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A roundup of the day's news focused around AI development with PyTorch, building AI agents, and controlling the costs of AI.
@AMD @Intel @SnapLogic @Flexera
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Nexus Repository Pro+ screens every open source package, AI model, and MCP server the moment a developer or AI agent requests it. Anything malicious, vulnerable or out-of-policy is automatically blocked or quarantined before it reaches a build. @sonatype
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The question is no longer whether engineering teams can build software quickly enough. The question is whether organizations can consistently identify the most valuable opportunities to pursue. @Gartner
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Here's this week's episode of "What the Dev?" looking at the issue of enterprise metadata management, with Christian Bremeau, founder and CEO of
@Meta Integration
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A new employee’s first weeks are spent getting access to tools, systems, and data. Agent onboarding is the same. @Solace
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The npm v12 release includes security that blocks malicious package life cycle scripts from being introduced at installation. That just means that attacks will now move to the runtime. @checkmarx
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These capabilities, GitLab said, help create a governed system “for orchestrating, securing and optimizing software development” and move organizations beyond shadow software factories. @GitLab
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Appfire’s latest products address distinct challenges across security, software development, and enterprise workflows — each rooted in real challenges Appfire encountered across its own engineering teams. @Appfire
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Organizations are facing a fundamental challenge: gaining the visibility needed to understand where technology investments are going, who owns them and whether they’re delivering meaningful business value. @flexera
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There is an AI confidence gap between leaders who have a lot or complete trust in AI testing its own code, and the developers who see things differently. @smartbear
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While quantum hardware keeps evolving, quantum software has gone much further than many realize. @classsiq
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This gives users more model choice to which to delegate their work, and according to Microsoft’s announcement, GPT-6 Astra will enable users to delegate larger tasks, while the users can review the work and make decisions that can move work forward.
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For years, AI on the mainframe was often experimental. Companies now view AI as an operational tool. However, they want AI to provide insight and advice, but they are not ready to give it full control. @BMC
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Wrapping a REST API as a Model Context Protocol server takes about 20 minutes. Wrapping it so that an agent uses it correctly takes considerably longer, and the difference is almost entirely in decisions that do not look like code decisions. @melissa
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Platform Engineering 2.0 extends the self-service, paved-road model of traditional platform engineering to support autonomous AI agents alongside human developers.
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One of the big complaints engineers working with AI have is that it has no memory, so users basically have to start from scratch every day. It was Groundhog Day for engineering. @Anthropic
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Modern distributed systems break the old rules of API testing, and static mocks simply can't keep up with today's complexity. Join us to see how service virtualization gives engineering and QA teams the ability they need to test with confidence.@Parasoft
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Maestro Flow closes that gap. Builders can use the coding agents they already rely on—including Claude Code, Cursor, GitHub Copilot, and Codex—to design, run, observe, and govern complete business processes as a single artifact @UiPath
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From a new @Linear report on who's using AI: "The suggestion that everyone in an organization is becoming a ‘builder’ seems to be directionally true Those gains haven’t shown up as time saved, though."
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