Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT</a> <font color="#6f6f6f">WSJ</font>
Could not retrieve the full article text.
Read on Google News: ChatGPT →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
productchatgpt
Developers Are Designing for AI Before Users Now
<p>A quiet shift is happening in modern web development.</p> <p>For years, developers designed applications with one priority: users.</p> <p>UI came first.<br><br> User flows came first.<br><br> User experience came first. </p> <p>Backend, APIs, and integrations were built around that experience.</p> <p>But today, something has changed.</p> <p>Developers are increasingly designing systems with <strong>AI in mind before users</strong>, and this is reshaping how frontend, UX, and fullstack engineering work.</p> <h2> The Old Way of Building Applications </h2> <p>Traditional product development looked like this:</p> <p>Design UI → Build frontend → Connect backend → Launch</p> <p>The focus was simple:</p> <ul> <li>What does the user need?</li> <li>How will they interact?</li> <li>What is the ea
The Hidden Cost of Copy-Pasting Code Into ChatGPT
<p>AI coding tools promise faster development. The <a href="https://metr.org/blog/2025-07-10-early-2025-ai-developer-study/" rel="noopener noreferrer">METR study</a> found the opposite: experienced developers were 19% slower on complex tasks when using AI, even though they perceived themselves as 20% faster. The biggest contributor wasn't bad code generation. It was the workflow around it.</p> <p>Every time you alt-tab from your editor to a chat window, paste a function, explain what it does, describe the bug you're seeing, read the response, mentally translate it back to your codebase, switch back to your editor, and apply the changes, you're paying a productivity tax that compounds across a day of work. <a href="https://www.microsoft.com/en-us/research/uploads/prod/2022/07/Disrupted_and_
The 5th Agent Orchestration Pattern: Market-Based Task Allocation
<p>Most conversations about agent orchestration patterns settle on the same four: pipeline, supervisor, router, blackboard. Each solves coordination differently. Pipelines chain steps linearly. Supervisors centralize control. Routers classify and dispatch. Blackboards let agents coordinate through shared state without direct communication.</p> <p>These four cover a lot of ground. But there is a fifth pattern that comes from an older field, and it solves a problem the other four handle poorly: dynamic allocation across heterogeneous agents when cost matters.</p> <h2> The Pattern: Auction-Based Task Allocation </h2> <p>Instead of a supervisor deciding which agent handles a task, you let agents bid on it.</p> <p>The mechanism works like this. A task enters the system. It gets broadcast to all
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
The 5th Agent Orchestration Pattern: Market-Based Task Allocation
<p>Most conversations about agent orchestration patterns settle on the same four: pipeline, supervisor, router, blackboard. Each solves coordination differently. Pipelines chain steps linearly. Supervisors centralize control. Routers classify and dispatch. Blackboards let agents coordinate through shared state without direct communication.</p> <p>These four cover a lot of ground. But there is a fifth pattern that comes from an older field, and it solves a problem the other four handle poorly: dynamic allocation across heterogeneous agents when cost matters.</p> <h2> The Pattern: Auction-Based Task Allocation </h2> <p>Instead of a supervisor deciding which agent handles a task, you let agents bid on it.</p> <p>The mechanism works like this. A task enters the system. It gets broadcast to all
The Stages of AI Grief
<blockquote> <p><strong>Assumed audience:</strong> People who work with AI daily — or are starting to — and have complicated feelings about it.</p> </blockquote> <p>I don't think I've ever had so much fun in my programming career as I do now. Which is strange, because a few weeks ago I was in a very different place. I was watching - in horror - as the machine on my desk was taking over my craft. Like most people I guess, I derive quite a lot of my identity from that craft; hence the horror. (Let's ignore for now whether that's a good thing or not.)</p> <p>I just watched it melt away. Like a block of ice in the sun; inexorable. In that moment it felt like I was witnessing an emerging god: an uncontrollable force in the sky asserts its influence over all it touches, and every day, it touches
Unlock the Power of Private AI: Build a Local RAG Pipeline with LangGraph, Ollama & Vector Databases
<blockquote> <p>I created a new website: <a href="https://programmingcentral.vercel.app/books/typescript/" rel="noopener noreferrer">Free Access to the 8 Volumes on Typescript & AI Masterclass</a>, no registration required. Choose Volume and chapter on the menu on the left. 160 Chapters and hundreds of quizzes at the end of chapters.</p> </blockquote> <p>Retrieval-Augmented Generation (RAG) is revolutionizing how we interact with AI, allowing models to provide more informed and contextually relevant answers. But what if you need to keep your data private and secure? This guide dives into building a <strong>Private RAG pipeline</strong> – a self-contained AI system that operates entirely on your machine, leveraging local embeddings, vector stores, and Large Language Models (LLMs). We'll
Stop Using Elaborate Personas: Research Shows They Degrade Claude Code Output
<blockquote> <p>Scientific research reveals common Claude Code prompting practices—like elaborate personas and multi-agent teams—are measurably wrong and hurt performance.</p> </blockquote> <h1> Stop Using Elaborate Personas: Research Shows They Degrade Claude Code Output </h1> <p>A developer who read 17 academic papers on agentic AI workflows has published findings that contradict much of the common advice circulating in the Claude Code community. The research-backed principles suggest developers are actively harming their output quality with popular prompting patterns.</p> <h2> What The Research Says — Counterintuitive Findings </h2> <p>The key findings, distilled from papers including PRISM persona research and DeepMind (2025) studies, are actionable for any Claude Code user:</p> <ol> <
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!