Smart Contract Engineer
Onchain
Joined August 2015
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D∆VE◢◤ 🦇🔊 retweeted
human-in-the-loop
└ human-on-the-loop
└ human-near-the-loop
└ human-cc'd-on-the-loop
└ human-legally-liable-for-the-loop
D∆VE◢◤ 🦇🔊 retweeted
If every function on your smart contract is named kimJongUnIsRetarded_1234 then you’re safe from North Korea.
They wouldn’t be allowed to execute contract calls.
D∆VE◢◤ 🦇🔊 retweeted
run agent swarms to optimize evm contracts verified w/ lean
github.com/figtracer/evm-gol…
D∆VE◢◤ 🦇🔊 retweeted
I just published my first technical article! 🔐🦀
From Mathematical Equations to Working Code: Implementing Classical Ciphers in Rust .
🔗 hashnode.com/@israel117
#Rust #Cryptography #Maths
D∆VE◢◤ 🦇🔊 retweeted
It's fucking 2026 man the breakfast menu should be available all day. You never know what someone is going thru
D∆VE◢◤ 🦇🔊 retweeted
You don’t fully grasp how much of your life is tracked until you’re sitting in a courtroom, watching federal prosecutors use your digital history against you, down to your most harmless Google searches. Your entire life becomes an open book, and even your most innocent actions can be twisted into something sinister.
They get to tell the most damning version of your story. To tell yours in your own words, you have to take the stand.
D∆VE◢◤ 🦇🔊 retweeted
ppl like to talk about "taste", but IMO what's important is vision
if you can sense/see/feel the _shape_ of a solution or problem, then in our new experimental era of software, once you can see the Opening Thread, you're nearly done
by consuming more high-signal non-slop fundamental papers/books/talks, you train your Wetwear pattern matching system (satori!) to recognize more and more shapes, to further identify new Opening Threads, leaving solutions you never actually had the time to work out in your wake
D∆VE◢◤ 🦇🔊 retweeted
i'm hiring a senior jev engineer
at least 2 years of experience with jev is a requirement
dm
D∆VE◢◤ 🦇🔊 retweeted
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.
D∆VE◢◤ 🦇🔊 retweeted
Debunking the Phantom Debts and Ecological Loan Fallacy
My attention has been drawn to trending claims by the Anambra State Government that my administration left behind inherited debts, including a ₦2 billion ecological loan, contractor liabilities, and unpaid salaries, gratuities, and pensions. This is completely false.
We systematically liquidated historical gratuities and arrears dating back several years, amounting to over ₦35 billion. At the point of handover, the state owed nothing in salaries, gratuities, or pensions, nor did we owe anything to any contractor for projects duly executed and certified.
The ₦2 billion ecological fund in question was released barely three months before the end of my tenure for the Oko/Umuchiana erosion crisis. Despite pressure to spend the money, I refused, insisting that it should be left for my successor because government is a continuum and the funds were tied to a specific project.
The money was therefore left 100 per cent intact in First Bank, in Account No. 2018779464, with a balance of over ₦2.13 billion.
It is important to stress that funds tied to specific projects or set aside for particular purposes were not even included in the over ₦75 billion in savings we left behind.
If anybody can establish anything to the contrary, I will stop campaigning. -PO
