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New agent framework matches human-engineered AI systems — and adds zero inference cost to deploy

Agents built on top of today’s models often break with simple changes — a new library, a workflow modification — and require a human engineer to fix it. That’s one of the most persistent challenges in deploying AI for the enterprise: creating agents that can adapt to dynamic environments without constant hand-holding. While today’s models…

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Alibaba’s Qwen 3.5 397B-A17 beats its larger trillion-parameter model — at a fraction of the cost

Alibaba dropped Qwen3.5 earlier this week, timed to coincide with the Lunar New Year, and the headline numbers alone are enough to make enterprise AI buyers stop and pay attention. The new flagship open-weight model — Qwen3.5-397B-A17B — packs 397 billion total parameters but activates only 17 billion per token. It is claiming benchmark wins…

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When accurate AI is still dangerously incomplete

Typically, when building, training and deploying AI, enterprises prioritize accuracy. And that, no doubt, is important; but in highly complex, nuanced industries like law, accuracy alone isn’t enough. Higher stakes mean higher standards: Models outputs must be assessed for relevancy, authority, citation accuracy and hallucination rates.  To tackle this immense task, LexisNexis has evolved beyond standard…

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Anthropic’s Sonnet 4.6 matches flagship AI performance at one-fifth the cost, accelerating enterprise adoption

Anthropic on Tuesday released Claude Sonnet 4.6, a model that amounts to a seismic repricing event for the AI industry. It delivers near-flagship intelligence at mid-tier cost, and it lands squarely in the middle of an unprecedented corporate rush to deploy AI agents and automated coding tools. The model is a full upgrade across coding,…

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OpenAI’s acquisition of OpenClaw signals the beginning of the end of the ChatGPT era

The chatbot era may have just received its obituary. Peter Steinberger, the creator of OpenClaw — the open-source AI agent that took the developer world by storm over the past month, raising concerns among enterprise security teams — announced over the weekend that he is joining OpenAI to “work on bringing agents to everyone.” The…

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SurrealDB 3.0 wants to replace your five-database RAG stack with one

Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has also become increasingly clear that agentic AI systems need memory, sometimes referred to as contextual memory, to operate effectively. The complexity and synchronization of having different data layers…

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Qodo 2.1 solves your coding agents’ ‘amnesia’ problem, giving them an 11% precision boost

As AI-powered coding tools flood the market, a critical weakness has emerged: by default, as with most LLM chat sessions, they are temporary — as soon as you close a session and start a new one, the tool forgets everything you were just working on. Developers have worked around this by having coding tools and…

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Most ransomware playbooks don’t address machine credentials. Attackers know it.

The gap between ransomware threats and the defenses meant to stop them is getting worse, not better. Ivanti’s 2026 State of Cybersecurity Report found that the preparedness gap widened by an average of 10 points year over year across every threat category the firm tracks. Ransomware hit the widest spread: 63% of security professionals rate…

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Nvidia, Groq and the limestone race to real-time AI: Why enterprises win or lose here

​From miles away across the desert, the Great Pyramid looks like a perfect, smooth geometry — a sleek triangle pointing to the stars. Stand at the base, however, and the illusion of smoothness vanishes. You see massive, jagged blocks of limestone. It is not a slope; it is a staircase. ​Remember this the next time…

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AI agents turned Super Bowl viewers into one high-IQ team — now imagine this in the enterprise

The average Fortune 1000 company has more than 30,000 employees and engineering, sales and marketing teams with hundreds of members. Equally large teams exist in government, science and defense organizations. And yet, research shows that the ideal size for a productive real-time conversation is only about 4 to 7 people. The reason is simple: As…

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