标签：#openai

Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI.

OpenAI's Fidji Simo is departing her full-time role as the company's AGI chief and is transitioning to being a "part-time advisor," she said on X. The news follows Simo's original announcement in April that she would take a few weeks of medical leave due to a neuroimmune condition, shortly after she had taken on the […]

The move comes after Simo took significant medical leave. She will stay on as a part-time adviser.

OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.

OpenAI is sunsetting its AI-powered browser after less than a year. But it's moving some agentic browsing features to its desktop app and a Chrome extension.

The most interesting model comparison of 2026 isn't a benchmark table. It's a product exec quietly changing the question everyone asks about models — and getting a completely different ranking as a result. Claire Vo (founder of ChatPRD, host of the How I AI podcast) ran a head-to-head between OpenAI's new GPT-5.6 lineup (Soul / Terra / Luna) and Anthropic's Claude Fable and Sonnet. The result was an upset: the most theoretically intelligent model, Claude Fable, lost to the one she could actually collaborate with, GPT-5.6 Soul. Here's what that upset actually reveals. She killed "vibes" — then bet 70% back on her own taste Tired of vibe-checking, Vo built a real benchmark across the work she does every day: writing PRDs, prototyping apps, debugging multi-step code, and talking to an agent. Scoring had two layers — an LLM-as-judge (she picked the harshest judge, GPT-5.5) and her own hand-graded "taste test," where she clicked through every artifact and wrote notes. Then the key move: she weighted the final score 70% her taste / 30% the machine. "It's my show. I trust my own taste more." That's the first insight. Benchmarks are getting more rigorous, but the final call is still human taste. The point of blind testing isn't to replace taste — it's to force it to be honest . Cover the labels, react to the work itself, then put your judgment back at the center. Theoretically brilliant vs. practically effective On raw intelligence, Fable is elite. But Vo's verdict is the sharpest line on models I've seen this year: Fable is theoretically hyper-intelligent. Soul is practically effective. She describes Fable as "an engineer who has never met a human." Precise to the point of pedantry — it scores every risk, hardens every edge. In one case it hardened a tool-calling loop so tightly that only one specific model could run it at all. It optimized itself into a corner. Soul's edge was the opposite: it gets out of its own head. Same stuck problem — she moved it to Codex, said "sto

OpenAI is already shutting down ChatGPT Atlas, its browser that could do tasks for you on your behalf, less than a year after launching it. Atlas was announced in October, but as part of its wave of news about ChatGPT Work today, the company confirmed that it will be "sunsetting" Atlas and is targeting an […]

About two weeks after OpenAI's GPT-5.6 was caught up in regulatory drama - rolled out only to government-approved organizations during a "limited preview" period - the company has received the Trump administration's greenlight for a public rollout of the model. OpenAI CEO Sam Altman called it "the best model we have ever produced." To celebrate, […]

Three big AI IPOs are set to generate more value than all the U.S. VC-backed exits since 2000.

OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps. By Steef-Jan Wiggers

Experiments in using AI to build AI show that the future doesn’t just belong to the frontier labs.

Elon Musk's tech company released the newest version of Grok on Wednesday, promising a cheaper, more efficient alternative to other powerful AI models.

OpenAI is overhauling ChatGPT's voice mode with a new model that it says is more like "talking to another person." The new GPT-Live-1 is designed to interrupt you less and will also wait for you to continue speaking if you pause mid-conversation. During a press briefing, OpenAI research lead Kundan Kumar called GPT-Live-1 the company's […]

We’ve all been there: staring at a stack of printed lab results or a folder full of cryptic report_final_v2_NEW.pdf files, trying to remember if our cholesterol was higher or lower two years ago. For developers, this isn't just a filing problem—it's a data engineering challenge. In the world of healthcare, data is messy, siloed, and often locked in "unstructured" formats. To build a truly personal Electronic Health Record (EHR) system, we need more than just a folder; we need a RAG (Retrieval-Augmented Generation) pipeline that can parse PDFs, map them to the FHIR (Fast Healthcare Interoperability Resources) standard, and provide natural language insights. In this guide, we’ll leverage Unstructured.io , Milvus , and DuckDB to turn chaotic medical PDFs into a queryable, structured knowledge base. The Architecture: From Raw Pixels to Structured Insights Before we dive into the code, let’s look at how the data flows from a messy lab report to a structured answer. graph TD A[Unstructured PDF Reports] --> B[Unstructured.io Partitioning] B --> C{Data Split} C -->|Textual Context| D[Milvus Vector DB] C -->|Tabular Data| E[DuckDB Structured Storage] D --> F[LangChain RAG Engine] E --> F G[User Query: Is my glucose trending up?] --> F F --> H[FHIR-Formatted Response] Why this stack? Unstructured.io : The gold standard for handling "ugly" PDFs (tables, headers, and nested lists). Milvus : A high-performance vector database built for scale. DuckDB : Perfect for running complex analytical SQL queries on the extracted "structured" parts of our medical data. FHIR Standard : To ensure our data follows global healthcare interoperability rules. Prerequisites Make sure you have your environment ready: pip install langchain milvus unstructured[pdf] duckdb openai Step 1: Extraction with Unstructured.io Medical PDFs often contain complex tables. Standard PDF parsers usually fail here. We’ll use unstructured to partition the document into logical elements. from unstructured.partition.pdf

Joshua Achiam spent nearly nine years at OpenAI researching AI safety and made a memorable appearance in the Musk v. Altman trial.

Microsoft is the latest Silicon Valley giant to cut back on its AI spending.

OpenAI Agents SDK 0.13 → 0.17: Three Breaking Changes You Will Hit The OpenAI Agents SDK moved from 0.13 to 0.17 in five weeks (April 9 – May 19, 2026). That's 19 releases, including three breaking changes. Two of them are silent — they change runtime behavior without raising an error at upgrade time. If you're running agents on 0.13.x, this is the migration guide you need before you upgrade. The Three Breaks Break #1: ModelRefusalError (v0.15.0) — Silent to Loud The change: Model refusals used to return empty-string output. Now they raise an exception. What this means: If your code treated refusals as output == "" or checked for empty responses, you were silent-failing gracefully. That behavior changed. A model refusal now raises ModelRefusalError and will crash your agent run unless you handle it. What you need to do: Register a "model_refusal" error handler before upgrading past 0.15.0: from openai.agents import Agent , RunConfig def handle_refusal ( error ): print ( f " Model refused: { error . reason } " ) return " Model refused to respond. Try rephrasing. " agent = Agent ( model = " gpt-4 " , tools = [...], ) config = RunConfig ( on_error = { " model_refusal " : handle_refusal , } ) response = agent . run ( " ... " , config = config ) Without the handler, you'll see: ModelRefusalError: model declined to process this request This is an intentional change — OpenAI wants refusals to be explicit, not silent. But it means your error handling has to change. Impact: Any agent that doesn't add a refusal handler will crash on a refusal. High-risk if your agents field user input directly. Break #2: Default Model Changed (v0.16.0) — Silent Model Switch The change: The default model switched from gpt-4.1 to gpt-5.4-mini . What this means: If you created an agent without explicitly setting model= , you were running on gpt-4.1. On upgrade to 0.16.0+, that same code silently switches to gpt-5.4-mini. This isn't just a version bump — gpt-5.4-mini ships with different defaults

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

OpenAI has floated giving the US government a 5 percent ownership stake as a way of easing tensions with the Trump administration and blunting mounting public backlash against AI, according to the Financial Times. CEO Sam Altman argued that giving the public a financial interest in the company would be the best way to share […]

We’ve all been there. You get a notification from your smartwatch saying your heart rate has been a bit funky, or your blood oxygen is dipping. Usually, we ignore it until it becomes a problem. But what if your personal AI was looking out for you? 🤖 In this tutorial, we are building an Autonomous Health Agent . This isn't just a notification bot; it's a proactive system that uses Playwright browser automation , OpenAI Function Calling , and Python to monitor your health trends and—if things look suspicious for three days straight—literally opens a browser and books a doctor's appointment for you. By leveraging Autonomous AI Agents and Playwright automation , we are moving from "Passive Monitoring" to "Active Intervention." This is the future of Health Tech Automation . 🏗 The Architecture Before we dive into the code, let's look at how the data flows from a "scary heart rate" to a "confirmed appointment." graph TD A[Wearable Data/Health Logs] --> B{3-Day Anomaly Check} B -- Normal --> C[Stay Healthy! 🟢] B -- Abnormal --> D[Trigger AI Agent 🤖] D --> E[OpenAI Function Calling] E --> F[Playwright Browser Automation] F --> G[Hospital Booking Platform] G --> H[Appointment Confirmation 🏥] H --> I[Notify User via SMS/Email] 🛠 Prerequisites To follow along, you’ll need: Python 3.10+ Playwright : The king of modern browser automation. OpenAI API Key : For the "brain" of our agent. A healthy dose of curiosity! 🥑 pip install playwright openai pydantic playwright install chromium 👨‍💻 Step 1: Defining the "Brain" (OpenAI Function Calling) We don't want the LLM to just "talk" about booking an appointment; we want it to actually execute the action. We'll use OpenAI's Function Calling to bridge the gap between text and code. import json from openai import OpenAI client = OpenAI () # Define the tool our agent can use tools = [ { " type " : " function " , " function " : { " name " : " book_doctor_appointment " , " description " : " Books a medical appointment based on department and s