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Your team doesn’t need to switch tools to try open models. Keep Codex and add open models served by Baseten. Same picker, nothing new to learn.

Open models served by Baseten are now in OpenAI’s marketplace. Get more model options in Codex without changing tools or workflows.

AI agents are starting to act on production systems faster than security standards can keep up. Our free checklist covers five pillars and 24 core standards every multi-user agent should meet before acting on production systems, plus 5 advanced standards for higher-security environments.

The Polish state is taking a stake in ElevenLabs. Through Vinci, part of the BGK Group, the Government of Poland is joining our latest round investors Sequoia, Andreessen Horowitz, ICONIQ and others as one of our backers. The teams at BGK and Vinci are great. More so than capital, they bring expertise and are phenomenal partners as we continue building this ecosystem. Together, we will help power AI Lab Poland: a new joint undertaking bringing together technology, science, talent and investment to support the next generation of AI innovation in Poland. We are already honoured to work with established partners such as Centrum e-Zdrowia, LOT Polish Airlines, InPost, Uniwersytet Warszawski and the iconic Piotr Fronczewski. Across all of them, one thing stands out: the ambition to build global technology from the start, lead with innovation and show what is possible from here. Piotr Dabkowski and I met in high school in Warsaw. Building on top of incredible foundations set by the generations that came before us, we had the privilege to study abroad, learn from many of the best people around the world and build a company with global impact from the start - from Europe. We now have an incredible talent network in Warsaw, London and across many capitals in the region and we’re committed to strengthening it even further in the years to come. We believe Europe has the potential to lead on technology and applied AI. Recently, we hosted the ElevenLabs Summit in Warsaw, and it felt like a homecoming - with such an incredible scene brewing here. With the Polish state now investing in ElevenLabs, we are proud to keep strengthening the connections between Poland, Europe and the global technology ecosystem. Thank you to Minister Andrzej Domański, to the teams at BGK and Vinci who believed in this, and to everyone who helped make it happen.

Find out where a lower-cost model can deliver the quality you need. The Specialized Intelligence Index compares models on benchmarks built by domain experts. Explore the trade-offs between quality, cost, and speed, and see where training gives specialized models an edge. Explore the index.

How well will a model handle the work you need it to do? Compare open, closed, and specialized models on the Specialized Intelligence Index, with benchmarks built by teams at Harvey, Replit, Genspark, and more. Explore the index.

You run Terraform, Kubernetes, and IAM. GPU clusters add Slurm, InfiniBand, and node health. The Nebius AI CloudOps Engineer exam covers that layer. 60 minutes online, Credly badge valid 3 years. Early-bird pricing until December 31.

Black Forrest Labs x NEBIUS: The European Computing & AI Powerhouse!! 🌲🖤 [Anzeige] Meet Black Forest Labs × Nebius! At NVIDIA GTC Berlin, I met Stephen, Developer Advocate at Black Forest Labs, one of Germany’s most exciting AI startups! And there’s a pretty interesting collaboration behind this picture! Black Forest Labs develops cutting-edge generative AI models, including the FLUX family for visual and Physical AI. But building these models requires ENORMOUS computing power! That’s where Nebius comes in! ☑️ GERMAN AI MODELS: Black Forest Labs develops frontier visual intelligence models. ☑️ NVIDIA BLACKWELL GPUs: Black Forest Labs used Nebius’s NVIDIA B200 infrastructure for research behind Self-Flow, a training method foundational to FLUX 3. ☑️ EUROPEAN AI INFRASTRUCTURE: Nebius provides the computing infrastructure needed to develop and scale AI models! Besides all the AI applications we build, Europe needs more companies building the models & infrastructure behind it. And I LOVE seeing these companies doing this by working together!! 🖤 P.S. Stephen, it was great meeting you! Maybe I’ll take you all on a little trip to the German BLACK FOREST soon to explore Black Forest Labs behind the scenes! 🌲 #Nebius #BlackForestLabs #NVIDIA #AI

Now your AI does, too. WRITER captures every signal and every insight your team generates, so agents act on your latest intelligence — not last quarter's.

We're giving engineering teams at enterprises $20,000 to train and deploy their own model on Arena. One grant per company, with our experts on hand at no extra cost, so the first experiments don't have to wait for budget approval. If your ML team has a task it wants a specialised model for, apply here and we'll get back to you within a week: https://lnkd.in/eMDCYpfV

If you lead AI at a bank or an insurer and you're planning to go agent-first, the model underneath it deserves a close look. Here is why: Runaway agents in a business can do great harm to a company's fiscal position whilst using a frontier model. With a frontier API, cost scales with usage, so as your usage grows, your token cost grows in a linear fashion, and you're always at the mercy of the provider if they change their token price. Open-weight, fine-tuned models on a company's own infrastructure allow them to really take advantage of an agent-first implementation. The initial cost is a little bit higher, but after that you pay for the hardware you run, so the more your agents do, the cheaper each unit gets. That only pays off if your agents are doing work that matters, which is why I would start where the data is richest. For banks and insurers, that's the repetitive work a few people do by hand, where you already hold years of previous outcomes. - Settling claims, where you know the value claimed, the correct value and what the policy covers. - Customer-service escalations. - A coding agent trained on your own codebase, so no third-party tool touches where your secrets live. Pick one of those, let the agent help the people doing it first, and over time let it do the work while they oversee it. This is exactly what we help enterprises do at AgileRL: fine-tune open models on their task and run them on their own infrastructure, so the cost of going agent-first stays predictable. Happy to chat if you're working through this.

Preparing for the EU AI Act? Keep a trace of every agent run and score it against your own tests before release.

Give every AI team the same tracing, evals and guardrails, with SSO, RBAC and self-hosting for the platform team.

Your AI agent will give you a confident reason for every dollar it moves. The reason is fiction. Ask it why it shifted spend out of brand media and into promotions, and it answers with total conviction. It just can't tell a good-sounding answer from a true one. That's the gap Tapati Bandopadhyay, PhD of Third Eye Advisory names in her new paper, "Beyond the Trust Wall: How Causal Reasoning Embeds AI TRiSM Into Autonomous Enterprise Action." She calls it fast to act, slow to explain. An agent reallocates a marketing budget at 2 a.m., and no one sits with it while it decides. The systems that take action are maturing faster than the systems that can explain the action. Her case makes the cost visible. Promotions looked like the biggest lever at 14.5% of new customers, with brand media at 10.6%. An agent running on standard prediction would have moved the budget into promotions. FOUNT found the other 7.1% of brand media was quietly generating months later, money last-click measurement simply could not see. She turns that into a test every agent should pass: explain the outcome "the way a controller explains a variance." Isolate the baseline. Remove the noise. Attribute each lever, now and later. What's left is the only number an agent is allowed to act on. A causal chain gives a guardian agent a checkable claim to test, so you can ask whether an agent "has earned the right to act on it" before the money moves. Tapati, thank you. The idea that every decision should come with its cause attached is the one DATA POEM was built on, and you've given it a sharp frame. Read the full paper here: https://lnkd.in/gA_ZqF4R #DecisionAI #CausalAI #EnterpriseAI #AIROI

What does building web search into agents look like in production? This Thursday at 10am PST, we chat with Victoria Plummer from the Hyperagent team about their learnings from shipping web-search-enabled agents into production. You’ll learn: - Which jobs depend on search: what Hyperagent's production usage shows - When your agent should search fast and cheap vs. deep research - New product capabilities that web search can bring to your agent Link to register in the comments!

A regulated iGaming operator now resolves +75% of its support tickets autonomously. It's Betsson, in one of its key regulated markets. Before a ticket even exists, 30%+ are solved proactively: the AI fixes failed identity checks before the player has to ask. Of the remaining incoming tickets, +75% are resolved end to end. The case study walks through the setup step by step: governance, ownership, and the limits built in by design. Free download for licensed iGaming operators.

6 iGaming support jobs Betsson's AI now runs end to end, from KYC to bonuses. Identity checks (KYC). Proof of address. Bonus eligibility and wagering. Login and account access. Player information updates. Betting history and account questions. Each one runs on rules the support team writes and owns. Bank-account changes, and anything the rules can't settle, go to a person. The case study shows the resolution rate for each job. Free download.

I've been going through the color explanations from our AI taste study (all 563 of them) and one pattern keeps showing up. A lot of them don't really say what the color is for. They say what it isn't. "Without the coldness of hospital blue." "Rather than corporate-cold." About 36% of them are written that way. Take the telemedicine brief, for example. The models were trying hard not to look like a hospital, and 8 of 10 landed on the same deep teal. Two of them were one hex digit apart. It's a good reminder that "avoid the cliché" is also something a model can learn, and once they all learn it, it stops being an escape. This blog dives deeper: https://lnkd.in/g7UuJdJf

You can't unit test whether a website feels right. You have to ask people. For this study we wrote a rubric, ran 450 head-to-head comparisons with ten votes each, and ranked the models with a Bradley-Terry fit. The full method and raw data are public if you want to check our work.

You have a limited budget for collecting hand data for physical AI. What do you spend it on? 10,000 more clean demonstrations of pick-and-place and grasping? Or a few hundred hard sequences: fingers self-occluded or hidden behind the object, hands truncated at the frame edge, off-nominal camera viewpoints, rare grasp types? What’s the trade-off in clean vs hard in terms of performance. We would like to put on the table at our research session at Humyn Labs this Saturday. We’re bringing researchers together to examine real failure cases and debate what the next round of hand data should prioritise. 24 October · Indiranagar, Bengaluru Researchers in hand pose estimation, egocentric vision and robot learning, this one is for you! Link to apply in the comments. Sandeep Reddy Sabbella, PhD Ishank Gupta Manish Agarwal Bhoomi Arora Aachman Maheshwari

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 researchers: literature, derivations and code in one place. Sign up free and get 1,500 credits.

100+ applications already. This Saturday, we're bringing together some very interesting folks: → An Adobe researcher who worked on egocentric hand tracking at CMU → A former CTO who built robots to help astronauts in space → A builder making mocap gloves that can sense touch → A PhD researcher at IISc's HiRO Lab An evening led by Dr. Sandeep, discussing all things egocentric hand tracking and where gaps are. Building in this space? Last few spots. We close applications in 24 hours. Sandeep Reddy Sabbella, PhD Ishank Gupta Manish Agarwal

A smile score isn’t the story. Context is. Hume measures expression across face, voice and language in real time, so your product responds to how people actually come across, not just what they type or say.