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Lenovo sellers are turning ThinkSystem GPU quotes into AI Factory deals. Attach Klique on the services contract and the deal grows from a server sale to an AI Factory, with software and services on top. Your customer gets a working AI Factory on Day 1. Lenovo AI Innovator. Lenovo AI Library.

Permission to read data isn’t permission to share it - or act on it. Access decisions alone aren’t enough to govern AI agents. IndyKite.ai brings data trust, context, and real-time control to every interaction with enterprise data. See how it enables agents act within clear authority and trust boundaries.

Every AI policy says approved models only. Very few teams can show the proof. Credo AI is the system of record for enterprise AI governance. Every use case, model, agent and connector in one place, with the approval trail and the evidence already mapped to the frameworks you report against.

The NAIC’s 12-state pilot is testing how regulators may evaluate insurer AI during market-conduct reviews, financial analysis and financial examinations. What information will insurers need to provide about their AI use, governance, models and data? Get an inside look in this on-demand webinar from Credo AI and the NAIC.

A model trained on generic data makes generic decisions. Adaptive Data and AutoScientist help train a model that’s yours. Turn company data into training-ready inputs. Then auto-train the model. Your model. Not the average.

Introducing Visualize, the first release in Adaption's Adaptive Interfaces. Ask Visualize anything about your data and explore the answers through interactive charts. Discover patterns, dig into the examples behind them, and follow your curiosity to new questions. Most tools stop at the chart. Go from understanding your data to shaping your intelligence. Visualize takes every insight into Adaption’s full AI system, so you can train a model that’s yours. See it. Shape it. Own it. Custom intelligence, by design.

Many neoclouds run KubeVirt under the hood. What separates fast from functional isn't the VM layer, but whether you can get GPUs through it cleanly. VFIO passthrough: direct PCIe access from the VM to the GPU, hypervisor completely off the data path. That's what we shipped in clusterdOS v0.3.17. clusterdos-kubevirt deploys KubeVirt v1.8.2 + CDI v1.65.0 as an ArgoCD gitapp. Enable it in your metarepo values and your cluster gets GPU passthrough to H100s, B200s, and B300s, plus live VM migration between nodes and declarative disk lifecycle via CDI. cert-manager is already there. Operators have memory limits set. CDI clone deadlock is patched. Upgrade tooling is automated. Phase 1 of the HyperConverged stack. That used to be weeks of custom configuration per cluster. Now it's a values flag in your metarepo. This is infrastructure complexity moving into the platform.

There’s a huge gap between what data centers offer and what AI workloads really need.

Today's AI builders need infrastructure that scales with their ambitions. IREN is a vertically integrated AI Cloud platform, delivering data centers, compute and software for AI training and inference. Direct access to NVIDIA GPUs, rapid scaling, predictable pricing.

AI Verse gave a German counter-drone company the detection capability its flight schedule could not deliver. Capture campaigns set the roadmap for you. Every new airframe, sensor or customer requirement means another flight window, another labelling contract, another quarter of delay. AI Verse removed that constraint. We generated roughly 17,000 labelled images to the company's spec: two camera settings, two drone models, both views rendered from the same scene, every frame labelled to the pixel. Four batches, each one shaped by their review of the last, from a 50-image sample to full delivery. No aircraft left the ground. What our data produced: 0.989 mAP50 on the narrow camera, and a working small-object detector for the wide search view. Both trained on AI Verse imagery alone. Our engine generates the dataset whenever the requirement changes. A new drone model becomes a spec change, not a headache. Tell us which capability you need to prove and we will scope the dataset. Link to the case study in the comments ⬇️

Watch everything in production without blowing your budget. Today we’re launching Always-on Monitoring 2.0 at Galtea. Continuous evaluation of your production traffic, with sampling, credit caps, and content filters built in from day one, so you're never choosing between watching everything and staying within budget. Sessions close automatically. Regressions surface in minutes, not in customer complaints. And most importantly, connecting production evals to your pre-production tests is what turns monitoring into a system that actually improves over time. The requirement regulated industries have is proof that a deployed AI system still behaves as intended, month after month. This is the last piece to close the loop for online and offline evals. Available at https://platform.galtea.ai

AIAIAI is gemaakt voor gereguleerde omgevingen. Alle AI op één veilige plek, waar data en software met elkaar praten. Naast onderwijs, vastgoed, zorg en (semi-)overheid is accountancy één van de sectoren die AIAIAI volledig hebben omarmd. Daarom organiseren we in samenwerking met accountancy-specialisten Docubird, Full Finance en Asista een klantendag met als thema ''Grip op AI in de praktijk.'' Verwacht live demo's, praktische inzichten en discussies over AI binnen de accountancy en advies markt. 📅 5 november 2026 ⏰ 13:00 - 17:00 📍Locatie Van der Valk Houten Aanmelden kan via: https://luma.com/o7d94bzz #Accountancy #AI #Digitalisering

Some weeks ago, I was on the phone with customer support at one of the biggest delivery services in the world, and the call was carried out entirely by a voice agent. Two things stood out: How good voice agents are getting by the day, and how bad they can still be with human interactions (this one failed with a very simple query). I think voice agents should be evaluated more carefully. A wrong answer from a voice agent in a bank or insurance call center doesn't feel like a bug, but more like a person who told them something wrong. The implications for the brand (and the costs attached to it) are something that big companies cannot overlook. To solve this, today we launch something big in Galtea: you can now run voice simulations against AI agents that answer the phone. Voice simulations are available today in every plan. Running a voice agent in a bank or insurance call center? Test it before it talks to a customer: https://lnkd.in/eCn_gwBY

🚀 The hardest part of AI isn’t the technology. It’s getting started. After years building DRUID’s product and working with teams across the US, I’ve learned one thing: everyone wants AI, but not everyone knows where to begin.💡 My advice? Start with one use case. Define success. Build from there. That’s exactly what Symbiosis 5 is about: real use cases, real experiences, real impact. 📍 October 20th | London Link in the comments 👇 #Symbiosis5 #AgenticAI #AIAgents

Everyone has an opinion on where AI agents should start. We have 15 months of data. At Symbiosis 5, Michael Yang, Chief Strategy Officer, will show where your peers started, what’s paying off, and the scorecard that tells a production-ready agent from a very convincing demo. Oct 20 · London or online · free ❇️ Registration details are in the comments section. #Symbiosis5 #AgenticAI

💰 We processed 52,406 API calls for $2.41. Here’s what we learned about AI cost structure. When building AI agents, model choice quickly stops being a matter of preference and becomes a core cost driver. To see what agent operations actually cost in practice, our marketing team built a research agent to process over 43,000 Hacker News posts on AI infrastructure. After running 50,000+ model calls, the entire bill came out to just $2.41. Here are 3 key takeaways from the experiment: 1️⃣ Cost is decided at the step with the highest volume Classification accounted for 83% of all API calls and half the total bill. If you want to cut costs, optimize the model behind your most frequent step rather than replacing the most expensive model in your stack. 2️⃣ One parameter tweak can beat switching models Turning off "thinking mode" for simple classification dropped average output tokens from thousands down to 21 per call—cutting token usage and output cost by an order of magnitude overnight. 3️⃣ Smart model matching delivers 16x savings with minimal accuracy loss Using a tiered stack (Qwen3.5-9B for classification and Qwen3.6-35B-A3B for extraction/summarization) cost 16x less than GPT-5 while outperforming lower-cost alternatives like GPT-5-Mini in classification accuracy. The goal isn't just to find the cheapest model—it’s matching the right model and settings to the difficulty of each task. How is your team optimizing API call costs and model pipelines for AI agents? We’d love to hear your approach! Read the full breakdown and prompt setups — link in the comments below!👇 #AIEEV #AirAPI #AIOps #LLM

You don't need an allocation to get Blackwell-class GPUs. You need a datacenter that already has the racks — compare live capacity before you sign.

Prices for the same GPUs vary a lot by datacenter. We update the index every 15 minutes so you can compare before you sign — B300s are listed at $3.70/GPU-hr today.

How do you know if your startup idea is actually worth pursuing? Nobody in your life can answer it honestly. Your friends will say yes. Your doubts will say no. Ask founders who've validated - and killed - their own ideas:

Inworld is expanding in Europe. We are building out a Berlin office and now have regional deployments that allow for high performance and compliance at scale. To celebrate this expansion we're hosting Backchannel Berlin, an event for top researchers and engineers in Berlin and across Europe. Join us on Friday, October 23rd: https://lnkd.in/gpKCYWjq

Raising, hiring, pricing, pivoting - the hardest startup calls are lonely. Ask a founder who's already made them. Describe your situation and get answers drawn from their own interviews and writing, then ask follow-ups.

💡 2 weeks. That is all it takes to turn an enterprise NPU hardware infrastructure into a production ready private AI platform. When enterprise teams want to run AI models on their own infrastructure, the real challenge begins after model selection: making those models smoothly accessible to internal applications. Here is how AIEEV connected the NPU infrastructure of Company A, Korea’s largest AI contact center (AICC) platform provider, to their internal applications. 1. Deploying Private Air Cloud We set up Private Air Cloud directly on their NPU environment, establishing a secure private cloud foundation for their data and operations. 2. NPU Model Serving Configured a tailored model serving environment so internal tools could send requests and receive model responses seamlessly on NPU hardware. 3. Intelligent LLM Routing Connected internal application requests to multiple models via an LLM router, allowing developers to call and evaluate different models for features like document summarization. How the system works together: Internal App → LLM Router → Model Serving → Response Building a private AI environment around your existing GPU or NPU resources does not have to be complex. Planning to deploy AI models on your own hardware? Talk to AIEEV today. Read the full case study in the comments below!👇

Join us in Berlin on Friday, October 23 for Backchannel Berlin, an afternoon of conversation about the research on speech-to-speech models. Our team will share how we're building and serving full-duplex speech-to-speech in realtime, including the things that haven't worked. Then the floor opens: bring your own work or a problem you're stuck on, or just come and listen. We'll be getting into turn-taking and interruptions, cascaded versus end-to-end, keeping an LLM's reasoning when you teach it to speak, cross-lingual transfer, and what we should actually be measuring for conversations. If you work on speech-to-speech, TTS, ASR, audio codecs, conversational modelling, LLM serving or evaluation, we'd love to have you. PhD students welcome. Food and drinks throughout. Register below.

Sometimes the shot is already working. You just want to see what happens under different light. Relight Studio in Luma lets creative teams explore new lighting directions from the shot they already have, without starting over. Try Relight Studio in Luma.