chartpath

I know nothing of BTC but how would you stack it against gold, real estate, other traditional stores of value as a hedge against hyperinflation?

We live in a numpy array.

Agree with all the comments like "where are the nd arrays?"

BUT, they have list comprehensions!! One of the main things I miss coming from Python.

Wasn't there an app on iOS way back at the start that was just an image of a ruby/gem of some kind that cost $999? Some people actually bought it before Apple took it off the App Store.

Love pg but lol at opening with "I write simple" and immediately throwing around some Italian words.

Interesting, thanks!

Not an index per se but the Canadian Centre for Policy Alternatives publishes living wage reports, here is how they calculated it in the last one (2019): https://www.policyalternatives.ca/sites/default/files/upload...

Is there any way to replicate this for housing speculation?

What? Yes we do, see drone strikes [0].

As a paranoid citizen I don't trust people with my privacy if they get off on punching down at other people's freedoms. Simultaneously I agree that Eich built some interesting technology. Just like Obama started to put a dent in mass incarceration [1].

[0] https://www.thebureauinvestigates.com/stories/2017-01-17/oba...

[1] https://www.vox.com/policy-and-politics/2017/1/6/14189862/ob...

I thought the meaning of life was negentropy. We seem to habitually build little corners of order in the eddies of the waves of entropy sloshing around. Even just by observing things.

If using entropy emission as the indicator, how would we distinguish the exhaust of life from any other energy source? Wouldn't it make sense to look for pockets of negative entropy or would those be hidden by all the opposite reactions?

Not a physicist obviously!

Thank you for bringing this up. It's outrageous that it isn't more known outside of Canada (and inside). The last one only closed in 1996.

EDIT: and good on you for honouring her.

Yes exactly, I want to be able to do rich auditing of the predictions. Not sure about performance yet, still prototyping!

Graphs to organize annotation labels are necessary to avoid concept drift! Even though BLAS on graphs and fancy JVM property graphs are cool and interesting, my small brain is fully satisfied with LTREE labels in Postgres :)

That's just lack of leadership skills and possibly incompetence about subfields of development they don't have experience with. It can become toxic in its own way when the first bully/troll is hired since that person will pull the wool over this poor dev manager's eyes and make everyone else's lives terrible through bikeshedding and pointless meetings to debate unnecessary architecture proposals.

Currently https://github.com/nilp0inter/experta but https://github.com/noxdafox/clipspy seems nice, I just shied away from using it due to uneasiness about FFI and debugging, even though the original CLIPS is still awesome and has a very interesting manual.

There's also https://github.com/jruizgit/rules but haven't tried it yet.

I still do this after 15 years! Commitment means creative autonomy though. If valid solutions get tossed out by impostors then it becomes impossible to sustain. Or when authoritarians and people low on agreeableness traits start trying to make everyone predict all implementation details up front under the guise of process improvement (it allows unexperienced managers to pretend they understand details).

There is a minimum level of mutual co-operation and diplomacy required to nurture commitment and ownership, and alas it seems so uncommon as soon as a team goes over 5 people IME, unless the business is REALLY careful about hiring for openness and agreeableness.

That's true, I'm making recommendations based on Multinomial Naive Bayes (and SGDClassifier) over custom TF-IDF bags of words, so it is like search plus text classification. And some endpoints do just check the cosine or Jaccard distance between things. There is a lot of overlap between search and NLP.

My approach to AI is somewhat conservative because of working in a law-adjacent field where explainability is paramount. When it comes to getting "smart" I prefer forward-chaining logic over facts, and facts include predictions from models too. But at least there is a "judge"/engine to coordinate how the predictions from the ensemble of models maps to actions. I love me some pertained neural nets, but use them more as black box appliances.

Yup

I've been using LASER from Facebook Research via https://github.com/yannvgn/laserembeddings to accept multi-lingual input in front of the the domain-specific models for recommendations and stuff (that are trained on English annotated examples).

True about Facebook. But didn't they completely re-invent themselves once the white suburban soccer moms took over, and then again when all the drunk racist uncles came on?

I feel like you are right about the need for an original purpose. And would add that at such a momentous time when everyone can see the consequences of private, centralized media selling attention so efficiently, is it really impossible that the situation rises to that level of original purpose? The seed of a new community might just need a great spark.

As the article says, that is not the kind of union they set up. They set up a "members only union" which is voluntary to join or not. Either you are unhappy with conditions and need protection so you join for the the support network, or you are happy with conditions but join anyway out of solidarity with the lower classes of employees. https://tcf.org/content/report/members-only-unions-can-they-...

Or not join at all, which is fine, but punching down and across at your coworkers comes across as not being a team player.

Not sure about actors but I just use joblib for parallelization. It is way lighter than Dask and friends but still depends on (cloud)pickle serialization.

I agree that premature abstraction is bad. Except when using a mature off-the-shelf tool, e.g. Keycloak. Sometimes if you know that you need to implement a standard and are not willing to put in the effort for an in-house solution, that level of complexity just comes with the territory, and you can choose to only use a subset of the mature tool's functionality.

I also have a lot of experience starting with very lo-fi and manual scripting prototypes to validate user needs and run a process like release management or db admin, which would then need to be wrapped in some light abstractions to hide some of the messy details to share with non-maintainers.

Problem is, I've noticed that more junior developers tend to look at a complex prototype that hits all the user cases, and see it as being complicated. Then they go shopping for some shiny toy that can only support a fraction of the necessary cases, and then I have to spend an inordinate amount of time explaining why it's not sufficient and that all the past work should be leverages with a little bit of abstraction if they don't like the number of steps in the prototype.

So, not-generic can also end up failing from a team dynamic perspective. Unless everyone can understand the complexity, somebody is going to come along and massively oversimplify the problem, which is a siren song. Queue the tech debt and rewrite circle of life.

Yeah totally! I have done a couple different LTREE label columns on the same table before but that was for separating named entity recognition from other things, so still just different kinds of class membership (e.g. people, places, things).

It is possible to join these across tables, and therefore treat the matching nodes as having the same meaning for classification. However, it doesn't make sense to concatenate trees with different roots because it would just look like regular nesting.

Even if I make it clear at the column level like resource_table.node_label::ltree and predicate_table.edge_label::ltree, there would still be no obvious way to parse where in the forest we traverse over nodes and edges, since they all look and function the same.

I feel like this calls for a well thought-out naming convention using the only thing LTREE allows, alphanumeric and underscore characters. Something like root0.__node__name0.__edge__name0.__node__name1. This "merged" forest could be a "projection" within queries/views.

Thanks again, happy topologizing :)

This is a really cool and very smart looking mathematical way of looking at something that I do with lower-tech taxon labels using LTREE in Postgres to store the annotations.

In PG you can do a kind of KNN for things that are X LTREE degrees away as well as ordering by distance from trigrams of text. With an ensemble of online learning models for different blobs of taxa, it is a way to prevent concept drift and give weights to the different subdomains of training data to fight bias.

This kind of topological approach is great. One challenge in SQL is that class membership can only be so expressive and I have a nagging desire for slightly more RDF-like edges of predicates/verbs, but that still feels like overkill to me since I can already do rule engines with facts based on the labels. Therefore I end up with class names like "things that verb" or "things that are verbable" like would be done for naming interfaces.

Scanning the paper I don't understand the math well enough (am more of a code plumber). How would different "types" of edges be encoded to get past the perceived limitations of class membership?

Sneakernet 4.0

Everyone on HN always says they are taking like 10x the RDA and it's the only thing that makes them survive the winter, blah blah blah. Intentionally hyperbolic, but not far off.

Every time I try just 400 IU per day for a week it makes me dizzy and woozy. I eat plenty of meat and veggies with K2, and nuts and fish with magnesium. Vitamin-D containing foods like milk, eggs and fish are totally fine. I can bake all day in the sun and don't burn easily.

There's something different about synthetic D3, and I wish I could get all the benefits that people here claim.

I keep waiting for each new generation to offer more RAM than my 2015 MBP which has 16GB and is still going strong. How else can I run a bunch of docker containers?

But every new release is the same or worse for memory. Is there something about these new architectures that I'm missing which should negate my desire?

Why can't I get a machine with 32GB RAM for under $3k or whatever they are now?

How do those boots taste?

How is being compiled the same turf as running in a VM?