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A production inference layer needs GPU compute sized to the model, a serving runtime such as vLLM or SGLang, orchestration that autoscales on queue depth, one endpoint with health-aware routing and failover, model weights cached near the GPUs, and observability, security, and cost controls.

Developers use Bitbucket Cloud every day to turn plans into working software. Code lives in repositories, feedback happens in pull requests, and Bitbucket Pipelines helps teams validate changes before they move forward. For an AI assistant to help with this work, it needs access to the same information. Without a direct connection, developers have to gather pull request details, pipeline results, logs, and links before the assistant can help.

You can now see which of your Multi-Region Cloud Storage buckets have cross-location read charges worth acting on, and start with the ones where a cache would save the most. It runs automatically for every organization with GCP Cloud Storage, so there’s nothing to set up. Google bills network data transfer when a Google Cloud service in one location pulls data from a Multi-Region bucket in another.

The best open source LLMs by family: DeepSeek V4 for frontier reasoning under MIT, Qwen3.8 and Gemma 4 for clean Apache 2.0 licensing, GLM-5.3 for coding, Kimi K3 for long context, Llama 4 for single-GPU scale. The real ranking depends on your deployment lane and token volume, and this guide prices all three lanes. At CloudZero, Bill Buckley, our SVP of Engineering, gives every engineer a $5,000 monthly AI token budget and published the math behind it.

At EVOLVE, Cortex’s annual engineering leadership conference, Nathen Harvey (Google Cloud’s DORA lead) asked the audience to name the biggest friction point in their software delivery lifecycle. Of 69 people who voted, none picked “building”. Reviewing changes and measuring impact nearly tied for first. Risk, security, ownership, and trust topped the EVOLVE audience's list of what keeps AI-built software out of production, and all four are platform problems.

In July 2025, nine days into a public build experiment, Replit’s coding agent deleted a user’s production database. The user had declared a code freeze and told the agent not to make changes without his approval. The agent ran destructive commands anyway, wiping records on more than 1,200 executives and over a thousand companies, and then wrongly reported that the data couldn’t be rolled back. The instruction was clear, and the agent had it in context.

Every age has had its spark, the moment an idea stops being an idea and becomes something a whole country can build on. Steam, electricity and the web all began with people who could see what was possible, but each one only changed the world once someone built the infrastructure to make it real. Britain has never been short of those people. Our universities produce world-leading AI research, our engineers are among the best anywhere, and our founders build companies the rest of the world wants to buy.

You already know the effectiveness of the JFrog Platform – blocking risky packages, speeding up builds, securing the supply chain, providing end-to-end governance and so much more. But when it comes time to renew the license, a new VP may ask: “What are we actually getting in return?” or finance wants to understand the ROI: can it really be proven by the numbers?

Today Canonical announced the release of Ubuntu 26.10, codenamed “Stonking Stingray,” available to download and install from ubuntu.com/download. Building on previous releases, Ubuntu 26.10 advances enterprise security features, completes the rollout of the Rust-based uutils coreutils, and strengthens integrations for user authentication using Microsoft Entra ID.