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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.

Launch day is calmer when the data is already validated. Get a Chemin sample dataset, on us, and see how our experts check it.

Tell us the basics – which GPU, count, duration. We will send back a quote.

Skip the intro call, the deck and the follow-up. Tell us the GPU, the count and the duration — we come back with availability and pricing.

Eyedea Recognition: AI Visual Traffic Analysis Software: E-Tolling & ITS, Parking & Access, Law Enforcement, Smart Cities, Police & Forensics. 🚘 🛣 Eyedea develops AI-based software solutions for traffic and mobility. Its flagship product, Make & Model Recognition (MMR), is one of the most detailed vehicle recognition solutions on the global market. Eyedea’s ANPR license plate recognition software is localized for individual countries and achieves accuracy above 98% in most regions. The system delivers reliable results even from low-quality data or images captured from sharp angles. 🚚 Additional Eyedea solutions cover forensic analysis, biometrics, and privacy-focused anonymization. ✅

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.

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.

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

On 2 August, three transparency obligations under the EU AI Act became applicable across the EU market: → If your AI interacts with a person, it has to disclose that it is AI. → If it generates synthetic images, audio, or video, that content has to be labeled. → If it runs emotion recognition or biometric categorization, the people exposed to it have to be informed. For most teams, disclosure was a design decision made once, at launch. Under the AI Act, it is now a behavior you are accountable for, on every release and in front of every user. And you have to verify it the same way you verify anything else your system does. Three questions worth putting to your AI team: → Does our assistant reliably disclose that it is AI across the real range of conversations users actually have, not just the scripted happy path? → Does every piece of synthetic content we generate carry the label, across every format it ships in: image, audio, and video? → When we run emotion recognition or biometric flows, does the required notice actually reach the person in the interaction? And the one that matters most under the new regime: → Can we produce the evidence to prove all of this, on demand, if a regulator or an auditor asks? None of this shows up in a standard accuracy or latency eval. Disclosure has to be tested against realistic scenarios, re-run on every new model version and every prompt change, and captured as evidence, not checked once at launch and assumed stable. Manual spot-checks do not scale to that. And "we designed it to disclose" is not the same as "we can show that it still does." P.S. I'm running a free workshop on building evals for behaviors like this, which an off-the-shelf metric can't measure. If your team is building AI in a regulated industry, join me: https://lnkd.in/et-RWCjm

We recently partnered with Sabadell Seguros to help them scale a GenAI assistant to 6,000 sales agents. A bit of context: SofIA is their generative AI assistant for Home, Life, and Income insurance. Before scaling to 6,000 agents, they wanted hard proof it was ready. These are the final numbers: 8% uplift in accuracy. 20% fewer refusals. Not everybody insists on this level of rigor before scaling. The team led by Joan Franco did, and it shows. You can read the full story here → https://lnkd.in/efQ7WUnR

💰 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.

We’re Making Advanced AI Drug Research Free for Everyone At Apodex, our goal is to build Discoverative AI—AI that can help humanity discover things we do not yet know, not just generate answers from what we already know. I believe this is one of the most valuable things AI can do for humanity. And drug research is one of the best places to start. Few things matter more than finding better ways to prevent and treat diseases. There are thousands of small biotech companies and research teams working on important medical problems. Many have good ideas but do not have enough AI experts, computing resources, or research tools to explore them. I don’t think a good scientific idea should have to wait for a big budget. That is why we decided to make a growing set of Apodex’s AI drug research tools available for free. One thing I want to emphasize: we are offering much more than free computing. At Apodex, we have a team of scientists and AI researchers who are constantly improving our models, algorithms, and research tools. We are starting with clinical trial forecasting, drug repurposing, drug interactions, and safety assessment. But this is just the beginning. What matters even more is what you need that we have not built yet. We want to listen and develop new tools and algorithms based on real research needs. Some may become free tools for everyone, while others may require deeper collaboration. Our goal is to make useful AI research capabilities available to more people, while continuing to build better ones. So please don’t just come to Apodex to use what we have built for free. Come and tell us what we should build next. https://drug.apodexlab.ai/ #AIforScience #DrugDiscovery #Apodex

Faster generative video means more variations, more testing, and campaigns that launch on your timeline. Higgsfield Enterprise gives your team priority speed when deadlines matter most.

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Where should your first AI agent start? Your peers have already made that call, and 15 months of production data show which choices are paying off. Michael Yang walks through Druid's 2026 AI Adoption Benchmark industry by industry, using deployments from around the world, and the AI Agent Reference Model: a blueprint for what every production AI agent needs, and a common scorecard for comparing one agent with another. On October 20th, at Symbiosis 5. Not to be missed! ❇️ Save your seat to attend online or in London. (we've added details in the comments section below) #Symbiosis5 #AgenticAI #DruidAI

There are rare driving scenarios that happen once every few million miles that more data won’t necessarily help. What would help would be to find rare scenarios, prioritize them by risk, and capture enough context to learn from them. We break down how it works in our latest article: https://lnkd.in/dukrywGK Want a second set of eyes on your AV data pipeline? Let’s chat: chemin.com/contact