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🤖 AI ran first autonomous hack on Taiwan
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- Suspected Chinese hackers ran the first publicly known near-autonomous AI cyberattack on a government, using open-source models to breach Taiwanese state infrastructure and extract over 2,500 personnel records, according to research from Israeli cyber firm Dream.
- The multi-agent system, built on the Hermes and OpenClaw frameworks, ran "Learning Cycles" that autonomously searched vulnerability databases, GitHub, and security publications, then expanded to IT supply chain vendors, a nuclear safety agency, and 7+ energy companies in parallel.
- Attackers bypassed safety guardrails by framing the operation as authorized penetration testing, though Dream stressed the system still required substantial human work, task-specific tuning, agent coordination, and decision-logic adjustment, rather than "just" running a model.
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🤖 Grok 4.6 matches GPT-5.6
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- SpaceXAI's Grok 4.6 scores 61 on the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol and trailing only Claude Opus 5 (63) and Fable 5 (62), a five-point gain over Grok 4.5.
- The model shines on agentic work, ranking second on GDPval-AA v2 with an Elo of 1,753 and finishing complex tasks in ~53 steps versus Claude Opus 5's ~103.
- Pricing holds at $2/$6 per million tokens, over 60% below Claude Opus 5 and GPT-5.6 Sol, and it's live via API, Cursor, Grok Build, OpenRouter, Vercel, and Cloudflare, with doubled quota the first week.
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🤖 Nvidia's new Nemotron model bets on speed
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- NVIDIA shipped Nemotron 3.5 Lightning on August 11, a 30B open-weight model distilled from its 550B Nemotron 3 Ultra and built for the execution layer of agent loops rather than general-purpose reasoning.
- Independent tests clock it near 670 tokens/second-roughly double Gemini 3.5 Flash-Lite-with strong agent scores like 824 Elo on GDPval-AA v2, priced on OpenRouter at $0.05/$0.20 per million tokens under an unrestricted-commercial OpenMDW-1.1 license.
- Its Intelligence Index of 24 trails Qwen 3.6-35B-A3B (32) and Gemini 3.5 Flash-Lite (37) on MMLU-Pro, GPQA Diamond, and SWE-bench Verified, though its 1M-token window retrieves at just 52.0 accuracy, behind smaller-context rivals.
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👁️ Liquid AI vision model runs on edge
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- Liquid AI released LFM2.5-VL-3B, a 3B vision-language model that runs fully on-device, decoding 228 tokens/s on an M5 Max, 20 tokens/s on a Galaxy S26 Ultra, and fitting in about 3 GB of memory.
- Built on a SigLIP2 400M NaFlex encoder and the LFM2.5-2.6B backbone, it was pre-trained on ~34T tokens with 4x more vision data, and leads its size class on screen/UI understanding, grounding, and document reading.
- The model hits ~11K output tokens/s at high concurrency on an H100, roughly 2x larger 4B-class models, and ships day-one on llama.cpp, MLX, vLLM, SGLang, and ONNX via Hugging Face.
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🐋 DeepSeek upgrades its V4 Pro model
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- DeepSeek has quietly shipped V4 Pro 0813, its latest reasoning model, available API-only through OpenRouter with no formal announcement page from DeepSeek itself.
- The model exposes three distinct reasoning levels, low, medium, and high, which produced noticeably different outputs, an unusual behavior compared to other models tested.
- Open weights haven't been confirmed but seem likely given prior V4-Pro and V4-Flash-0731 releases, though benchmark numbers only surfaced secondhand via WeChat, a deleted Reddit post, and a Hacker News ASCII table.
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