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[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6
OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.
At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.
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NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut
NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.
In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.
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Why you should work on AI for AI Research — Richard Socher of Recursive
Richard Socher's new lab Recursive, backed by $4.65B seed, targets AI systems that automate AI research itself.
Latent Space interviews Richard Socher, founder of You.com and AIX Ventures, about his new venture Recursive, which raised a $4.65 billion seed round to build the 'Eureka Machine' — a superintelligence for automating invention and AI research. Early claimed results include an AI research system outperforming humans and their agents on optimization tasks within two days, and NVIDIA GPU kernel improvements discovered without CUDA experts. Discussion spans reward hacking, constitutional AI critique, AI regulation, open-source models as geopolitical soft power, and hard-takeoff constraints.
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Meta Introduces Muse, a Personal AI Agent That Runs on Its Own Dedicated Secure Cloud Computer
Meta launched Muse, a proactive personal AI agent running in an isolated per-user cloud VM with a Sentinel approval agent and surrogate credentials.
Meta introduced Muse, a consumer agent that performs long-horizon tasks like email, travel booking, and bill negotiation, rolling out in the US on iOS, Android, muse.ai, and WhatsApp with free and paid tiers. Each user gets a dedicated Muse Secure VM where the agent runs in a systemd-nspawn cell, while a separate Sentinel agent approves every network request at layer 4/7 and injects real credentials only at the network boundary. The underlying Muse Spark 1.3 model, which Meta says cuts tool calls by ~20% and tokens by ~25% versus 1.2 and is near state-of-the-art on prompt-injection resistance, is available via Meta Model API, with open weights on the roadmap.
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Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026
NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.
At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.
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Why I'm still bearish on LLMs after Navier-Stokes
Essay argues frontier LLMs remain far from autonomous knowledge-worker replacement because reward hacking and specification costs limit reliability to narrow, well-specified domains.
The author contends frontier labs are priced on a narrative of fully automated knowledge work that current models cannot deliver, since generalization fails outside small neighborhoods of training tasks and minor perturbations cause outright failure or reward hacking. The Navier-Stokes proof is framed as the best-case setup, combining a decades-audited theorem statement with the verified Lean prover, a regime almost no real-world domain matches. Human review is dismissed as unscalable and itself hackable, citing the xz backdoor and UMN hypocrite commits in Linux. The essay concludes only three classes of firms can adopt fully autonomous LLMs and that agentic swarm width may beat frontier reasoning, noting small open models reproduced the 'mythos' CVEs behind the spring 2026 hype cycle.
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Creepy crawlies
git.kernel.org spends more CPU rendering commit pages for abusive AI-era scrapers than on all legitimate access including git clones.
Konstantin Ryabitsev reports that abusive crawler 'background radiation' at git.kernel.org consumes more CPU cycles rendering commit HTML than all legitimate access, with 14 cores across five geo-distributed nodes dedicated to scraper traffic. Simon Willison highlights the implications for crawlable web services like Datasette.
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AIR raises $50M to help companies vet the skills and add-ons AI agents use
AI security startup AIR exits stealth with $50M across two seed rounds to continuously vet skills, plugins, and MCP servers used by enterprise AI agents.
AIR, founded by Unit 8200 veterans Yair Saban and Niv Hoffman, raised $10 million led by Sequoia and $40 million led by Greenoaks to secure the emerging AI agent software supply chain. Its platform discovers agents in enterprise environments, intercepts their actions, and continuously vets skills, add-ons, and MCP servers against a maintained whitelist, filtering out about 27% of found add-ons. The company claims over 20 customers, roughly a quarter large enterprises, with strong demand in financial services and pharma, and competes with Noma Security, Zenity, Astrix Security, and Operant AI.
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