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Liquid AI, founded by former MIT computer scientists, today released its smallest AI language model yet, LFM2.5-230M, and enterprises would do well to consider it for their uses in data extraction and local deployment on smartphones, laptops and robotics.This is a 230-million-parameter foundation model explicitly designed for on-device agentic workflows, and as Liquid states in its release blog post, that small size makes it possible to run nearly "anywhere." According to Liquid, it also outperforms models.
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VentureBeat
Presented by Oracle NetSuiteEvery major technology transition produces a set of assumptions about where the market is headed. The assumptions are often directionally correct, but they tend to underestimate the degree to which organizations adapt new technologies to their own circumstances. AI is following a similar trajectory.Many current discussions about enterprise AI assume a future in which employees interact with business systems through a common interface. The details vary depending... Read more ›
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Presented by SAPGenerating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate. While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP's Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality... Read more ›
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A team routing queries across a coding specialist, a logic specialist, and a generalist model assumes each will cover the others' blind spots. A new study evaluating 67 frontier models from 21 providers shows that assumption is mathematically flawed — and the flaw has a name: the co-failure ceiling.The assumption works like this: as long as two models don't usually fail on the exact same prompts, combining them is supposed... Read more ›
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Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six... Read more ›
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OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users' email, calendars, code repositories, and messaging apps.The product is powered by OpenAI's latest flagship model, GPT-5.6, and is designed to go far beyond generating text. ChatGPT Work can gather context from connected apps,... Read more ›
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An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did.In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it... Read more ›
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Slopsquatting represents an emerging supply chain threat made possible by AI hallucinations. As developers increasingly rely on AI coding assistants, they unknowingly grant cybercriminals access to their software from day one. Understanding what slopsquatting isSlopsquatting is a new type of supply chain attack that uses large language model (LLM) hallucinations to inject malicious code into development workflows. The term combines "AI slop" and "typosquatting," a deceptive practice where attackers register Read more ›
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