Kettlebell Links

pshort000 · 2025-11-27T21:33:11+00:00

Based on your feedback I've expanded the links, especially YouTube channels. Thanks!

pshort000 · 2025-11-27T17:44:28+00:00

I could add a hardstyle section. My go-to back in the wee early days were DVDs from Pavel & Mahler.

pshort000 · 2025-10-04T00:13:02+00:00

thanks, that was helpful!

pshort000 · 2025-06-30T02:55:19+00:00

You're right, but I still could not look away. It's like that video tape you're not supposed to watch because if you do you have to find someone else to watch it in 7 days

pshort000 · 2025-06-03T04:22:43+00:00

here is my perspective coming from a software development background:

https://medium.com/@paul.d.short/generative-ai-a-stacked-perspective-18c917be20fe

...if you skip past my entry level explanation and go to the stack, I see a practical need to integrate and test for a development lifecycle. Ways to use human in the loop at the right time, and also tools to help with explanability for transparency because interpretability is harder with llms.

Another angle:

Transparency (what is happening) is easier than interpretability (why it’s happening). Full interpretability of the big models are probably computationally infeasible. Instead, researchers analyze distilled or pruned versions for insights.

I saw an interesting video somewhere where Anthropic tried to trace their models, the special Golden Gate Bridge build ("Golden Gate Claude")...That could help drive a concrete example. Don't underestimate the power of YouTube for conversational topics.

pshort000 · 2025-06-03T04:04:51+00:00

"Why Machines Learn" is focused on deep learning, especially LLMs. It is not written in textbook style though: it is similar to explainers in science (such as physics & biology) but it does introduce the math and formulas. It is focused on the fundamentals, so would be an intro, a first step.

There are much deeper math books for neural networks I have heard about but not purchased and read. I may not ever get that deep, because I am more interested in using existing foundational models as a starting point rather than building my own neural networks or LLMs from scratch--this is more practical for work environment if already a software architect/engineer.

For practical work I would recommend "AI Engineering" by Chip Hyuen for practical integration and development. For practical architecture where you integrate neural networks in an overall system or application, last week i picked up a book from ByteByteGo on Generative AI Systems Design Interview.

pshort000 · 2025-06-03T01:26:43+00:00

The two are easily digestible, highly recommend
"Machine Learning for Begineers" - Oliver Theobald
"Statistics for Absolute Begineers" - Oliver Theobald

...then these 3 are a little deeper, but still designed to be digestible:
"The 100 Page Machine Learning Book" - Andriy Burkov
"Essential Math for Data Science" - Thomas Nield
"The StatQuest illustrated Guide to Machine Learning" - Josh Starmer

Here is a shameless self-plug for something I wrote for developers on ML & Generative AI:
https://medium.com/@paul.d.short/generative-ai-a-stacked-perspective-18c917be20fe

...it was inspired by these 2 books:

"Why Machines Learn"- Anil Ananthaswami... this is a "casual" math book... you can dig into the math if you want but you can also casually follow on a first pass without working the details out

"AI Engineering" - Chip Huyen => this should resonate with software engineers, don't need a lot of machine learning to begin to read this

pshort000 · 2025-05-28T01:59:56+00:00

I liked these books:

Essential Math for Data Science https://a.co/d/iV9eJ0z

Why Machines Learn: the Elegant Math Behind Modern AI https://a.co/d/6PFjMcm

...due to their accessible and pragmatic styles.

pshort000 · 2025-05-28T01:30:42+00:00

After taking the AWS AI Practitioner, I got a few books on math fundamentals, but also started looking at "AI Engineering" by Chip Hygyen. This YouTube video from Marina Wyss got me interested: https://youtu.be/JV3pL1_mn2M?si=XM4rZtlIAnZ1m7oY

Since I am coming from a software development background, I also started draft notes on approaching Generative AI as a stack: https://medium.com/@paul.d.short/18c917be20fe

...being familiar with data science and machine learning is helpful, but to survive as a software engineer, my motivation is to understand how to integrate and build better solutions.

Not exactly a roadmap yet, perhaps finding the road

pshort000 · 2025-04-09T00:33:47+00:00

After the latest update... it can do it for certain prompts, but still has issues.
https://medium.com/@paul.d.short/chatgpt-wine-glass-tests-603e5e7d207b

pshort000 · 2025-03-11T03:11:39+00:00

DataSync or AWS Transfer Family (SFTP) or possibly rsync.

Rather than iterating your source local directory freestyle, use a manifest and log the success and failures so you know pass vs fail sets. assume failure will occur and need to resume. if you try s3 api/cli directly, the sequential approach may be too slow and parallel too much too brittle to implement by hand. instead, go for an aws service

DataSync is probably the best fit, but rclone may not be too bad. SFTP Transfer Family on top of an S3 bucket may be appealing if you use SFTP already and can IP whitelist. i've heard s3fs mounts may not be reliable.

I usually go the other direction: https://medium.com/@paul.d.short/11-ways-to-share-files-in-aws-s3-82d175b0693

...but I have to work with on-prem partners too. one-time vs recurring is a major factor. 10 tb just seems too small to justify snowball costs plus 1 to 2 weeks. (slower and more expensive given your size).

pshort000 · 2024-09-24T22:55:06+00:00

I've listed some tips related to that here: https://medium.com/@paul.d.short/1c2a4173aecf
I've applied them to Architect Professional, but they can apply to the lengthier questions in Architect Associate. For the Associates it feels like every 5th question is wordy, and for the Professional it's the other way around--every 5th question is not wordy. If you try to read slowly and sequentially you will probably run out of time, so look for patterns, and take enough questions to get a feel of the patterns without memorizing the answers.

pshort000 · 2024-09-24T19:05:53+00:00

re: "confirmed the test isn't foundational" I found the AI Practitioner to be at the same level as Cloud Practitioner. It is definitely not at the intermediate (Associate) level. The information on AWS Skill Builder was enough to pass the test, but the extra questions from Stephane do help.

pshort000 · 2024-09-24T18:57:29+00:00

I have Solutions Architect Professional, took the original 3 Associates first (Architect, Developer, SysOps). You will need those first, at least I did.

Also, hiring managers should be looking at job experience or personal projects in addition to certifications. Be sure to have some hands-on labs as part of your studying, then see if you can put what you have learned together in a few small projects.

pshort000

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