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Keith Ballinger, VP & GM of Google Cloud Platform Developer Experience, reminds us that AI isn’t a detached layer — it’s woven into the developer’s workflow itself. In this clip from The New Stack Agents, he explains to Alex Williams and Frederic Lardinois why the best coders of tomorrow will think in systems, not syntax. Watch the full interview: https://lnkd.in/gRQnxwA8

Tired of open models lagging behind proprietary ones? Bee-8B-RL by Open-Bee changes the game. An 8B-parameter Multimodal LLM trained on the meticulously curated Honey-Data-15M corpus, built using their transparent HoneyPipe data curation framework. Unlike noisy open datasets, Honey-Data-15M blends short and long Chain-of-Thought (CoT) reasoning over 15M clean, enriched samples that power Bee-8B-RL to deliver SOTA reasoning, visual understanding, and factual accuracy rivaling closed models like InternVL3.5-8B. Now, you can run it locally, fast, efficient, and fully open. In our latest guide, we show you how to install and run Bee-8B-RL on your own machine with NodeShift Cloud, unlocking a smooth, high-performance environment for experimentation, deployment, and innovation. 🔗 Read the full guide: https://lnkd.in/gk7hW6EG #mllm #multimodal

The AI race isn’t about intelligence. It’s about territory — just like cloud in 2010. Back then, AWS wasn’t just selling servers. It was staking ground. Azure and GCP joined later, fighting for land, not innovation. Then came layers of partners, resellers, consultants — the “cloud economy.” AI’s following the same path. OpenAI, Anthropic, Perplexity — all fighting for market share. You’ve got: ▶️ Tier 1: Model providers ▶️ Tier 2: Infra and orchestration layers ▶️ Tier 3: Builders turning cognition into products Every cycle starts the same way — chaos, capital, consolidation. The winners won’t just train better models. They’ll own the distribution layer of intelligence. So if this really mirrors the cloud era… We’re still pre–AWS Marketplace. The moats haven’t formed yet. The map is being drawn. And every player’s staking land, not just training models. If this really is 2010 all over again… what would you build this time?

$118 billion USD. That’s how much AWS is investing in AI infrastructure — and it’s just one player in what’s fast becoming the biggest tech arms race since the birth of the internet. We’ve entered the era of the AI Infrastructure Gold Rush — where AWS, Microsoft Azure, and Google Cloud are spending tens of billions to power the future of artificial intelligence. This isn’t just about data centres anymore. It’s about who controls the compute, the chips, and the developer ecosystems that will shape every AI service on Earth. The question isn’t who builds the best model. It’s who builds the digital empire it runs on. With OpenAI’s latest £6.4B funding round and partnerships deepening across the board, the global AI economy is consolidating fast — and the stakes couldn’t be higher. Will this “gold rush” accelerate innovation — or concentrate power in the hands of a few tech giants? Let’s talk. #AIConversations #AIInfrastructure #CloudWars #AWS #Azure #GoogleCloud #AIInvestments #FutureOfTech #AIEconomy

Fidelis Ngede , this post captures the essence of the current AI infrastructure gold rush perfectly. The unprecedented investment from AWS and its peers is not merely about enhancing capabilities but is fundamentally reshaping the digital landscape. Allow me to inflect a parallel thinking in our Subsaharian African context: As we witness this monumental shift, the implications for governance, accountability, and economic development, especially in developing countries like Cameroon, are profound. This surge offers a unique opportunity to foster local innovation and entrepreneurship. However, with power concentrating among a few giants, there’s a critical need for robust governance frameworks to ensure that AI advancements serve broader societal interests, rather than just enriching a select few. For developing nations, it’s not just about catching up, but strategically leveraging these advancements to build sustainable ecosystems that empower local communities, create jobs, and promote equitable growth. Let’s engage in this conversation: how do we ensure that the AI revolution is inclusive and equitable for all? #AIConversations #AIInfrastructure #CloudWars #AWS #Azure #GoogleCloud #AIInvestments #FutureOfTech #AIEconomy #TechGovernance #DigitalEquity #InclusiveInnovation #EconomicDevelopment #Cameroon #EmergingMarkets #DataForDevelopment #ResponsibleAI

Your ML models are worthless if they never make it to production. You spend weeks training a model. But getting it deployed is chaos. Juggling different tools for data prep, training, and hosting is a nightmare. Google Cloud’s AI Platform (now Vertex AI) fixes this. It’s a single, unified platform to build, deploy, and scale ML models. No more duct-taping services together. Here’s how it simplifies the entire process: 1. It’s one platform, not ten. Everything is in one place. From data labeling to training and deploying your model, you use one interface and API. It saves a massive amount of time and reduces complexity. 2. It’s fully managed. Stop worrying about servers. Whether you're using AutoML or custom training, Google handles the underlying infrastructure. You can focus on building models, not managing clusters. 3. It's built for MLOps. Production ML is more than just a model. It’s about maintenance and automation. The platform has tools for: ✔️ Building repeatable pipelines. ✔️ Registering and versioning models. ✔️ Monitoring models for drift and performance. It’s about shipping better models, faster. If your team is struggling to get ML projects out the door, this is what you need. Repost if you believe in efficient MLOps. #GoogleCloud #VertexAI #MachineLearning #MLOps #AIPlatform #CloudComputing #DataScience #Engineering

If you're looking for a path to scale your AI workloads without adding operational complexity, you should seriously consider S3 Vectors on Amazon Web Services (AWS). You can get vector search capabilities built-in. No new database to manage, no separate system to learn, no cluster provisioning to worry about. As your vector dataset grows from thousands to millions to billions of embeddings, S3 handles the scaling automatically. You don't hit capacity limits or need to re-architect your storage layer. For RAG applications, this means you can focus on improving your retrieval quality and model performance instead of managing infrastructure. The operational overhead is minimal, which matters when you're trying to move quickly from prototype to production. S3 Vectors removes a significant barrier to scaling AI workloads. If your bottleneck is vector storage complexity, this is worth a closer look. #S3Vectors #AWS #VectorSearch #RAG

Anthropic just locked in a multibillion-dollar deal with Google for up to 1 million TPUs to power Claude. This isn't just a partnership—it's a statement about where AI infrastructure is heading. The scale here is staggering: Anthropic + Google Cloud TPUs = Up to 1M specialized chips = Multibillion $ commitment = Claude LLM at unprecedented scale 🔹 **Why this matters**: We're seeing the emergence of AI-native infrastructure partnerships that dwarf traditional cloud deals 🔹 **Impact for builders**: The bar for competitive AI training just jumped—specialized hardware partnerships are becoming table stakes 🔹 **This week's move**: Audit your AI infrastructure roadmap. Are you building on platforms that can scale with these new realities? The race isn't just about better models anymore—it's about who can secure the compute to train them. What's your take on these mega-infrastructure deals? #aiinfrastructure #anthropic #cloudcomputing #tpu #aibuilders #startupstrategy Sources: anthropic.com, siliconangle.com, morningstar.com

Anthropic just locked in a multibillion-dollar deal with Google for up to 1 million TPUs to power Claude. This isn't just a partnership—it's a statement about where AI infrastructure is heading. The scale here is staggering: Anthropic + Google Cloud TPUs = Up to 1M specialized chips = Multibillion $ commitment = Claude LLM at unprecedented scale 🔹 **Why this matters**: We're seeing the emergence of AI-native infrastructure partnerships that dwarf traditional cloud deals 🔹 **Impact for builders**: The bar for competitive AI training just jumped—specialized hardware partnerships are becoming table stakes 🔹 **This week's move**: Audit your AI infrastructure roadmap. Are you building on platforms that can scale with these new realities? The race isn't just about better models anymore—it's about who can secure the compute to train them. What's your take on these mega-infrastructure deals? #aiinfrastructure #anthropic #cloudcomputing #tpu #aibuilders #startupstrategy Sources: anthropic.com, siliconangle.com, morningstar.com

Founders love chasing new AI features. But you know what really buys you more AI time? → Efficiency. Here are 3 Google Cloud tools that quietly extend your runway: 1️⃣ #Sustained-#use #discounts - You get automatic savings the longer your instances run. 2️⃣ BIGQUERY #Flex #Slots - Pay for compute only when you actually query. 3️⃣ #Storage #Lifecycle #Policies – Automatically move cold data to cheaper tiers. These small tweaks can easily save startups across all tiers(from pre-seed to Series indefinite 😂 ) tens of thousands a year - without writing a single new line of code. We’ve compiled the real numbers inside the Cloud Survival Playbook (releasing on Nov 17). And if you’d rather not dive into the dashboards yourself, we’ve got you. At Enhub.ai, we help founders uncover these hidden wins, fix what’s broken, and make Google Cloud actually work for your balance sheet. #GoogleCloud #InfraOptimization #StartupGrowth