Fascinating. Seems like similar issue risk management issue to silicon valley bank where they didn't account for a jump in interest rates and increase in inflation.
Calling such shameless pocket-stuffing a "risk management issue" seems to rather be like calling a drink-driving accident that kills a pedestrian an "unforeseen kinetic event".
That doesn't make any sense though. What benefit would DOJ get from getting the IP address of everyone who downloaded ytp-dlp? They aren't the enforcement arm of google's terms of service, which is a civil matter.
Even if they were, and the DOJ was going for a dragnet operation to go after tools that could potentially infringe terms of service of big corporations, they would go after every tool and every fork. Not just 1 package. But again, what court would allow such action and why?
If I was in the DOJ and was investigating a malicious package uploaded to PyPI, I would ask for the IP's of the downloaders to see if the uploaders dun goofed and downloaded their package shortly after uploading off VPN. Or to find out if any major corporations were impacted by downloading the malicious package and to inform them.
This is one of the reasons Databricks created Dolly, a slim LLM that unlocks the magic of ChatGPT. A homegrown LLM that can tap into/query the datasets of all the data in an organizations Data Lakehouse will be hugely powerful.
I am working with customers that are looking to train a homegrown LLM that they host and have blocked access to ChatGPT.
“My grand, macro thesis is that real interest rates have to stay low, and that’s because the rich have all the wealth and like saving,” he reflected. “Now, no matter how hard you work, how smart you are, if you come from the ‘wrong’ family you’ll probably never own property. That is feudalism. We’re going back into a world of aristocracy. Capitalism’s over.”
> Now, no matter how hard you work, how smart you are, if you come from the ‘wrong’ family you’ll probably never own property. That is feudalism. We’re going back into a world of aristocracy. Capitalism’s over.
As of 2021, 47.9% millenials in US own homes. So "new feudalism" has ~50% aristocracy rate (compare: 2-3% in times of real feudalism). This is probably partially due to increasing urbanization and higher life expectancy (alive grandfathers = no inherence).
Millennial home ownership lags sharply behind previous generatons. He's not saying that we have feudalism. He's arguing that is the direction the world is heading with increasing inequality. This video does a good job of explaining his views:
I particularly like his point about why homes have risen from 2-3x wages to 20-30x wages, this was a question I had always pondered about and could never find a good explanation for. The GINI data you posted is interesting but ends at 2018 and if I had to guess the pandemic has only worsened inequality. Asset prices such as homes have risen sharply putting them out of reach for many families.
Edit:
Looks like has worsened signficantly:
The United States’ Gini coefficient is .484, the highest it’s been in 50 years according to the U.S. Census Bureau. The U.S. has the highest Gini coefficient among the G7 nations. The top 1% of earners in the United States earn about 40 times more than the bottom 90% of earners.
Thats where Databricks comes in though, you can implement row/column based security on your data on cloud object storage and use it for all your downstream use cases (Not just BI/SQL but AI/ML without piping data over JDBC/ODBC).
According to their documentation [1], Databricks does not have this capability even for their own engines, and definitely not for "without piping data".
This is what I've personally seen few times - Databricks claiming they can do something and then it turns out they can't. Buyer beware lying salespeople and HN shills.
I don’t understand what capability you are saying Databricks lacks. This capability is literally the entire premise of the Data Lakehouse. With Snowflake you need to export data out/or pipe data over jdbc/odbc to an external tool. With Databricks you can use SQL for data warehousing and when you need you can work with that same data using python to train an ML model without piping data out over jdbc (using the spark engine). One security model, one dataset, multiple use cases (AI/ML/BI/SQL) on one platform.
They're still lacking things in the SQL space. For example, Databricks say they're ACID compliant, but it's only on a single-table basis. Snowflake offers multi-table ACID consistency, which is something that you would expect by default in the data warehousing world. If I'm loading, say, 10 tables in parallel, I want to be able to roll-back or commit the complete set of transactions in order to maintain data consistency. I'm sure you could work around this limitation, but it would feel like a hack, especially if you're coming from a traditional DWH world (Teradata, Netezza etc.).
Snowflake now offers Scala, Java and Python support, so it would seem their capabilities are converging even more, but both with their own strengths due to their respective histories.
Actually, you would expect that in an OLTP world. DW's for the longest time, even Oracle, recommends you disable txn to get better performance. The logic is implemented in the ETL layer. Very rarely do you need multi-table txn in large scale DW.
Sure! Full Disclosure: I run frontend engineering for Zoomdata. Zoomdata is powering the backend, it's a server that connects to big data sources. In this case, Cloudera Impala, but we also connect to plenty of other big data stores like Elastic Search, Solr, etc. Zoomdata queries those big data stores, then streams the results over a websocket to visualizations. We have a web interface for analyzing your data in standard charts, but we also allow for total customization of visualizations (like for building this dashboard). Those visualizations can be embedded using iframes, or a more advanced approach using a javascript library. I used our javascript library to embed these visualizations into my own web based dashboard.
It's not like they are looting them to Philly to put them on display. They shipped them to Drexel university to do additional research like extracting proteins and trying to extract DNA so they can make Jurassic Park.
"The researchers performed laser scans of all of the bones and published 3-D models of each. That could allow other paleontologists to study the fossil from afar and even print three-dimensional replicas of the bones. Dr. Lacovara and his collaborators are undertaking additional research to use the models to study how Dreadnoughtus moved.
Even some softer tissues like tendons were preserved, and the scientists are trying to extract proteins and possibly DNA from some of the bones."
https://www.mit.edu/people/dpolicar/writing/prose/text/think...