与「idle」相关的搜索结果

@aavegotchi is voting to put its idle treasury to work earning yield. The DAO holds ~345k $DAI alongside other stablecoins and $ETH, a portion of it sitting idle and earning nothing. The plan converts that $DAI to $USDC and whitelists two lending markets, @aave and @Morpho, where deposited funds earn interest. Directors could then deposit, withdraw, and reallocate between the two without a fresh vote each time, on the logic that "requiring governance for every deposit or withdrawal creates unnecessary friction." Borrowing, leverage, and looping stay banned outright. This builds on two 2024 votes that converted the treasury's $DAI to $USDC and a 2022 vote to deposit 3M $GHST into Aave, so lending out idle funds is well-trodden ground here. 64 votes have cast 1.53M $GHST so far, 93.7% in favor, with quorum not yet met. Voting closes June 20th at 6:16pm UTC. Proposal:

Good evening 🧢 Most users don’t fully understand what their collateral is doing anymore. And honestly, modern DeFi made that harder to track. The same asset can now simultaneously: earn yield, support borrowing,route liquidity, collect incentives,and influence exposure across multiple positions. Which means collateral increasingly behaves less like “stored value” and more like an active financial component inside a larger system. Protocols like @Dolomite_io make this shift more visible because capital doesn’t necessarily sit idle after deposit. The interesting part? A lot of users still think they’re holding assets passively while the protocol architecture is already treating those balances as productive infrastructure underneath. That gap in understanding is probably larger than people realize.

Understanding, accepting, and working with reality is both practical and beautiful. I have become so much of a hyperrealist that I’ve learned to appreciate the beauty of all realities, even harsh ones, and have come to despise impractical idealism. Don’t get me wrong: I believe in making dreams happen. To me, there’s nothing better in life than doing that. The pursuit of dreams is what gives life its flavor. My point is that people who create great things aren’t idle dreamers: They are totally grounded in reality. Being hyperrealistic will help you choose your dreams wisely and then achieve them. By interacting with my digital twin, you can evaluate your own decision-making processes and evolve your approach in real-time. The faster you evolve, the faster your results will follow. Click the link below/in my bio to start our comversation now. #principleoftheday#

The hardware in old Chinese cloud accelerator cards never fails to impress me. If you go on Chinese ebay (idlefish) you can get a Xilinix UltraScale FPGA for ~$50 USD. For perspective, the same raw chip is currently ~$2,100 on Mouser.

今天 CLARITY Act 重点有四块，而且即便通过了后边还要去参议院全院投票 第一，稳定币收益。 这是最大争议。现在的妥协方向是仍然是禁止 idle stablecoin balance 的利息，也就是稳定币躺着不动不能给利息，但允许交易奖励、使用奖励、活动奖励。 银行业不满意，担心这会变相吸走银行存款。 第二，AML / KYC / 反洗钱。 民主党，尤其是 Warren 阵营，认为现在版本对反洗钱、制裁规避、非法融资的约束不够强，要求把更多数字资产平台纳入 Bank Secrecy Act 等合规义务。 第三，特朗普家族 / 政治伦理条款。 民主党想加入更强的道德规范条款，来限制总统、政府高官及其家族从 crypto 项目中获利。这个是当前党争最明显的部分。 第四，SEC 和 CFTC 的监管边界。 也就是哪些 Token 算证券，哪些算商品。交易所、经纪商、DeFi 平台到底归 SEC 管还是 CFTC 管。CLARITY Act 的核心就是把大多数数字商品交易放到 CFTC 框架下，同时保留 SEC 对证券型 Token 和 tokenized securities 的监管。 另外，现在有 100 多个修正案，Warren 一个人就提交了 40 多个，所以今天的重点是这些修正案哪些会被接受，哪些会被否决。 即便今天通过，代表的也只是 CLARITY Act 通过了参议院银行委员会这一关，接下来法案从委员会被正式送往参议院全院。 而参议院全院通常需要 60 票，共和党票数不够，还需要民主党支持。所以今天即便是通过了，也并不代表是法案正式成为法律，后边的路还长着呢。

Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)

@nummanali tmux grids are awesome, but i feel a need to have a proper "agent command center" IDE for teams of them, which I could maximize per monitor. E.g. I want to see/hide toggle them, see if any are idle, pop open related tools (e.g. terminal), stats (usage), etc.