GPT-Live (8 minute read)
OpenAI introduced a full-duplex voice model that can listen and speak simultaneously, handle natural conversational cues, and delegate complex tasks to GPT-5.5 while maintaining the flow of dialogue.
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Grok 4.5 (3 minute read)
SpaceXAI launched Grok 4.5 as its strongest model for coding, agentic tasks, and knowledge work, noting that it was trained alongside Cursor.
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ByteDance debuts Seedream 5.0 Pro with advanced reasoning (2 minute read)
Seedream 5.0 Pro is a multimodal image-creation model that offers precise edits, multilingual support, and advanced production-design features. It is designed for production design work rather than one-shot image output. The model is targeted at creators, designers, marketers, educators, product teams, and developers. It supports more than 10 languages, as well as some right-to-left layouts and accents.
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Meta launches Muse Image across its apps (3 minute read)
Muse Image is an image-generation model that can create and edit images. It is now available in the Meta AI app, Instagram Stories, and WhatsApp. The model can perform multi-reference composition, room redesigns, prompt-based image creation and editing, and more. It can draw from public Instagram photos when a user tags an account. There will be an invisible watermark in the images it generates.
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Auditing the Reliability of Coding Benchmarks (9 minute read)
OpenAI examined SWE-Bench Pro's construction, model failures, and task metadata, concluding that roughly 30% of its public tasks were broken. The analysis showed how flawed evaluations could distort assessments of coding ability, safety, and model progress.
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An off switch for dual use knowledge in AI models (8 minute read)
GRAM (Gradient-Routed Auxiliary Modules) enables the benefits of training many separately-filtered models at the cost of training only one model. It gives models dedicated, removable compartments for each category of dual-use knowledge and only updates those compartments when learning from dual-use data. It allows parts of the model's knowledge to be deleted after training, providing a stronger framework for model safety.
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A Taxonomy of Self-evolving Agents (15 minute read)
Shilong Liu proposes a taxonomy that classifies self-evolving agents into three categories: artifact optimization, harness self-improvement, and model learning. The framework distinguishes whether evolution occurs in outputs, agent infrastructure, or model weights, providing a common language for emerging agent research.
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SWE-1.7: Frontier Intelligence at a Fraction of the Cost (22 minute read)
SWE-1.7 reaches frontier-level intelligence at a much lower cost. It is the result of broad improvements across Cognition's RL pipeline, including better infrastructure, more stable training, higher-quality data, and new techniques for long-horizon tasks. The model is available now in Devin on the web, desktop, and CLI. This post contains details on the infrastructure, algorithms, and data work behind the model.
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Data for Agents (6 minute read)
NVIDIA underscores the importance of open and synthetic data for developing robust AI agents, highlighting its use in the Nemotron datasets for enhancing capabilities like reasoning and tool use. Open datasets contribute to AI system transparency and reproducibility, allowing developers to inspect and understand agent behavior.
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Data At The Edge (9 minute read)
AI is unlocking valuable datasets from the physical world by combining cheaper sensors, robotics, and multimodal models. New data flywheels around infrastructure, healthcare, and industrial automation could create the next generation of enduring AI businesses.
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