Google was writing papers about it. Then something changed.
I don't think it was the tech. It was a realization around the power and societal impact.
> A gun is a metal tube that uses a tiny, controlled explosion to shoot a small piece of metal (called a bullet) forward at very high speed.
If the gun doesn't work as intended, you can take it to a shop and someone can fix it so it works as designed.
All I'm saying is AI should be designed the same way. Treat AI as normal tech like any other and use similar language.