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The latest update to Microsoft’s code editor previews an automatic model selection capability and improvements to agent security.
Working with AI at scale requires that you use models and their APIs like any other components in a software development stack.
LLMs are memory hogs, but PagedAttention fixes that, making AI apps faster, cheaper and way more efficient.
JFrog Fly offers small development teams an AI-driven development experience tightly integrated with GitHub and native AI tools like GitHub Copilot, JFrog said.
As developers lean on Copilot and GhostWriter, experts warn of insecure defaults, hallucinated dependencies, and attacks that slip past traditional defenses.
Familiar patterns—and familiar lessons—are emerging as enterprises get serious about agentic AI and Model Context Protocol and Agent2Agent implementations.
More companies are moving to integrate custom AI agents into business operations. Here are eight essential capabilities to look for when evaluating AI agent development tools and platforms.
Are AI projects a success? The answer depends on who is funding the study. It’s no surprise that enterprises are struggling to align resources with expectations.
The multilingual text embedding model suitable for retrieval-augmented generation and semantic search runs on less than 200MB of RAM with quantization.
The multitude of Python tools makes for many choices and many potential pitfalls. Streamline your AI projects by standardizing on tools, approaches, and a ‘golden path’ for development.