Being alive is expensive.
Time to look for a @COLDCARDwallet because fuck @Ledger
What accessories should I buy?
So... I benched 275lbs for 2 reps tonight. Barbell to chest, 2 second pause and back up. Proud of myself. I'll record a video next time.
Just bought Mother's Day gift using
@bitrefill and USDC (Polygon). Whew... so easy.
I'm currently learning from Udemy. The course is called "Docker for the Absolute Beginner" by Mumshad Mannambeth. It's a decent primer to learn Docker. You might be able to buy the course for under 15$ as these courses are always on sale.
Crash coursing a course on Docker. Wished I had done this long ago. Might need it for an upcoming project at work.
Blah.
I loathe Ethereum. Why are gas fees still high?! I thought ETH 2.0 was supposed to fix this.
Dang it. I didn't know I could buy gift cards through @bitrefill on the Polygon network (USDC). Lost money bridging from the Polygon to the Ethereum network. Lesson learned.
Heck ya! I made it to #4 this evening. Been having a crappy week of playing, but tonight made up for it.
#icepoker #decentraland #decentralgames
ChatGPT is amazing. I asked it to re-write an email I was about to send to a co-worker, but to make it sound like I am smart and know what I am doing. Wah-lah! Magic. The email was re-written to sound more professional with clarity. What a time to be alive.
Huh?! Apple allows you to use up to 8 Apple gift cards to purchase a Mac. Neato! It would be nice not have a payment on a new Mac. Hmm... to buy or not to buy, that is the question?
Getting weak. On the verge of ordering a Mac mini w/ all the features. Can I still do .Net development on it though?
Ugh, crash coursing on Big O notation.
Here are some common examples of Big O notations for different algorithms:
O(1): Constant time complexity, meaning the algorithm takes the same amount of time regardless of the input size. Examples of O(1) algorithms include accessing an element from an array or checking if a number is even or odd.
O(log n): Logarithmic time complexity, meaning the algorithm's runtime grows logarithmically with the input size. Examples of O(log n) algorithms include binary search and finding the height of a balanced binary tree.
O(n): Linear time complexity, meaning the algorithm's runtime grows linearly with the input size. Examples of O(n) algorithms include iterating through an array and finding the maximum or minimum value.
O(n log n): Log-linear time complexity, meaning the algorithm's runtime grows in proportion to n times the logarithm of n. Examples of O(n log n) algorithms include quicksort and mergesort.
O(n^2): Quadratic time complexity, meaning the algorithm's runtime grows in proportion to the square of the input size. Examples of O(n^2) algorithms include bubble sort and selection sort.
O(2^n): Exponential time complexity, meaning the algorithm's runtime grows exponentially with the input size. Examples of O(2^n) algorithms include the traveling salesman problem and the knapsack problem.
It's hard to get most of my friends off there, because they are settled.
Hadn't logged into LinkedIn in a long time. Dang, I missed so many messages. I should visit it from time to time.
Is there a good reason to use BlueSky when I am already on Nostr? Is BlueSky just a Nostr client? I am not sure to be honest.
A good way to spend a Sunday. #icepoker and beer from @TheBrewtorium. You should try the Captain Hoof or the Das Daydrinker (highly recommended)
#decentraland #decentralgames
I cannot believe how silky smooth my Macbook Air can run Windows for ASP.NET development using
@parallels
! This is insane how smooth it is.
I supposed it helped I maxed out my Macbook Air M2, the 24 GB of RAM is a blessing.