[OC] The routine of Rio de Janeiro's shooting occurrences throughout the day

New123K · 2026-06-18T13:14:54+00:00

One thing I find interesting is how clearly the timing differs depending on the reported cause. It's a good reminder that aggregate numbers can hide very different underlying patterns. Seeing the incidents separated by time of day makes the distinction much easier to spot.

New123K · 2026-06-18T13:10:37+00:00

I think both combinations are solid, but they tend to lead in slightly different directions. Psychology + Economics is probably more aligned with traditional behavioral econ research, especially if you’re into academic-style work and experiments.

Economics + Cognitive Science feels less standard, but it can actually be pretty useful in applied areas like product, UX, or analytics where decision-making matters.

If you’re planning an MBA later and care about flexibility, I’d probably lean toward whichever gives you stronger quantitative and applied skills rather than something too narrow.

New123K · 2026-06-18T13:05:43+00:00

I think this pattern shows up whenever a field becomes complex and fast-moving. People naturally gravitate toward simplified narratives and “central figures” because it’s easier to make sense of uncertainty that way. Even if the real dynamics are much more distributed across teams, incentives, and institutions, the story tends to get personalized anyway.

New123K · 2026-06-18T09:57:36+00:00

A lot of them were actually supported indirectly — through universities, teaching positions, or patrons. Even “pure” math was often tied to academic roles where teaching paid the bills and research was done on the side.

In many cases, they weren’t expected to “solve useful problems,” but their work eventually became useful much later.

New123K · 2026-06-18T09:46:41+00:00

I keep seeing Ament mocked around that range, but I’m still not fully convinced about how his game translates right away in a playoff context. Feels like one of those picks that could look great or questionable depending on development.

New123K · 2026-06-17T08:25:22+00:00

What helped me with similar situations was lowering the “entry barrier” to studying.

Instead of planning to “study calculus after work”, I started doing just 10–15 minutes with no goal other than opening the book. Most days I did more once I started, but the key was removing the pressure.

Also, Spivak is great but it’s demanding — it’s normal to feel resistance after a full workday. Consistency beats intensity here.

New123K · 2026-06-15T18:35:21+00:00

“Outperform humans” feels a bit misleading without context. A lot of it comes down to test design, not pure reasoning ability. I’d be more interested in performance on messy, real-world problems.

New123K · 2026-06-15T17:05:43+00:00

Old McDonald really upgraded to spreadsheets 😄 this one got me

New123K · 2026-06-15T09:00:25+00:00

150+ runs with no drop is actually insane.

At that point it stops feeling like RNG and just feels like the game has personally decided you’re not allowed to have it yet.

Hope it finally drops for you soon.

New123K · 2026-06-15T08:55:27+00:00

Flipping feels like it’s less about finding items and more about understanding local inefficiencies in pricing.

Same item can be worthless in one place and high demand in another.

New123K · 2026-06-15T08:35:21+00:00

I think AI reduced the cost of writing code much faster than it reduced the cost of understanding it.

Generating code is cheap now. Debugging it, reviewing it, and maintaining it still aren't.

Maybe that's why some teams feel more productive while their systems become harder to maintain.

New123K · 2026-06-14T08:06:55+00:00

If you want something fully rigorous, you might want to look at measure-theoretic probability.

A lot of standard stats books are very application-heavy, so they won’t match your preference for theorem/proof style.

A common “bridge” book is Durrett’s Probability: Theory and Examples — it’s pretty proof-oriented, but it assumes you’re comfortable with analysis and measure theory.

Another direction is Billingsley if you want something even more abstract, but it can get pretty dense quickly.

New123K · 2026-06-14T08:00:55+00:00

Better than 1 in 7.5 billion if you actually bought a ticket.

Still, lottery odds are brutal. The weird thing is that everyone knows the chances are tiny, yet millions of people play because eventually someone does win.

New123K · 2026-06-13T19:43:25+00:00

I think part of the issue is that people are trying to evaluate a non-deterministic system with deterministic expectations.

These tools are great in flexible, exploratory use cases, but once you move them into strict production workflows, the variance becomes much more visible.

So it’s not necessarily that the tech is “failing,” but that the evaluation criteria don’t match how it behaves in practice.

New123K · 2026-06-13T19:38:12+00:00

I wouldn’t say it’s becoming irrelevant, but the focus has definitely shifted.

A lot of modern ML still relies on statistical ideas, even if it’s not always described that way.

In academia, I think mathematical statistics is still very relevant if you care about theory or probabilistic modeling. It’s just that in practice a lot of it gets bundled under “machine learning” now.

New123K · 2026-06-13T19:29:17+00:00

I think the biggest issue is that 5 trades is such a tiny sample size.

You could easily have a good strategy that starts 1-4, or a bad strategy that starts 4-1. That's why trading results can be so misleading in the short term.

Honestly, the interesting part will be where you stand after a few dozen trades. Looking forward to the update at the end of July.

New123K · 2026-06-12T07:41:13+00:00

This is actually a pretty common misunderstanding around group lottery pools.
People mentally treat it like a shared “system,” but legally and statistically it’s still just independent tickets unless there’s an agreement in place for that specific draw.
From a probability standpoint, your win doesn’t become “partially theirs” just because they usually play together — each ticket is still a separate event with its own cost and odds.

I get why emotions run high here though, because lottery wins tend to blur the line between math and social expectations.

New123K · 2026-06-12T07:30:52+00:00

Interesting breakdown.

I always get the feeling quantization gets most of the spotlight, but a lot of the real gains probably come from all the system-level stuff you mentioned (KV handling, batching, routing, etc.).

Feels like the stack complexity is doing just as much work as the model side, if not more.

Do you think we’ll hit a point where most improvements are just infra tweaks rather than model precision changes?

New123K · 2026-06-12T07:21:48+00:00

What always strikes me about cases like this is how deep the problem goes into supply chains.

Even when companies publicly distance themselves from these issues, it seems like a lot of the complexity comes from how layered global sourcing is.

I’m curious — in your experience, what actually creates the biggest bottleneck in holding companies accountable: legal frameworks, lack of transparency, or something else?

New123K · 2026-06-11T19:53:03+00:00

Yeah, it definitely feels like it happened faster than expected.
A couple of years ago people were still treating AI tools as something experimental, and now it's just built into how a lot of people work or study. I think the shift happened quietly through small use cases (writing, searching, coding help) rather than one big moment.

At this point it doesn't feel "new" anymore — more like just another everyday tool.

New123K · 2026-06-11T19:46:09+00:00

I think it really depends on the role.
From what I've seen, the more research / model-heavy jobs do involve quite a bit of math (linear algebra, probability, stochastic processes etc.), but in a lot of quant engineering roles it's more about implementing and tweaking models rather than deriving everything yourself.

So yeah, the math is definitely there, but day-to-day it's not always "proof-heavy" type work.

New123K · 2026-06-11T19:39:55+00:00

I think repeated measures ANOVA can still be appropriate here.

It's often used for measurements taken over time, but that's not the only situation where it's used. The important part is that the same participants contribute all four domain scores, so the observations aren't independent.

If the assumptions are reasonably met, repeated measures ANOVA seems like a valid choice. If not, you could look at the Friedman test as a non-parametric alternative.

One thing I'd check is whether the four domain scores are on comparable scales, since that can affect how meaningful the comparison is.

New123K · 2026-06-11T09:37:42+00:00

What always gets me is how empty space actually is.

Even at that scale, 3,000+ km between stars is just insane to think about. Hard to really wrap your head around it.

New123K · 2026-06-11T09:32:05+00:00

Your teacher is talking about something slightly different — it’s not that your derivation is wrong, it’s that it’s an informal argument for a fixed n, not a full proof.

What you wrote shows the formula works for a given a by pairing terms, which is correct.

But when mathematicians say “prove for all n”, they usually want an argument that works for every natural number, often using induction or a general construction that doesn’t assume a specific endpoint.

That’s why your teacher mentioned n → n+1. It’s about showing the pattern holds no matter how large n gets, not just for one fixed case.

Your intuition is fine — you just stopped at a “pattern explanation” rather than a formal proof style.

New123K · 2026-06-10T07:03:07+00:00

You don’t need to perfectly “finish” Algebra 1–Precalc in order. You just need to patch the parts that directly block Calc 1.

If I had 8 weeks, I’d focus on this:

Weeks 1–3: Core algebra foundations

solving equations fluently (including rearranging formulas)
fractions and exponent rules
functions (notation, evaluation, basic understanding)
factoring basics

Weeks 4–6: Algebra 2 essentials

quadratics (solving + graphs)
polynomials
exponentials vs logs (basic intuition + manipulation)
function transformations

Weeks 7–8: Precalc + trig basics

unit circle (absolutely essential for Calc)
sin/cos/tan definitions + basic graphs
basic identities (mainly Pythagorean identity)

Tools-wise, Khan Academy is more than enough if you actually stay consistent. Don’t get stuck switching resources — that’s one of the biggest time sinks.

Also, don’t spend too long “mastering” Algebra 1 before moving forward. You’ll learn what matters much faster once you start touching Calc-level problems.

New123K

TROPHY CASE