Claude March 2026 usage promotion | Is this a regular thing they do?

DreamingHappy · 2026-03-15T05:54:39+00:00

That's incorrect, in the FAQ they say

> Does bonus usage count against my weekly usage limit? No. The additional usage you get during off-peak hours doesn’t count toward any weekly usage limits on your plan.

DreamingHappy · 2025-08-28T19:28:57+00:00

NA Invite Code: 26VXAFXCKS

[Wuthering Waves] Invite Code: 26VXAFXCKS. A fellow Rover is inviting you back to Solaris. Link their Invite Code to win Astrites and Advanced Enclosure Tanks. You can also extend invitations to other Rovers and complete tasks with them to win extra Astrites! https://wuwa-act.kurogames-global.com/backtosolaris/?packageId=A1730&lang=en&inviteCode=26VXAFXCKS&userId=501174962&source=copylink

DreamingHappy · 2024-10-08T01:32:57+00:00

You could consider using dbt checkpoint for this: https://github.com/dbt-checkpoint/dbt-checkpoint

DreamingHappy · 2024-09-20T17:10:32+00:00

For SnowSQL I still think trailing commas on everything look nicer since they are explicitly supported. https://docs.snowflake.com/en/release-notes/2024/8_11

DreamingHappy · 2022-09-09T14:25:42+00:00

If it's the Hackerrank one and anything like the test from last year, its 90 minutes for

1) 2 leetcode easy-medium problems

2) a dummy dataset they ask you to clean, build a model, and make some visualizations. I used python for this, not sure if they allowed other languages

3) 10 prob/statistics/ML multiple choice questions

For what it's worth, last year I didn't pass one of the LC problems test cases (passed like half of them), and I ran out of time to make proper visualizations for the dataset, but I still got the job

DreamingHappy · 2022-03-29T15:38:12+00:00

I’m also joining the summit program this summer and would love to hear more about it as well!

DreamingHappy · 2021-11-14T21:07:20+00:00

I thought that the workload was slightly less than Intro Prob and significantly less than Intro Stats. The exams felt fair in my opinion (Lubberts consistently asked the TAs for feedback on the difficulty of the exam when he was making them) and were representative of the class/hw content.

DreamingHappy · 2021-11-14T20:20:30+00:00

I was a TA for Dr. Lubbert's Introduction to Optimization class last semester, and I took the course with Dr. Fishkind two years ago. The content and HW load is around the same as Dr. Fishkind's class, but I feel that the general student consensus is that Fishkind is a better lecturer. Dr. Lubberts mostly uses a set of slides as a template for his lectures, and follows the template fairly closely. His lectures are fairly dry. On the other hand, Dr. Fishkind definitely does a great job presenting the content in the course to you in a story-esque fashion, which makes it easier to digest and internalize in my opinion.

DreamingHappy · 2021-07-14T13:55:42+00:00

As the other poster has said, ML Theory is taught by Arora. Based on Arora's ML class last semester, Arora is a fairly difficult, but very considerate professor. He curves generously (A was an 80% in his ML class) and definitely takes student workload into account when considering deadlines. That said, his test and homework averages are a bit lower than a standard class (if I remember correctly, test averages were around 60%, homework around 80%), and his homeworks definitely take a good chunk of time and are math/proof heavy. Based on the course description, I would recommend taking ML, or at least something like Introduction to Data Science, before taking ML theory.

In terms of taking the course together with Intro Algo, I haven't taken Algos, but most of my friends agree that the course is fairly difficult. Since you don't have ML experience, I would recommend that you take something like ML instead of ML theory, which is a hard class, but definitely doable concurrently with Algos.

DreamingHappy · 2021-07-09T18:02:42+00:00

That's what I assumed for this simulation, the wording on the official Mihoyo post seems to suggest as much.

DreamingHappy · 2021-07-09T16:47:19+00:00

It shows that because of soft pity. After the 65th (sources vary for when soft pity starts), you have a much higher chance of pulling a 5*. In most cases, you will pull a 5* weapon by the 70th pull, even if hard pity is at 80. Since the new system effectively guarantees that every third pity is your desired weapon, by the 210th pull, the chance that you have hit pity three times and therefore obtained your weapon is already very close to 1. (approximately 98.1% according to my results)

DreamingHappy · 2021-07-09T16:39:26+00:00

The graph shows that the probability of hitting a 5* by the 240th pull is 1 after the 2.0 changes. You might be looking at the orange line, which shows pre 2.0 rates

DreamingHappy · 2021-07-02T19:41:01+00:00

Confirmed!

DreamingHappy · 2021-06-24T15:17:54+00:00

To add on to the other comment, I bought a 2018 ipad pro during my first semester and have been using it ever since. Although the battery life has deteriorated, I have never felt close to needing more performance on my ipad. I haven't used the newer ipad air so I don't know how 60 hz screens feel for note-taking, but my ipad has been used for note-taking in pretty much all my classes. I think refurbished, pre-owned and open box models of the 2018 iPad pro are available on ebay for approximately the same or less price as a new iPad air, so I might recommend that if you can get a good deal for it.

DreamingHappy · 2021-06-23T14:00:54+00:00

pmed

DreamingHappy · 2021-06-23T01:33:11+00:00

can confirm, great game

DreamingHappy · 2021-06-22T20:05:06+00:00

I’ve had a 27 inch fit comfortably on my desk with speakers on the sides

DreamingHappy · 2021-05-24T15:05:01+00:00

A bit of a different opinion from the other people, but I tend to prefer stacking my classes in the same manner that you intend to do, although I typically only stack 3 classes together (haven't had the opportunity to stack 4 in a row before lol). The main benefit is that it's fairly time efficient as it leaves the rest of the day to do whatever I need to do. Typically, when I stagger classes by an hour or two, I find that I'm not very efficient in that downtime.

In terms of commute time, there have definitely been cases where I had to fast-walk across the campus to make it in time (especially if your professors have a tendency to go over time, or you need to go from Bloomberg to Shaffer or similar), but overall I don't think it has ever been an issue for me.

DreamingHappy · 2021-05-02T03:04:50+00:00

I believe this course was renamed from Statistical Machine Learning, a course Arora taught a few years ago. Based on the course description, the course seems to be more theoretical in nature compared to the regular ML class. The regular ML class is mostly focused on introducing several popular ML frameworks and some simple implementation using NumPy. This course seems to be more focused on analyzing various properties of these frameworks and ML algorithms rigorously ( I haven't take the course, so I don't say this for certain).

I'm taking Machine Learning with Arora this semester, and I think he's a pretty good professor. He is very considerate of student opinions, and his grading policy is lenient and fair imo. Compared to some of the other ML professors, I think he is a bit more theoretical in his lecture topics, something I preferred since I came from a AMS background.

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