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phone: shortcuts gcp command- pre commit
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bryanwhiting committed Aug 9, 2024
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56 changes: 28 additions & 28 deletions .obsidian/plugins/recent-files-obsidian/data.json
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**Tuesday, August 6th, 2024**
[⏪ Mo 8/5](goals/2024-08-05) | Let God Prevail | [We 8/7 ⏩](goals/2024-08-07)


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26 changes: 26 additions & 0 deletions posts/data-science-structure.md
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title: "Data science structure"
date: "2024-08-04 13:00:01"
created: "2024-08-04 13:00:01"
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Over my career I've thought a lot about "where should a data scientist sit in an org?" 👇 5 lessons from what I've learned after working in 6 very different companies.

1. DS under an MBA: maybe okay, probably bound for failure.
2. DS under the PM: def bound for failure.
3. DS under the DS: good for the junior in the short term.
4. DS under Eng: good.
5. DS under a people manager, but works closely integrated team with a DS-experiences tech lead: best.

Interdisciplinary teams do the best work. It maximizes labor efficiency to have engineers, PMs, and DS's working on the same problem but doing different things.

When you federate DS, they don't care as much about what they're working on. The incentives are off.

When a DS works for a PM the PM will make wild assumptions about what they should do. PMs oversimplify problems and DS find that unsatisfying.

DS reporting to DS means you throw stuff over the fence and the Eng do the real work. And the Eng don't capture all the nuance the DS would like to implement.

DS reporting to business types is unfulfilling because DS love technical stuff.

best scenario? Have a DS work in a pod with experienced tech leads. DS reports (HR purposes) to someone outside of the team who can give them guidance but is incapable of micromanaging them.
18 changes: 18 additions & 0 deletions posts/scientific-method.md
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title: "Scientific method"
date: "2024-08-06 14:11:23"
created: "2024-08-06 14:11:23"
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I use these seven steps to the scientific method every single day:

1. What’s the problem?
2. Hypothesis: What’s the believed answer?
3. Metrics: how do I measure with data?
4. Methods: how do I test the belief?
5. Findings: what are the unemotional facts?
6. Discussion: what are my opinions about the findings? (These are usually more hypotheses.)
7. Next steps: self explanatory 🤣

It helps me think more clearly when diving into data! Hope it helps.
27 changes: 27 additions & 0 deletions posts/this-is-an-ab-test.md
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title: "This is an A/B test"
date: "2024-08-05 20:38:38"
created: "2024-08-05 20:38:38"
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Dude. You're being experimented on. Aren't you a little bit curious how it works?

Bruh, they're experimenting on you. Yes you. Now. Here's how it works.


Im A/B testing out some clickbait, let's see if it works 👇👇

This post is not AI generated. I wrote it. But I'm testing an idea: what if I write and publish two posts at the exact same time but with different clickbait hooks? Same content (what I'm writing here.)

Hypothesis: I'm guessing LinkedIn will naturally favor one post over the other. Hard to know which.

A/B testing is all about this every day. Newspapers play with their bylines. Search engines play with their titles. Ad copy is all one massive experiment to see what clicks the most.

So drop a like if you like A/B tests. And keep an eye out for my other identical post, launched at 10am on Tuesday PST. Let's see which gets the more impressions on LinkedIn!

Here are the two intros:

Dude. You're being experimented on. Aren't you a little bit curious how it works?

Bruh, they're experimenting on you. Yes you. Now. Here's how it works.
24 changes: 24 additions & 0 deletions silvermineai/give-it-time-and-retry.md
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---
title: "Give it time and retry "
date: "2024-08-01 23:07:46"
created: "2024-08-01 23:07:46"
categories: silvermineai
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---
A week and a half ago I wanted to shut it all down. Fire all my guys. I was burned out.

I kept the lights on though. I narrowed in. I worked on my system.

I remembered I have a lot to learn.

I'm terrified of getting rejected. But im not a sales person. I don't have to sell anyone. I can treat the call as a free consultation.

Free is soft. It lets their guard down.

"Hey this is a free consultation. Just curious to learn what you've done so far, bounce some ideas off you, and see if we can create some value for you. If so, we'll send you some more material and we can do a follow up call with our founder. So why are you interested in marketing services at this time? What's working, what's not working for you?

What have you tried in the past?

Great, our founders busy serving customers so we want to make sure we use both of your time wisely. Let's fix a time for a 15-30 min zoom call to review anything and discuss next steps.

"Well, calling you at random hours isn't really conducive. You might be busy, or I might be busy. So if you're serious, let's commit to a time to chat and we'll set up a zoom link for 15 minutes."
28 changes: 28 additions & 0 deletions tech/forget-pythonic-write-code-that-works.md
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---
title: "Forget Pythonic. Write code that works"
date: "2024-08-01 16:21:00"
created: "2024-08-01 16:21:00"
categories: tech
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---
For the Python nerds: I've seen data scientists bashing notebooks for years. I was one of them.

Then I learned yesterday about nbdev and how Jeremy Howard and folks at Nixtla are building production grade software using…notebooks.

I was taken aback by Jeremy's YouTube "I don't write code like most Python programmers, but I have a reason for how I write it".

I was also shocked how easy it was to read Nixtla's documentation and source code. Shouldn't that be the goal??

Unit tests at the bottom of the Python files where the functions are defined. Wait…that makes a ton of sense.

Wirh nbdev there's no major emphasis on pep8. No black formatting. No like wrap at 80 characters.

And I realized: we put so much emphasis on convention and doing what others are doing.

I'm not saying pep8 or black are wrong. I like them. But Python is just a language. Don't get sucked into "being pythonic". If the code works, and it creates value, it serves its purpose.

I come from R where there are so many "dialects". Compare base R vs the tidyverse. Worlds apart in how they build and test their code.

So…make your own dialect. Just don't waste time trying to keep up with all the formatters and linters out there. Because who cares? It's your code.

It's okay to have you or your team have your own style.

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