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You didn't become a marketing leader to manually pull campaign reports or chase down content updates all day. Imagine AI workers handling content repurposing, lead scoring, campaign analytics, and social scheduling while you focus on strategy, creative direction, and driving pipeline. This eBook shows you exactly how to build 6 AI marketing workers (no code needed) that eliminate repetitive work and give you time back for high-impact campaigns and team leadership.

You didn't become a marketing leader to drown in spreadsheets and status updates. Imagine AI workers handling content production, campaign reporting, lead routing, and data analysis while you focus on strategy and growth. This eBook shows you how to build 6 AI marketing workers (no code needed) that handle the repetitive work so you can lead.

While the AI world was racing to build better language models, we were building something different entirely. Dr. Uri Yerushalmi, our Co-Founder & Chief Scientist, explains why - and what our Market Model is trained on instead. 🎥 Watch the full keynote from the Berkeley Agentic AI Summit: https://lnkd.in/dVq8RcE8 #Market #Model #Agentic #AI #Berkeley

Exascale Labs Holdings Inc. (Nasdaq: XLAB) today announced a non-binding memorandum of understanding with EnergyBank to explore the development of grid-independent AI compute infrastructure powered by floating offshore wind and long-duration energy storage. The collaboration contemplates starting with an initial pilot alongside Zephyros-One, the world’s first floating offshore wind turbine, operated by Sustainable Energy Norway. That pilot targets up to 800kW of compute-ready power on an existing floating turbine. EnergyBank is expected to fund the initial pilot through a combination of private investment and potential public funding. Beyond the pilot, the companies intend to develop a repeatable reference architecture for future multi-turbine, multi-module projects, potentially targeting up to 10MW of firm compute capacity per turbine. From our CEO, Dr. Hoansoo Lee: “AI infrastructure growth is not constrained by the lack of availability of advanced chips. Rather, it is due to the inability to secure and deliver power at the required scale and within commercially viable timelines. Currently, GPUs ship in weeks, and the facilities to house and power them take months or years to build. What makes this collaboration different is that we are assembling power generation, storage, cooling, and compute into a single integrated system from the outset. Our modular platform is purpose-built to operate off-grid, which means floating offshore wind farmed by EnergyBank’s technology can deliver compute-ready power without waiting years for a grid connection.” EnergyBank CEO Tim Hawkey framed the same problem from the generation side: taking floating wind off-grid and integrating generation, storage, and modular data centers so variable offshore power can become firm compute without a multi-year interconnection wait. Next steps under the MOU are joint technical feasibility work, a reference architecture for the pilot, and a scaling roadmap. The MOU is non-binding and does not obligate either party to proceed with the pilot or any later deployment. Any pilot or subsequent project remains subject to further technical evaluation, definitive agreements, financing, and applicable regulatory and other required approvals. There can be no assurance that the collaboration will result in a binding agreement, a completed project, or any commercial deployment. Read the full release: https://lnkd.in/gZsgHEPg #AIInfrastructure #DataCenters #OffshoreWind #EnergyStorage #XLAB

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You create premium research reports. Track downloads. Then... nothing. You don't know: → If anyone actually reads them → Which sections resonate → Who's a real buyer vs. tire-kicker → If they're being shared with competitors Join research marketing leaders Nov 5 (11am EST) discussing: ✅ How to capture leads INSIDE your reports (not just at download) ✅ Track page-level engagement across all your research ✅ Stop readers from copying to ChatGPT with in-doc AI ✅ Real case studies from research firms solving this

You spend months creating research. Someone downloads it. Then... nothing. You don't know: → If they actually read it → Which sections they care about → Whether they're a qualified buyer → If they shared it with competitors Factify changes this. ✅ Capture leads directly inside your reports (not just at download) ✅ Track page-level engagement across all your research ✅ Embed AI chat so readers don't leave for ChatGPT

AIxBio Hackathon. April 24-26. Co-organized with BlueDot Impact and Cambridge Biosecurity Hub. New rewards on the table: top teams get fast-tracked into programmes from our co-organizers. ERA:AI Fellowship interviews at Cambridge. The Apart Fellowship. CBAI. BlueDot's talent and funding pathways. That stacks on top of: - $11,000 in prizes across hubs and tracks - Four tracks: DNA Screening, Pandemic Early Warning, AI Biosecurity Tools, Benchtop Synth Security - In-person hubs in SF, Berlin, London, and Boston Three days. One weekend. Real research output on one of the most neglected intersections in AI safety. Who on your team should be building here? Sign up: https://lnkd.in/dZjwEBsj

AIxBio Hackathon, T-3 days | With BlueDot Impact and Cambridge Biosecurity Hub. Three days out. Here's what's on the table this weekend: New keynote: Dr. JAIME YASSIF, Senior advisor at NTI. She co-chaired the NTI-RAND side event on AIxBio Governance at the Paris AI Action Summit and leads the Global Forum's push for tiered access to AI-bio tools. That makes three keynotes, alongside Prof. Kevin Esvelt (MIT Media Lab) and Dr. Oliver M. Crook (Oxford). Funding runway talk: Conor McGurk, Chief of Staff, Biosecurity at Coefficient Giving, on their biosecurity RFP. The RFP closes May 11, two weeks after the hackathon ends. Teams can prototype across the weekend and submit real applications two weeks later. Fast-tracks stack up: - ERA:AI Fellowship, 10 weeks of funded research at Cambridge. Top teams get fast tracked. - ERA AIxBio Fellowship, bio-focused summer cohort. Top teams get fast tracked. - CBAI fellowship fast-track. - Apart Fellowship invitation for top teams. Plus $13,000 in prizes across four biosecurity tracks and local hub prizes, in-person hubs in SF, Berlin, and London, and full remote. Who on your team should be building here? Sign up: https://lnkd.in/dz8fYGBv Local hubs: https://lnkd.in/dHWCJZjj

Speed is only valuable when it comes with accuracy. For enterprise-scale AI, “close enough” simply isn’t good enough. EigenInference uses advanced NVFP4 / FP3 quantization to deliver significant speedups while maintaining the model integrity your customers expect. Scale your AI services with the confidence that your outputs remain enterprise-grade. Fast, without the "correctness tax."

Enterprise AI has moved beyond simple chatbots. To stay competitive, your product roadmap likely spans vision, video, speech, and text. Your infrastructure needs to be as ambitious as your vision. EigenInference is optimized for multimodal workloads, so as your product evolves from text to video or speech, performance stays consistent and seamless. Build for the future of AI, not just today’s requirements.

【ELYZA Works 】日々の報告書作成、管理業務を効率化

You saw the problem at HumanX — everyone's shipping AI, nobody's governing it. Empromptu gives you evaluation on every output, full audit trails, compliance-ready governance, and real-time monitoring. See what production-grade AI infrastructure looks like.

I was cleaning data from 6 sources in spreadsheets. Debugging hallucinations at 2am. Getting 70% accuracy and thinking that was normal. It's not normal. I plugged our data into Empromptu and hit 98% accuracy on real user queries. Context that actually persists. Data pipelines that just work. I'm not going back to building the hard way.

【ELYZA Works with KDDI】日々の報告書作成、管理業務を効率化

Reduce manual finance work by 70%+, live in only 6–10 weeks. Endeavor AI agents work across your ERP to lower finance ops cost, reduce DSO/DPO, and unlock savings through spend intelligence.

Every invoice that needs manual coding, approval chasing, vendor follow-up, or exception review slows the finance team down. Endeavor AI agents handle the repetitive AP work end to end, so your team can spend less time pushing paper and more time managing cash, controls, and strategic priorities.

Still debating whether to build your edge AI stack from scratch? Discover how Edge Impulse helps you: ✅ Accelerate time to market ✅ Slash development costs ✅ Scale effortlessly across devices ✅ Tap into expert support and a thriving dev community Skip the complexity. Start innovating faster with Edge Impulse.

Engineering teams face a critical choice: invest months building infrastructure—or accelerate innovation with a proven platform. With Edge Impulse, you get: 🔧 Production-ready pipelines for data ingestion, model training, and deployment 📈 Proven performance benchmarks across real-world edge devices 🔒 Enterprise-grade security and compliance baked in 🧑💻 Full transparency and control over your ML lifecycle Don’t reinvent the wheel. Focus your team on what matters: launching great products, not building AI models.

"Why can't we just build this ourselves?" is the first question we hear in almost every Physical AI conversation. Marc Rouanne, veteran of Nokia and DISH, breaks down the complexity - all 15 layers of the DIY stack - and why most DIY pilots stall out after 18-24 months of lost effort.

If your team is weighing a DIY approach to Physical AI infrastructure, read this first. We walked through all 15 layers - firmware, security, connectivity, release management, and more - before you commit budget and headcount to building it yourself.

Emergence World Season 2 results are now live. In Season 1, we asked what happens when autonomous AI agents operate together for weeks rather than being tested for seconds or minutes. For Season 2, we asked a harder question: as AI models become more capable, do they actually become safer? We took 8 different worlds powered by 7 leading frontier models (Claude Opus 4.8, Gemini 3.5 Flash, OpenAI GPT 5.5, Grok 4.3, Mistral Medium 3.5, Qwen 3.7 Max, and DeepSeek V4 Pro) from the US, Europe and Asia, alongside a mixed-model world, and observed how autonomous agents behaved over time. We also introduced Black Swan events: a phishing attack, a misinformation campaign and a memory breach designed to test how these systems respond when conditions become unpredictable. What we found was that every model showed vulnerabilities. These findings have implications for how autonomous AI is being deployed today. As AI systems become more capable moving beyond answering questions to taking actions in real-world enterprise environments, evaluating the model alone is no longer enough, guardrails needs to be put in place. At Emergence, our research points to six actions we believe the industry needs to take to make long-horizon autonomous AI safer, more trustworthy and more reliable: 1. Define the boundaries. Architect the multi-agent system around clear trust boundaries, communication policies, authority delegation, and information-sharing rules. Ensure persistent goals, tools, and permissions can be inspected, reviewed, revoked, and reset as requirements change. 2. Enforce the boundaries. Build AI Runtime Governance into infrastructure through policy enforcement, approval gates, action authorization, and audit logging that operate independently of the LLM. 3. Verify the work. Require independently verifiable evidence for critical actions, including database changes, customer communications, financial transactions, and code deployments, rather than relying solely on model reasoning or explanations. 4. Control the memory. Apply provenance, verification, ownership, retention, expiration, and quality controls to agent memories, summaries, and shared knowledge. 5. Prepare for failure. Build AI Reliability Engineering and continuous operational capabilities for behavioral drift, policy compliance, fault recovery, graceful degradation, rollback, checkpointing, and long-term agent health. 6. Stress-test the system. Use adversarial and chaos testing to expose autonomous systems to phishing, misinformation, compromised memory, outages, conflicting instructions, and other Black Swan scenarios before they reach production. Emergence World is designed to surface these risks before autonomous systems encounter them in the real world and to help establish the systems, safeguards and engineering practices needed to deploy long-horizon autonomy responsibly. Read the Emergence World Season 2 paper here: https://lnkd.in/gmSRexKB

Have 200+ ChatGPT Enterprise licenses? You qualify for a 3-day complimentary Enablement Package from Eliza, designed to accelerate adoption and unlock ROI. 1. Use Case Discovery & Prioritization (with Lighthouse Wins) We co-create a catalog of high-impact AI opportunities, prioritize by ROI, and deliver a quick “lighthouse win” so teams see immediate success. 2. Training & Coaching Role-based workshops and 1:1 coaching help employees build confidence, turn theory into practice, and embed AI into daily workflows. 3. Lasting Momentum We leave behind champions, playbooks, and repeatable patterns so adoption compounds instead of stalling after the first week. 4. Health Check & Best-Practice Setup We review your environment, security posture, and configuration to ensure your deployment is compliant, scalable, and ready to expand. This package is complimentary for qualifying customers. Reach out to [email protected] to learn more.

AI doesn’t fail because of models: it fails because data isn’t ready to move. Our platform brings data, automation, and governance together so agents can reason, plan, and execute across workflows without manual handoffs. That’s what true data readiness looks like.