Apple says its AI is still private, even when it's running on Google's servers
Some models run in Google's cloud, but without giving Google any kind of access.
InfoWorld AI·
Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands. Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand. The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use ca
Read full articleSome models run in Google's cloud, but without giving Google any kind of access.
Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.
We’re seeing an interesting infrastructure tug of war today where GPU clouds are being pulled in two directions. For the economics of AI to work, the enterprise market needs to carve expensive hardware into smaller, shareable units and hand it to customers on demand, similar to how CPUs are doled in public cloud infrastructure. But the more the providers push GPUs to behave like elastic cloud infrastructure, the more they run into the reality that this GPU hardware was never built for safe multitenant use, fast fault recovery, or clean isolation between workloads. That tension is becoming one of the defining operational problems of the AI infrastructure market. When a gamer launches Steam or the Epic Games Store on their laptop, they don’t have to worry about which GPU is being scheduled, how memory is going to be divided, or really any of the security boundaries or hardware assignment issues on their PC. For consumer PCs, these issues are not just hidden from view, they are irrelevant
Kimi Code CLI is Moonshot AI's open-source terminal coding agent, written in TypeScript with subagents and MCP configuration. The post Moonshot AI Releases Kimi Code CLI: A Terminal AI Coding Agent Built in TypeScript for Next-Gen Agents appeared first on MarkTechPost.
You can use the new console experience to browse and compare the latest AI models on Amazon Bedrock side by side, organize work into projects with streamlined evaluation workflows, and access project-aware documentation with auto-prefilled code snippets ready to copy and run.
Microsoft has identified seven new failure modes in agentic AI systems, in addition to those it identified last year in its first Taxonomy of Failure Modes in Agentic AI Systems. Four things contributed to the growing list of ways agentic AI can go wrong: the speed at which the technology went mainstream, the growing maturity of the Model Context Protocol (MCP) ecosystem, the rise of computer-use agents, and finally the gathering of more empirical evidence as researchers obtained more real-life findings. The seven new failure modes it has identified are: Agentic Supply Chain Compromise —agent behavior can be affected by natural language rather than malicious code; Goal Hijacking — adversarial instructions appear aligned with legitimate task completion, while silently redirecting the agent’s terminal goal; Inter-Agent Trust Escalation —a compromised agent asserts false identity or inflates claimed permissions to an orchestrator; Computer Use Agent (CUA) Visual Attack — agents operating
Most AI agent failures don't happen during the demo. They happen when APIs fail, context windows explode, costs spiral, and nobody can explain why the agent made a decision. Here are five questions that separate production-ready platforms from expensive experiments.
By Liam Reid, Senior Product Manager, Legatics. Most law firms now have at least one generative AI tool in production. Many have several. The frontier ...