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From Mixture of Experts to Mixture of Agents: Building Smarter AI Systems * How Cerebras is revolutionizing AI inference with ultra-fast hardware and innovative agent architectures* The evolution of large language models has reached an inflection point. As models grow larger and more capable, we face fundamental challenges in scaling them efficiently. At a recent Cerebras workshop, researchers demonstrated how to move beyond traditional monolithic models toward a new paradigm: Mixture of Agents (MoA). The Evolution of Large Language Models The journey from GPT-3 to today's frontier models tells a story of relentless scaling. GPT-3 started at 175 billion parameters, Llama 3 reached 400 billion, and DeepSeek-V3 now boasts 600 billion parameters. But simply adding more parameters isn't sustainable without architectural innovations. Three key factors have driven model improvements: 1. ** Model Size** : Larger parameter counts generally lead to better performance 2. ** D...

--- How I Accidentally Created a “PhD-Level” Chatbot Style (And Why It Works Better Than Default AI) --- The Problem with Generic Chatbot Responses If you’ve ever used a chatbot—whether it’s Claude, ChatGPT, or another AI—you’ve probably noticed something: their responses often feel watered down, vague, or overly simplistic. That’s not because the AI lacks knowledge. It’s because ** Reinforcement Learning from Human Feedback (RLHF)* * trains these models to “regress to the mean,” delivering responses at about a ** fifth- or sixth-grade reading level **. The result? Generic, cautious, and often unhelpful answers—especially when you’re diving into complex topics like health, science, or niche academic fields. I stumbled onto a solution by accident. And it’s changed how I use AI forever. --- The Accidental Discovery: A “Scholarly Dialogue” Style A few months ago, I was recovering from ** chronic health issues **, specifically ** H. pylori treatment **. Thirteen weeks post-trea...

  Beyond the Vibes: The Shocking Security Risks of AI-Powered "Vibe Coding" (And How to Fix Them) You’ve probably heard the buzz: **"Vibe coding."** Popularized earlier this year by figures like Andrej Karpathy (who famously claimed he could "speak to AI" to build apps), it’s the hot trend where developers describe an application in plain English, and an AI like Cursor or GitHub Copilot writes the code. It’s fast, it’s exciting—and **it’s quietly creating "vulnerability as a service" for 99% of its users.** If you’re building with AI but *haven’t* woken up to a $10,000 cloud bill or a database flooded with 1,000 fake sign-ups per minute—congratulations. But your luck might be running out. As one developer tweeted: *"Guys, I’m under attack ever since I started to share how I built my SaaS using Cursor."* Another pleaded: *"Okay, someone is attacking my database with 1,000 new signups per five minutes. Bro, can you please...

The Race for the World's Fastest Supercomputer: How the US Reclaimed the Crown For five years, China dominated the supercomputing landscape, holding the title for the world's fastest machine. But in a dramatic shift, the United States has surged back to the top with Summit, a groundbreaking $200 million government-funded supercomputer that represents the cutting edge of computational power. ## Meet Summit: A Technological Marvel Developed at Oak Ridge National Laboratory in partnership with tech giants IBM and Nvidia, Summit is more than just a computer—it's an engineering feat that pushes the boundaries of what's possible. This massive machine consists of over 36,000 processors working in harmony to achieve an astounding 200 quadrillion calculations per second. To put this in perspective, consider that the first supercomputer, the CDC 6600 released in 1964, could perform three million calculations per second using a single processor. While impressive for it...

Google's Titans: Revolutionary Transformer Architecture with Long-Term Memory *A breakthrough in AI architecture combining the best of RNNs and Transformers for unprecedented context handling* --- The AI community has been buzzing with excitement over Google Research's latest breakthrough: **Titans**, a revolutionary new transformer architecture that fundamentally changes how large language models handle memory and context. This isn't just another incremental improvement – it's a paradigm shift that combines decades of research into a powerful new approach to AI reasoning.   The Genesis: From RNNs to Test-Time Training To understand Titans, we need to appreciate the journey that led here. Back in 2019, Recurrent Neural Networks (RNNs) were our go-to solution for sequential data processing. RNNs had a beautiful simplicity: they used hidden layers as memory mechanisms to retain past context and influence future predictions. These hidden states compressed all p...

  The Tiny AI That Outreasoned Goliath: How a 27-Million-Parameter Model Beat Tech Giants at Their Own Game You’ve likely marveled at AI’s ability to generate poetry, compose symphonies, or even build functional apps from a single prompt. Yet, ask the same colossal model to solve a basic logic puzzle, and it might utterly crumble. It’s a paradox that’s stumped researchers: **Why do trillion-parameter behemoths, trained on nearly the entire internet, hit a brick wall when faced with pure step-by-step reasoning?** This isn’t just a quirky limitation—it’s one of the final frontiers on the path to *true* artificial intelligence. And now, a small team of researchers from Singapore-based **Sapiant** has shaken the AI world with a radically different approach. Their brain-inspired model, called **HRM (Hierarchical Reasoning Model)**, isn’t just nibbling at the giants’ heels—it’s *beating them*. And it’s doing it with **less than 0.0007% of their size**. Why Giant AI Still Stum...

Understanding Mixture of Experts: Beyond the Hype The world of large language models (LLMs) is constantly evolving, and one of the most intriguing architectural innovations making waves is the concept of Mixture of Experts (MoE). While rumors have long swirled around whether models like GPT-4 use this approach, the reality of how MoE works is far more nuanced than many realize. What is Mixture of Experts? At its core, Mixture of Experts is a neural network architecture designed to solve a fundamental problem in large language models: computational efficiency. Traditional "dense" models like LLaMA 2 and LLaMA 3 activate every single parameter when processing a query. If you're working with a 70 billion parameter model, all 70 billion parameters need to process information for each token generated, requiring enormous computational resources. MoE models take a different approach. Instead of activating all parameters, they use a "sparse" activation patte...

Revolutionizing AI: How Intelligent Systems Design Their Own Multi-Agent Communication Networks The world of artificial intelligence is evolving at breakneck speed, and one of the most fascinating developments is the emergence of AI systems that can design their own optimal communication protocols. Today, we're diving deep into groundbreaking research that's changing how we think about multi-agent AI systems.   The Communication Challenge in Multi-Agent Systems Imagine you have seven AI agents working together on a complex task. How should they communicate? Should they form a simple chain, where each agent talks only to the next one? Or should they have a dense network where everyone talks to everyone else?  Until now, we've relied on predefined, static topologies that remain the same regardless of the task complexity. But here's the problem: a simple query doesn't need the same communication overhead as solving general relativity equations. We're es...

Link:  https://github.com/Zie619/n8n-workflows/tree/main

The Accidental Discovery That Made Modern AI Possible: How Attention Sinks Saved Large Language Models The attention mechanism that powers today's AI chatbots—enabling them to solve PhD-level questions, generate code, and engage in sophisticated conversations—continues to surprise researchers with its hidden complexities. Despite massive investments and research efforts, this nearly eight-year-old technique remains largely unmatched in performance. But what if the secret to its success was discovered entirely by accident? The Mystery of Long Context Windows Modern AI models like Gemini 2.5 Pro can handle context windows of 64k tokens or more, a capability that seems almost magical when you consider the technical challenges involved. While other attention mechanism alternatives have failed to scale effectively, the original transformer architecture scales up as if it was designed for this purpose from the beginning. The answer to this puzzle was accidentally uncovered in...

Why AI Fails at Complex Tasks: The Missing World Model Problem and a Revolutionary Solution *August 25, 2025*   The Problem: When AI Recommendations Go Wrong Picture this: You're planning a vacation and ask GPT-5 to recommend an affordable, beautiful destination. It confidently suggests a place with incredible prices and glowing reviews. You follow the link, excited about your discovery, only to find that the article it quoted is from 2015—a decade old. Those "amazing" prices? They're completely outdated. This frustrating scenario, shared by AI researcher Gary Marcus on August 12, 2025, perfectly illustrates a fundamental flaw in current AI systems. As Marcus pointed out, GPT-5 doesn't understand that a 10-year-old article about pricing is essentially useless for current decision-making. **The reason? AI systems lack functional world models—they don't understand timing, pricing, economics, or how the world actually works.** Current AI systems are l...

TL;DR Link: https://towardsdatascience.com/what-if-i-had-ai-in-2020-rent-the-runway-dynamic-pricing-model/

TL;DR Link:  https://www.marktechpost.com/2025/08/09/graph-r1-an-agentic-graphrag-framework-for-structured-multi-turn-reasoning-with-reinforcement-learning/

TL;DR Link:  https://www.anthropic.com/news/building-safeguards-for-claude

TL;DR Link:  https://www.pcworld.com/article/1681251/10-awesome-things-you-can-do-with-chatgpt.html

Use the following prompt: Abstract human silhouette made of glowing energy waves and sacred geometry patterns, vibrant frequency lines flowing through the body, cosmic and holistic vibe, modern metaphysical art, futuristic yet organic style.

TL;DR Link:  https://www.tomshardware.com/tech-industry/artificial-intelligence/95-percent-of-generative-ai-implementations-in-enterprise-have-no-measurable-impact-on-p-and-l-says-mit-flawed-integration-key-reason-why-ai-projects-underperform

TL;DR Link:  https://www.theregister.com/2025/08/20/kpmg_giant_prompt_tax_agent/

Research Paper:  https://arxiv.org/pdf/2508.12538