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Privacy matters most where data matters most. ⬟ Healthcare data cannot leak. Regulated industries require private inference by default, not policies layered on later. Encrypted computation is what makes real compliance possible. Build where compliance is native.

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25,934 görüntüleme • 7 ay önce •via X (Twitter)

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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

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UB1UB2 West London (Southall)

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Many immigrants claim that white Brits are racist. But here’s the reality I witnessed: A man collapsed on the street-an immigrant. And who rushed to help? Ordinary British people. No questions about his background.( legal, illegal, rapist or high on drug’s) No judgement about his skin colour. They stayed with him, cared for him, and called an ambulance 🚑 until help arrived. That’s real British values. Humanity first. Compassion first. Not division. Not hate. This is what truly makes Britain great, people who stand up for each other when it matters most, regardless of who you are or where you come from. Respect 🫡✊

Many immigrants claim that white Brits are racist. But here’s the reality I witnessed: A man collapsed on the street-an immigrant. And who rushed to help? Ordinary British people. No questions about his background.( legal, illegal, rapist or high on drug’s) No judgement about his skin colour. They stayed with him, cared for him, and called an ambulance 🚑 until help arrived. That’s real British values. Humanity first. Compassion first. Not division. Not hate. This is what truly makes Britain great, people who stand up for each other when it matters most, regardless of who you are or where you come from. Respect 🫡✊

Harman Singh Kapoor

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$EPIC Is Now Live on One of Southeast Asia’s and Thailand’s Leading Fiat Exchange, Bitkub.com Epic has officially launched on a major regulated exchange with 5M+ users and billions in annual volume, unlocking direct THB access in one of Asia’s most institutionally and retail-driven markets. What this activates: → EPIC/THB trading now live → Seamless fiat on-ramps for millions of local users → Native entry into a market where 80%+ of activity is retail → Regional campaigns, liquidity programs & activations underway Stay tuned for more! Trade now →

EPIC Travel

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In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

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Tokenized Stocks: Coming to RWAX Next Month Real-world asset tokenization is projected to reach trillions in value over the next few years. Stocks are the most understood and traded form of 'real assets' - exceeding the trade value of real estate. Despite the momentum, most of the space is still dominated by institutional players with limited access for everyday users. That’s where RWAX comes in. By building a Tokenized Stocks Trading Terminal that offers diversified exposure to real-world assets on-chain, RWAX makes it simple for anyone to get involved in this emerging category. The countdown to launch starts now.

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