Eero

Influenced by Hayek and Wittgenstein.
Founder @catallaxyz
The mind behind StarMine – Director | Screenwriter | Producer

[Eero @eero_chi]

@temawincj87259 @EasonnLi Seedance2现在也有NSFW的版本了吗？

[Eero @eero_chi]

If you're tired of insider manipulation and oracle manipulation on Polymarket, come check out @catallaxyz .

[Catalla @catallaxyz]

If you're tired of insider manipulation and oracle manipulation on Polymarket, come check out Catalla.
https://t.co/5CoypszrI4

[Eero @eero_chi]

@krate27 @fussonX I’m really curious to know: what was the reason you decided to switch from the Yolomarkets prediction market to Prep?

[Quinn @quinnofsol]

Check out Quinn (QUINN) on https://t.co/Zb5T82bGmU https://t.co/6QIq0LOu1O

[Catalla @catallaxyz]

I'm building in the Solana Frontier Hackathon, powered by @Colosseum!

https://t.co/2MpkbygIlh

[Eero @eero_chi]

work hard，play hard

[Eero @eero_chi]

@Solana_zh 请问推荐渠道在哪里写呢？

[Eero @eero_chi]

@binancezh 3号马

[币安Binance华语 @binancezh]

🐎2026 马年第一签，领取你的新年上上签！

#币安马上有礼 Day2️⃣：

📷点击视频开始并暂停+截屏，RT本条并在评论区晒出你截屏的「新年上上签」即可参与

我们今日将抽出💻 MacBook Air x 1台和🧧 66 USDT 红包 x 3个

祝愿大家新年马到成功，六六大顺 👇

[Eero @eero_chi]

@binancezh 祝大家满载而归

[Eero @eero_chi]

@MEJ50749 新年快乐啊，希望能中~

[Eero @eero_chi]

滚滚长江东逝水，浪花淘尽英雄。是非成败转头空。青山依旧在，几度夕阳红。
Crypto充满魅力就是因为这个行业符合Hayek的Catallaxy，秩序是自发构成的，由市场决定的，而不是自上而下的，是让更多人实现更多可能性的。
在市场变换如狂风暴雨像你袭来的时候，你要做的就是保持耐心，不要迷失方向

[Pickle Cat @0xPickleCati]

https://t.co/2ghAA5aLZB

[Eero @eero_chi]

@mablemeibao 做梦都在想着KPI

[Eero @eero_chi]

@shanshan2018n 玻璃心？巨蟹座吗？其实容易内耗的话，写日记，写推都是好习惯，可以把情绪发泄出来~

[Eero @eero_chi]

https://t.co/AMpJqcjRIk这篇文章就很好的解释了为什么intospace注定会失败，大家也不做技术分析，盲目跟风，我是没在intospace的doc里看到他们如何处理价格跳跃���题，预测市场和杠杆是不会兼容的

[Eero @eero_chi]

@thecryptoskanda @mia_okx @0xPickleCati adobe character animator可以实现这个，能后期做，但是直播的话，得用live 2d比较好

[Eero @eero_chi]

学会了

[vixhaℓ @TheVixhal]

I found out my girlfriend cheated on me. Instead of breaking up right away, I made a fake account, sent her the proof anonymously, and told her that if she didn’t send me money, I’d tell her boyfriend everything.

I shared this whole plan with my best friend for advice, but this mf went behind my back and shared everything with my girlfriend.

When confronted, he said
Why does it matter? I thought she deserved to know.

He wasn’t just betraying me. He was behaving like a random variable after you marginalize out all the hidden information.

In probability, to understand what you actually know, you marginalize over hidden variables.

That means you sum over all the possibilities you can’t observe to compute the probability of what you can observe.

Marginal probability is a statistical measure that represents the probability of a single event by aggregating over all possible values of other variables.

Formula
P(A) = Σ P(A, Bi)

Where
P(A) = Marginal probability of event A
P(A, Bi) = Joint probability of A and B
Σ = Summation

Let's take an example and solve step by step

A dating app wants to find the probability of users sending messages, regardless of whether they get a response. The data shows message sent vs response received:

Short forms
- M = Message
- R = Response

Joint Probability Table
- M (Yes), R (Yes) = 0.30
- M (Yes), R (No) = 0.25
- M (No), R (Yes) = 0.10
- M (No), R (No) = 0.35

Step 1 What we want to marginalize
- We want P(M = Yes)

Step 2 Joint probabilities for M = Yes
- P(M = Yes, R = Yes) = 0.30
- P(M = Yes, R = No) = 0.25

Step 3 Apply marginal probability
- P(M = Yes)
- P(M=Yes, R=Yes) + P(M=Yes, R=No)
- 0.30 + 0.25 = 0.55

P(Message = Yes) = 0.55

Final Answer
The marginal probability of a user sending a message is 0.55 or 55%, regardless of whether they receive a response.

Congratulations, you've just learned Marginal Probability.

Bonus: Applications in AI/ML

1. Bayesian Networks:
Computing marginal probabilities by summing out irrelevant variables to make predictions and inferences in graphical models.

2. Latent Variable Models:
In topic modeling (LDA) and hidden Markov models, marginalizing over hidden states to find the probability of observed data.

3. Feature Selection:
Identifying which features independently correlate with target variables by computing marginal distributions, helping reduce dimensionality.

4. Probabilistic Classification:
Naive Bayes classifiers use marginal probabilities of features to classify data, assuming independence between features.

[Eero @eero_chi]

虽然用好几个账号跑，但是3000赚10万也可以了

[Moses @holy_moses7]

Ever wonder why he had a $3k balance in his first post?

The answer is simple: he’s running a massive Sybil farm. He didn't start with $12, he started with hundreds of accounts funded with $10-$20 each. Once one hit $2,900, he started posting.

Since then, he had seven trades and seven wins. But notice that he bets his entire balance every single time. No real trader does this. He’s chasing clout and is willing to do whatever it takes to get it.

He even appears to have wash-traded some of his last bet using his other accounts to get filled at his desired price, since he couldn't get enough fills.

Do your own research before trusting "influencers" blindly. The attached pictures show some of his failed Sybil accounts that only managed to reach up to $1k.

[Moses @holy_moses7]

Ever wonder why he had a $3k balance in his first post?

The answer is simple: he’s running a massive Sybil farm. He didn't start with $12, he started with hundreds of accounts funded with $10-$20 each. Once one hit $2,900, he started posting.

Since then, he had seven trades and seven wins. But notice that he bets his entire balance every single time. No real trader does this. He’s chasing clout and is willing to do whatever it takes to get it.

He even appears to have wash-traded some of his last bet using his other accounts to get filled at his desired price, since he couldn't get enough fills.

Do your own research before trusting "influencers" blindly. The attached pictures show some of his failed Sybil accounts that only managed to reach up to $1k.

[Eero @eero_chi]

@MewsWorld