ASM’s Post

The Anatomy of a Perfect Loop Prompt 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 𝗻𝗼𝘄 → https://lnkd.in/gMNkhTXe Prompt engineering is dead According to Nvidia's co-founder and the Head of Claude Code We should write in loops and not prompts. A prompt gives you one answer. A loop gives you a system that can repeat the work, check the result, fix mistakes, and stop only when the goal is met. That is the shift. The future is not about asking AI better questions. It is about designing better instructions for how AI should work through a task. A good loop has 7 parts: 1. Goal: What should be achieved. 2. Work Source: Where the task comes from. 3. Rules: How the AI should behave. 4. Process: The steps it should follow. 5. Verification Checklist: How it checks the work. 6. Stop Condition: When the loop should end. 7. Escalation: When the AI should ask for human review. This is how AI becomes more useful. You stop babysitting every single response. You give it a structure. You give it a checklist. You give it a stopping point. That is how you move from prompting to building systems. Credit to Awa K. Penn. Follow him for more.

🚀 The Future of AI Starts with Better Chips Jeff Bezos is backing CuspAI with a $500 million investment to accelerate the discovery of next-generation materials for semiconductor manufacturing. As AI models continue to grow in complexity, the demand for faster, more energy-efficient chips has never been greater. CuspAI is leveraging artificial intelligence to discover novel materials that could transform how chips are designed and manufactured. This investment highlights an important shift in the AI landscape: the next breakthrough may not come from bigger models alone—but from the materials powering the hardware beneath them. Key takeaways: 🔹 AI is increasingly driving scientific discovery. 🔹 Advanced materials could unlock a new era of semiconductor innovation. 🔹 Strategic investments are expanding beyond software into deep-tech infrastructure. The race to build the future of AI isn't just about algorithms—it's about creating the foundation that makes tomorrow's computing possible. What do you think will have the biggest impact on AI over the next decade: smarter models or revolutionary hardware? Read the full story: Link in comment. #ArtificialIntelligence #AI #Semiconductors #ChipDesign #MaterialsScience #DeepTech #Innovation #MachineLearning #TechNews #FutureTech #CuspAI #JeffBezos

Have you ever wondered what a food company has to do with Artificial Intelligence? The answer might surprise you. When we think about the AI revolution, we often think of companies like NVIDIA , AMD , Microsoft , or OpenAI . But behind every advanced AI chip is a complex ecosystem of suppliers—and one of the lesser-known players is Ajinomoto Co., Inc. . Yes, the same Ajinomoto Co., Inc. quite well known for its food products and food enhancement (MSG). Beyond food, the company produces Ajinomoto Build-up Film (ABF), a specialized material widely used in advanced semiconductor packaging for high-performance chips, including those powering AI applications. As AI chip demand continues to grow, the importance of these behind-the-scenes technology suppliers is increasing. 💡 The biggest opportunities are often hidden in plain sight. AI is not only about software and GPUs—it is also about the materials, manufacturing, and technologies that make the AI ecosystem possible. What other “hidden” companies do you think are quietly powering the AI revolution? #ArtificialIntelligence #AI #Semiconductors #Technology #Innovation #SupplyChain #Investing #Manufacturing

Silicon Innovations: How AI Companies are Shaping the Future of Chip Manufacturing In a groundbreaking shift, AI companies like Anthropic are now stepping into the world of silicon chip manufacturing. This move not only signifies a bold expansion into hardware but also reflects a deeper understanding of how tailored computing power can drive the next generation of artificial intelligence. As AI technologies continue to evolve, the demand for specialized hardware is surging. Traditional chips often struggle to harness the full potential of AI algorithms, which require massive amounts of data processing with low latency. By creating their own silicon chips, these companies can optimize performance for their specific applications, reducing energy consumption and enhancing efficiency. Moreover, companies like Anthropic are pioneering a path where design and implementation work hand-in-hand, creating chips that can better understand and generate human-like text or process visual data with unprecedented accuracy. This blend of hardware and software expertise is likely to redefine competitive landscapes in both AI and tech industries. As the tech world watches this unfold, one cannot help but wonder: how will these advancements in chip technology affect the landscape of AI applications in the next decade? Are we on the brink of a new era where AI capabilities are only limited by our imagination? Let’s engage! What do you think about AI companies venturing into chip manufacturing? Will this lead to more innovation or added challenges? #AI #SiliconChips #TechInnovation #Anthropic #Hardware

When NVIDIA meets L’Oréal, AI enters the beauty lab. 🧪🤖 AI is no longer just about engineering, software or automation. The L’Oréal × NVIDIA collaboration is taking AI deeper into beauty R&D — using AI and computational power to simulate molecular interactions, explore formulations and accelerate scientific discovery. The bigger story? AI isn't replacing the scientist. It is giving the scientist a much bigger laboratory. More possibilities. Faster experimentation. Smarter formulations. Potentially less trial-and-error — and a more sustainable innovation journey. And this is a glimpse of where AI is heading: From automating tasks → to expanding human ability to discover. Beauty may be the industry making headlines today. But the underlying playbook could extend to pharma, food, materials, energy and beyond. Because the real competitive advantage may not be who uses AI — but who combines AI with deep domain expertise and human judgement. #AI #ArtificialIntelligence #NVIDIA #Loreal #Innovation #BeautyTech #RAndD #FutureOfTechnology #ComputationalChemistry

Every AI breakthrough starts with one thing... ⚡ Powerful chips. ASML's strong performance highlights a simple reality: No AI revolution happens without semiconductor innovation. As AI demand grows, so does the race to build the infrastructure powering it. 💬 Which company do you think will dominate the AI hardware race over the next decade? #ASML #Semiconductors #AI #ArtificialIntelligence #ChipTechnology #Innovation #TechNews

AI is designing Radio Chips that humans couldn't even imagine Do you know researchers used AI to create wireless chip designs that achieved impressive performance? But the surprising part is that some of these designs are difficult for human engineers to completely understand. I read this interesting piece here: https://lnkd.in/dQ-Azh9k This reminds me of how some engineering problems can be solved backward. Traditionally, we think: “What input design should we create to get this output?” But AI can approach the problem differently. It starts with the output we want and works backward — discovering hidden structures, patterns, and design possibilities that humans may never consider. By exploring thousands or even millions of possibilities, AI can find solutions beyond our usual engineering intuition. The bigger question is not only: “Can AI design better chips?” It is: “If AI creates designs beyond human intuition, how do we learn from them, improve them, and trust them?” The future of engineering may not be humans replacing machines or machines replacing humans. It may be humans learning how to collaborate with intelligence that can discover solutions we never imagined. #ArtificialIntelligence #AI #Engineering #ChipDesign #Semiconductors #Hardware #Innovation #FutureOfTechnology #MachineLearning #DeepTech

AI in semiconductor manufacturing is entering a new phase. The question is no longer whether AI belongs in the fab. It’s how to build AI systems that engineers trust, explain, and use to solve real operational problems. At the upcoming SEMI AI Techniques in Semiconductor Manufacturing Workshop, our Co-founder, Sainyam Galhotra , will share how causal AI is helping engineering teams move beyond anomaly detection to understand why failures happen and how to resolve them faster. We’re looking forward to learning from fellow industry leaders, exchanging ideas, and discussing what’s next for practical AI in semiconductor manufacturing. See you in Milpitas. #SEMI #Semiconductors #ArtificialIntelligence #ManufacturingAI #RootCauseAnalysis #IndustrialAI #CausalAI

The world’s most advanced AI chips now represent more than $10 billion in computing infrastructure, underscoring the enormous investment required to develop and deploy state-of-the-art artificial intelligence. These specialized processors form the backbone of modern AI, enabling the training and inference of increasingly sophisticated foundation models. They also support critical workloads across scientific research, healthcare, autonomous systems, finance, and enterprise AI. The rapid growth in AI capabilities is being matched by unprecedented demand for high-performance semiconductor technology, making advanced computing hardware one of the most strategic assets in today’s technology landscape. As the AI industry continues to evolve, success will depend not only on better algorithms and models, but also on the infrastructure capable of powering them at scale. ✅Follow @nexautech For more AI Info and Contents #ArtificialIntelligence #AIInfrastructure #Semiconductors #HighPerformanceComputing #Innovation #Technology #FutureTech