Nikolaj-K

{
    "aixbt@aixbt_agent": {
        "user_id": "1852674305517342720",
        "username": "aixbt_agent",
        "yaps_all": 24515.98,
        "yaps_l24h": 64.35,
        "yaps_l48h": 107.54,
        "yaps_l7d": 363.15,
        "yaps_l30d": 1701.74,
        "yaps_l3m": 8689.35,

Nikolaj-K

"""
Below you find the Somnia 7-day top-100 yappers rankings for several dozen of days.
Ask me for an update at @ErnstKummer on X:
https://x.com/ErnstKummer/status/1909233792771936471

The last entry is currently from the 7th of April.
I started grabbing them on 22th of March, one or two per day. 

The data here is written down in the form of a python dict which one can simply import.
This format is really almost the same as a JSON, 

Nikolaj-K

"""
Code explained in 

https://youtu.be/tSyMnVd6DsY

* Theorem:
V := CDF_X^{-1}(U), with U from the uniform distirbution on [0,1], has same distribution as X.
Proof:
* Note: {r \in Q | sqrt{3} < r} = {r \in Q | 3 < r^2}
* Pr(V <= x) = Pr(CDF_X^{-1}(U) <= x) = Pr(U <= CDF_X(x)) = CDF_X(x)

Nikolaj-K

Denote the expectation by ${\mathbb E}[f(X)]:=\sum_x f(x)\cdot p(x)$, where $\sum_x$ is the sum (or integral) over all $x$ and $p(x)$ is the probability distribution in question.

Denote the variance by $\mathrm{Var}[X]:={\mathbb E}[(X-{\mathbb E}[X])^2]$

Further, define the standard deviation $\sigma[X]=\mathrm{Var}[X]^\frac{1}{2}$ (Which, if we use units, has the same units as $E[X]$ or $X$ itself.)

Nikolaj-K

import re
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.service import Service
import time


class Config:
    URL = "https://testnet.somnia.network/memecoins"
    TMP_FILE_PATH = "/path/to/write/somnia_memecoin_page_tmp.html"

Nikolaj-K

Script discussed in the video:

https://youtu.be/tqmtZ82WHJc

==== Bayesian calculus vs Propositional logic ==== Idea: Logical implications are akin to Conditional probabilities. Reading: $P(B \vert A)=1 \iff A\to B$ $P(B \vert A)=0 \iff \neg(A\to B)$ "$P(B \vert A)$ ... $P(A\to B)$"

Nikolaj-K

import random
from scipy import special
import matplotlib.pyplot as plt
import numpy as np


def l1_normalize(finite_stream):
    lst = list(finite_stream)
    return np.array(lst) / sum(lst)

Nikolaj-K

==== Integration by parts ====

$\int_a^b (p\cdot f)' dx = (p\cdot f)_a^b = (p\cdot f)(b) - (p\cdot f)(a)$

$\implies$

$\int_a^b p\cdot f', dx = -\int_a^b p'\cdot f, dx + (p\cdot f)(b) - (p\cdot f)(a)$

=== Score ===

Nikolaj-K

Mappings out of V_6

input cardinality = 0

(#1)  []  ↦  []

input cardinality = 1

(#2)  [[]]  ↦  [[[]]]
(#3)  [[[]]]  ↦  [[]]

Nikolaj-K

Links of the pages in the video:

https://youtu.be/Q_c0n1d5x3I

########## ########## ########## ##########

• Paper:

Interpreting and Improving Diffusion Models from an Optimization Perspective >