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We ran a major bug bounty programme. Significant prize pool. Zero findings. But that's just one layer. Internal security audits, external audits, pen testing, chain integrity is our number one priority by a long way. If the base layer is compromised, nothing else matters.
13,164 просмотров • 4 месяцев назад •via X (Twitter)
Комментарии: 27
@Somnia_Network Really awesome to see the security measures taken for Somnia I believe the chain will definitely shine harder in the near future
THIS is Somnia🖤
Respect bro, strong security first is how real trust gets built. Somnia matters most
I think the biggest challenge is yet to come, the taller the tree the stronger the wind that hits it.
respect that approach
Agree with you Peter 🤝
Somnia should be safe.
How often are you rotating auditors or refreshing your testing approach?
audits are great but users need to stay vigilant too
Zero findings on a big prize pool is genuinely impressive. Most programmes get flooded with low-hanging fruit.
@Somnia_Network Excellent result Peter
somnia in safe hand
@Somnia_Network You failed to invite Lazarus!
Great step for a big adoption 🤝
based
good to always host bounty programs like this security matters a whole lot
Now, April is coming to an end, and Somnia has never gone up properly, but it continues to fall. This time, I got it right as I said. Some Somnia advocates said they'd watch for two weeks until the end of April. I guess it's gone now?As expected, the price of Somnia doesn't go up
Great Approach by Somnia
Great to know this... The best chain needs to have the best security systems too
Massive captain 🫡
Bảo mật hết sức quan trọng, hãy làm cho SOMI ko thể xâm nhập từ những kẻ xấu
لا تنخدع بهؤلاء المحتالون انظر الى طريقته في الكذب والاحتيال على الناس فتح شمعه من 17 سنت الى 25 سنت ثم عاد مره اخرى اياك ان تدخل هذه العمله المحتاله فلو ان لديهم مشروع حقيقي لوجدتهم حريصين على ان لا يكون شكل الشارت بهذا العفن عمله احتيال حتى لو شاهدت انك تربح سينزل بالسعر
Somnia is also falling in price. Somnia has never been maintained because the price has gone up properly. The price goes down right away. Don't believe Somnia if it doesn't rise or hold above $0.5. It's definitely going down.
We take our security incredibly seriously, constantly testing and updating to ensure the protection of our systems
@Somnia_Network I like what you are working on.
Excellent result Keep it up💪
Believe in Somnia
![Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!](https://image.24vids.com/tw-2079208495094677511/ext_tw_video_thumb/2079208483669315584/pu/img/dDD0x6lHYHuV1POQ.jpg)
