https://www.casticle.fm/baleen-1/
推荐一下糊糊的新邮件通讯。

I especially liked: https://www.youtube.com/watch?v=HeQX2HjkcNo
Great explanation of Gödel’s incompleteness theorem

再次推荐，这篇是非常好的了解灵感买家俱乐部的方式。https://bobfu.zhubai.love/posts/2201061462389899264

Very interesting film, recommended: https://www.imdb.com/title/tt9764362/

https://coke.do/issues/115-1468280
推荐可乐周报，质量很棒，比如最新这期就有两个非常好的 insight：

- 免费贡献99%的部分，对剩下的1%收费。https://dailywritinghabits.substack.com/p/give-away-99-for-free-monetize-the
- 四种作者，你想成为哪一种？https://qr.ae/pvux48

https://evantravers.com/articles/2022/06/30/dating-other-task-managers/

Great insights. The main takeaway for me is to have "the List" and "the Brain", where "the List" is things you committed to do soon (say today).

I have been using a similar system where there is a list of "immediate todos" and a backlog. The difference is my "immediate todos" are not actually what I committed to do soon. And that sometimes leads to procrastination. I will be trying out this new "the List vs. the Brain" approach to see if it helps.

> Still more intriguing: Brain and List could be separate systems.
E.g. copy items from the List to a piece of paper first thing in the morning.

！
https://youtu.be/l-iHDj3EwdI

感谢 limboy 主持录制和剪辑！
https://bytetalk.fm/posts/episode-10/

https://lloydtabb.substack.com/p/data-is-rectangular-and-other-limiting
A primer on where SQL falls short especially with hierarchical / nested repeated data and an attempt to solve it.

Two things that are particularly interesting:
- Malloy introduces the concept of aggregate locality [1] to make it much easier and less error-prone to express query intent with nested repeated data. See also symmetric aggregates [2].
- Malloy not only understands nested data, but also writes nested data.

There are also the VS Code Extension [3], native DuckDB support, ... that makes it easy to work with Malloy.

[1] https://malloydata.github.io/documentation/language/aggregates.html#aggregate-locality
[2] https://cloud.google.com/looker/docs/best-practices/understanding-symmetric-aggregates
[3] https://marketplace.visualstudio.com/items?itemName=malloydata.malloy-vscode

Disclaimer: Malloy is an experimental project created by lloyd tabb, the co-founder of Looker which was acquired by Google. I currently work in the Looker organization but don't directly work on or with Malloy. Opinions are my own.

The annual must-watch: https://www.youtube.com/watch?v=c0Eo7fwGBJ4

The problem with stupidity, though, is that it often goes hand-in-hand with power. Bonhoeffer writes, “Upon closer observation, it becomes apparent that every strong upsurge of power in the public sphere, be it of a political or of a religious nature, infects a large part of humankind with stupidity.”

This works in two ways. The first is that stupidity does not disbar you from holding office or authority. History and politics are swimming with examples of when the stupid have risen to the top (and where the smart are excluded or killed). Second, the nature of power requires that people surrender certain faculties necessary for intelligent thought — faculties like independence, critical thinking, and reflection.

https://bigthink.com/thinking/bonhoeffers-theory-stupidity-evil/

A great overview/analysis piece
https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/

https://motherduck.com/blog/big-data-is-dead/

> MOST PEOPLE DON’T HAVE THAT MUCH DATA: In order to understand why large data sizes are rare, it is helpful to think about where the data actually comes from. Imagine you’re a medium sized business, with a thousand customers. Let’s say each one of your customers places a new order every day with a hundred line items. This is relatively frequent, but it is still probably less than a megabyte of data generated per day. In three years you would still only have a gigabyte, and it would take millenia to generate a terabyte.

> WORKLOAD SIZES ARE SMALLER THAN OVERALL DATA SIZES: There are acute economic pressures incentivizing people to reduce the amount of data they process. Just because you can scale out and process something very fast doesn’t mean you can do so inexpensively. If you use a thousand nodes to get a result, that is probably going to cost you an arm and a leg. The Petabyte query I used to run on stage to show off BigQuery cost $5,000 at retail prices. Very few people would want to run something so expensive.

> DATA IS A LIABILITY: An alternate definition of Big Data is “when the cost of keeping data around is less than the cost of figuring out what to throw away.” I like this definition because it encapsulates why people end up with Big Data. It isn’t because they need it; they just haven’t bothered to delete it.