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🚨SlowMist TI Alert🚨 💸 @LienFinance Loss: ~542k USD 🔍 Root Cause: The `exchangeEquivalentBonds` function in BondMakerCollateralizedEth lacks proper multiset integrity checks. It only counts total exception occurrences instead of verifying each bondID's appearance per group. By repeating a single exception bondID in the output group, attackers consumed the exception count twice, masking a missing input exception. This allowed minting new non-exception BondTokens without burning the corresponding input bonds, which were then sold for USDC from a pre-approved victim address. 📌 Attacker: 0x0d7d9023531ad1a88414e216ee2715f63561808a 📌 Victim: 0xa961684a3a654fb2cca8f8991226c0cefc514d80 📌 Vulnerable Contract: 0xda6fc5625e617bb92f5359921d43321cebc6bef0, 0x843225cf6e663e4454732d6b551a737ac7b47de0 Attackers exploited the flawed exception-counting logic to mint unbacked bond tokens, swapped them for USDC via three pre-authorized endpoints, and drained 542,144.628604 USDC from the victim. Powered by Tx:

Elon Musk: Tesla’s Secret AI Chip Factory Could Change Everything That said, we expect to announce their location soon and provide more details about our plans in that regard, but we'll leave that to the product launch announcement. Rather than trying to squeeze it into an earnings call, I think this is a very big announcement and deserves to have its own spotlight, instead of being squeezed into an earnings call. But I do think this is going to be an amazing initiative, and a necessary one. Without it, we will be constrained in our ability to scale up mass production because we simply won't have enough AI chips. So, it's crucial to solve that. And we'll have to solve memory, logic, and packaging in order to scale. We'll leave the rest for its own announcement. We've placed equipment orders for our development fab in Austin. And that development fab, I think, is pretty cool because it's intended to have lithography mask production, logic, memory, packaging, and chip testing — all under one roof. So you can have a very fast iterative cycle, try out new chip designs very quickly, and see if they work. I don't think such a building exists anywhere on Earth. So this is really going to be super helpful as we try some exciting, adventurous, high-risk, high-payoff bets.

Former $CRWV employee on why neoclouds are far more exposed to GPU generation cycles than hyperscalers ( $MSFT, $AMZN, $GOOGL ): - The expert describes GPU utilization tracking at hyperscale as a continuous and disciplined process built around two lenses. The first is infrastructure utilization, covering GPU occupancy, idle time, and memory utilization, noting that 95% booked usage can still mask inefficiency if jobs stall or batches have idle gaps. The second is outcome utilization, asking whether the compute is actually generating business value, measured by metrics such as tokens trained per dollar, time to reach target accuracy, and tokens per second per GPU. - The expert sees a meaningful difference between hyperscalers and neoclouds on GPU investment economics. Hyperscalers like $MSFT, $GOOGL, and $AMZN can tolerate a 3-5 year payback period given their ability to monetize the same infrastructure across multiple revenue streams. Neoclouds like $CRWV operate on a tighter 2-3 year window. - With higher financing costs and direct dependence on infrastructure cash yield, neoclouds are far more sensitive to utilization and GPU residual risk. The biggest risk is GPU generation cycles, where a slow payback means newer chips could erode pricing power before the asset has paid itself off. - The expert explains that for hyperscalers, roughly 60% of GPU capacity is allocated to external monetization, including GPU rentals, managed AI services, enterprise inference workloads, and startup model training. The remaining 40% is used internally, and of that internal portion, the majority is still indirectly monetized through products like M365 Copilot or GitHub. Around 40% is dedicated to pure R&D. - The GPU pricing mix has shifted meaningfully over the past few years. In 2023, around 70-80% of revenue was hourly as customers paid a premium just to get access to scarce GPUs. By 2025 that had moved to roughly 50% hourly and 30-50% committed, and the expert expects 2026 to tip further toward committed at around 65% for hyperscalers as AI matures and inference becomes more predictable. Neoclouds are moving in the same direction but more slowly. - By 2027-2030, the expert sees committed contracts settling at 55-65% as the norm, with hourly pricing remaining but losing its scarcity premium as more supply comes online.

China's AI shock. Wall Street's blockchain milestone. 2X Levered ETFs. Ben Emons (@Marcomadness2) breaks down the market fallout, @nchakar explains @The_DTCC's move into live tokenized securities, and @DirexionLETFs's @BilalSLittle unpacks the first 2x leveraged spot Bitcoin and Ether ETFs — on Public Keys at the @NYSE with @JennSanasie. Chapters/Timecodes: 00:00 Welcome to Public Keys 00:22 Ben Emons (FedWatch Advisors) Joins Public Keys 00:48 China's Kimi K3 Rattles Chip Stocks Friday 01:42 Bitcoin Holds $64K, While AI Sells Off 02:59 Crypto Miners Pivot to AI: HUT8 Up 14% 03:58 Kimi K3 vs. DeepSeek: This Time It's a Price War 05:03 AI Selloff Tightens Financial Conditions, Helps Fed 06:32 Fed Now Debating Rate Hikes, Not Cuts 06:58 CLARITY Act Deadline: August 7th 09:15 DTCC Takes Tokenized Securities Into Live Production 09:36 Nadine Chakar on the 10-Year Journey to Live Tokenization 13:45 DTCC's Multi-Chain Roadmap 15:26 DTCC's Digital Twin Framework 18:14 Bitcoin ETF Flows: $76M Week Masks $425M Monday Outflow 18:49 Ether ETFs Outpace Bitcoin; Robinhood Chain Hits $800M+ Daily Volume 19:35 Bilal Little (Direxion) on the First 2x Spot Crypto ETF Debut 22:42 How BTCU and EVMU Work — and Why Not Just Use Margin? 25:50 Do Bitcoin ETFs Mirror Gold? 27:38 Fear and Greed Index at 29

Juan Soto (and Mr. Met!) starred in a new Spider-Man commercial 👀 The world may no longer know the man behind the mask, but Soto and New York still have love for Spidey 🕸️