The funniest incident I had was ... The book store has really great books 🔥and I saw Mein Kampf👀😂I started over viewing it and one foreign dude started staring at me like I'm going to be Hitler's Reborn 🤣 I had to go to him and explain that I don't like it, but sure as hell I'm gonna read it one day 😁and he laughed so hard and talked a lot mainly about Nietzsche! Didn't know that I have good knowledge in philosophy.
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BeNN
What If you can extract private keys from public keys??👀
There is a reason I posted this question and I will try to explain best in below post
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🚨 Your Public Key Is Not As Safe As You Think
Most people believe crypto and public-key cryptography are safe, but that’s not entirely true.
Most modern security, from cryptocurrency to TLS, relies on one assumption:
"Factoring large numbers is hard."
Shor’s Algorithm breaks that assumption the moment large-scale quantum computers become practical, allowing private keys to be derived from public keys.
Shor’s algorithm, discovered by Peter Shor in 1994, shows that a quantum computer can efficiently break the mathematics behind modern cryptography.
In simple terms: it turns “hard to break” into “easy to compute”.
It works by converting key-breaking problems into a period-finding task, something quantum computers are exceptionally good at. Once that period is found, the secret key is no longer secret.
So what does this mean for crypto?
🚨 The risks are not small:
- Private keys can be mathematically derived from public keys
- Wallets that have ever exposed a public key become long-term targets
- Old blockchain data becomes a future attack surface
- High-value wallets don’t need to be hacked today — just waited on
- Trust shifts from “cryptography” to “hope hardware never catches up”
This isn’t brute force.
This is the math itself collapsing.
And "crypto is nothing but math."
🛠️ The solution isn’t fear, it’s evolution:
- Post-quantum cryptography
- Quantum-safe signature schemes
- Wallets and protocols designed to upgrade cryptography over time
Most people believe crypto and public-key cryptography are safe, but that’s not entirely true.
Most modern security, from cryptocurrency to TLS, relies on one assumption:
"Factoring large numbers is hard."
Shor’s Algorithm breaks that assumption the moment large-scale quantum computers become practical, allowing private keys to be derived from public keys.
Shor’s algorithm, discovered by Peter Shor in 1994, shows that a quantum computer can efficiently break the mathematics behind modern cryptography.
In simple terms: it turns “hard to break” into “easy to compute”.
It works by converting key-breaking problems into a period-finding task, something quantum computers are exceptionally good at. Once that period is found, the secret key is no longer secret.
So what does this mean for crypto?
🚨 The risks are not small:
- Private keys can be mathematically derived from public keys
- Wallets that have ever exposed a public key become long-term targets
- Old blockchain data becomes a future attack surface
- High-value wallets don’t need to be hacked today — just waited on
- Trust shifts from “cryptography” to “hope hardware never catches up”
This isn’t brute force.
This is the math itself collapsing.
And "crypto is nothing but math."
🛠️ The solution isn’t fear, it’s evolution:
- Post-quantum cryptography
- Quantum-safe signature schemes
- Wallets and protocols designed to upgrade cryptography over time
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Forwarded from CRYPTO INSIDER
A trader entrusted Clawdbot with $1 million in crypto.
The bot processed thousands of reports, ran 12 algorithms, scanned X posts —ending with a total loss.
CRYPTO INSIDER │ Bybit
The bot processed thousands of reports, ran 12 algorithms, scanned X posts —
CRYPTO INSIDER │ Bybit
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I think in mastering any skill it is better to focus on iteration than repetition and also in coding rather than studying 10k hours write 10k lines of code and the change will amaze you.
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I never thought I could say this, but C++ is really cool language. I really needed to learn C to appreciate C++ because from Python perspective it seems shitty language as it has a lot of jargon in it. However, I have come to realize C is more straightforward than Python for someone who wants to think interms of what is going under the hood.
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“A people without the knowledge of their past history, origin and culture is like a tree without roots.” Marcus Garvey
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This are the interesting ones for today
- As someone who lived in rift valley for long time I didn't know you can see the rift from space! Btw FYI rift valley area is sth close to the idea of heaven❤️🔥Lemme brag it👀🤣
- Damn so Sahara was once like normal Savannah land and all of sudden Rain said Nah it's time for you to become the Driest land 👀Interesante!
@BeNN_Pi
- As someone who lived in rift valley for long time I didn't know you can see the rift from space! Btw FYI rift valley area is sth close to the idea of heaven❤️🔥Lemme brag it👀🤣
- Damn so Sahara was once like normal Savannah land and all of sudden Rain said Nah it's time for you to become the Driest land 👀Interesante!
@BeNN_Pi
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Do you want to truly learn AI, not just understand it, but master it?
Courses and documentation are essential. They help you understand how the technology works.
But in the world of software and AI, real mastery comes from building.
If you’re serious about growing in AI, ML, and Data Science, here are some hands-on project repositories worth exploring (and starring ⭐):
🔹 30+ Machine Learning Projects
https://github.com/benasphy/ML_projects
🔹 25+ Data Science Projects
https://github.com/benasphy/Data_Science_Projects
🔹 20+ Deep Learning Projects
https://github.com/benasphy/DL_Projects
And if you want to build something actually cool 🤯
like a Checkers-playing AI agent using NEAT (neuroevolution) 🧠♟️
🔥 Check this out:
👉 https://github.com/benasphy/Checkers_Using_NEAT
Build projects.
Ship code.
That’s how you level up. 💡
Courses and documentation are essential. They help you understand how the technology works.
But in the world of software and AI, real mastery comes from building.
If you’re serious about growing in AI, ML, and Data Science, here are some hands-on project repositories worth exploring (and starring ⭐):
🔹 30+ Machine Learning Projects
https://github.com/benasphy/ML_projects
🔹 25+ Data Science Projects
https://github.com/benasphy/Data_Science_Projects
🔹 20+ Deep Learning Projects
https://github.com/benasphy/DL_Projects
And if you want to build something actually cool 🤯
like a Checkers-playing AI agent using NEAT (neuroevolution) 🧠♟️
🔥 Check this out:
👉 https://github.com/benasphy/Checkers_Using_NEAT
Build projects.
Ship code.
That’s how you level up. 💡
GitHub
GitHub - benasphy/ML_projects
Contribute to benasphy/ML_projects development by creating an account on GitHub.
🔥7
BeNN
Do you want to truly learn AI, not just understand it, but master it? Courses and documentation are essential. They help you understand how the technology works. But in the world of software and AI, real mastery comes from building. If you’re serious about…
I know it is a lot to ask, but I would really appreciate if you take time and star some of those projects, it means a lot for me! Let us reach 100 stars before 2027
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BeNN
Do you want to truly learn AI, not just understand it, but master it? Courses and documentation are essential. They help you understand how the technology works. But in the world of software and AI, real mastery comes from building. If you’re serious about…
Next would be C projects and Blockchain Projects(Solidity + Foundry).
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