搜索“AWS”

End: “R loves” confirms their affection for the series).\n3. Juvenile Graphic Novels: 7+ J GRAPHIC physical books in logs, including Katie the Catsitter, Witches of Brooklyn, and PAWS – these are only juvenile, not read by the TEEN lone wolf.\n4. Middle Grade Humor: Jeff Kinney’s Diary of a Wimpy Kid (10+ audio entries, binged Feb 2025) and Judy Blume’s Fudge series, matching the 8–12 target age for the 12 and 10-year-olds.\n Timeline Evidence Summary\n| | |\n|:-:|:-:|\n|Confirmation|Timeline De

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application. Users should be aware of the potential risks and ethical considerations when using this project, and should use it responsibly and in compliance with all applicable laws and regulations. Prohibited Use: This project must not be used to generate content that is illegal, defamatory, pornographic, harmful, or that violates the privacy, rights, or interests of individuals or organizations; User Responsibility: Users are solely responsible for any content generated using this project. T

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4 beats MXFP4, with up to about 8% faster decode at small batches (@StasBekman); Grid constraints: Mississippi’s Entergy warned it would curtail Amazon data centers at peak times. AWS responded with Project Falcon, which lets the utility switch sites to backup power itself (report); Agent infrastructure: Perplexity runs each project as a Git forge, with ephemeral agent sandboxes committing to S3. Fewer than 2% of commits collide, and grep beat semantic search 63% to 37% on financial document que

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rforming actually harmful acts; and work to improve security and strengthen defenses against cyber attacks. Developers and security teams can use models like Muse Wonder to find flaws in their own code and strengthen their systems, so we made sure the models can still do that work. We train, evaluate, and release our most capable AI models in line with the standards set out in our Meta Superintelligence Scaling Framework. A full report with additional safety evaluations will be available soon. F

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a significant step up in cyber capabilities and meets our Critical threshold. This means that, with the right tools and access, GPT-6 Astra can find previously unknown security flaws and develop new ways to exploit them across many well-protected systems without a person guiding each step. Accordingly, we significantly strengthened our protections against the model taking harmful cyber actions, whether that’s due to misuse or misalignment. We also took steps to secure our internal development a

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4 beats MXFP4, with up to about 8% faster decode at small batches (@StasBekman). Grid constraints: Mississippi’s Entergy warned it would curtail Amazon data centers at peak times. AWS responded with Project Falcon, which lets the utility switch sites to backup power itself (report). Agent infrastructure: Perplexity runs each project as a Git forge, with ephemeral agent sandboxes committing to S3. Fewer than 2% of commits collide, and grep beat semantic search 63% to 37% on financial document que

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sted uses include vulnerability reproduction, exploit development, penetration testing, security auditing and cyber reasoning. The 3 design goals: Find what others miss: unknown flaws like RCE, sandbox escapes, auth bypasses, privilege escalation and attack chains; Go beyond detection: reason through attack paths, challenge its own hypotheses and rank flaws by demonstrable exploitability; Built for autonomous agents: 1M-token context, native tool calling and extended reasoning for large codebase

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llion valuation led by a16z. Microsoft is about three and a half weeks behind the startup that started this category, and in that time OpenAI, Upstage, Perplexity, Cloudflare, and AWS have all shipped decision models of their own . TypeSafe says nearly 30% of the Fortune 500 has tried Jev, though it hasn’t named any of them, and TypeSafe CEO Diogo Almeida posted on X that “29.4% of the Fortune 500 showed up” in the three weeks since launch. launched a model accidentally served trillions of token

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2), editing, and Genmoji, we developed ADM 3 Cloud to deliver strong controllability and parameter efficiency. It generalizes across different aspect ratios and resolutions, and draws on the broader Apple Foundation Model family to guide both creation and editing. While the base model natively handles image creation, editing, and Genmoji, we also use specialized adapters to power specific downstream editing experiences, such as Spatial Reframing in Photos, along with touch-based image modificati

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91. On JevBench (231 public items), Drex scores 86.2% against Jev’s 87.0%. Both reach 73.9% on hard items. In a head-to-head across 8 OpenSpiel games, Drex recorded 122 wins, 47 draws and 87 losses against Jev (56.8%). Long documents are a clear strength: 8K to 32K tokens: 89.5% accuracy, median 0.65 s; 32K to 128K tokens: 93.4% accuracy, median 2.0 s Truncating the same requests to 8K tokens drops accuracy to 76.5% and 78%. How can developers run Drex 1.5? There are 4 deployment paths, all serv

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truction following. K2.6 excels at maintaining architectural integrity over extended coding sessions, making it a stable foundation for autonomous agent pipelines, like all the "claws". It demonstrates a measurable leap over K2.5 in long-context tasks, achieving state-of-the-art performance in complex reasoning. Got an early look at K2.6 and ran it through Hermes Agent. Tool calling and agentic loops feel noticeably tighter, coding is a clear step up, and the creative range surprised us. We're s

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is a finite "fossil fuel", and its growth is lagging far behind the pace of compute. This makes token efficiency during pre-training a new critical coefficient in the AI scaling laws. Post-training is pivotal in the "Era of Experience" (David Silver, Richard Sutton, 2025). In this era, LLMs increasingly learn from their own self-generated interactions, receiving rewards that free them from the limits of human data and enable them to surpass human capabilities. Kimi K2 is forged from these very

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on Knowledge (23.8). Across seven public text benchmarks, d1-3B averages 82.9, ahead of Decider 4B at 81.1. d1-omni-600M averages 78.4 and posts the top Civil Comments (95.8) and PAWS-X (79.5) scores. On 11 image benchmarks, d1-3B averages 74.1 against 73.9 for its base model. Liquid AI calls audio decision benchmarks an open problem. How fast is d1-3B on NVIDIA hardware? Liquid AI measured end-to-end latency, one warm request at a time. One question takes 8 ms on an RTX 4090 and 9 ms on an AMD

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forming Decider 2B (77.1) at a quarter of the parameters. It also posts the highest score in the table on toxicity detection (Civil Comments: 95.8) and paraphrase identification (PAWS-X: 79.5). Vision and audio performance. The Decision Index v0.3 includes a private vision split, which we do not report on in this release. Instead. we validated that d1-3B retains the vision capabilities of its LFM2.5-VL-3B backbone on standard vision benchmarks, and that d1-omni-600M handles all three modalities.

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ty ML4 is one of the world's strongest AI models for cybersecurity. On the Artificial Analysis Cyber Index, an independent evaluation of how well AI models find and fix security flaws in real software, it ranks among the top five models globally and leads open-weight models developed outside China by a wide margin. On one of the index's tests, which asks a model to reproduce a real vulnerability in open-source software and then patch it, ML4 scores 82%, the highest of any model. It also solves 9

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moves those handoffs. Text, vision and robotic actions become tokens inside one context window. According to Reka’s research, one unedited session shows the full loop. The model draws a lighthouse, boxes it, animates it, edits the video into a snowstorm, and explains the difference. That all happens in 5 turns, with no tool call and no second model. Every input and output uses one of two native formats: Discrete tokens carry text, symbolic reasoning and high-level commands; Continuous tokens car

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vers 150 tasks built from 50 real vulnerability cases. Each case appears in 3 variants: guided whitebox, focused whitebox and focused blackbox. Authorization, identity and scope flaws make up 72% of cases. Accounting and numerical precision bugs add 18%. Time validation, business rules and SSRF cover the rest. As per the model card on HF, RL rollouts ran inside the Codex agent harness on production-like software and protocol environments. Benchmark Results Cantina evaluated 60 tasks from 20 held

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00 or H200 via vLLM Asif Razzaq Asif RazzaqInside NVIDIA’s IsaacTeleop: From Hand and Controller Tracking to Robot Actions with the Graph-Based Retargeting Engine; Asif RazzaqDecision AI Models Explained: TypeSafe Jev vs Fastino GLiDE, GLiNER2.5-Decide and Open-Source Competitors; Asif RazzaqDatalab Introduces OmniExtractBench to Fix Bias and Opacity in Extraction Benchmarks; Asif RazzaqAWS Strands Labs Releases Strands Decider 2B: An Open Source Decision Model That Picks Options in About 115 ms

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development, and the President indicated that he felt there would be no “second place” in AI development. Regarding safety, Vice President J.D. Vance said that there are already laws from the FTC and similar agencies requiring companies to develop products with user safety in mind, and that government regulation would hinder progress. Zach: What was it like watching the big names share a stage? How did they seem to get along? Nick: I was in the room with Elon Musk, Jensen Huang, and (Anthropic

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ion. At a Sichuan restaurant, a foreign diner is stumped by a Chinese-only menu. SeedRealtime identifies the dishes straight from the scene, recommends them in English, and even draws on cultural background to explain "why there is no fish in fish-fragrant pork (yuxiang rousi)" and "how century eggs are made." As the server sets down a dish and casually says it "goes great with rice," the model understands the line in the context of the dish on screen and translates it for the guest. Visual info

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