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Ask who owns the AI running in your organisation and you get three answers, or none. Deeploy registers every model and agent, assigns an owner and keeps the controls in one place.

Every new AI use case adds a blind spot: another model, another owner, another set of obligations nobody is monitoring. Runtime AI governance puts the controls where your models actually run.

Co lidem brání používat AI v práci? Z více než 3 000 dotazníků a online rozhovorů jsme zjistili: 🟣 21 % lidí neví, k čemu AI použít. U začátečníků je to dokonce 48 %. 🟣 Zhruba třetinu pokročilých brzdí firemní pravidla, zákazy a licence. Samotné licence nestačí. Lidé potřebují zjistit, k čemu jim AI pomůže v jejich vlastní práci. V e-booku Školení nestačí najdete čtyři kroky, osm zásad z praxe a plán pilotu s jedním týmem. Zdarma, 20 minut čtení.

Ptáte se, co vám AI školení přineslo? Často tohle: lidé AI pár týdnů zkoušeli a vrátili se k tomu, jak pracovali dřív. V e-booku zjistíte, proč se to děje a jak tým naučit používat AI na konkrétních úkolech. Čtyři kroky, osm zásad z praxe a plán pilotu s jedním týmem. Zdarma.

The best agent design is selective about what it loads, calls and computes.

We pointed Crusoe Managed Inference at Wiz's Day-One CTF. With the right browser tooling and a run-code primitive, the agent solved all seven challenges in less than 2 hours for ~$13, showing a strong harness matters more than a frontier model.

ComfyUI for Animation: Concept Art to Any Angle Concept art → 3D proxy → any angle you want. One graph, two methods. Step 1: load your concept art. Tripo generates a 3D model and saves it as a GLB. Drag it into Load 3D and set material mode to Normal. Step 2 (stills): rotate the viewport to your angle. Load 3D outputs the view, and GPT Image 2 renders it back as a polished on-style illustration. Step 3 (camera moves): record a path in the viewport. That clip becomes the motion reference for Seedance 2.0, which generates the move through the stylized scene. Click the link below to run the workflow ⬇️

In 'From electrons to tokens,' five Crusoe leaders share what they're actually seeing and betting on right now. Not a survey. Not a synthesis of secondary research. An authentic view from inside the machine.

What if we could more accurately predict what comes next? That's the question that started Fundamental. A couple of years later, it’s changing what AI can do.

The Blender video defines the camera movement and perspective, giving the generation a clear motion reference while Seedance 2.5 handles the final visual output. Prompt + workflow included. To try this workflow, link below 👇 https://lnkd.in/eGRmC3BC

Getting GPU allocation is hard. Paying a premium for it makes it worse. io.net secures the compute you need from our own inventory and partner network, at 50% less than big cloud.

Tell us what you need to run. We match it to our inventory and partner network and lock in your allocation. No waitlist, no premium, 50% less than AWS.

You can now use your ChatGPT subscription to hire AI staff on Hyperagent. 🚀 Was excited to attend DevDay and watch OpenAI launch Sign in with ChatGPT yesterday - with Hyperagent's logo popping up on the big screen among the first products where it's live. Our customers use Hyperagent to hire agents into real jobs all across their businesses: → A pavement company's sales agents are running outbound at 3X the response rate, adding $2M in revenue. → A glass installer's quoting agents are bidding projects 6X faster. → An AI startup's marketing agents are getting 5X the industry-average CTR on perf mktg ads. So now, with your existing ChatGPT subscription, there's no barrier to your own business's first AI employee. Sign in and start hiring. Link below 👇 https://lnkd.in/gCvTYfsZ

🎙️Voice Agents That Actually Work: Cartesia × Coval 📍Coval HQ, SoMa · Thu, Oct 8 · 6:30 PM Demos are easy. Production is hard. We're getting real about what it takes to build voice agents customers actually trust. Drinks included, spots are limited. RSVP: https://lnkd.in/gyFD74Rh

☕ COFFEE CARTesia 📍 Salesforce Plaza · Wed, Oct 7 · 8:30 AM–12:30 PM Walk up, tell our voice agent your order (oat milk, extra matcha shot, "surprise me"), and it's on us. It's SF's newest coffee stand and we're in business. RSVP: https://lnkd.in/gMFDtY-R

Our revenue more than doubled in FY2026. Exascale generated $14.8 million in revenue, up 111.3% from the previous year. Existing customers spent more with us, and our customer base expanded, driving growth in our intelligent compute service. After fiscal year-end, we completed our business combination and began trading on Nasdaq as $XLAB on August 28. Hoansoo Lee, our CEO, on what comes next: “Our focus now is on converting that growth into disciplined, scalable execution as a public company.” Read the full financial results and business update: https://lnkd.in/gMYGF3im

Accuracy is everything when you are forecasting your company's future. The M5 is the world's benchmark for it. Google, Amazon and Salesforce all compete there. The winning entry used more than 200 models. POEM365 uses one. It beat them all: 36% average superiority across standard benchmarks, and 90%+ accuracy for its Fortune 500 clients. One causal model, outperforming hundreds. See what that accuracy looks like on your numbers. The paradigm is changing.

For twenty years, analytics only got more detailed about the past. More data, better dashboards, still describing what already happened. POEM365 is not an LLM or a predictive model. It is a large causal model, built on FOUNT, our Large Causal Architecture, and trained on billions of transactions. It maps cause and effect across your entire business to show what happened, why it happened, and what to do next. It is not a better dashboard. It is the decision layer that comes after it, where you see as one, act as one, and grow as one. The paradigm is changing.

The hardest part of data science is figuring out what problem to solve. Gael Varoquaux says the real work is figuring out what the data is, what question you're asking, and what an answer is worth. In science it's called framing the problem. In business, he says, it's the same job.

How can your Kansas City bank benefit from Hapax? Here are 5 specific use cases. Request a demo: https://lnkd.in/dxwb4y58

How can your Kansas City credit union benefit from Hapax? Here are 5 specific use cases. Request a demo: https://lnkd.in/dxwb4y58

Upstream oil & gas is one of the hardest environments to deploy AI in. Thousands of distributed wells, sensor data in every format, constantly shifting conditions, and decisions where being wrong shows up as lost production, a safety incident, or environmental damage. The conventional response has been a model per asset, per failure mode, per site. Each one needs its own development, customization, and retraining. That approach never reaches fleet scale, which is why most operational sensor data still goes unused. Foundation models change the economics. Newton, our Physical AI foundation model, reads vibration, pressure, flow, current, and acoustics across drilling, production, and rotating equipment. It ports from one well to the next without a rebuild each time, so a use case proven on one asset extends to hundreds at a fraction of the engineering cost. On June 18, Archetype AI walks through what it actually takes to design, adapt, and operationalize Physical AI across upstream use cases — from drilling and production monitoring to pumps, compressors, and other critical equipment. The session remains practical: preparing industrial sensor data, choosing between zero-shot deployment and machine-specific adaptation, and configuring Physical Agents across well fleets. What you'll leave with: 🔹 A framework for deploying foundation models across upstream assets and workflows 🔹When to use zero-shot, few-shot, or fine-tuning 🔹Best practices for multimodal sensor inputs and agent configuration 🔹Lessons from scaling AI across equipment fleets

Physical AI is moving into the factory. Join NTT DATA and Archetype AI for a live conversation on how AI reasoning over sensor data is catching issues earlier, verifying critical work, and keeping operations safe. Featuring lessons from our Hyster-Yale collaboration. Thursday, July 30, 9:00 AM PDT. Register at the link in the comments.

Wie nutzt man KI operativ, ohne die Kontrolle über besonders schützenswerte Daten abzugeben? 🔒 Gemeinsam mit FAST LTA haben wir eine Antwort: eine vorkonfigurierte, betreute KI-Appliance – einsatzbereit ab Tag eins. 🚀 Mehr dazu im neuen Artikel 👇 #DigitaleSouveränität #KI #Datenschutz #Gesundheitswesen #basebox #FASTLTA #SilentAIappliance #KHIT