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注意到 V4 预览版论文里的 Table 6，V4 Pro (Preview) 和 Opus-4.6 的 SWE Verified 跑分， 80.6 比 80.8 正好差 0.3%。 这也不能说有些做中介的人写一句什么“性能跻身全球第一梯队，编程能力仅弱于 Claude 旗舰0.3%”是在纯瞎编是吧？

We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container. So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness. TL;DR of this special prompting: - Trust source code over the user prompt, so read every call site and existing tests before starting the task - Weigh edge and error cases as heavily as the happy path - Always reproduce the bug before fixing - Don't trust the first passing test suite, and verify suspicious looking half-baked tests - Never stop at just editing, keep working until the change is verified complete. We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness. Results: - Used 2.7x fewer tokens (19.7M → 7.2M) - Finished 2x faster (49min → 24min) - Cost 2.4x less ($7.69 → $3.25) Same Muse Spark 1.2 model, same task, only the prompting changed. Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

华为的盘古大模型 openPangu-2.0-Pro 还真的开源了 华为官方称：第一个完全在非 NVIDIA 硬件（华为昇腾 NPU）上完成训练的 500B+ 级别前沿模型；MoE 架构，505B/A18B，512K 上下文窗口。 对比 DeepSeek V4 Flash、Kimi K3、GLM 5.2 等国内头部开源模型，看看是否能「遥遥领先」： 略显诡异的是，华为公布的 benchmark 评估维度，和 DeepSeek 几乎完全不重叠 😂，和其他模型也只有很少相同，咱们先简单对比看看，等后续有更多推理服务商接入后再做评测（505B 自己也推不起。。） 在唯一有交集的公开指标上，openPangu 处于"第二梯队"。 GPQA-Diamond 87.9 明显低于 Kimi K3（93.5）和 GLM-5.2（91.2）；BrowseComp 65.7 远低于 K3 91.2；SWE-bench Verified 68.5 与 DeepSeek V3.1 时代水平（66.0）相近，低于 V4 自报的 80.6。 但考虑到 openPangu 的参数量级和 Kimi K3、GLM-5.2 差了几倍，这样的表现也属意料之中吧，毕竟 DeepSeek V4 Flash 这样的意外惊喜，也不多。

The machine economy does not begin when robots get smarter. It begins when a machine can be paid to do something, and every party involved can prove what happened. Made with @FabricFND. Two systems meet in the middle of it: Agent Passport issues the agent a verifiable identity and a spending authority its owner defines, and RoboPay actuates a robot after the payment behind the request checks out. What happens before the robot moves: ▷ Authority is granted once, and it is bounded. The human signs a spending session with a passkey: a total budget, a per-transaction ceiling, the assets allowed, and an expiry. The agent holds no card number and no wallet key. It holds a delegation it cannot exceed. ▷ Two payments, because these are two different obligations. One settles with the merchant for the goods. A separate x402 payment pays the robot for the work of moving them. Buying a thing and hiring a machine to carry it are not the same transaction, and the receipt keeps them apart. ▷ Verification comes before motion. The request arrives with an x402 payment header. The facilitator checks the network, the price, the payee wallet, and the signed payload. Only then are the transaction details sealed onto the robot action event and the command published. Until that clears, the robot sits still. ▷ Every step leaves a receipt. Identity, scope, approval, both payments, verification, dispatch. When you need to know why a machine did something, the answer is a record rather than a guess. (This run was executed in a demo environment.) Agents have been paying for software for a while now. Paying for physical work is a harder problem, because a delivery cannot be rolled back. The guarantee has to sit in front of the action instead of behind it. Authorization before payment, payment before motion: that ordering is what makes it safe to let autonomous systems spend in the world we live in. It is also the layer the machine economy has to get right before anything else in it can work. Scoped by Kite. Verified by RoboPay. Delivered in the real world. 🪁

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🚨 SlowMist TI Alert 🚨 💸 @VerusCoin Loss: ~$7.5M ⚠️ Unlike the prior 0x6990…b321 exploit, which decoupled the validated proof from the executed transfer payload, this attack hash-bound the transfers to the CCE but failed to validate the CCE’s economic backing; both exploit flawed cross-chain import validation. 🔍 Root Cause: `VerusProof.checkExportAndTransfers` verified selected CCE fields—including `hashReserveTransfers` against attacker-supplied serialized transfers and the source/destination IDs—but did not enforce the CCE’s accounting semantics. It failed to parse or validate `totalamounts`, `totalfees`, `totalburned`, CTxOut `nValue`, or whether the prior CCE outpoint carried sufficient value and assets to cover the claimed transfers. As a result, a matching transfer hash was incorrectly treated as authorization to release bridge assets, rather than merely a commitment to the requested transfers. 📌 Attacker EOA: 0xbda71b58cec0b1c20a8f87ccd52fa0679747855c 📌 Victim Bridge: 0x71518580f36feceffe0721f06ba4703218cd7f63 📌 Vulnerable Contract: 0x54e03a1682fd0bb065b669f6296f97028dcfd4ce 📌 Fund Receiver: 0xcfd0a20703cd11e0b9f665e1c3f1ef989c142d54 Impact: The attacker submitted a successor CCE anchored to an accepted Verus state root, containing a hash commitment to eight attacker-defined reserve transfers. Because the bridge did not verify whether the CCE’s economic fields backed those transfers, it executed eight payouts from bridge custody to the attacker-controlled receiver—releasing ETH, DAI, USDC, USDT, and four additional tokens without enforced cross-chain asset backing. Powered by Tx:

Power In The Numbers (PITN) is building the semantic infrastructure for the humanoid robotics ecosystem—connecting companies, AI, research, investment, and innovation through structured knowledge, digital identity, and intelligent business intelligence. Our focus spans humanoid robot manufacturers, AI developers, suppliers, system integrators, enterprises, universities, investors, and public sector organizations. As the ecosystem grows, is creating the foundation for verified connections, AI-ready knowledge graphs, multilingual concept mapping, and strategic industry matchmaking. The opportunity isn't just to participate in robotics—it's to help shape the infrastructure that enables the next generation of collaboration across AI, Web3, and humanoid robotics. #HumanoidRobotics# #AI# #Robotics# #Industry40# #DigitalIdentity# #KnowledgeGraph# #Innovation# #BusinessDevelopment#