I shipped a production-ready authentication flow in 22 minutes today. The secret isn't better prompts; it's using natural language to define the state machine first. Let the AI handle the boilerplate while you focus on the logic flow. Vibecoding is just extreme modularity at the speed of thought. Would you do this?

Is GPT-5.4 turning into an operating system? This could fundamentally reshape how we interact with technology. The evolution of Large Language Models into centralized control planes signifies a major leap, moving AI from isolated tools to the orchestrators of complex multi-agent systems. Will natural language truly become the dominant interface for software development? https://lnkd.in/gKrKtPsx @OpenAI

Ever tried generating text with an LLM that feels like watching paint dry one token at a time? Inception Labs just dropped Mercury 2, a diffusion-based reasoning model that flips the script. Instead of predicting the next word sequentially like GPT or Claude, it starts with noise in latent space, refines it iteratively, and produces coherent text, code, or responses in parallel. It's 5x faster than top speed-optimized models, matches Claude Haiku's quality, and handles prompts like a pro. Developers are buzzing because it turns image-gen techniques into text-gen magic. Try it free at : https://lnkd.in/g4CDV7mR and watch the diffusion effect live. Learn more here : https://lnkd.in/g4f4VxDz The question is, how can we harness the power of these models to create meaningful and impactful applications? What are the potential use cases for diffusion-based language models, and how can we ensure that they are used responsibly and ethically? I'd love to hear your thoughts on this topic. Are you more interested in exploring the technical aspects of these models or discussing their potential applications and implications? #ai #0to100xEngineer #generativeai #100xEngineers #inceptionlabsai #mercury2

The paper I liked (together w. my AI ResearchMate) (3/4) Recursive Language Models https://lnkd.in/grQ99pC2 (papers.lunadong.com) #LongContext This paper introduces Recursive Language Models (RLMs), which rethink the traditional LLM workflow by placing the model inside a Read–Eval–Print Loop (REPL) environment. In this setup, the input prompt becomes part of the environment itself. The model writes code to inspect (“peek into”) the environment, evaluates the result, and can recursively invoke itself until the task is completed. At first glance, this feels almost like a familiar programming trick, but the experimental results suggest big potentials: better scaling to inputs beyond 10M tokens, improved generalization, and lower overall cost---likely enabled by the external "memory", plus the cleaner structure that is favorable for fine-tuning. In a world where the web, personal folders, and social accounts increasingly become part of an LLM’s accessible context, it is not surprising that the chat itself becomes part of the environment too—even the current turn of the conversation.

How Large Language Models (LLMs) Work Internally — Explained with a Simple Example Recently I spent some time understanding how Large Language Models actually work internally. Sharing a simple explanation of the pipeline using an example. Learning this pipeline really changed the way I think about modern AI systems. Curious how others approached learning the Transformer architecture #AI #LLM #Transformers #MachineLearning #GenerativeAI

EZ-Check, is a framework that combines Knowledge Graphs and Large Language Models to enable Zero-shot fact checking. Read our recent article published in the Information Sciences journal here: 
https://lnkd.in/efdnrKc3 As AI systems are increasingly deployed in high-stakes domains, explainability and trust are no longer optional. EZ-Check addresses the trade-off between reasoning power and transparency by grounding language model outputs in structured knowledge. #KnowledgeGraphs #ExplainableAI #FactChecking #NeuroSymbolicAI #AIResearch #AI

As a PM, I don't need to train models. But I do need to understand how they work. Just finished 'How Transformer LLMs Work' by DeepLearning.AI. What stuck with me most was the historical evolution — seeing how language models went from Bag-of-Words to embeddings to transformers made everything click in a way isolated explanations never did. Also: Mixture of Experts (MoE) completely changed how I think about model performance and cost tradeoffs as a product decision. Knowing enough to ask better questions — that's the bar. #ProductManagement #AI #LLM #GenerativeAI https://lnkd.in/dNMgPR5q

"Claude is calling everything in our ecosystem via MCP and agents are providing wild answers. Sometimes they're right, and other times they don't come close. Then it runs the same calls for the next question." "It took us three months to build and test one agent to make sure it would provide the right information. We have a major backlog and our business isn't willing to wait that long." Agents need direction and Context to know how to answer natural language questions. On April 16, we'll show how to build the Context Layer for AI agents in Atlan. Improve accuracy, test, and deploy your agents at scale. April 16 | 11 AM ET | Virtual https://lnkd.in/g-xycdGy

AI is no longer just answering questions. It’s starting to think with us. 🧠⚡ NotebookLM + long-context LLMs may change how humans read, learn, and create knowledge. Read here: https://lnkd.in/djgpAAEC #AI #NotebookLM #FutureTech

AI is no longer just answering questions. It’s starting to think with us. 🧠⚡ NotebookLM + long-context LLMs may change how humans read, learn, and create knowledge. Read here: https://lnkd.in/djgpAAEC #AI #NotebookLM #FutureTech