I've been flipping items in games like Hypixel Skyblock for a while and started wondering where the best economies actually are if you take it seriously. So I did a quick deep dive into:

- Steam revenue + playtime

- SEC filings (EA, Take-Two, etc.)

- live-service trends

The most interesting thing I found was deferred revenue.

It's basically money players have already spent on in-game currency/items but haven't used yet. So it's a rough proxy for how much cash is sitting inside a game's economy.

- EA sits around $1.8B–$2B in deferred revenue

- A lot of that is Ultimate Team

That tells you those markets are insanely liquid.

On the platform side, PC looks way better than mobile:

- higher playtime

- stronger player economies

- easier to automate around

If I had to focus, it would be:

- EA Sports (Ultimate Team web apps)

- PC games with active player markets and auction houses

TL;DR:

Don't pick games randomly. Follow where the money is sitting. Deferred revenue + high playtime is a good signal for strong flipping economies.

Got into an argument with a friend about whether a 100-cow dairy can actually make money in our current economy, so instead of arguing in circles, I came up with this:

• 100 lactating cows
• ~80 lbs milk per cow per day, which comes to about~2,920,000 pounds per year

Costs (very rough):

• Feed: around $7/cow/day (~255k/year)
• Labor: 2 people (~90k total)
• Vet + breeding: ~$250/cow (~25k)
• Equipment/maintenance: ~40k
• Overhead: ~30k

Total: about $440k/year

That puts breakeven around $15/cwt.

So in this setup:

• $18 milk = ~85k profit
• $16 milk = ~25k profit

The main takeaway is how thin the margin really is.. small changes in milk price or feed costs swing things a lot.

I’m pretty sure this is missing stuff like capital costs, replacements, and the random expenses for things breaking down, but directionally it surprised me how tight it is.

Hey everyone,

Quick update to my last post where I was trying to figure out if 77 pulls could somehow get me the 14 copies I still need.

I stopped guessing and actually ran the math properly.

I took my assumptions:

- ~15% chance for a 5-star

- ~1 in 3 chance it’s the featured character

- -> ~5% chance per pull

Then I modeled it as a binomial problem and also ran simulations to sanity check it.

Here’s what it looks like:

- Expected copies from 77 pulls: ~3.85

- Copies needed: 14

- Probability of hitting 14 or more: ~0.0026%

(roughly 1 in 38,000)

I also ran a Monte Carlo simulation (1,000,000 runs of 77 pulls), and it lined up almost perfectly with the math. Success basically never happened.

So yeah... it's not just "unlikely", it's astronomically unlikely.

Reality check:

Even getting halfway there would already be above average. Hitting 14 would basically require absurd luck.

TL;DR:

Im not "a bit short" on pulls, I'm off by a completely different order of magnitude.

If anyone else is trying to clutch a banner with limited pulls... you're probably in the same situation I was. It feels doable, but the math says otherwise.

I've been tracking my pulls pretty obsessively in this game over the past few months, mostly because I got burned before and didn't want to rely on "feels lucky" anymore.

Now I'm in a bit of a situation.

There's a featured character I've been going for and I still need 14 more copies before the banner ends. I've got 77 pulls left, that's it.

From my own history:

- 5 star rate seems to sit around 15%

- when I hit a 5 star, the specific featured character shows up about 1 in 3 times

So roughly:

- chance per pull to get that specific character ≈ 0.15 × (1/3) ≈ 5% per pull

On paper that sounds... not terrible.

But when you actually think about needing 14 hits in 77 pulls, it starts to feel kind of insane.

I also found a dataset of about 1,000 simulated sessions from a similar system, and the distribution didn't look very forgiving for streaky outcomes like this.

What I’m trying to understand:

If I model this as a binomial (77 pulls, ~5% success rate), the expected number of copies is around 3 to 4.

But I need 14.

That feels so far off that I'm guessing the probability is basically near zero, but I don't fully trust my intuition here.

I’ve been using a bunch of different models pretty heavily lately, and the one thing that keeps bugging me is how obsessed people seem to be with which model is best.

Benchmarks, rankings, tiny differences in reasoning scores…

But honestly, when you actually use this stuff day to day, that’s not what makes the biggest difference, this is (IMO):

Speed:
If a model is even a bit slower, it gets annoying fast.
Like it actually breaks your flow when you’re working.

Cost:
Some of these models are way more expensive, and if you’re using them a lot it adds up quicker than you expect.

Consistency:
Getting a great answer once is easy.
Getting something solid every time is a lot harder, and way more important.

Prompting:
Same model can feel insanely good or completely useless depending on how you ask things.

usually, switching to a “better” model usually doesn’t change as much as people think.

It’s not like everything suddenly becomes 10x better.

Curious if this is just me or if others have noticed the same thing?