and "the Digital Antiquarian" just did a great, deep two-parter on the history of Looking Glass last autumn. (covers Thief, Thief 2, System Shock, and some forgotten oddities)
Author here, yes you are correct that it's Simplex noise. I also use a method of domain warping, I used this article as reference https://iquilezles.org/articles/warp/
I remapped the UV coords based on the spherical projection of the mesh after subdividing, so there should be minimal distortion, especially compared the UV sphere. There is a slightly higher density of vertices where the edges of the cube used to be, but it's small compared to the UV sphere's extreme convergence at the poles.
100% agree. I've found no theory as to what correlates with my particular issues: hives and elevated heart rate when consuming processed food/gluten/I don't know what over a long period and also difficulty sleeping. If I go pure low-carb, I'm fine, even if I consume large amounts of sugar. However, stress can also induce it even if I am eating properly. Additionally, evaporation of sweat also causes it in certain cases.
Nobody seems to have any idea what the issue is, but I've learned to manage it by controlling diet and stress basically. I'm still not 100% why/how it happens.
I've learned that these sorts of issues may be something science will never figure out for me since they focus on populations rather than the individual. I expect in 10 years some studies at some point will start isolating some
of these triggers as they become more prevalent. I've talked with a few people who've had similar issues.
Nvidia recently rolled out upscaling in video too with new drivers and latest Chrome/Edge [1]. Honestly, it's pretty decent from my usage. Required a 3080 or above, however.
I've tried the nvidia one and it's really not particularly noticeable. On low resolution media (eg below 720) it really looks like it has removed a lot of the details - mostly as at such a small resolution, there aren't any details haha, but up scaling makes it very noticeable.
asyncio is _not_ implemented as threads. It has features where it can wrap a sync function inside a thread in order to turn it into an async function, but if you just write "normal" async code, all your code is running in a single thread.
We're currently looking into datalake implementations. Right now, we only have 1 or 2 data sources. Current thinking is reading them on the fly, combine them using pandas dataframe and query that. Anyone have experience with doing something similar?
Depends on the type of data you're processing, your business goals and the existing consumers that you need to support.
At a minimum I'd suggest planning to load the data from the data lake into an RDBMS (OLAP/columnar preferably). Then it's accessible to more than just Python scripts (BI tools, users of other languages, etc).
Depending on how much data there is, should also plan on data summarization strategies. You can either build some common rollups to ensure that consumers are all looking at the same summaries or you can let consumers build their own transform/load pipelines from the raw data lake or you can let consumers build their own transform pipelines from the data in the data warehouse (using something like dbt).
The benefits of a data lake architecture really appear when you have lots of sources, lots of disparate consumers, and lots of data, with some schema evolution & unstructured parts thrown in. If you only have 1 or 2 sources, small enough data to query raw data in Pandas, and consumers are restricted to Python scripts, then you can skip a lot of the architectural headache of building a data lake for now (just make sure to archive your raw data somewhere if you want to be able to pull it into a data lake in the future).
You can do this for small datasets and I built an app around exactly this use case [0].
As your data gets bigger (just over time even if individual days don't emit more data) you'll end up building out partitioning schemes yourself or you'll move to a system that does that for you like bigquery/snowflake/etc.
It can seem like overkill, but I can't recommend Snowflake enough. It's so simple to setup and manage, and JSON support makes it easy to just drop JSON line files into a table and query on the fly.
Depending on your data/query volume, it can also be very cheap.
However, this article was about table formats so maybe OP wasn't thinking about JSON. If so, looks like Snowflake only supports read operations atm. Snow Summit this week, so maybe that will change?
https://www.youtube.com/watch?v=T1tXepGXDDM