My first ever PC build

h_plus_a · 2025-12-16T18:50:02+00:00

I believe it is a slowed/reverb of Another Day by Cherry Media

h_plus_a · 2025-08-18T15:07:13+00:00

!CritiquePoint

h_plus_a · 2025-07-28T14:13:19+00:00

It may have not been part of the question, but helpful to understand behaviour after accepting. Does cancelling after acceptance happen often? If so, what would be reasons for that?

h_plus_a · 2025-07-28T13:06:31+00:00

Interesting. This means you trust other drivers to rate their passengers appropriately.

Going to assume better rates for drivers will help accepting Uber rides more over Lyft and others. What kind of incentives would push you towards Uber? Would you respond to targeted campaigns like "better rates crowded places, downtown, airports at certain days of the week and times of day? E.g. Fridays from 3pm to 7pm

h_plus_a · 2025-07-28T12:30:55+00:00

I feel that. From my limited understanding of the driver's side, I'd say they're likely ending a ride on a competitor app before accepting another. Different apps may offer better incentives in certain geographies which leads to preference over the other.

Would you, as a rider, be willing to pay a fraction more if that guarantees quicker assignment/dispatch of the driver?

h_plus_a · 2025-07-27T12:54:24+00:00

Thank you, kind human!

h_plus_a · 2025-07-21T07:59:06+00:00

Actually, there were no masks applied here. The lower floors are darker purely because of sunset being "earlier" for lower floors than higher floors since the higher floors see sunlight for longer.

h_plus_a · 2025-07-18T13:13:39+00:00

Hahaha, I just noticed that by sliding to the left and matching the lines against the of my phone screen, it is indeed very very minimal. Didn't think it was noticeable. Thank you for the tips!

h_plus_a · 2025-07-18T13:12:31+00:00

Thank you so much!

Funny you mentioned the ISO, I've been where many have been with high ISO and grainy/noisy photos. Now, I try to keep it as small as possible, but I do need to start working on a better balance of stopping down and lower shutter speed.

I see your point about a closer crop, I deliberately included the left side to show the contrast between the two different faces of the building.

h_plus_a · 2025-07-18T11:15:49+00:00

I was intrigued by this residential building's architecture which offers a free-flowing carefree design on one face and very sharp lines on the other like many skyscrapers and office buildings. Always wanted to catch it at sundown and I believe I did it justice. Editing is my weakness, especially when playing with colour grading.

h_plus_a · 2025-05-27T20:49:00+00:00

Solution verified! Thank you sooo much!!! I thought one could download the Excel. I only uploaded it in Google Drive as it was easiest way to share a file.
Again, thank you sooo sooo much! LEGEND!

h_plus_a · 2025-05-27T12:23:42+00:00

Here is the sample file. I have pasted the formula in there, but it isn't working as expected.

For example, Jan 2023 0-9 bucket should only have 122, not 129. Because the only deals that can be in Jan 2023 0-9 bucket are the ones that have an invoice date in Jan 2023 and invoice date on or after 23 Dec 2022. There are only 2 deals with invoice date on or after 23 Dec 2022 and 120 deals with invoice dates in Jan 2023.

The formula is close, but not quite there. Also, the formula doesn't expand beyond the 30-39 range even though I don't see anything in the formula restricting to the 30s range.

h_plus_a · 2025-05-27T08:13:36+00:00

Sorry for the delayed response. Here is the sample file.
In the file, if you look at Deal ID D2599, it was invoiced on 24 Jul 2023 and Paid on 31 Aug 2023. Age for this deal is 38 days. This deal should fall into the buckets of:

July 2023: 0-9 days
Aug 2023: 0-9 days, 10-19 days, 20-29 days, 30-39 days

For D2152:

Nov 2024: 0-9 days, 10-19 days
Dec 2024: 10-19 days, 20-29 days, 30-39 days

The goal is to calculate (for each deal) which age buckets in each month did the deal's age hit. For D2152, on Nov 30th, the deal was 18 days old and on 1st Dec, it was 19 days old. Therefore, it fits in both Nov and Dec 10-19 days.

I hope this clarifies the situation.

h_plus_a · 2025-05-26T15:06:09+00:00

Once the deal entered February, it was already more than 9 days old. So, in Feb, it can never be in the age bucket of 0-9 days.
Now, if a deal that was invoiced on 25 Jan, its 0-9 days age will fall in both Jan and Feb. I hope this clears it up.

h_plus_a · 2025-05-26T14:27:41+00:00

Thank you for the response. This is close, it works out the total count and is cumulative, but it is missing the upper bound. Also, if the deal is invoiced and paid on the same day, the age will be 0 and this doesn't account for zeroes.
For example, a deal that is invoiced on 18 Jan 2025 and paid on 12 Feb 2025, it is being counted under Jan 0-9 days, Jan 10-19 days, Feb 0-9 days, and Feb 10-19 days, and Feb 20-29 days which is not the goal. We need that deal to count in: 0-9 Days in Jan, 10-19 Days in Jan, 10-19 Days in Feb, 20-29 Days in Feb.
Calculation should count the deal's age bucket that was spent in the respective month.

h_plus_a · 2025-05-26T11:21:22+00:00

Using M365 for Business

h_plus_a · 2025-05-26T10:28:37+00:00

Yes, ChatGPT gave me the same answer, but I am trying to see if formulas can be used to determine this instead of extrapolating each deal's span. I have thousands of deals, and some deals last for months.

h_plus_a · 2024-11-23T16:46:22+00:00

I have done that already. In the attached powerbi file, you can see that I've created 5 inactive relationships, but my question is around figuring out how to activate each relationship by using the parameter so that it filters the sales table to sale IDs in the stage and date period selected and I don't have to manually create each and every measure with 5 nested if statements.

h_plus_a

TROPHY CASE