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LEGACY MEDIA TRIED TO SMEAR ELON AGAIN — AND HE SHUT IT DOWN In the Economist interview, the editor went straight to the usual playbook: “People say that you’re a racist.” Elon’s response was simple and factual: “My partner is half Indian, and I have four children with her. One of them was named after a famous Indian physicist. So I would say I’m not racist. And also, if you look at the people that are employed at my companies, we have senior executives of all races. I don’t think there’s any racism there.” Then came the follow-up: “Are you anti-Muslim?” Elon: “If people are coming to a country with antithetical views, I am against that. I’m against rape and murder. I’m against the imposition of rules and laws that are contrary to what we’ve come to accept in the West.” That’s it. No hate. No racism. Just a clear distinction between people and values. Yet legacy media still runs with the same tired narrative because it fits their script. They don’t want the nuance — they want the smear. Elon keeps pointing out the obvious: opposing certain cultural values that clash with Western norms is not the same as hating an entire group of people. The media refuses to acknowledge the difference. The more they push these accusations, the more transparent their bias becomes.

ELON JUST EXPOSED LEGACY MEDIA AGAIN In the latest Economist interview, the editor-in-chief tried the usual line: “People loathe you.” Elon’s response was perfect: “More people like me than don’t. Far more people hate you and the media than you realise.” That’s the quiet part out loud. Legacy media has spent years pushing distorted narratives, selective outrage, and outright misrepresentations about Elon — and it’s not working the way they think it is. People see through it. The constant framing, the loaded questions, the attempt to turn every achievement into a threat… it’s gotten so predictable it’s almost comical. Elon didn’t need a long speech. He just pointed out the obvious: the public’s trust in legacy media is collapsing, and they still act like they hold the moral high ground. The more they attack him, the more transparent their bias becomes.

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The American Bar Association claims on their website that all of us have "implicit biases." I asked their President what groups she has implicit biases against and if she'd like to reflect on them.

Nonce-Generation Vulnerability in the Zilliqa Ledger App: A critical vulnerability has been identified in the Zilliqa Ledger application affecting the generation of Schnorr signatures for native (non-EVM) Zilliqa transactions. The vulnerability causes signatures to be generated with a predictably weakened ephemeral nonces, from which an attacker can recover the signer’s private key using only publicly available on-chain data. Protective measures are already in place to prevent further loss, and a coordinated remediation plan is being finalised. Users who have signed native Zilliqa transactions with a Ledger device should await official guidance before taking any action. Impact: The vulnerability affects private keys used to sign native Zilliqa transactions with a Ledger device. Any account that has broadcast approximately five or more native transactions signed through the Zilliqa Ledger app should be considered compromised. Its private key can be reconstructed from signatures already recorded on-chain, regardless of any subsequent software update. The issue is confined to the Ledger app’s native signing path. EVM transactions are unaffected. Zilliqa software development kits, including zilliqa-js, gozilliqa-sdk and pyzil, generate nonces correctly and are not affected. Root cause: Zilliqa native transactions are authenticated using EC-Schnorr signatures over secp256k1. Each signature requires a fresh, uniformly random 256-bit ephemeral nonce, (k). The secrecy and full-width randomness of (k) are essential, as any systematic bias can allow the private key to be recovered. The signing routine generated 40 bytes of randomness and reduced them modulo the curve order, correctly producing a uniform 256-bit value. However, when copying this value into the nonce buffer, the code copied the wrong 32 bytes of the 40-byte output. This retained the eight zero-padding bytes introduced by the reduction and discarded eight bytes of entropy. As a result, the most significant 64 bits of every generated nonce were fixed at zero, meaning (k < 2^{192}). A nonce with 64 known bits leaks information about the private key with each signature. With five or more affected signatures, the private key can be recovered in seconds using commodity hardware by solving the resulting Hidden Number Problem through lattice reduction - a well-documented technique for attacking biased-nonce signatures. Because the affected transactions are permanently recorded on-chain, this exposure cannot be reversed by updating the signing application. The affected keys must be retired. Timeline 2019-2026: The defect was present in every released version of the Zilliqa Ledger app across all supported devices. 19 July 2026: On-chain activity consistent with active exploitation was observed. 21 July 2026: The root cause was isolated to the app’s nonce-handling code and confirmed by reproducing the issue against on-chain signatures. Ongoing: A corrected version of the app is being prepared in coordination with Ledger. Release details will be announced separately. Remediation: As soon as the issue was identified, native (non-EVM) transactions were suspended as a protective measure. This has halted further draining of affected accounts while a solution is prepared. Affected accounts cannot be secured through an ordinary transfer. Because their private keys can be derived from data already recorded on-chain, an attacker with access to the same key could attempt to front-run a legitimate transfer as soon as transactions resume. Advising users simply to move their funds would therefore be ineffective and potentially unsafe. A corrected build of the Ledger app has been prepared, restoring full-width nonce generation and preventing further weakened signatures from being produced. However, this does not protect keys that have already been used to sign affected transactions. Those keys must ultimately be retired. A coordinated remediation plan to secure affected balances is being finalised and will be published separately. Until then, users who have signed native Zilliqa transactions with a Ledger device should take no independent action and should rely solely on official Zilliqa channels for instructions. Users who hold or transact with ZIL exclusively through EVM-compatible tooling are not affected. Acknowledgments: @kucoincom played a key role in pinpointing the root cause in the Zilliqa Ledger app nonce generation, recovered affected private keys from publicly available on-chain signatures, and confirmed ongoing exploitation. KuCoin’s timely reporting and responsible collaboration enabled rapid protective measures, helping safeguard users, ecosystem participants, and the broader Zilliqa ecosystem while the remediation plan was being developed. We sincerely appreciate the KuCoin team’s professionalism, technical expertise, and cooperation throughout this process.

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!

I spoke with @jvisserlabs to break down the AI stock slowdown, why China's new Kimi K3 model is upending the AI trade, and the hidden cultural bias baked into these systems. We also discuss the cooler inflation report, Fed Chair Kevin Warsh's early moves, Bitcoin's reaction, and why robotics could be the next big AI trade. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:50 - AI stock unwind & the summer slowdown 5:45 - Framework for picking AI winners 7:1 3 - China's Kimi K3 & open source vs. closed models 11:09 - Model routers & how many models is too many? 17:46 - The hidden "cultural weights" in AI models 22:37 - Inflation report & Fed reaction 32:28 - Bitcoin's reaction & crypto allocation 39:26 - Nasdaq outlook & where he's avoiding 45:09 - Software stocks & what is the next trade?