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With Microsoft, we are building a long-term partnership spanning cloud, compute, governance and real-world deployment. At its core is a shared belief that trusted infrastructure, responsible innovation and the capability to scale are fundamental to the future of AI. Foundations built to serve governments, industries and communities for years to come. #G42 #Exponentialai #AI #Microsoft #Technology #InnovationHub

Fusemachines’ Global Reseller Network enables companies to become authorized resellers of AI Studio and AI Engines, enabling enterprises in retail, real estate, and food and beverage to build and deploy highly accurate, industry specific AI solutions at scale. Become an authorized reseller. Apply here: https://bit.ly/4oMcTgu

An exciting new chapter begins for Fusemachines. Here’s to growth, innovation, and #DemocratizingAI.

When does it actually make sense to move off pay-per-token to dedicated GPUs? 🤔 Pay-per-token is the easiest way to start building with LLMs — no GPUs to provision, and costs scale directly with usage. But as traffic grows, the economics shift. We modeled a representative GLM-5.1 workload (10K input / 500 output tokens per request, 50% cache hit rate) and compared the two pricing models: 🔵 Model APIs (pay-per-token): cost scales linearly with token volume 🔵 Dedicated Endpoints (reserved GPUs): fixed hourly cost, independent of throughput The two curves cross at a point. Below it, pay-per-token is cheaper because you only pay for what you use. Above it, dedicated capacity wins because you're utilizing GPUs more efficiently. In our example, the effective cost on a Dedicated Endpoint came out to ~$0.64 per million tokens — compared to $1.40 for the equivalent GLM-5.1 input price on Model APIs. There's no universal crossover point. It comes down to your tokens per request, concurrency, traffic patterns, and latency targets. The honest answer is to measure your workload, benchmark both, and decide on real numbers. FriendliAI makes migration straightforward. And because both run on an OpenAI-compatible API, switching is just a base URL and model ID change. Full breakdown in the comments below👇

Tiny, maybe? Hiring, definitely. https://lnkd.in/eXi_PUPa

Join our live webinar to see how Fiddler unifies evaluations and production monitoring into a continuous feedback loop that improves accuracy, safety, and deployment speed. Few spots left. Claim yours👇

Your agents are only as good as your visibility. See how Fiddler helps teams diagnose failures fast, understand agent decisions, and boost performance across production systems. Register for our Agentic Observability webinar👇

🧸 𝐀𝐈 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐮𝐬𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚 𝐭𝐨𝐲 Many organisations are rushing to deploy #AI tools. But the real challenge isn’t generating answers, content, or translations. The challenge is 𝘵𝘳𝘶𝘴𝘵: 🔹 Where did the information come from? 🔹 Which model produced the output? 🔹 Who had access to the data? 🔹 Can the result be traced and verified? 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲, 𝐚𝐮𝐝𝐢𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐀𝐈 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐥𝐢𝐭𝐭𝐥𝐞 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐚 𝐜𝐥𝐞𝐯𝐞𝐫 𝐭𝐨𝐲. At Exfluency, we focus on making AI usable in serious organisations – where confidentiality, multilingual knowledge, and verifiable sources actually matter. That means: 🔹 controlled AI workflows 🔹 trusted knowledge bases 🔹 model choice per task 🔹 and traceable output with clear data provenance Because without trust and transparency, AI doesn’t scale beyond experimentation. #AIGovernance #AgenticAI

Scale AI and multilingual workflows without losing control. We help organisations govern AI agents, automate multilingual workflows, and turn scattered knowledge into operational advantage.

Tried an AI SDR and got ChatGPT in a trench coat? You're not alone. Most "AI SDRs" are glorified copy assistants, they draft emails and hand the work back to you. EverWorker's AI SDR is an autonomous AI Worker. It researches accounts, personalizes every touch, sends, replies, books, and follows up. On your CRM. On your cadence. 24/7/365. Skeptical? Good. Don't take our word for it. See it built on your stack in a 30-minute live demo. No slides. No "roadmap." Just your pipeline, moving.

You didn't become a marketing leader to spend hours formatting blog posts or manually segmenting email lists. Imagine AI workers handling SEO optimization, lead nurturing sequences, competitive research, and performance reporting while you focus on positioning, messaging strategy, and revenue growth. This eBook shows you exactly how to build 6 AI marketing workers (no code needed) that eliminate the busywork and give you time back for the creative and strategic work that actually moves the needle.

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

Set up daily alerts for new qualified leads

Set up daily monitoring for new leads

Most research marketers nail the download gate. But here's what you're missing: • Leads captured at actual moments of interest (not just downloads) • Engagement data showing what prospects care about • Intelligence on which readers are qualified buyers This webinar covers: ✅ In-document lead capture strategies (gate specific sections, insert forms anywhere) ✅ Analytics that show WHO reads WHAT and for how long ✅ How to keep readers engaged without them leaving for AI tools

If you only track downloads, you're missing the full picture. What research marketers really need to know: • Are reports actually being read? • Which insights resonate with different audiences? • Who's a qualified buyer worth following up with? • Where content loses reader attention This webinar reveals: ✅ Advanced analytics for research reports (beyond basic download metrics) ✅ Lead capture strategies inside documents (not just at the gate) ✅ How to prevent content leakage to external AI tools ✅ Real examples from research firms getting 3x more qualified leads

The AIxBio Hackathon (April 24-26) is virtual-first, but if you want to work alongside other participants in person, we have confirmed hubs in four cities: San Francisco Berlin Cambridge/Boston London All talks, submissions, and collaboration happen on Discord and Zoom. Remote participants have the same experience. $6,000+ in prizes. Top teams: invitation to the Apart Fellowship and fast-track to the CBAI fellowship. Sign up: https://lnkd.in/dJ48CTmr Local hubs: https://lnkd.in/dHWCJZjj

AIxBio Hackathon, April 24-26 | Co-organized with BlueDot Impact and Cambridge Biosecurity Hub Two keynotes: Kevin Esvelt (MIT Media Lab) runs the Sculpting Evolution Group. He was the first to identify the potential for CRISPR gene drives to alter wild populations, and has spent the last decade arguing for open research that advances biotech without expanding catastrophic risk. Oliver M. Crook (Oxford, Kavli Institute for Nanoscience Discovery) is an MRC Career Development Fellow. His group works at the intersection of Bayesian machine learning, structural biology, and biosecurity. Two angles on the same question: how do you build tools that keep biosecurity ahead of the risk curve when AI is accelerating both sides? Four tracks. $11,000 in prizes. Track sponsors: CBAI (DNA Synthesis Screening), Sentinel Bio (Benchtop Synthesizer Security), Fourth Eon Bio (AI Biosecurity Tools), Measuring AI Progress (Pandemic Early Warning). London 1st: $3,000. Top teams: invitation to the Apart Fellowship, Fast track to Era-AI and CBAI Fellowships. Four days to go. Hubs in SF, Berlin, London + full remote. Signup Here: https://lnkd.in/d6pfAGaj What is the biosecurity problem most worth a weekend of work?

【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.

【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.

Satya Nadella just made the case for everything we've built at Empromptu: "There should be as many models in the world as firms in the world. Because after all, what is a firm? A firm is a learning system." The whole article reads like a blueprint for what we ship. Line by line, we check all the boxes: "I don't want to be locked into any one model" ✅ "I want to use my own context, my own data" ✅ "...cost-efficient model" ✅ "...fine-tuned model" ✅ And the line that puts it into a sharper perspective: "If you outsource your learning, then why exist?" Exactly. So we turn every correction, edge case, and real-world use into training data that builds custom AI your company owns, not rents. Our Self-Managing Context tech holds up to 98% accuracy in production. In the wild, that's 50,000+ AI requests a day across 1,600+ store locations, and 20,000+ creators managed with zero AI engineers on the client side. You own your context. You own your learnings. Take it from Satya. This is the future. Source in the comments. Want to see it work on your data? 👇