In case it helps as a tool recommendation, I’ve had some success having ChatGPT writing & rendering MermaidJS (diagrams as code)
72 karma · joined June 22, 2017
In this context EDA is Electronic Design Automation, not Exploratory Data Analysis
I’ve worked (mostly on business/data teams) in companies for 10 years, and have to say, saving on technical debt and human inefficiency w/ a codebase typically far outweighs the computational metrics
How might we incorporate that in a metric-like way?
Alright, little finance whiz, let's break it down for you. Imagine you have some money, and you can use it to buy something called an "option." An option is like a special ticket that gives you the right to do something later, but you don't have to do it if you don't want to.
Now, in the quote, instead of money and options, we're talking about computer code. When people write code, sometimes they write it in a way that's not very good. We call this "bad code." It's like having a sloppy and messy way of writing instructions for a computer.
But here's the clever part: good code can be like a special kind of option for the future. It means you have the choice to make things better later on. It's like having a ticket that allows you to improve your code and fix any problems.
Now, when code is really bad, it's like not using that special ticket. It's like not taking advantage of the chance to make things better. So, when we say bad code is an "unhedged call option," it means you're not using that ticket and missing out on the opportunity to make things right. It's like not taking the option and potentially causing more problems down the road.
Here’s the book synopsis in case anyone else is interested:
How honeybees make collective decisions―and what we can learn from this amazing democratic process
Honeybees make decisions collectively―and democratically. Every year, faced with the life-or-death problem of choosing and traveling to a new home, honeybees stake everything on a process that includes collective fact-finding, vigorous debate, and consensus building. In fact, as world-renowned animal behaviorist Thomas Seeley reveals, these incredible insects have much to teach us when it comes to collective wisdom and effective decision making. A remarkable and richly illustrated account of scientific discovery, Honeybee Democracy brings together, for the first time, decades of Seeley's pioneering research to tell the amazing story of house hunting and democratic debate among the honeybees.
In the late spring and early summer, as a bee colony becomes overcrowded, a third of the hive stays behind and rears a new queen, while a swarm of thousands departs with the old queen to produce a daughter colony. Seeley describes how these bees evaluate potential nest sites, advertise their discoveries to one another, engage in open deliberation, choose a final site, and navigate together―as a swirling cloud of bees―to their new home. Seeley investigates how evolution has honed the decision-making methods of honeybees over millions of years, and he considers similarities between the ways that bee swarms and primate brains process information. He concludes that what works well for bees can also work well for people: any decision-making group should consist of individuals with shared interests and mutual respect, a leader's influence should be minimized, debate should be relied upon, diverse solutions should be sought, and the majority should be counted on for a dependable resolution.
An impressive exploration of animal behavior, Honeybee Democracy shows that decision-making groups, whether honeybee or human, can be smarter than even the smartest individuals in them.
Love this site
Honest question: why do developers insist so much on using command line based tools? What's the advantage over using an IDE? (e.g. VS Code or PyCharm)
""" Fourier analysis is like playing with music notes. We have different ways of looking at these notes, like how high or low they are, or how long they last. In the same way, we have different ways of looking at signals and how they change over time or space.
Just like how we can make different types of music by combining different notes in different ways, we can make different types of signals by combining different types of Fourier analysis.
There are four classic ways to do Fourier analysis, and each one is like a different way of looking at the signal. They're like different types of glasses we can put on to see the signal more clearly.
But there are also other ways to do Fourier analysis, like looking at signals on different types of spaces or using different types of math. It's like having even more types of glasses to look at the signal!
So when you ask a mathematician what Fourier analysis is, they might give you a different answer depending on which type of glasses they like to wear. """
But yeah, totally into auto-generated (great example is dbt’s docs command: https://docs.getdbt.com/docs/collaborate/documentation)
And granted, this is a complaint for base R and other older spaces in the R world...the tidyverse packages (and modern ones inspired by it) tend to have pretty great examples with lots of iron
I'm seeing stuff I probably need to go clean up from like 10 years ago that's no longer relevant