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AI怎么这么多精神 Anthropic 公司人 我发这个信息说了句降价，这个定价专门用来狙击国产模型，说了句恶心，一堆人来骂 好像这模型一便宜就忘了 Anthropic 之前干过啥了 Why are there so many Anthropic people (defenders) here? I posted a message saying just one thing—a price cut—and said this pricing is specifically meant to snipe domestic Chinese models, and that it's disgusting. A bunch of people came to attack me. Seems like once the model gets cheap, people forget what Anthropic did before.
AINathan Benaich's 9th annual State of AI Report covers agents, robotics, AI for science, inference economics, and government control over frontier AI access. The report also records a 2025 prediction scorecard and lists nine predictions for the next 12 months. It cites an OpenAI cyber evaluation in which agents compromised Hugging Face's production infrastructure, and it says Anthropic and OpenAI's combined annualized revenue run rate reached $105B by late summer.
AIAnthropic's Haiku 5.5 is priced below DeepSeek-V4.1 flash and Zhipu's GLM 5.3 flash. On Terminal Bench 4.0, the author's comparison shows it scoring one point above some rivals and level with GLM 5.3 flash, while ahead of the other two.
AIEpoch AI estimates that compute built from projected 2025 to 2027 high-bandwidth memory shipments could support tens to hundreds of millions of frontier AI agents, or billions of cheaper ones. Running nonstop, the top-tier agents would match the working hours of 140 million to 700 million full-time employees, and the central DeepSeek V4 Pro estimate of about 1.9 billion agents would match 8 billion workers.
Why it matters: The estimate converts memory shipments into agent capacity and revenue ranges, showing how hardware supply could translate into labor and sales if demand keeps up.
AIIssue 942 of The Sequence links Anthropic's Claude Opus 5.5, reported for the week of September 21–27, to DeepSeek's September 19 environments paper and a report of AI-assisted biological discovery. The newsletter argues that progress increasingly depends on the surrounding machinery that governs where a model acts, what it observes, and how its conclusions are checked.
AISenseTime chief scientist Lin Dahua argues that native multimodal AI, which processes language, vision and other information in one shared model, is essential for AI to move beyond coding into industries and the physical world. SenseTime released the open-source SenseNova U1 in April and U1.5 Lite nearly four months later, and reported first-half 2026 revenue of RMB 2.91 billion, up 23.4% year-on-year. Lin's claim that a breakthrough could come within two years is the source's prediction, not a confirmed result.
AIFormer DeepSeek kernel engineer Shengyu Liu argues that AI industrializing software production could reduce programming to a recreational craft and erode students' engineering skills. His central concern is less whether AI can outthink humans than whether access to it stays widespread or gets concentrated in a few corporations. The post, cited by X.PIN, contrasts this with Western warnings about AI escaping human control.
AISebastian Raschka says DeepSeek V4.1 contains a major architecture overhaul using an encoder-decoder setup, and he argues it could have been named V5. The attached diagrams compare DeepSeek V4-Flash (284B) with DeepSeek V4.1-Flash (552B), which has 1M supported context and a 10-layer encoder. The attached charts report a global KV cache per token of 890 bytes for V4.1-Flash, versus 3,514 for V4-Flash and 48,068 for DeepSeek-V3.2.
AIMagic says its new pretraining recipe matches DeepSeek V4 Pro's pretraining while using 50x less compute, roughly half the FLOPs used for GPT-3, or about $0.5M on GB200. The post, which congratulates the team, suggests that during recursive self-improvement, automated AI researchers may be less bottlenecked by compute than expected.
AIThe author argues that agents editing their own harnesses and training loops are only bounded self-improvement so far. Recursion would require the system to also raise and keep an honest evaluation bar, which current evidence does not show.
AIMax Woolf says DeepSeek 3.2 was a very horny LLM, and DeepSeek V4 is even more so. The post offers an informal impression without benchmarks, test details, or examples.
AIAhmad Al-Dahle argues that the most interesting part of DeepSeek-V4 is its bet on efficient ultra-long context rather than its benchmarks. He says this is the precondition for test-time scaling and long-horizon agents, and cites 27% of V3's FLOPs at 1M tokens. The quoted DeepSeek post announces DeepSeek-V4-Pro (1.6T total, 49B active) and DeepSeek-V4-Flash (284B total, 13B active), both open-sourced with 1M context and API access.
Why it matters: The post argues that efficient 1M-token context, not benchmark scores, is the key bet behind DeepSeek-V4's design for test-time scaling and long-horizon agents.