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Terra Classic is back online after its security fork... The terra-luna:native chain halted at block 30,544,730 around 17:51 CEST today to switch to the mandatory v4.0.1-patch.3 release. Blocks were flowing again by 18:17 CEST, with 57 of 87 validators signing so far. The patch fixes a flaw in the...

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The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using Claude Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in Zcash 🛡️’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As Anthropic framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): Bug bounty program and honeypot details:

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$BNB turns 9 today! On July 14, 2017, $BNB launched as an ERC-20 token on Ethereum. It started out as a utility token for a young exchange, and nine years later it sits at the center of one of the most active onchain ecosystems in crypto. Here’s a quick breakdown of that journey: 2017 - Launched on Ethereum at $0.15 with a 200M total supply 2019 - Migrated to its own chain and became a native asset for the first time 2020 - Binance Smart Chain went live, bringing EVM smart contracts and making $BNB the gas of a fast-growing dApp ecosystem 2021 - The DeFi and GameFi boom put the chain at the center of onchain activity, and $BNB reached $690 2022 - Binance Smart Chain became BNB Chain, and BNB took on a new meaning: Build N Build 2023 - The stack expanded with opBNB for scale and BNB Greenfield for storage 2024 - Beacon Chain fusion brought staking, governance, and everything else onto one chain 2025 - Pascal, Lorentz, and Maxwell cut block times from 3s to 0.75s, DEX volume set records, and $BNB hit a new all-time high of $1,370 2026 (so far) · Blocks now land in 450ms with 650ms finality, roughly double the throughput the chain had in January Along the way, over 65M of the original 200M $BNB supply has been burned, on the road to a final supply of 100M. The H2 2026 roadmap keeps the focus where it has been all year: doubling mainnet throughput again, and building a next-generation L1 designed for 100K+ TPS with transactions confirmed in under 50ms. Nine years in, and $BNB has never been faster to use, cheaper to move, or scarcer to hold. Happy birthday, $BNB.

BNB Chain

95,995 Aufrufe • vor 2 Monaten

Monthly WINR Protocol Development Update: To begin with, the WINR Protocol has distributed $900,000 to token holders, generated more than $500,000 in pure profit for liquidity providers on WLP, acquired more than 5,000 users, and has almost 9% of the supply burned. In the upcoming months, the WINR Protocol, which has been in production for years, will introduce a range of new products and deployments. These developments will represent the practical and technical evolution to V2 of the protocol. Here are the latest updates and further details as they progress: Progress on WINR Bonanza, Casino Hold'em, and Blackjack is nearing completion. These games are in the final stages of testing. Additional games, including a new type of crash game, have been finalized and are set to debut with the JustBet v2 launch. Take a look at the gameplay videos for a preview. 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37,919 Aufrufe • vor 2 Jahren

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BRAVE U Fund

20,257 Aufrufe • vor 1 Jahr

Transformer by hand ✍️ ~ 6 steps walkthrough below Open the hood of a transformer and the parts list is overwhelming: embeddings, positional encoding, attention weighting, self-attention, cross-attention, multi-head attention, layer norm, skip connections, softmax, linear, Nx, shifted right, query, key, value, masking. Which of those actually make the car run? Two of them. Attention weighting and the feed-forward network. Everything else is an enhancement to make it run faster and longer, which is how we got from a car to a truck, and to the word "large" in large language model. So I drew and calculated those two parts entirely by hand. Goal: push five features through one transformer block, filling in every cell yourself. 1. Given Five positions of input features, arriving from the previous block. 2. Attention matrix Let us feed all five features to a query-key module (QK) and read back an attention weight matrix, A. The details of that module are a post of their own. 3. Attention weighting We multiply the input features by A to get the attention weighted features, Z. Still five positions. The effect is to combine features *across positions*, horizontally: X1 becomes X1 + X2, X2 becomes X2 + X3, and so on. 4. First layer Let us feed all five weighted features into the first layer of the FFN. Multiply by the weights and biases. This time the combining happens *across feature dimensions*, vertically, and each feature grows from 3 numbers to 4. Note that every position goes through the same weight matrix. That is what "position-wise" means. 5. ReLU We cross out the negatives. They become zeros. 6. Second layer Let us bring it back down: 4 dimensions to 3. The output feeds the next block, which has a completely separate set of parameters, and the whole thing runs again. You have just calculated a transformer block by hand. ✍️ The takeaway: the two parts are doing two different jobs, and neither one alone is enough. Attention mixes *across positions*, so a feature can see its neighbours. The FFN mixes *across feature dimensions*, so each position can think about itself. Horizontal, then vertical. Then that pattern repeats N times, each block with its own separate set of weights. That is the Nx from the list up top, and that is what makes the transformer run. 💾 Save this post! #AIbyHand #Transformers #DeepLearning

Tom Yeh

26,211 Aufrufe • vor 2 Monaten