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800+ engineers and AI leaders gathering in London this November.

Fully Connected London is back! The AI cloud conference. Two days that will change the way you build AI.

Unlock 24/7 employee support through AI assistants. Learn how connecting AI to your SOPs and internal policies can create a continuous, always-available help desk for new and existing employees.

Abacus raised $5 million in Series A funding. Honestly, it feels surreal to write that. I’m incredibly grateful to Jay Eum and the team at GFT Ventures for leading this round, and to Carey Ransom at BankTech Ventures and the LAUNCH Fund for believing in what we’re building at Abacus. For anyone who doesn’t know our story: my co-founder Lisa Gillespie was my accounting professor at Loyola University Chicago. (Yes, really.) People ask us all the time how that’s worked out. She was tough then, and she’s still tough now, but in the best way. The rigor, precision, and accountability she demanded in the classroom is exactly what we’ve built into Abacus from day one. Here’s what we’re solving: Regulated industries – banks, credit unions, healthcare, insurance – can't afford black-box models or data leaks. They need AI they can actually trust. That's Abacus. Abacus is on-prem AI infrastructure that keeps your data within your control. The demand from banks and credit unions has been overwhelming (in the best way possible) and now we’ll be able to meet that demand. This round lets us grow our team, deepen our platform, and keep pushing on what we believe in: AI assurance. Excited to see where this journey takes us.

Most AI companies ask enterprises to do something completely unrealistic: “Just upload all your documents to our platform and we’ll take care of the rest.” For an enterprise company with tens of thousands of documents across hundreds of different data sources??? That’s not happening. It would mean: - Internal alignment meetings across every department - Changes to established processes and procedures - Moving sensitive data outside your firewall - Getting compliance to sign off on all of it What a nightmare. This is why we built the Abacus Indexer to be decentralized. Instead of asking you to bring your data to us, our Indexer goes to where your information lives today – indexes it right there – and brings it together. The Indexer stays completely inside your infrastructure. So that you have peace of mind and a sense of security when using Abacus. To check out our integrations, go to the link in our comments.

Proud to see Kevin Scaman present ‘What LLMs Learn (and Don't) from Tables’ at the [ICML] Int'l Conference on Machine Learning The work shows that general-purpose LLMs rely heavily on memorization when reasoning over tabular data, with performance deteriorating as table complexity grows. Progress on tabular AI won't come from simply scaling language models. Structured data presents a different set of challenges, and solving them requires purpose-built architectures. If this sounds interesting, come join our research team

AI at scale needs the right foundations. Our long-term partnership with Microsoft brings together trusted infrastructure, responsible innovation and the capability to scale. Across cloud, compute, governance and real-world deployment, we are building foundations that can serve governments, industries and communities for years to come. Built to last. #G42 #Exponentialai #AI #Microsoft #Technology #InnovationHub

The partnerships that matter are built to last. With Microsoft, we've built more than a collaboration. We've built a long-term partnership grounded in a shared belief that the future of AI depends on trusted infrastructure, responsible innovation and the capability to scale. From cloud and compute to governance and real-world deployment, we're building foundations that will serve governments, industries and communities for years to come. #G42 #G42ai #Exponentialai #AI #Microsoft #Technology #InnovationHub

𝐓𝐢𝐜𝐤𝐞𝐫 ‘𝐅𝐔𝐒𝐄’ 𝐥𝐢𝐯𝐞 𝐨𝐧 𝐍𝐀𝐒𝐃𝐀𝐐 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰! We have successfully completed our business combination with CSLM Acquisition Corp., and starting 23 October 2025, Fusemachines' common stock will trade on the Nasdaq Global Market under the symbols “FUSE". Behind every milestone is a team of extraordinary minds. At Fusemachines, our journey has always been guided by collaboration, innovation, and a shared belief that AI should be accessible to all. As we begin this exciting new chapter, we celebrate our global team, partners, and supporters who have fuelled our mission to bring the power of AI to every corner of the world. #DemocratizeAI Press release: https://lnkd.in/gisW854h

Fusemachines welcomes Genese Solution, an Amazon Web Services (AWS) Premier Tier Services Partner, to its Global Reseller Network as an authorized reseller of Fusemachines' AI Studio and AI Engines.

Fluidstack builds infrastructure that works — from the ground up. Explore open roles turning bare metal into gold.

Build ClusterMAX™ Gold rated infrastructure with Fluidstack. Check out 50+ open roles across the United States.

NVIDIA Nemotron 3 Ultra — 550B total, 55B active, 1M token context, Hybrid Transformer-Mamba MoE model is available Day 0 on FriendliAI’s frontier inference cloud 🙌 We’re built for the hardest part of agentic AI: orchestration. It reads your full context, breaks down complex goals, delegates across sub-agents, and synthesizes everything back at a speed that makes long-running agents viable in production. What FriendliAI brings to production-ready: ⚡ 2×-5× faster 💰 50–90% cost savings ⛑️ 99.99% uptime 🔒 Deploy on your own infra Deploy Nemotron 3 Ultra → https://lnkd.in/gXt3T_CK Find your Nemotron model → https://lnkd.in/g3Ttd9Di Link to blog in the comments 👇 #NVIDIA #Nemotron #AgenticAI

One benchmark won't tell you which inference provider to pick. The Pareto front will. A single point—best TTFT, peak throughput—only describes one operating condition. Your workload doesn't run at one point. Vary batch size and concurrency and you get a whole curve: the Pareto front of latency vs. throughput. Each non-dominated point is the best throughput achievable at a given latency. That's the thing to compare. ❓ Why it matters: Providers can swap places along the curve. Provider A wins at the benchmark point; Provider B owns the rest of the frontier your traffic actually hits. 💡 Pick your point by workload: → chat / voice agents → minimize TTFT → batch / summarization → maximize TPS → search / RAG → somewhere in between Real question isn't "who wins the benchmark?" It's "who has the better frontier for my model, my SLA?" That's the question worth asking — and worth measuring. Full methodology in the comments below 👇

Debugging AI agents with logs is like searching in the dark with a flashlight. You only get clues, not context. Other monitoring tools hand you a flashlight. Fiddler turns on the lights. See the entire agentic hierarchy at once and find the root cause in seconds.

Are you shipping AI agents that your system flags as "healthy," even when they're failing in production? Fiddler Agentic Observability closes this go-live gap by providing the unified platform you need to maintain full visibility, context, and control. With Fiddler, you can: 🎯 Test What Matters: Evaluate agents with curated datasets and run experiments before launch. 🕵️♀️ Catch Hidden Failures: Get full visibility into reasoning chains, dependencies, and cross-agent coordination that traditional monitoring tools can’t see. 🔍 Find the Root Cause: Diagnose and pinpoint issues across the entire agentic hierarchy. 🎛️ Control Costs: Eliminate hidden costs related to calling external models with our “batteries included” Fiddler Trust Models and maintain data privacy. See how Fiddler Agentic Observability can help: https://lnkd.in/dThZ32qR

𝐖𝐡𝐲 𝐇𝐢𝐠𝐡-𝐅𝐢𝐝𝐞𝐥𝐢𝐭𝐲 𝐒𝐞𝐧𝐬𝐨𝐫 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐈𝐬 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐟𝐨𝐫 𝐀𝐕 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 In his latest blog, Sebastian Klaas explores how high-fidelity sensor simulation is becoming essential for autonomous vehicle development. He describes how virtual testing helps validate cameras, lidar and radar faster, more flexibly and at lower cost than purely real-world campaigns. 👉 Read the full blog here: https://lnkd.in/dJBTMZ89

𝐖𝐞’𝐫𝐞 𝐞𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐢𝐧𝐯𝐢𝐭𝐞 𝐲𝐨𝐮 𝐭𝐨 𝐦𝐞𝐞𝐭 𝐮𝐬 𝐚𝐭 #𝐂𝐄𝐒𝟐𝟎𝟐𝟔 𝐢𝐧 𝐭𝐡𝐞 𝐋𝐕𝐂𝐂 𝐖𝐞𝐬𝐭 𝐇𝐚𝐥𝐥, 𝐛𝐨𝐨𝐭𝐡 𝟒𝟕𝟔𝟕! 🚀 We’ll be demonstrating the Foretify Physical AI toolchain and showing how it accelerates the training, validation, and safety evaluation of autonomous vehicles. Want to dive deeper into your specific AV development challenges? Secure 1:1 time with our experts. Reserve your meeting here: https://lnkd.in/dQCDqMqs Let’s drive the future of autonomy together at CES. We hope to see you there! #Innovation #AI #AutonomousDriving #AV #ADAS

𝐖𝐞'𝐫𝐞 𝐞𝐱𝐜𝐢𝐭𝐞𝐝 𝐭𝐨 𝐬𝐡𝐚𝐫𝐞 𝐭𝐡𝐚𝐭 𝐰𝐞'𝐫𝐞 𝐩𝐫𝐞𝐬𝐞𝐧𝐭 𝐚𝐭 𝐭𝐡𝐞 𝐖𝐨𝐫𝐥𝐝 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐅𝐨𝐫𝐮𝐦 𝐀𝐧𝐧𝐮𝐚𝐥 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐯𝐨𝐬 𝐭𝐡𝐢𝐬 𝐰𝐞𝐞𝐤 🌍 As organisations move from experimenting with #AI to embedding it into core operations, questions around governance, data ownership, accountability, and trust become decisive – not optional. Our CTO, Jaromir Dzialo, will be in #Davos for conversations across technology, enterprise, and policy, exchanging perspectives on how AI can be applied responsibly and effectively in real organisational settings. Looking forward to the discussions ahead. #WEF2026 #WEF26

𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐢𝐬 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐡𝐚𝐫𝐝𝐞𝐫. 𝐓𝐫𝐚𝐜𝐞𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫. For organisations operating in regulated environments, it’s not enough to say what happened in an AI workflow. ➡️ You need to prove it. At Exfluency, #blockchain is used to ensure the veracity and provenance of data within AI-driven workflows. Every step can be traced, verified, and audited – creating a clear record of how content, knowledge, and decisions were produced. This matters for organisations navigating frameworks such as #GDPR and other compliance requirements where transparency and accountability are essential. The result: #AI systems that are not only powerful, but auditable and trustworthy. ➡️ 𝐋𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐛𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐭 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: https://lnkd.in/drQCY5ym #DataGovernance

"The only AI SDR that actually books meetings." Every vendor says it. We'll do something different, we'll show you in 30 minutes. Watch it research accounts, write personalized outbound, send, handle replies, and book meetings end to end. If it's not better than what you're running now, you've lost half an hour. If it is, you've just found your next hire.

Every sales newsletter this quarter has declared the AI SDR dead, reborn, and inevitable, usually in the same week. Here's what's actually true: The tools that draft email copy and hand it back to a human? Still require the human. The autonomous kind that researches, personalizes, sends, replies, and books meetings on your stack is the one rewriting the role. EverWorker is that kind. See it run on your ICP in a 30-min live demo. No deck. No whitepaper. Your pipeline, your cadence, your data.

Over a decade of working with organisations where getting decisions right genuinely matters, at scale and under pressure, has left me with one consistent observation. The sectors look very different from the outside, and this holds regardless of industry: financial services, energy, pharma, national infrastructure. But the underlying challenge is remarkably similar. The number of decisions organisations must make has multiplied. The stakes of getting them wrong have increased. The operating model for making them has barely changed. When we founded Faculty, our mission was the safe and widespread adoption of AI. What's become increasingly clear to me is that "widespread" won't come from better models alone. It will come when organisations are willing to rethink how decisions are made, coordinated, executed, and learned from over time. That is the shift most enterprises are still yet to make. I've tried to put some of my thinking on what it actually requires here: https://lnkd.in/eF-UVzzB

Today, Exascale Labs Holdings Inc. closed its previously announced business combination with D. Boral ARC Acquisition I Corp. Our Class A common stock and warrants begin trading on Nasdaq tomorrow, August 28, 2026, under XLAB and XLABW. This does not change the work. AI demand is still constrained by deployable compute- power, cooling, cluster operations, and the time it takes to bring capacity online. That is the problem we built the company around. We operate an asset-light, software-defined GPU platform: reserved and on-demand GPU compute sourced from third-party data centers, plus cluster management and related infrastructure solutions for training, fine-tuning, and inference. We have also developed modular data center, high-density cooling, HVDC power, and energy storage approaches intended to address deployment bottlenecks. Dr. Hoansoo Lee, our CEO: “We are entering the public markets at a time when demand for AI compute is being shaped not only by GPU availability, but also by the ability to deploy reliable infrastructure at scale. As a public company, we remain focused on executing against our qualified customer pipeline, expanding our platform, and delivering high-performance AI compute infrastructure for enterprise, research, and AI-native customers.” Full announcement, forward-looking statement language, and risk factors are in the press release and in documents filed with the SEC. This post is not investment advice. https://lnkd.in/gC-Peh-Z #AIInfrastructure #Nasdaq #XLAB