“knowing Jeff as a friend and colleague for almost 20 years. I have had many opportunities to benefit from his knowledge and advice. I am a huge admirer of his drive, spirit and dedication to all aspects of his life. His successes and achievements inspire me to continue the entrepreneurial life and never quit. I am proud to call Jeff a great friend, a wonderful mentor and a great resource for expertise.”
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Taylor Black
AI-assisted coding isn’t about fewer keystrokes, but greater clarity, deeper thought, and scalable precision. Diwank Singh’s "Field Notes From Shipping Real Code With Claude" guides developers through battle-tested AI workflows: 🍃 Playground Mode for swift exploration. 📐 Pair Programming Mode using CLAUDE.md to shape architecture and uphold style. 🚦 Production Mode fortified with human judgment, stringent tests, and anchor comments. This is developers orchestrating AI to amplify craftsmanship. If you're exploring how AI can elevate your coding practice, start here. Read more → https://lnkd.in/gG68gjxr #AICode #EngineeringBestPractices #DevOps
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Sebastian Hunte
How much of AI's next step change is a compute problem to solve and how much is a connectivity problem we've been misattributing to compute because compute is what we've been measuring? We scaled aggressively on the axes of more GPUs, more parameters, more training data but the fundamental network topology stayed largely unchanged with local connectivity fast and remote slow leading to scale-up and scale-out being separate worlds. We treated connectivity as infrastructure to be optimised within rather than a constraint worth removing and leveraging for performance gains. This post is an attempt to take network and connectivity seriously. Some companies working in this and adjacent spaces are Oriole, Callosum and Nexthop AI. Oriole Networks is building at the fabric layer. Callosum is demonstrating what heterogeneous compute orchestration can do for performance and speed across AI workloads. Nexthop AI is focused on the Ethernet switching layer. Each rethinks this constraint from a different angle. https://lnkd.in/eHsbsCwh
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Nate Nead
📖 https://lnkd.in/g5quijW2 🤖 Transformer models are powerful—but bloated. Billions of parameters might sound impressive, but try deploying one to a mobile app or edge device and you’ll hit a wall. Enter weight clustering: a lesser-known model compression technique that groups similar weights to drastically shrink model size without tanking performance. This blog dives into: What weight clustering is (hint: think k-means meets model compression) Why it can outperform expectations on memory, latency, and cost How it stacks up (and plays nicely) with pruning, quantization, and distillation Real-world tips to make it part of your dev workflow 🚨 Hot take: Clustering isn’t just “academic.” With the right cluster count and fine-tuning, it’s production-ready and surprisingly simple to implement. 📌 Are you using weight clustering in your ML stack—or still sticking to pruning and quantization? Let’s talk trade-offs, tooling, and what’s working for you. #AIOptimization #TransformerModels #ModelCompression #MachineLearning #DeepLearning #WeightClustering #NLP #EdgeAI #TensorFlow #PyTorch #MLDeployment
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Grant Straker
This is a great article on why small language models outperform large language models in many use cases. Faster, cheaper AI is ultimately the outcome from SLMs, because LLM's can burn through thousands of times as many tokens to complete a task as they burn through them in an internal monologue they use to reason through an answer. SLM's are focused on solving very specific problems and do it much faster using far less power. https://lnkd.in/gH4UF-gB
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Eric Lutkin
interesting read from The New York Times about AI.... "Throughout his 40-year career as a computer scientist, Yann LeCun has earned a reputation as one of the world’s leading experts on artificial intelligence and a man with a penchant for throwing verbal grenades. He was one of three pioneering researchers who received the Turing Award, often called “the Nobel Prize of computing,” for their work on the technology that is now the foundation for modern A.I. For more than a decade, he also served as chief A.I. scientist at Meta, the parent company of Facebook and Instagram. But after leaving Meta in November, Dr. LeCun has become increasingly vocal in his criticism of Silicon Valley’s single-minded approach to building intelligent machines. He argues that the technology industry will eventually hit a dead end in its A.I. development — after years of work and hundreds of billions of dollars spent. The reason, he said, goes back to what he has argued for years: Large language models, or L.L.M.s, the A.I. technology at the heart of popular products like ChatGPT, can get only so powerful. And companies are throwing everything they have at projects that won’t get them to their goal to make computers as smart as or even smarter than humans. More creative Chinese companies, he added, could get there first. “There is this herd effect where everyone in Silicon Valley has to work on the same thing,” he said during a recent interview from his home in Paris. “It does not leave much room for other approaches that may be much more promising in the long term.”" #AI #bubble #AWS #GCP #AZURE #meta #nvidia #google #llm #amazon #microsoft #openai https://lnkd.in/gdywmQSx
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