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Headlines · 33
- Chinese GPU makers race on supernodes as buyers weigh cost and ecosystem
At the 26th China International Industry Expo, where the integrated-circuit zone debuted as a standalone exhibition, domestic GPU vendors focused on rack-scale "supernodes": MetaX showed its 64-GPU fully interconnected Xijing S600 plus a 16-GPU S600 Lite, Enflame showed the Yunsui ESL64-O built with ZTE, and Iluvatar CoreX packed 144 GPUs and 18 CPUs into one 44U rack, saying the TianGai 300 IXL144 reaches 100-nanosecond single-hop latency and a 15% effective bandwidth gain in certain MoE All-to-All scenarios (all vendor claims). Citing an IDC report via Reuters, the article says China's data-center AI accelerator shipments reached 2.008 million units in the first half of 2026, with Nvidia at 678,000 (34%) and Huawei at 572,000 (28%), while China GPU shipments fell 26% year on year to 991,000 units and other AI accelerator types rose nearly 80% to 1.016 million. The report argues that beyond performance and price, stable supply, software ecosystem maturity and the ability to bring real customers and revenue to compute centers will shape commercialization, with vendors ultimately answering how to lower customers' total cost of ownership.
36氪 人工智能 · 🔥 7 - 17-year-old Kimi researcher behind Attention Residuals leaves for Silicon Valley
A report republished by 36Kr says 17-year-old Kimi machine-learning researcher Chen Guangyu announced his departure in an Oct. 15 post on X and replied to Anthropic scalable reinforcement learning lead Sholto Douglas that he is heading to the Bay Area. He joined Moonshot AI as a 16-year-old intern and is one of the co-first authors of the Attention Residuals paper, whose Block AttnRes variant, proposed with Zhang Yu, splits networks into blocks and cuts memory complexity from layer-scale to block-scale; the article says it matched a baseline using about 80% of the compute on a Kimi Linear 48B model trained on 1.4 trillion tokens, with under 2% added inference latency. The piece says the technique is one of the foundations of Kimi K3, described as a 2.8-trillion-parameter, 3T-class open-weight model released in July 2026, and uses the move to examine whether Chinese AI firms can retain elite talent, drawing parallels to hiring battles in smart-driving and robotics.
36氪 人工智能 · 🔥 7 - China proposes export controls on 23 AI technologies, including LLMs and chip designs
China's commerce and science ministries unveiled a draft plan on October 16 to add 40 technologies to the export control list — 23 of them AI-related, covering large models, AI agents, chip designs and computing clusters — with public comment open to November 15. For large models, curbs would cover weights, source code and core parts of models with at least 2 trillion parameters or top-10 rankings on performance and safety tests; chip know-how for 14-nanometre-or-below designs or 2.5D/3D stacked chips, plus some memory chip designs, would also be limited. Open-source code and published research are exempt, and overseas users can still reach Chinese models via apps, websites and APIs, though running a covered model on foreign servers counts as an export; firms using their own overseas cloud may get automatic licensing. A Commerce Ministry spokesperson said most AI technology can still be exported freely, that API calls, app interaction, SaaS and on-device deployment fall outside model-technology export management, that the plan targets no country or firm, and that a temporary export registration rule took effect the same day.
36氪快讯 · 🔥 55 - TokenSpeed: open-source inference engine for agentic workloads
TokenSpeed, an open-source LLM inference engine from the non-profit LightSeek Foundation, is trending on GitHub; it is pitched as a "speed-of-light" engine for agentic workloads aiming for TensorRT-LLM-level performance with vLLM-level usability. Its architecture is described as the first to separate a C++ control plane, implemented as a finite-state machine that uses the type system to enforce request lifecycle and KV cache resource safety at compile time, from a Python execution plane, with kernels handled as pluggable modules behind a portable API and centralized registry. The project's blog says TokenSpeed joined the PyTorch Ecosystem and reports 685 tokens/s per user serving Qwen3.8-Flash-Next on NVIDIA GB300 and Kimi K3 inference work on AMD Instinct MI355X, with engineering support from NVIDIA and AMD; those performance figures are self-reported.
GitHub Trending（每日） · 🔥 7 - Ex-OpenAI researcher says 90% of LLM neurons have one clear job
Nick Cammarata, who helped found OpenAI's interpretability team and now works at Goodfire, published a long essay titled "Neural Networks Are Already Clean," arguing that neural networks are not as entangled as the widely held superposition hypothesis suggested, so researchers can study individual neurons directly instead of first decomposing features with tools such as sparse autoencoders. He estimates that in a preliminary review of Qwen3-32B roughly 90% of neurons have a single main job at each polarity, about 7% have two related jobs, and 2%–4% remain unexplained, with similar patterns reported in trillion-parameter models including Kimi K3. The essay also describes agent tools that inspect neurons, propose explanations and design falsification tests, though he stops short of declaring interpretability solved and notes that attention mechanisms and full model auditing remain open.
36氪 人工智能 · 🔥 17 - vLLM: Kimi K3 on AMD Instinct MI355X tops NVIDIA HGX B300 on AgentX
A vLLM blog post details the engineering work behind serving Kimi K3 on AMD Instinct MI355X. It reports that on the AgentX benchmark at 50 tokens/s/user P90 interactivity, an eight-GPU MI355X system delivers roughly 7,567 total tokens/s per GPU versus 6,083 for NVIDIA HGX B300, a 24.4% throughput advantage, and 87.4% more tokens per dollar under SemiAnalysis's modeled TCO of $1.50 versus $2.26 per GPU-hour. The optimizations described include DCP8 partitioning of MLA history across GPUs, removing duplicated MoE output-projection work, fusing the single-token KDA decode path, and using AMD AITER assembly for MLA long-context decoding, which cut time per output token from 43.8 ms to 23.6 ms.
vLLM Blog · 🔥 5 - 17-year-old Kimi researcher Nathan Chen leaves Moonshot AI for the Bay Area
On Oct. 15, 17-year-old Nathan Chen announced on X that he had left Moonshot AI's Kimi team, describing the past year as the best of his life and thanking the team for hiring him at 16. He is a co-first author of the Attention Residuals paper, which Chinese reports describe as one of the foundational technologies behind Kimi K3, and he also worked on KDA kernel optimization, optimizer research and Tilde Research's MoMoE project. Anthropic researcher Sholto Douglas invited him in the comments to work with teams including Prime and Base10 in San Francisco; Chen confirmed he is heading to the Bay Area but replied "no" when asked whether he was joining Anthropic, leaving his next employer, role and start date unconfirmed.
36氪 人工智能 · 🔥 23 - StepFun opens Step 5 Preview weights: 600B MoE, 1M-token context
StepFun released BF16 and FP8 weights for Step 5 Preview, its flagship long-horizon agent model, on Hugging Face and ModelScope, plus a technical blog and deployment guides; the sparse MoE model has 600B total and 27B active parameters, supports up to a 1M-token context with text and image input, and targets software engineering, professional knowledge work and finance. According to StepFun, the design uses a 92-layer narrow-deep architecture with 3:1 gated sliding-window to sparse global attention and Sparse GQA, where 8-token block indexing cuts index and top-k selection cost to one eighth, plus FP32 key states, fine-grained gradient clipping and long-horizon RL that trains context compression; it can be served via vLLM or SGLang with an OpenAI-compatible API, or run offline with Transformers. StepFun-cited data puts it at 44 on Artificial Analysis's Intelligence Index and second on OpenRouter's weekly and daily token charts (about 18.8T tokens in seven days), at roughly $0.71 per task versus $5.86 for Claude Opus 5, and reports it above Kimi K3 and GLM-5.3 on ALE-CLI, FrontierFinance and DRACO, behind GPT-6 Astra or Claude Opus 5.
阶跃星辰 · Hugging Face · 🔥 38 - Meta releases Muse Spark Open, an 816B open-weight multimodal model
Meta released Muse Spark Open on October 15, an open-weight multimodal mixture-of-experts model with roughly 816 billion total parameters and about 42 billion active per token, drawing on 256 experts per layer with 8 selected per token and accepting text, images, audio and video with a context window of up to 1 million tokens. It was trained for agentic coding and long-horizon work such as tool calling, computer use and web browsing, and the weights ship on Hugging Face in BF16 and NVFP4 (NVIDIA Blackwell) formats, with Together AI and Modal hosting it from day one; Together AI named Factory and Ollama as early-access customers. Meta's own evaluations put it at 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1, and the company calls it "the most capable US open-weight model to date"; Engadget notes local runs need about 1TB of unified memory, such as two 512GB M5 Ultra Mac Studios, whose pricing and ship date Apple has not announced.
Hugging Face · 🔥 136 - Artemis launches Orion-1, a security model it says beats GPT-6 Sol on its own benchmark
US startup Artemis Security has launched Orion-1, a purpose-built security model that investigates and responds to attacks on corporate networks on its own. It pulls log data from auth services, cloud environments, corporate endpoints, SaaS apps and network gear, links signals such as an account login, new cloud permissions and a freshly created inbox rule to a single attacker, then assigns a confidence score, locks in a verdict and recommends or kicks off countermeasures. On Artemis' own Decision-Grade Readiness benchmark, Orion-1 scored 80.8 in attack reconstruction versus 58.3 for the next-best model, GPT-6 Sol, and also faced Claude Opus 5.5, Grok 4.7 and Kimi K3; Orion-1 is in private preview with select customers, where high-impact actions require human approval by default.
The Decoder · 🔥 0 - DeepRSI: Claude Fable 5.1 tops 48-hour AI research benchmark, Kimi K3 leads open weights
Hillclimb, a Y Combinator-backed startup, released DeepRSI, a benchmark of 15 tasks drawn from real machine-learning research that tests models' recursive self-improvement. Models run offline with the same Pi Agent framework at maximum reasoning effort for 48 hours, proposing approaches, writing code and running experiments; scoring uses public plus private verifiers and is converted to Elo via a Bradley-Terry model. Claude Fable 5.1 ranked first with 1548 points, GPT-6 Astra second at 1232, and Moonshot's Kimi K3 third at 820 — the top open-weight model, ahead of Grok 4.7 and Gemini 3.8 Flash. Reported run costs were about $627 for Claude Fable 5.1, $404 for GPT-6 Astra and $159 for Kimi K3, though the article says these reflect test overhead rather than real research return on investment; hillclimb lists Jeff Dean among its investors.
36氪 人工智能 · 🔥 0 - Chinese low-cost open models pressure cloud giants' AI returns, Jefferies and Bain warn
TechNews reports that analyses from Jefferies and Bain argue the rise of low-cost Chinese and open-source AI models is driving down token prices, threatening US cloud providers' ability to recoup trillions of dollars in data center investment. Jefferies estimates roughly half of the about $2.3 trillion in contracted backlog held by Microsoft, Alphabet, Amazon and Oracle comes from OpenAI and Anthropic, while Chinese models now process 76.12 trillion tokens a week versus 4.37 trillion before late April, against just 14.2 trillion for the top four US providers; it estimates Moonshot AI's open-source Kimi K3 reaches 95% of Anthropic's flagship Claude Fable 5. Bain says the AI industry must generate $6 trillion in annual revenue by 2031, but existing AI application revenue is only about $1.8 trillion, leaving a $2.7 trillion gap even after adding $1.5 trillion from search ads, autonomous driving and AI simulation.
科技新报 · 🔥 0 - MoneyPrinterTurbo: open-source AI short-video generator trends on GitHub
MoneyPrinterTurbo is an open-source, all-in-one AI short-video generator that is currently trending on GitHub's Python list. According to its project description, users provide only a topic or keyword and it automatically writes a script, matches footage, generates subtitles and background music, and renders an HD short video, via an AI Agent, WebUI, API or CLI. The project says it supports multilingual scripts and can connect to model services including Kimi/Moonshot AI, OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Qwen, Azure OpenAI, Volcano Engine Ark, xAI Grok, MiniMax and Xiaomi MiMo, with footage from local uploads or Pexels, Pixabay and Coverr, in 9:16, 16:9 and 1:1 formats, plus one-click posting to TikTok, Instagram and YouTube Shorts. The README also carries extensive sponsor promotions, including Kimi K3 and MiniMax H3 video-generation APIs and various discounted prices, which are promotional claims and not independently verified.
GitHub Trending · Python · 🔥 0 - Intel CEO calls Chinese AI models 'good enough' and cheaper
Intel CEO Lip-Bu Tan named DeepSeek, Moonshot's Kimi and Z.ai's GLM at a DBS panel, saying their performance is "already good enough," their token costs are lower and adoption has been "very fast," while adding that the market should decide which model fits which use case. Third-party price trackers put DeepSeek V4.1 Flash at $0.15 per million input tokens and $0.60 per million output tokens in off-peak hours, versus $2 and $10 for OpenAI's GPT-6.1 Sol, though DeepSeek doubles its rates at peak times, Kimi K3's output price is also $10, and GLM 5.3's $4.40 output price is about 44% of GPT-6.1 Sol's. Consulting firm Digital Applied, citing OpenRouter rankings, said Chinese AI providers together passed 45% of weekly token usage in early April, up from under 2% a year earlier, but attributed the rise to per-token cost and specific strengths such as coding and long context rather than benchmark leadership.
科技新报 · 🔥 0 - Report: China's six AI leaders earn about 10% of OpenAI and Anthropic revenue
An Epoch AI report estimates that, as of September 2026, the combined AI-related revenue of Alibaba, ByteDance, Zhipu, Moonshot AI, DeepSeek and MiniMax was roughly 10% of OpenAI and Anthropic's combined total. It puts Anthropic's annualized revenue at about $65 billion and OpenAI's at about $40 billion, against roughly $10 billion for the six Chinese firms: ByteDance about $4 billion, Alibaba about $2.4 billion, Zhipu about $1.8 billion, Moonshot AI and DeepSeek about $1 billion each, and MiniMax about $0.8 billion, with the ByteDance and Alibaba figures mainly reflecting model-as-a-service revenue. The report maps five monetization paths — consumer apps, API access, enterprise and government custom work, licensing and indirect gains through existing ecosystems — and notes that open-weight releases speed diffusion while eroding API pricing power, leaving pure model makers to weigh openness against monetization.
36氪 人工智能 · 🔥 0 - Moonshot AI reported at $50B valuation, targeting Q1 2027 Hong Kong IPO
Bloomberg, citing unnamed people, reported that Moonshot AI has closed a final funding round at a valuation of about $50 billion and plans to list in Hong Kong in the first quarter of 2027; the report follows an earlier one that the company filed a confidential A1 listing application. The article says Moonshot released Kimi K3 in July, with annualized recurring revenue rising from $100 million in March to $1 billion in August and a possible $2 billion by year-end, while September brought the K2.8 Preview rollout, an enterprise partner program, and Kimi K3 on Amazon Bedrock, lifting its valuation from roughly $10 billion at the start of the year to about $50 billion. The funding, valuation and IPO timeline all rest on anonymous sources and have not been confirmed by the company.
36氪 人工智能 · 🔥 0 - Jensen Huang: Chinese open models like Kimi are 'good enough,' backs open source
Speaking at a forum hosted by DBS Bank, NVIDIA CEO Jensen Huang said Chinese AI models including DeepSeek, Kimi and GLM are "already good enough." He described himself as a firm supporter of open source, calling it the best way to drive efficiency gains, and said the market will decide which models best fit different use cases. Huang also rejected the framing of AI competition as "a race with a finish line," saying both the US and China will be long-term players, and argued that cheaper, more open models will let more people and companies benefit from AI, increasing demand for NVIDIA chips, data centers and compute. The report says he argued the market first misread DeepSeek's impact and that Wall Street is now misreading Kimi's, and that quantum computing could reach mainstream adoption within three to five years.
cnBeta · 🔥 0 - Zhipu releases GLM-5.3 and multimodal GLM-5.3-Flash open-weight models
Zhipu (Z.ai) published GLM-5.3 and GLM-5.3-Flash on Hugging Face. GLM-5.3 is a 753B-parameter open-weight model that reuses the GLM-5.2 base model, with all gains coming from post-training: the team says it improves 50% over GLM-5.2 on its in-house Z.ai Code Bench and reaches open-source state of the art on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam, while expanded post-training also produced unexpected cyber capability, leading on CyberGym vulnerability discovery and more than doubling GLM-5.2 on exploitation benchmarks. GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series, with 320B total and 18B active parameters, hybrid sparse/linear attention and mHC; Zhipu says it outperforms GLM-5.2 at one-tenth the price and approaches Claude Opus 4.8 on coding and agentic benchmarks. Both support local deployment via SGLang and vLLM and expose a reasoning_effort parameter (low/high/max, default max).
智谱 GLM · Hugging Face · 🔥 10 - Arena weekly: Claude Opus 5.5 Max debuts first on agent leaderboard
Arena published its Week 41 AI model leaderboard (Oct 5–11, 2026), where Anthropic's Claude Opus 5.5 Max debuted at No. 1 on the agent board with a 15.53% net improvement, a 17.29% task-confirmation success rate and a 0.37% tool-hallucination rate. On the overall board Gemini 4 Argon (High) stayed first at 1525 ELO with Anthropic models filling the rest of the top four, while new entries gpt-5.5 and gemini-3.5-flash-medium placed 29th and 30th. Chinese models sat in the upper-middle tier and moved little: Moonshot's kimi-k3-max 16th overall, Alibaba's qwen3.8-max 22nd, Xiaomi's mimo-v2.6-pro 26th and Z.ai's glm-5.3-max 27th, with glm-5.3-flash the highest Chinese model on the coding board at 27th. Rankings use anonymous blind-vote ELO scores (a percentage signal for the agent board) and reflect user preference rather than absolute capability.
IT之家 AI · 🔥 0 - AirLLM claims 2.8T-parameter Kimi K3 runs on a single 4GB GPU, adds low-VRAM training
AirLLM is an open-source inference and training library that streams weights one layer at a time, keeping only a single layer resident on the GPU to cut VRAM use sharply. According to its repository, it runs 70B models on a single 4GB card without quantization, distillation or pruning, and reports running Kimi K3 (2.8T) in 3.72GB, Qwen3.8-Flash-Next (125B) in about 5.95GB and DeepSeek-V3 (671B) in roughly 12GB. Recent updates also add small-VRAM training, fine-tuning Qwen3.8-Flash-Next (125B) under 6GB and Qwen3.8-27B at about 2GB. All VRAM and training figures are the project's own reported measurements and have not been independently verified.
GitHub Trending（每日） · 🔥 0 - Washington Post probe: US AI industry leans heavily on Chinese models and parts
A Washington Post investigation reported that the US AI industry is deeply intertwined with Chinese supply chains at the model, hardware and component levels, with US companies including DoorDash and Airbnb using Moonshot AI's Kimi K3 and other Chinese open-weight models for everyday tasks because they cost far less than closed models from Anthropic and OpenAI; when the Trump administration weighed a national-security ban on Chinese AI in July, a coalition of nearly 300 US companies publicly objected, the report said. It said Congress began examining US firms' use of Chinese AI models in April, and that the FBI, NSA and a DHS cyber agency issued a joint advisory last month alleging Moonshot AI, DeepSeek, Alibaba, MiniMax, StepFun and Zhipu trained on US models without authorization and breached US companies' terms of service — allegations attributed to US agencies. The report also cited Epoch AI in putting six leading Chinese AI firms' disclosed annual revenue at roughly 10% of Anthropic and OpenAI combined, and a Morgan Stanley estimate that making Tesla's Optimus 2 with Chinese suppliers costs about $46,000 in materials versus nearly three times that, $132,000, without them.
cnBeta · 🔥 0 - Mistral releases trillion-parameter Large 4, the best model outside the US and China
European AI lab Mistral has released Large 4 (also called ML4 and nicknamed "Le Chonk" on social media), a trillion-parameter model. Testing lab Artificial Analysis scored it 38 on its overall intelligence index, according to Tom's Hardware, leaving it behind Chinese models such as Kimi K3 (max), GLM-5.3-Flash and MiMo-V2.6.Pro, though it performs strongly on cybersecurity tasks. Tom's Hardware reported that independent tests rank ML4 as the best AI model outside the U.S. and China, and said Mistral expects the exact weights to be released before the end of October.
Tom’s Hardware · 🔥 0 - PPT Master: open-source AI workflow builds natively editable PowerPoint decks
PPT Master is an open-source Python workflow by developer Hugo He that runs inside any agent-capable AI tool (such as Claude Code or Codex CLI) and turns PDFs, DOCX files, web pages or a bare topic into a natively editable PowerPoint deck — including slide masters and layouts, native shapes and connectors, data-backed charts and tables, transitions, animations and narration from speaker notes, plus support for a user's own .pptx templates. The project bills itself as free and open source with no PPT subscription, with everything except model communication running on the user's machine to avoid platform lock-in. The author recommends a large-context model (Kimi K3 or Claude) paired with an AI image generator such as gpt-image-2 or gemini-nano-banana-2.1, and warns that a single pass will not produce a flawless deck; the page also carries sponsor promotions for Kimi, PackyCode, APIKEY.FAN, RunAPI and APIMart.
GitHub Trending · Python · 🔥 0 - AWS Bedrock September recap: GPT-6 Astra, Claude Opus 5.5, Kimi K3 go GA
AWS's September roundup says OpenAI's Astra, Sol and Luna models are now generally available on Amazon Bedrock, including GPT-6 Astra with up to 1 million input tokens and a GPT-6 Astra Ultrafast tier with up to 6x faster inference and up to 300 tokens per second. Claude Fable 5.1, Claude Opus 5.5 and Claude Sonnet 5.5 also arrive, alongside Moonshot AI's Kimi K3 — which the vendor calls its first open model at 2.8 trillion parameters — and xAI's Grok 4.6 and 4.7. On the platform side, AWS launched a public preview of OpenAI-powered Bedrock Managed Agents, an AgentCore runtime with lower cold-start latency and scale-to-zero sessions, an open source Strands agent harness that it says uses 28% fewer tokens, and the 2B-parameter Strands Decider 2B decision model running locally in about 115ms. Managed Knowledge Base added automatic sync scheduling and native ServiceNow, Salesforce and Zendesk connectors.
AWS Machine Learning Blog · 🔥 0 - SGLang and Miles tuned for NVIDIA Vera Rubin, serving Kimi K3 NVFP4
The SGLang & Miles team says it received early access to two NVIDIA Vera Rubin nodes (8 GPUs) and brought both SGLang inference and Miles RL training up on the platform. For serving the Kimi K3 NVFP4 checkpoint, it retuned attention, MoE/communication and KDA kernels, reporting a 16% faster deeper MLA pipeline at batch 16 and 128k context, a 20% faster split-KV reduction, a 5.9% end-to-end gain from fusing the MoE tail, and 276 fewer kernel launches per decode step. On the training side, Miles uses SGLang for rollout and Megatron for training, and ran end-to-end RL out of the box on a single 4-GPU tray with Qwen3-30B-A3B and DeepSeek-V4-Flash, plus agentic RL with 64 sandboxes on the Arm-based Vera CPU on SWE-bench Verified. The team also cites Cognition as reporting up to a 4.8x increase in total token throughput on Vera Rubin NVL72 versus GB200 NVL72 using its own private SGLang fork.
LMSYS Blog · 🔥 0 - PaperBenchX: best AI agent fully reproduces only 14% of papers
UniPat AI's PaperBenchX benchmark gives 10 frontier agent configurations a published paper and asks them to re-run the key experiments in real scientific software, across 93 tasks covering 12 research directions. According to the team's published results, 70 of the 93 tasks were not fully reproduced by any configuration, and the top performer, GPT-6 Astra, fully cleared only 14% (13 tasks), while Chinese models clustered around 7% — roughly half that. Scoring separates modeling, execution and scientific verification across 3,168 scorable criteria, with a 4–24 hour per-task budget and outputs wiped and re-run in an isolated environment before grading.
品玩 实时要闻 · 🔥 0 - Goodfire launches inside-out monitors for AI agents
Interpretability startup Goodfire released "inside-out" agent monitors that read a model's internal activations via probes rather than having a second AI reread every output, and they are available to Baseten customers. In Goodfire's tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51, versus about $233 for a cheaper model checking every step and about $10,000 for a top-tier one; the probes caught 94% of malicious hacking sessions while flagging 8.7% of harmless ones for a second look, and running four probes added under 2% to response start time. Customers choose which risks to monitor, including offensive hacking, chemical and biological weapons misuse and reward hacking, and set the response: log, send for human review, or refuse the request.
TechCrunch AI · 🔥 0 - Step 5 Preview free for a week across several coding tools
StepFun says its flagship model for agentic and professional work, Step 5 Preview, is free for one week in OpenCode, Nous Portal (with Hermes Agent), Cline and Kilo. Partners describe it as a 600B-parameter MoE with 27B active per token, a 1M-token context and vision, with OpenCode noting zero data retention. Cline claims it scores ahead of Kimi K3 and GLM-5.3 on DeepSWE, while Nous says its 33.89 Hermes Index score matches GPT-6 Luna — both are partner claims.
阶跃星辰 StepFun · 🔥 0 - Interconnects: open-weight cyber-risk discourse is broken
An Interconnects opinion piece argues the public debate over open-weight models' cyber risk has gone off the rails: Anthropic's report on GLM-5.3 as an offensive cyber tool is largely reasonable technically, but ducks cross-cutting questions such as what happens if open models are banned and why Chinese labs consider their models safe to release. The author notes that documented cyberattacks so far mostly involve closed models, so "open dangerous, closed safe" may really be "open unsafe, closed unsafe," and says Chinese companies must register major model releases with the government along with evaluations, though it is unclear whether those cover cyber or bio risks. He adds that GLM-5.3, not Claude Mythos, is the model that crossed the capability threshold, and that more than a month after its weights were released there is little public evidence of a step change in harm.
Interconnects · 🔥 0 - Mistral launches Mistral Large 4 preview, a 1T-parameter open-weight model
Mistral AI has opened a public preview of Mistral Large 4 (nicknamed Le Chonk), a 1-trillion-parameter natively multimodal open-weight model with roughly 49 billion active parameters; the weights are due by the end of the month. Mistral claims state-of-the-art open-model performance on enterprise workloads such as cybersecurity, finance and law, and says it beats even closed frontier models on visual grounding — though it notes that some cyber-test leads come from Claude and GPT-6 refusing the tasks. Independent measurement is more sobering: Artificial Analysis's Intelligence Index puts ML4 at 38 points, a big jump from Mistral Large 3's 9, but well behind leader Claude Opus 5.5 (Max) at 58.
Mistral AI News · 🔥 0
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