Sajjaad Khader

😴 Your GPUs are sitting idle. Even at peak demand. Most AI companies are only utilizing 50-70% of their GPU capacity during inference. That's millions of dollars in compute just... waiting. The throughput bottleneck isn't about buying more GPUs. It's about fundamentally rethinking how your infrastructure handles LLM requests. We just published a deep dive on why traditional scaling approaches fail at production scale and what actually works. The companies that solve throughput now will serve more users, deploy better models, and spend less on infrastructure. Want to see the difference? 💸 We're offering $100 in free GPU credits so you can test Tensormesh's distributed caching architecture yourself. 🔎 Read the full article: https://lnkd.in/gDmn3WX5 👉 Claim your credits: https://lnkd.in/gmKa8chj #AI #MachineLearning #LLMs #AIInfrastructure #GPUs

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Learn the critical difference between passive LLMs (like ChatGPT) and active AI Agents. This article from CoLab is a practical guide for engineering leaders on automating workflows. https://lnkd.in/gDmn2DpH

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Top stories in AI: Major breakthroughs, bold launches, and strategic shifts shaping the future of intelligence. 🔹 xAI launches Grok 4 Elon Musk's latest model claims superhuman reasoning and post-grad-level performance across disciplines. With 100x more training and 10x more RL compute than Grok 2, it’s built for real-world intelligence. 🔹 NVIDIA hits a $4 trillion market cap Now the world’s most valuable company, driven by surging demand for AI infrastructure. A strong signal that foundational hardware continues to lead the AI value chain. 🔹 Anthropic publishes alignment study Following concerns around Claude 3 Opus and “deceptive alignment,” Anthropic analyzed 25 leading models. Findings suggest evasiveness and misalignment are more common than we think pushing transparency to the forefront. 🔹 OpenAI reportedly developing its own AI-powered browser A browser integrated with ChatGPT and Operator (its web agent) could disrupt traditional search models and challenge Chrome's dominance. 🔹 Open-source momentum is building Rumors hint at a new open-weight model (akin to o3 mini) coming soon potentially unlocking new possibilities for researchers and developers alike. The AI space is evolving faster than ever, from infrastructure to alignment, intelligence, and interface. Which of these developments do you think will have the biggest impact? #AI #xAI #Grok4 #OpenAI #Anthropic #NVIDIA #LLMs

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DeepSeek AI Signals a New Direction in Efficient Model Design : Latest Paper Suggests a Rethink of Large-Scale Training A newly published paper on arXiv proposes structural changes to neural networks that could meaningfully improve training stability and cost efficiency and it feels like a familiar pattern. The paper introduces mHC, a technique designed to stabilize large-scale training with minimal additional compute overhead. Tested on 3B, 9B, and 27B parameter models, mHC delivered stronger benchmark performance, particularly on reasoning-heavy tasks. Notably, CEO Liang Wenfeng co-authored and personally uploaded the paper, reinforcing how hands-on DeepSeek leadership remains with core research. If this sounds familiar, it should. Similar research breadcrumbs appeared ahead of R1 and V3, both of which preceded major releases. Why this matters Last year’s “DeepSeek moment” showed how R1 approached frontier-level performance at a fraction of the cost. This paper suggests they’re still unlocking efficiency gains. Combined with greater access to advanced AI chips, these architectural improvements point to something bigger. Looking ahead to 2026 Between cost-efficient training methods and accelerating hardware access, Chinese AI releases are set to become even more competitive—not just cheaper, but smarter and more stable at scale. This doesn’t look like a one-off paper. It looks like a preview. Read the paper here : https://lnkd.in/gR222q9P #AIResearch #DeepSeek #NeuralNetworks #LLMs #AIInfrastructure #MachineLearning #AITrends

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Our former intern, Selena, spoke with Carter Abdallah to share her advice for students. ✨ How to take advantage of the NVIDIA DGX Spark? Experiment. With preloaded tools, unified memory, and no GPU queues, the only limit is what you can imagine. 💭 See the demo Selena worked on during her internship here: https://lnkd.in/gc83sG4j #SparkSomethingBig 💫

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Anthony Levandowski previously helped build Uber’s self-driving effort after leaving Google, where he co-founded the project that became Waymo. https://thein.fo/4usT54S

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LLMs are surprisingly capable coding partners. UMBC CSEE Prof. Tim Oates gives a short, technical overview of why and how coding assistants work and where they predictably fail. Most of the session is supported with live, terminal-based demos using Claude Code. 1-2:30pm ET, April 3, ITE 325b & online https://lnkd.in/eengnYuP

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The evolution from custom training loops to frameworks like Ultralytics, which uses PyTorch as an underlying framework, has revolutionized ML development. Today, we focus on strategic parameter optimization rather than low-level scripting. Our team member Ali I. explores Ultralytics' training function, examining key parameters and optimization strategies to maximize model performance and training efficiency. blog link: https://lnkd.in/dwzEt94s #MachineLearning #AI #Ultralytics #ModelTraining #TechInnovation #deeplearning #linkedin #machinelearning #AI #automation #itconsulting #consulting #aboutme #introduction #struggle #Innovation #DigitalMarketing, #Economy, #LeanStartups, #Entrepreneurship, #Technology, #Programming, #ArtificialIntelligence #Engineering #Sustainability  #Startups, #Entrepreneurship, #VentureCapital.

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We’re excited to lead Deeptune's Series A. Deeptune is building leading RL environments for computer-use and code. More data, more compute, and better architectures can only take us so far. As models move into real-world task execution, they need structured environments where they can learn to fully control computers and perform knowledge work tasks. Designing high-quality RL environments is extraordinarily difficult to get right, but Deeptune has proven they can, working closely with leading AI labs and developing environments for computer use that are already showing up in benchmark improvements. Deeptune’s founder and CEO, Tim Lupo, is an exceptional leader with a rare combination of technical expertise and product intuition. We’re thrilled to partner with Tim and the Deeptune team as they build this critical layer of the AI stack. By Marco Mascorro and Martin Casado

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AI does not run on hype. It runs on infrastructure. And infrastructure is where SambaNova Systems decided to play. Founded in 2017 by Rodrigo Liang, Kunle Olukotun, and Christopher Ré, SambaNova Systems was built on a simple premise that most people underestimated at the time. Training grabs headlines. Inference pays the bills. Rodrigo Liang, CEO and Co Founder, brought 20+ years of semiconductor experience, including leading Oracle SPARC server and ASIC programs. Kunle Olukotun, Co-Founder and Chief Technologist, brought the architectural depth of a Stanford pioneer in multicore and domain specific systems. This was not a science project. It was a deliberate strike at the AI stack. The company built its own Reconfigurable Dataflow Units, deployed through SN40 and SN50 systems inside SambaRack, then wrapped it all in SambaStack, SambaOrchestrator, and SambaCloud. Full stack control. Hardware to API. OpenAI compatible endpoints so enterprises do not have to rip and replace to move fast. When latency, governance, and tokens per watt actually matter, inference optimized stops being marketing copy and starts being operational leverage. Capital noticed. On Feb 6, 2026, Reuters reported SambaNova Systems is in an oversubscribed Series E targeting more than $350M, led by Vista Equity Partners and co-led by Intel Corporation. Intel is reportedly committing around $100M, with flexibility up to $150M, with terms still being finalized. When Vista leans in, it signals enterprise scale. When Intel steps in, it signals belief in the silicon strategy. This follows a $676M Series D in April 2021 at a $5.1B valuation, led by SoftBank Vision Fund 2 with participation from Temasek, GIC, Intel Capital, BlackRock, GV (Google Ventures), Walden International School, and WRVI Capital. By 2021, total capital raised exceeded $1B. Wired later cited roughly $1.1B by late 2025. This is not incremental funding. This is conviction capital backing a thesis about where AI economics are heading. And the thesis is clear. Enterprises and sovereign AI customers need performance without surrendering control. Governments want models running within their borders. CFOs want throughput without runaway energy costs. Developers want compatibility without friction. SambaNova Systems is positioning itself as the backbone for that reality, powering sovereign AI providers across Australia, Europe, and the UK, while partnering with Hume AI and Canvass Analytics to push vertical workflows into production environments. The real takeaway is not the $350M headline. It is the pattern. Build proprietary silicon. Own the stack. Optimize for inference where value is captured. Attract long horizon investors who understand infrastructure cycles. Then scale into the parts of AI that enterprises cannot afford to get wrong. #ArtificialIntelligence #Semiconductors #EnterpriseInfrastructure #DataCenters #DCTalks

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Traditional prompt engineering led to an endless loop of fixes and new errors, so the system never reached production-level reliability. This team turned to RL, fine-tuning a Llama model with an automated system of verifiers that checked responses against source documents, which eliminated the need for manual prompt engineering. The resulting model, now better able to reason independently rather than simply memorize responses, doubled its effectiveness, boosting its win rate against GPT-4o from a baseline of 27% to 58%.

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We’re excited to launch the open-source initiative from Agentic AI @ UIUC  Our club was founded on a simple belief: students shouldn’t just learn about modern AI systems — we should build real ones that people can use. Much of today’s most impactful AI infrastructure is closed source, which makes it difficult for students and builders to understand how these systems actually work. So we decided to change that. Our mission: • Recreate and reimagine widely used closed-source AI tools • Build production-style multi-agent systems in the open • Contribute meaningful infrastructure to the developer community • Give students real experience shipping software used beyond campus And today we’re excited to share our first open-source project Introducing Aegis — Self-Healing Data Quality Monitoring with Autonomous AI Agents  Data pipelines rarely fail loudly. A column gets dropped upstream, a table stops refreshing, and by the time someone notices, dashboards are wrong and decisions are already compromised. We built Aegis to fix this. Aegis is an open-source data intelligence platform that uses a coordinated multi-agent system to automatically detect, diagnose, and recommend fixes for data quality issues before they reach stakeholders. How Aegis works: Aegis runs five specialized agents in a pipeline: 1)Investigator — Connects to warehouses (Snowflake, BigQuery, Postgres) and automatically discovers which tables to monitor using LangChain. 2)Sentinels — Continuously monitor schema drift and freshness violations. 3)Orchestrator — Deduplicates anomalies into incidents and coordinates response. 4)Architect — Performs LLM-powered root cause analysis and traces the blast radius across the lineage graph. 5)Executor — Generates remediation plans with actionable SQL ready for human approval. Key design principles • Deterministic fallbacks — works even without an LLM • Human-in-the-loop — agents propose, never auto-modify data • Structure-only discovery — minimal warehouse load, no profiling • Full lineage graph built from query log parsing Stack: Python, FastAPI, SQLAlchemy, LangChain, React, TypeScript This is just the beginning of our open-source journey. We’re building real, production-style AI systems in public and would love feedback from anyone working in data engineering, data quality, observability, or agent architectures Github Link - https://lnkd.in/gbdeiM2Q Developer : Ashleyn Castelino, Anirudh Konidala #OpenSource #DataEngineering #AI #MultiAgentSystems #LangChain #Python #BuildInPublic

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We are seeing an emerging 3-layer OSS stack for AI compute: 🔧 PyTorch +  🧠 vLLM  + ⚡ Ray  + 📦 Kubernetes 🎥 Robert Nishihara gives a quick breakdown of how this stack works together to scale LLMs + GenAI workloads The AI compute software stack consists of 3 specialized layers: 🔧 Layer 1: Training & Inference Framework (PyTorch + vLLM) • Runs models efficiently on GPUs • Handles model optimization and model parallelism strategies • Manages accelerator memory and automatic differentiation ⚡Layer 2: Distributed Compute Engine (Ray) • Schedules tasks within jobs and coordinates processes • Ingests and moves data • Provides workload-aware failure handling and autoscaling 📦 Layer 3: Container Orchestrator (Kubernetes) • Provisions compute resources • Schedules entire jobs • Manages user and workload multitenancy Each layer handles what it does best. The separation of concerns makes this stack so powerful. 📖 Read the full blog post with examples from Pinterest, Uber, and Roblox: https://lnkd.in/eyn3jBfU

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Rootly AI Labs Fellow Laurence Liang is live at ACL 2025 in Vienna presenting our latest work on applying ML to SRE, and catching up with researchers pushing the boundaries of AI. Matthew Toles, ML Researcher at Columbia University, is tackling one of the biggest gaps in LLMs today: their failure to ask clarification questions. His new paper explores why it matters and how to address it. Find his paper linked in the comments.

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At Ray Summit 2025, Elizabeth Hu, Goku Mohandas Mohandes and Akshay Malik from Anyscale will announce Lineage Tracking on Anyscale, a new observability tool for end-to-end ML lineage powered by OpenLineage. It maps datasets and models across Workspaces, Jobs, and Services and renders an interactive lineage graph for fast reproduction, audit, and debugging. It also integrates with your existing catalogs and registries like MLflow, Weights & Biases, and Unity Catalog. What you will learn: - How OpenLineage automatically captures datasets, models, code versions, and parameters across development to production on Anyscale - How to navigate the lineage graph to reproduce runs, trace failures upstream, and support governance and audits - How to connect lineage to MLflow, W&B, and Unity Catalog to keep one source of truth for artifacts and metadata Nov 3–5 at the San Francisco Marriott Marquis. Register now: https://lnkd.in/ghAANMa6

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