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At first I assumed fixed-rate protocols always leave large piles of capital earning nothing while waiting for borrowers.
Spent time with the design and noticed the opposite. Unborrowed capital does not sit idle.
It is automatically moved into floating-rate markets so it continues earning until a fixed-rate borrower appears. Liquidity providers no longer face a hard choice between locking a rate and holding dead money. The two sides stay connected without forcing that trade-off.
I keep wondering how cleanly the hand-off works when rates on the floating side shift suddenly.
Is the deeper limit in fixed-rate markets the matching problem, or simply the cost of capital that stays unused?
#DRAM coin is an AED-backed stablecoin, not a typical speculative crypto like BTC or ETH. Current market data places DRAM at about $1.00 per token, with total supply around 5.5 million DRAM. Each DRAM is designed to be backed by 3.67 UAE dirhams, keeping its value close to the U.S. dollar because the AED is effectively pegged to USD.
The main opportunity is utility rather than price appreciation: cross-border payments, regional digital transactions and stable on-chain settlement. Its biggest strengths are low volatility and predictable value. However, liquidity, adoption, reserves, regulatory compliance and the ability to maintain the peg remain key risks. Therefore, DRAM should be viewed primarily as a digital payment/stability asset, not a coin expected to deliver 10x–100x returns. DYOR before investing.
AI attack capabilities surge! Cybersecurity is at a turning point, and the time left for defenders is running out
OpenAI CEO Greg Brockman defined an incident in which his company’s internal large model successfully compromised Hugging Face as a watershed moment in the field of cybersecurity. The event also made OpenAI realize that they seriously underestimated the destructive attack power of cutting-edge AI models in real-world scenarios. Even more worrying is that open-weight open-source models’ attack capabilities have narrowed the gap with closed-source top-tier models to just a few months.
Brockman calls the current period a window for defenders. He warns enterprises that they must deploy AI-driven automated security defenses at scale within the coming months. Once AI attack capabilities spread widely, traditional patterns that rely on manual discovery and patching of vulnerabilities will be unable to keep up with the pace of attack iteration.
He offers practical guidance for enterprises: deploy AI intelligent agents to the security team so they can read code and infrastructure configurations, proactively scan and remediate vulnerabilities, clear accumulated historical risk, and embed security reviews into the entire software development lifecycle. After the agents identify risks, they can help generate remediation patches and complete testing. Then, step by step—from read-only scanning to more advanced stages like alert triage and limited automated responses—high-risk actions still retain human final decision-making authority.
OpenAI has already rolled out this approach internally: most initial security alerts are first handled by AI for classification, while the model continuously reasons through potential attack paths. At the same time, the team is conducting focused training on a specialized model capable of producing “ultra-high security level code,” with the goal of eliminating some software security vulnerabilities at the source. #ai
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