Kalanos

I tried WAL, but like you said, it froze up during multiple writes (when creating records, not updating). I guess what I needed was a write queue - not sure if that exists.

sharding locally defeats the purpose of sharding; splitting it up across machines to get more resources

It's great! However, it's only meant for local systems. Once you need to connect over a network or robustly handle simultaneous requests, you need something like postgres.

It's an MVC that handles realtime frontend without React?

Please refer to this as GenAI

https://newsroom.heart.org/news/long-term-use-of-melatonin-s...

Good idea. Previously, I joined a python slack community and it helped a lot

pypi put out a survey a while back that was full of bs questions about dei fluff. the lack of subject matter made me really question the competence of the project staff.

Yes! Why is every company hiring for LLM talent? Companies that have no business doing so. They probably don't even know that supervised machine learning exists.

In the US, there is no male test for HPV

"FTC made a procedural error by failing to come up with a preliminary regulatory analysis, which is required"

The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.

Despite an aging population

No. Pharma acquires these gov-funded companies. The gov de-risks them for pharma.

somatic = cancer. germline = inherited.

You're right, DNA damage is just one of the types of genetic variation in cancer. There are many other structural variations that act like remixes.

"Maybe we need to start culturing and DNA testing cancers." I assure you this is being done at a massive scale.

Due to cellular stress, cancer cells disobey multi-cellular governance. They behave more like independent organisms fighting for survival, reverting to primal programming.

Haven't tried it. S3 Tables sounds like a great idea. However, I am wary. For it to be useful, a suite of AWS services probably needs to integrate with it. These services are all managed by different teams that don't always work well together out of the box and often compete with redundant products. For example, configuring SageMaker Studio to use an EMR cluster for Spark was a multi-day hassle with a lot of custom (insecure?) configuration. How is this different from other existing table offerings? AWS is a mess.

16GB base!

I really like this surgeon general. Hope he doesn't get axed.

He is also taking a stance on alcohol in relation to hormonally driven tumor development.

For normies, does this work on Ubuntu?

does that have the same degree of cloud integrations as airflow?

It's producing more in-depth answers than alternatives, but the results are not as accurate as alternatives.

+1 "The hardest part isn't the building"

That's the first thing I thought when I read the title.

So without the two-lang problem, I think all of these low-level optimization efforts across dataframes, tensors, and distributed computing would be part of a unified ecosystem based on shared compatibility.

For example, the reason why numfocus is so great is that everything was designed to work with numpy as its underlying data structure.

With some serious repositioning, I think there is still an opportunity for Julia to displace Python tools like polars/pandas/numpy, airflow, and pytorch -- with a unified ecosystem that makes it easy to transition to GPU and lead a differentiable programming revolution. They have the brain power to do it.

The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language. Python succeeds because the language and its tools are *EASY TO USE*. It has nothing to do with computer science sophistication or academic prowess - it humbly gets the job done and responds to feedback.

In comparison to mojo/max/modular, the julia community doesn't seem to be concerned with capturing share from python or picking off its use cases. That's the real problem. There is room for more than one winner here. However, have the people that wanted to give julia a shot already done so? I hope not because there is so much richness to their community under the hood.

In your opinion, is this the very best use of our time right now? Why is it better than every other alternative?

https://en.wikipedia.org/wiki/Holiday_heart_syndrome

You can create a dataframe from a list of dictionaries in pandas

`df = pd.DataFrame([{},{},{}])`

That is to say, the investments are bad because most of them are are overvalued. Who do you think is writing the Series B checks that puff them up to $1B?

Who would have thought that a decade of: a bunch of consultants running copycat investment strategies, flushing money down the drain on crypto + indefensible AI wrappers, throwing cash at kids from Stanford with no real world experience, chasing near term exit multipliers, and ignoring biotech wouldn't pay off big?!

https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExZ2pmN296bjRqM28...