Yun Jin

LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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John K. Waters explores how JFrog is tackling one of AI development’s biggest challenges: managing and securing AI-generated code. The company’s new platform, JFrog Fly, is designed for “agentic workflows,” automating repository setup, versioning, and integration with tools like GitHub Copilot and Anthropic’s Claude Code. Read how JFrog Fly aims to bridge the gap between AI coding and real-world deployment: https://lnkd.in/eJFJkjvD #AI #DevOps #SoftwareDevelopment #JFrog #AICoding

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JFrog Powers AI Developer Workflows with MCP Server “The developer tool stack and product architecture has fundamentally changed in the AI era. With the launch of the JFrog MCP Server, we’re expanding the open integration capabilities of the JFrog Platform to seamlessly connect with LLMs and agentic tools,” said Yoav Landman, Co-Founder and CTO, JFrog. “This allows developers to natively integrate their MCP-enabled AI tools and coding agents with our Platform, enabling self-service AI across the entire development lifecycle, which helps increase productivity and build smarter, more secure applications faster.” Read More: https://lnkd.in/dWVcrTPf #AITech365 #ITandDevOps #JFrog #JFrogPlatform #LLMs #MCP #news #SoftwareSupplyChainPlatform

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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Julia Blase, Product Manager at Chronosphere, discusses how observability needs to evolve alongside Kubernetes. She explains that Kubernetes brought dynamic, scalable infrastructure, and monitoring systems must follow suit. The future requires on-demand instrumentation and dynamic data collection that can scale up and down based on needs, helping teams optimize costs while getting the right insights at the right time. Watch the full interview: https://ku.bz/X6tgWrG0P

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How Large Language Models Are Making Software Greener AI is quietly driving sustainability. Large Language Models (LLMs) now go beyond generating text—they’re optimizing code. By detecting inefficiencies and refining logic, they cut CPU cycles, reduce energy use, and lower costs. Cleaner code means greener software. At scale, these improvements shrink carbon footprints, turning LLM-assisted refactoring into an environmental win. Explore how AI is shaping a future where innovation meets sustainability. https://lnkd.in/dfwy_Yhq #AI #SustainableTech #GreenSoftware #CodeOptimization #Innovation Md Siddiqur RAHMAN, Mouna Ben Mabrouk Ha Nhi NGO Ginel Dorleon Azade Fotouhi José Carlos Rosales Núñez Ines BEN KRAIEM

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We co-authored this new white paper with JBF Consulting exploring what changes when transportation optimization moves beyond CPU-based architectures to GPU-accelerated optimization. One insight stands out clearly: the historic trade-off between optimization runtime, scale, and solution quality isn’t a law of physics - it’s an architectural limitation. In the paper, we go deeper into why CPU-based solvers hit that ceiling, how GPU-accelerated optimization changes the equation, and what that means for real-world, highly constrained transport networks. 👉 Read the full white paper - see the link in the first comment. #SupplyChainTechnology #TransportationOptimization #LogisticsStrategy #GPU #TMS #Chora #Tramm

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𝗔𝗜 𝗱𝗶𝗱𝗻’𝘁 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂𝗿 𝗙𝗶𝗻𝗢𝗽𝘀. 𝗜𝘁 𝗼𝘂𝘁𝗴𝗿𝗲𝘄 𝗶𝘁. Model training can run for hours or weeks. Inference costs change with user behavior. Experiments share GPU infrastructure across teams and timelines, making ownership and accountability unclear. GPU costs are 10–20x higher than standard compute, yet most cost tools only show totals - not intent, impact, or outcomes. Why Enterprise FinOps Needs an AI-Oriented Architecture explains why cost governance breaks under AI workloads and how AI-aware visibility, guardrails, and outcome-based tracking help enterprises regain control before budgets spiral. 𝘙𝘦𝘢𝘥 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 𝘣𝘭𝘰𝘨 𝘣𝘦𝘭𝘰𝘸 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘸𝘩𝘢𝘵 𝘍𝘪𝘯𝘖𝘱𝘴 𝘧𝘰𝘳 𝘈𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘴. #FinOps #AICostManagement #AIInfrastructure #CloudCosts #EnterpriseAI #CAIStack

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🧠 AI Infrastructure most markets underprice Amazon isn’t renting the future — it’s building it: ⚙️ Trainium & Inferentia for AI training and inference ⚙️ Graviton powering cost-efficient cloud compute ⚙️ Bedrock enabling enterprise-grade generative AI at scale This is AI leverage embedded directly into AWS 📊🤖 ⸻ 📢 Advertising powered by real purchase intent Unlike social platforms guessing intent, Amazon knows it: 🛒 Amazon DSP 🛒 Retail Media Network Every ad is tied to actual buying behavior, not just clicks — a data moat most ad platforms can’t replicate 🔒 ⸻ 🚚 Logistics as a Service Amazon is quietly becoming the backbone of global commerce: 📦 FBA 📦 Buy with Prime 📦 Amazon Freight Merchants don’t just sell on Amazon anymore — they run on Amazon 🌍 ⸻ 🛰️ Project Kuiper long-duration optionality Satellite broadband isn’t about next quarter revenue — it’s about: 🌐 Global connectivity 🌐 AWS edge expansion 🌐 Infrastructure dominance over decades This is classic Amazon thinking ⏳ ⸻ 🏥 Health + data land grab Early innings, but very on-brand: 🩺 One Medical 💊 Amazon Pharmacy 🤖 Care AI Data + logistics + consumer trust = a powerful long-term health platform ⸻ 🔑 The big picture Amazon has fused: Compute + Commerce + Logistics + Data into one massive operating system for the global economy That’s not a company. That’s infrastructure 💡 ⸻ ✨ Follow Hamza Ishaq for more informative and colorful insights on AI, Cloud Computing, Big Tech, and Future Technology ♻️ Repost to help investors and builders understand how real digital empires are quietly engineered behind the scenes 🧠📈 #Amazon #AMZN #AWS #ArtificialIntelligence #CloudComputing #BigTech #DigitalEconomy #FutureOfBusiness #TechInfrastructure #InvestingInsights

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Excited to see real-world proof of ARM’s impact on streaming. This collaboration between Amazon Web Services (AWS), Playtech, and the Wowza team makes this a must-watch for anyone focused on streaming performance and cost.

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World being super charged by AI, exponential growth of log/metrics must be processed and analyzed in an entirely new concept. Ingession based license model, Kafka based platform, is not a sustainable solution in the next era of exponentially growing log/metrics environment from cost and CPu resource perspective. FFWD brings a game changing solution holistically to the ever growing complex environment from network layer all the way up to AI Agent based on their in-house developed AI Native Pipeline framework, Anomally Detecion, and Correlation. When operating a large complex Enteprise environment, Data Center, AIDC, Service Infrastructure, SOC, etc., facing Mass Log/Metrics which is creating ever increasing license fees, servers, and etc. Check out how FFWD may solve your problem! https://www.core0.io/

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Jensen Huang dropped a number at #GTC 2026 that stopped me mid-scroll. "I believe computing demand has increased by 1 million times over the last two years." Jensen Huang, GTC 2026 Not over a decade. Not someday. Two years. Then came the slide that reframed everything for me. The entire AI infrastructure thesis in one frame: ▸ AI Factories are the Industrial Infrastructure of the AI Era ▸ Inference is the Workload ▸ Tokens are the New Commodity ▸ Compute is Revenue And the line Jensen said out loud: "It's now a factory to generate tokens." on the All-In podcast at GTC, Jensen laid out a thought experiment. If a $500K/year developer spent less than $250K on tokens by year end, he'd be "deeply alarmed." My read on that: it's not a pricing benchmark. It's a provocation. It's Jensen saying we are still dramatically underestimating how much value a token can unlock. Here's where I land on all of this. The cost of generating tokens is dropping fast. The value of what those tokens can do is rising exponentially. The gap between those two curves? That's where the real opportunity lives. But and this is the part most people skip the gap only gets captured if the infrastructure beneath it is built right. #Tokens don't generate themselves. #Inference doesn't optimize itself. And enterprises won't run mission-critical agentic workloads on infrastructure that can't guarantee latency, throughput and cost-per-token at scale. Whoever builds that layer , reliably, efficiently and at enterprise grade #owns the #AIstack. The inference delivery platform. At NeevCloud®, this is exactly what we're building toward on our AI SuperCloud platform. Because Jensen is right the AI factory era is here. The question is who builds the pipes that make the factory run. What's your take is token cost the most underloved problem in enterprise AI right now? Drop your thoughts below 👇 Note: I didn't attend GTC in person. Insights are from the official NVIDIA GTC keynote on YouTube and public coverage. Screenshot credit: NVIDIA GTC 2026 official livestream. #NvidiaGTC #AIInference #TokenEconomy #AgenticAI #EnterpriseAI #AISuperCloud #NeevCloud

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