jlelonm

I'm a young developer and I feel like I have such little context for where and why we are today.

A few examples:

- what tradeoffs did companies examine before concluding to move to microservices from monoliths? Which companies decided not to? Why?

- When Angular.js first came out, what made companies adopt it? What were they using before?

- Why did everyone leave PHP?

- What made Postgres become popular? What were they using before? Who decided to stick with their original tech?

Does there exist a chart with maybe

- industries on the x axis (healthcare, transportation, ecommerce, finance...) and

- technologies on the y axis (Next.js, React, Angular, Postgres, AWS, MySQL, Jenkins, etc.), and

- each cell is did this industry adopt this technology in general? How long did it take? Why not initially? What changed?

I'd love to hear more about any/all of

- what motivated you to learn to draw

- what you were interested in drawing

- what your biggest roadblock/weakness to getting better was

- how many times you've tried to learn

As I talk to different folks from different domains, it's always interesting to me to learn what they're optimizing for and what the variables are.

Usually there's a sweet spot between two extremes.

Examples:

- Bias-Variance trade off from statistics and machine learning

- Freedom vs Security from philosophy

- Explore vs Exploit from computer science

I'd love to hear some other examples from e.g. anthropology, chemistry, physics, bio, marketing, etc.

What I'm really hoping is that this happens as much as possible:

- person from field A posts

- person from field B replies to field A post: hey! that's kind of like X from field B

- person from field C comments: Or like Y from field C!

etc

Hello HN!

I'm interested in tricks, tools, or mental models to help answer subjective questions with accuracy and precision (and eliminate biases).

For example, how do you turn the following questions into insightful, actionable data?

- How much does this hurt?

- How much do you like this product?

- What are you feeling right now?

Backstory:

I'm 28 and I developed tinnitus out of nowhere a few months ago (I don't go to concerts, I don't sit in server rooms, etc).

It's "mild" to "moderate" right now, I'm terrified of it getting "worse" as I age. I'd like to track it over time (months, years) to see if it gets quantitatively worse.

The issue: it's really hard to

1) control for all environmental variables (sleep, exercise, diet, etc), and

2) answer the question "how bad is it right now?" with consistency (i.e. however bad it is today, I will also give that same response when it is also that bad n days from now)

At first, I recorded (twice daily) my answer to the question:

Scale 1-10 how bad is it right now?

But then I realized - what if it's just not there at some point? I could do mental gymnastics and normalize it so that 1 corresponds to not there but it seemed easier to add 0 to make it 0-10.

But then, when the question became

Scale 0-10 how bad is it right now?

I realized I had no visceral, high-fidelity understanding of what a 3 was, nor a 4, etc.

So I shrank it to 0-5

(I think I'm sacrificing granularity for precision?)

Now, it's easier to assign a number to a subjective experience, but it got me thinking:

This is interesting from a customer / product research perspective and from a scientific research prospective. How do you phrase survey questions to maximize accuracy and precision? Is there a tradeoff? I'd love resource recommendations for studying this idea.

http://www.powersearchingwithgoogle.com/

Thoughts?

I know it's not https, but it is Google's course. It's linked to by a Google domain:

https://edu.google.com/openonline/course-builder/index.html

I started a job, and I was told that I couldn't leave my laptop on my desk for security reasons.

I find this strange, since you need badge access to get in, there's security cameras, the hard drive is encrypted, the OS is password protected, etc. It seems redundant.

It made me curious - what does your work let you bring in and bring out of the building?

I love great explanations for things.

By extension, I love documentation, diagrams, plots, blog posts, presentations, etc that take complex topics and explain them efficiently and effectively.

Is there a name for the study of what makes explanations good? I'd really like to study this. Can anyone recommend any resources?

I'm a SWE, and part of my job right now is migrating a legacy data pipeline to the cloud. Part of that is understanding layers of complex bash scripts that have tons of side effects and do lots of things at once (make 2 copies of this, awk that, over write this, copy it again, rename, append to this one, change the name, append again, copy again, etc).

I'm a very visual person, and my dream goal would be to have some robust diagramming system that allows me to read the script, and, line-by-line, physically diagram it out in such a way that it's very easy for anyone who reads the diagram to understand what's going on.

Some goals I have for this system:

- never having to erase what I've already drawn as I iterate through each line

- the end result makes it easy to spot unnecessary logic

- borrows a lot of symbolism from existing diagrams (state machines, UML/Sequence/etc)

Does this exist? Do I need to invent this? Does this seem useful?