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Theo is an AI workspace for researchers: literature, derivations and code in one place. Sign up free and get 1,500 credits.

Theo is an AI workspace for physics research: literature, derivations and code in one place. Sign up free and get 1,500 credits.

Many AI teams are trying to reduce dependency on unpredictable cloud availability, on-demand shortages, or marketplace-based GPU access. Dedicated infrastructure can help teams plan training, inference, and production workloads with more confidence. CambridgeNexus helps AI companies access dedicated NVIDIA GB300 infrastructure, including bare metal and larger reserved clusters. We currently have access to 350+ NVIDIA GB300 bare-metal racks for teams planning capacity over the next 6–12 months.

My keynote at Dataiku Succeed came down to one point: Vibe coding is becoming the next generation of Excel workbooks. Fast. Useful. Hard to inspect. The answer isn’t stopping business teams from building. It’s giving them a governed way to build under IT oversight. At Succeed, we expanded Cobuild to support that: • Dataiku Headless: Build and manage Dataiku projects from tools like Claude Code, Codex, and Cursor - coming soon. • Agent Management: Discover, monitor, and improve agents across the enterprise, wherever they were built - available now. More people building is inevitable. The real question is whether they’re building in a system you can trust. More on what we shared at Succeed: https://lnkd.in/eprXtXXN #DataikuSucceed26

KI-Modelle verändern sich. Datengrundlagen bleiben. Schaffen Sie ein Fundament für GenAI, um mit Oracle Cloud Infrastructure zukunftssicher zu sein.

Erfahren Sie, wie Unternehmen Oracle AI-Lösungen nutzen, um praktische KI-Anwendungsfälle zu implementieren, die Workflows und Entscheidungsfindung verbessern.

NVIDIA B300s are LIVE at Cyfuture’s Tier III Data Centre in Noida. And these machines get even more interesting when you connect them through InfiniBand or RoCEv2. That’s when individual GPU servers start working together as a massive compute powerhouse, ready to take on demanding AI workloads. More GPUs. More interconnect bandwidth. More compute power.The real magic isn’t just in the GPUs. It’s in how you connect them. 😉 The GPUs bring the muscle. The interconnect brings them together. The real beast is the cluster. 🔥 Cyfuture.ai

It's official: the Arkane Cloud extension for VS Code is live! Millions of developers open VS Code every morning. Starting today, they can use the best open-source AI models right inside their editor, with inference running on our GPUs. No complicated setup. Install the extension, pick your model, start coding. This launch is first and foremost the work of a team. A small team that gave everything over the past few weeks, across infrastructure, product and testing. Arkane Cloud, thank you. I'm proud of what we've built together. And this is only the beginning. Several announcements are coming in the next few weeks, and some of them will make some noise. Stay tuned. The install link is in the comments. Try it, push it to its limits, and tell us what you think: every piece of feedback counts.

Von Grund auf privat und sicher. Mit Cohere KI nutzen und Autonomie bewahren.

Your AI agents shouldn’t feel like a black box; they should feel like a cockpit you know how to fly. On 10 Nov, WPP’s CAIO walks you through a clear six-stage lifecycle for governing agents, so you can set boundaries, define escalation paths, and prove to your stakeholders that your AI is both powerful and well-governed.

You wouldn’t run a business without a process; why run AI agents without one? In this live session with WPP’s CAIO on 10 Nov, you’ll get a practical, six-stage lifecycle for governing agents, covering when they should act, when they must stop, and how they hand off to humans - so your AI strategy is structured, auditable, and ready to scale.

Your RAG pilot worked. ✅ Now the agent needs access to tools, internal data, and business systems. 🔐 That is where the architecture gets harder: 1. Who can the agent act for? 2. What can it access? 3. How do you trace its decisions? 4. And what evidence will risk and compliance teams need before approving production deployment? This session explains how to design governed agentic systems - from MCP and access controls to auditability, reliability, and production readiness. Watch the full session: 👉 https://lnkd.in/ekZj-e3S/?utm_campaign=%255Bvideo%255DLLM&utm_source=linkedin&utm_medium=paid&utm_id=776239064&hsa_acc=508184568&hsa_cam=776239064&hsa_grp=416564784&hsa_ad=1468807454&hsa_net=linkedin&hsa_ver=3

Enterprise data can be available, but still hard to use. 👇 In one recent project, we built an MCP server on Azure that connected ChatGPT with Microsoft Fabric for a US-based spirits producer and distributor. The goal was simple: give business users a 𝐬𝐞𝐜𝐮𝐫𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐚𝐬𝐤 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐚𝐛𝐨𝐮𝐭 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 without having to rely on a technical team every time. Users could query approved datasets, compare information, and get structured answers, summaries, and reports in ChatGPT. This setup can also work for other companies with 𝐥𝐚𝐫𝐠𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬. Examples include: • operational and supply chain analysis • sales and commercial reporting • financial analysis • recurring management reports • internal data search • AI agents that need access to company systems 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐩𝐚𝐫𝐭 𝐢𝐬 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. An LLM should not have open access to business systems. Companies need clear rules for authentication, permissions, available actions, and data access. That is where a custom MCP server can help. It creates a controlled connection between AI tools and enterprise systems. Read more about how we build 𝐌𝐂𝐏 𝐬𝐞𝐫𝐯𝐞𝐫𝐬 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 👉 https://lnkd.in/dxFCnXve/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=887012544&hsa_ad=1541830714&hsa_net=linkedin&hsa_ver=3

You plan training runs, releases, and hiring with care—so why should GPU capacity be a wild card? By shifting to dedicated NVIDIA GB300 bare-metal racks and reserved clusters for the next 6–12 months, you trade on-demand uncertainty for predictable throughput, controllable costs, and reliable performance SLAs. CambridgeNexus lets you line up infrastructure with your pipeline, so every experiment and production deployment runs when you planned—not when a marketplace says you can.

Tired of choosing between security, speed, and performance? North 2 by Cohere is the answer.

AI strategy is not the finish line. See how manufacturing teams can start with one practical use case, prove value, and scale what works.

Fast Company just named Aranya as one of The Next Big Things in Tech! Big thanks to the team and everyone who's helped us get here. Link to the full article in the comments ↓

“Fine” never means the same thing twice. Expression Measurement hears the difference, natively in 50+ languages. The same word can carry relief, frustration or doubt depending on who says it and how. Our API measures the expression behind the words across voice, face and language, with no translation layer and no English bias.

Five cuts of your next ad. Test them on measured human reactions instead of gut feel, before you spend on media. Expression Measurement reads facial and vocal expression frame by frame, so you can see which moments land and which ones lose people.

A lot of this week's datacenter news comes back to one date: when the power turns on. But for operators who already have power, there’s a second date that matters just as much: the day the GPUs are online and actually usable. The industry norm from racked servers to a production-ready cluster is weeks, and until then, those GPUs aren't earning. We do it within 48 hours. The only requirement? Hardware is racked. We handle the rest: Bare metal provisioning, GPU orchestration, CPUs, networking, and storage. From there we build the architecture the tenant needs, whether that's Kubernetes, Slurm, VMs, or storage, and hand it over production-ready. Power decides when you can build. The cluster decides when you can earn.

AI strategy is not the finish line. See how manufacturing teams can move from one practical use case to a governed AI workflow.

NVIDIA B300 on demand at $7.79* per GPU-hour Reserved terms quoted in USD Each node: 8x NVIDIA B300 GPUs with 2,304GB of GPU memory, 2TB of RAM, and 30TB of local NVMe, on a non-blocking 800GbE RoCEv2 fabric Reserved capacity stays yours for the full term and is never reclaimed Spot can be paused if capacity is reallocated Elice owns and operates the B300 nodes and is ISO 27001 and ISO 27701 certified Planning reserved capacity? Get a quote for a dedicated NVIDIA HGX B300 cluster by answering 3 quick questions: GPU model, GPU count, and timeline Most of your contact details are prefilled, and our team will follow up by email with a quote in USD *On-demand list price in USD as of October 2026, billed per second, excluding taxes

Sometimes you just want to change one thing. The light. The angle. The room. Keep the shot. Keep working from it.

Buy NVIDIA B300 capacity from the company that owns it Elice built and runs its own liquid-cooled modular data center (PMDC) and owns the B300 nodes in it Elice engineers support bare metal, VMs, and multi-tenant isolation Your contract is with Elice, with no reseller or marketplace in between - Dedicated 8x B300 nodes - Lower rates on 3- to 5-year terms with prepayment - Quoted and contracted in USD - ISO 27001 and ISO 27701 certified Get a quote for a dedicated NVIDIA HGX B300 cluster by answering 3 quick questions Most of your contact details are prefilled, and our team will follow up by email with a quote in USD