标签：#openai

The hire marks OpenAI's latest push into India, expanding offices, partnerships and hiring.

The White House asked OpenAI to delay the rollout of its GPT-5.6 AI models, two weeks after Anthropic had to take its most advanced AI models offline.

Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]

AI models have progressed to the point where their capabilities have real political consequences. Dealing with those consequences will require collective action.

It's been two weeks since Anthropic took its Mythos-class models offline after a Friday evening ultimatum from the Trump administration. The company sprang into action immediately, sending a barrage of executives to Washington, DC. But updates have been suspiciously lacking, with no resolution in sight. Anthropic declined to comment multiple times this week about the […]

Nine jurors. Two hours of deliberation. Twenty-six claims at the original federal complaint's peak. Three surviving claims at trial. Zero claims surviving the verdict. One hundred fifty billion dollars of maximum disgorgement exposure if the verdict had gone the other way. One hundred thirty billion dollars of OpenAI Foundation equity stake under the October 28, 2025 recapitalization. Thirty-eight million dollars of total Musk contributions per his sworn trial testimony. Forty-four million per the legal complaint. Eight years from the January 2, 2016 Sutskever-Musk "less open / Yup" email exchange to the August 2024 federal filing date. Three years of statute-of-limitations runway on the breach-of-charitable-trust claim; two years on the unjust-enrichment claim. The verdict in Musk v. Altman came in this morning at the federal courthouse on Clay Street in Oakland, before Judge Yvonne Gonzalez Rogers in the Northern District of California. The companion piece, The Calendar Technicality , makes the doctrinal argument that the procedural dismissal is the substantive determination California charitable-trust law would have produced on the merits as well. This piece takes the same conclusion through the numbers. The dollar-and-time math closed the merits door before the doctrinal door even came into view. Two hours, in context Federal-court civil-trial deliberations on complex commercial cases typically run between one and five days. The Administrative Office of the U.S. Courts' annual judicial-business reports show median civil-jury deliberation in the multi-day range for cases with three or more issues to resolve and dollar exposure above one billion. The two-hour deliberation in Musk v. Altman is roughly one to two standard deviations below the median for cases of this complexity. The brevity is not a function of jury inattention. The trial ran three weeks. Roughly four hours of testimony came from Altman alone on May 12, with cross-examination opening with Musk's lea

penAI reportedly plans to share its newest model, GPT 5.6, with a select group of partners instead of to the broader public. The reason: the Trump administration told it to.

The Trump administration, apprehensive of potential security issues, has reportedly asked OpenAI to stagger the release of its next big-ticket model, GPT-5.6. The Information reported that OpenAI CEO Sam Altman told employees Wednesday in a company Q&A that it would release GPT-5.6 in limited preview form - granting access only to a small group of […]

Despite ChatGPT's commanding market lead, consumers who pay for AI have been increasingly choosing Anthropic's Claude, data shows.

Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI's inference systems.

OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]

The Complete Guide to OpenAI-Compatible APIs for Chinese LLMs One of the smartest decisions OpenAI made was making their API the de facto standard for LLM interaction. The openai Python package, the ChatCompletion interface, and the message format have become the HTTP of AI — nearly every major model provider now supports some form of OpenAI compatibility. This means you can swap models without changing your code. Here's how to use that to access China's best LLMs. The OpenAI SDK Pattern If you've used OpenAI's API, you already know the pattern: from openai import OpenAI client = OpenAI ( api_key = " sk-... " ) response = client . chat . completions . create ( model = " gpt-4o " , messages = [{ " role " : " user " , " content " : " Hello! " }] ) To access Chinese models through an OpenAI-compatible gateway, you change exactly two things : client = OpenAI ( base_url = " https://api.tokenmaster.com/v1 " , # ← Changed api_key = " tm-... " # ← Changed ) Everything else stays the same. The same SDK, the same method calls, the same message format. What This Unlocks By switching to an OpenAI-compatible gateway for Chinese models, you gain access to: Model Family Top Models Competitive Advantage OpenAI-Compatible DeepSeek V4-Pro, V4 Flash, Coder Coding, math, reasoning ✅ Qwen (Alibaba) 3.7-Max, 3.5-Flash Long context (256K), multilingual ✅ GLM (ZhipuAI) 4.5, 4-Flash Reasoning, structured output ✅ Baichuan Baichuan 4 Chinese content generation ✅ All accessible through the same SDK, the same API key, the same base URL. Migration Guide Step 1: Get Your Gateway Key Sign up at an OpenAI-compatible gateway for Chinese models. Most offer free trial credits: # I use TokenMaster # Sign up at https://api.tokenmaster.com # Get your API key from the dashboard Step 2: Update Your Client Instantiation Python: # Before: OpenAI only import os from openai import OpenAI client = OpenAI ( api_key = os . getenv ( " OPENAI_API_KEY " )) # After: Multi-model access TM_KEY = os . getenv ( " TOKENM

Netflix, A24, Focus Features, and Warner Bros.' Clockwork have all reportedly decided to pass on picking up Artificial - director Luca Guadagnino's new biographical drama about OpenAI cofounder / CEO Sam Altman - for distribution deals. And while Neon and Mubi are still said to be interested in the film, this situation makes it seem […]

Nine years ago, I built two versions of Highlander: an original jQuery application and a React/Redux version that used the same backend concepts. After successfully reviving and deploying the jQuery version, I turned to Highlander-react-redux. The goal was not simply to make an old repository run again. I wanted to improve the product, modernize its architecture without rewriting everything, and deploy something people could actually explore. This time, I used Turtle AI: a plan-driven engineering workflow built around Codex. It gave the AI explicit phases for planning, implementation, verification, testing, documentation, security review, and performance review. The process worked—but it also taught me that more guardrails do not automatically create a more efficient workflow. The Problem: More Than an Old React App The application had the typical problems of a nine-year-old project: Legacy React class components Complicated Redux connections Hardcoded localhost API URLs Authentication state disappearing after refresh Unprotected client routes Large Express route files mixing routing and business logic Inconsistent API errors No API versioning Limited filtering and pagination Outdated deployment assumptions UX gaps for demo users The app also had useful product ideas that had never been fully developed. Coaches could manage teams, players, and stats, but the experience needed stronger analytics, season support, collaboration, and more reliable workflows. I did not want to throw away the existing application and replace it with a new stack. The challenge was to preserve its original value while making targeted improvements. The Approach: Plan-Driven Product Engineering My previous Highlander revival prioritized: Get the app running locally Stabilize authentication and data Improve the demo experience Harden security Deploy For the React/Redux version, I followed a more structured workflow: Analyze the repository Create an implementation plan for one feature Implement

OpenAI is attempting to tackle the security issues of the open source software community.

Amid concerns about AI models’ cybersecurity capabilities, OpenAI revealed an improved version of GPT-5.5-Cyber and its “Patch the Planet” initiative to fix open-source software bugs.

If you've worked with multiple AI APIs, you know the pain: different auth flows, different SDKs, different billing dashboards, different rate limits. You end up with a providers/ folder full of wrapper code just to normalize the responses. Onlist solves this by putting 40+ AI providers behind a single OpenAI-compatible endpoint. One API key, one billing account, same chat.completions.create() call you already know. We just shipped official SDKs for Python and JavaScript/TypeScript, so I wanted to walk through what they look like in practice. The 30-Second Setup Python: pip install onlist from onlist import Onlist client = Onlist () # reads ONLIST_API_KEY from env response = client . chat . completions . create ( model = " openai/chatgpt-5.5 " , messages = [{ " role " : " user " , " content " : " Hello! " }], ) print ( response . choices [ 0 ]. message . content ) TypeScript: npm install @onlist/sdk import { Onlist } from " @onlist/sdk " ; const client = new Onlist (); const response = await client . chat . completions . create ({ model : " openai/chatgpt-5.5 " , messages : [{ role : " user " , content : " Hello! " }], }); console . log ( response . choices [ 0 ]. message . content ); That's it. No base URL to configure, no special headers to set. If you've used the openai package before, you already know how to use this. Why Not Just Use the OpenAI SDK Directly? You absolutely can. Onlist is fully OpenAI-compatible, so this works fine: from openai import OpenAI client = OpenAI ( base_url = " https://onlist.io/v1 " , api_key = " your-key " , ) The SDK adds three things on top of that: Default configuration. No base_url to remember. The ONLIST_API_KEY env var just works. Marketplace API. A .marketplace namespace for browsing models and providers programmatically. Proper User-Agent. Helps us debug issues when you reach out for support. If you're already using OpenAI or OpenRouter, switching takes one line: - from openai import OpenAI + from onlist import Onlist - clien

Luca Guadagnino's film about OpenAI CEO Sam Altman, Artificial, has reportedly been dropped by Amazon MGM. The film, which stars Andrew Garfield and covers the rollercoaster five days in 2023 spanning Altman's termination and reinstatement as CEO, had been in the works for about a year. The cast also includes A Complete Unknown actress Monica […]

Five months after returning to OpenAI, Barret Zoph - the company's head of enterprise AI sales - has departed, The Verge has learned. Zoph returned to OpenAI in mid-January after a stint as co-founder and CTO of Thinking Machines Lab, the competing AI company founded by former OpenAI CTO Mira Murati. Shortly after Zoph returned […]

On June 12, 2026, enterprise developers using the Codex API started seeing an unfamiliar response header: X-Model-Version: kindle-alpha . It appeared on a subset of requests for roughly 18 hours, then vanished. That's the release candidate for GPT-5.6 — OpenAI's next flagship model — leaking through the staging layer. OpenAI's Chief Scientist publicly called the upcoming release "a meaningful leap" the following day. By OpenAI's historically understated communications standards, that's loud. This post covers what the backend traces, developer reports, and Polymarket odds (currently ~80% for a pre-June-30 launch) actually tell you about the model — and what to do before it drops. How the Leak Surfaced Three separate sources converged in the 72 hours after the June 12 header incident. First, developers with ChatGPT Pro OAuth access reported hitting context windows significantly beyond GPT-5.5's supported limit. At least four documented cases logged successful 1.5M-token completions before the backend silently downgraded them to the production model. Second, the Codex enterprise API logs — accessible with full response header exposure enabled — confirmed the kindle-alpha codename across US-east-1 and us-west-2 endpoints. Third, the Polymarket market for "GPT-5.6 public release before July 1, 2026" moved from 61% to 80%+ within 48 hours of the header reports circulating on developer forums. None of this is from OpenAI's press office. No model card, no official benchmark numbers, no pricing. The specifics below are high-confidence inference from multiple corroborating signals — not official spec. Treat it accordingly when making production decisions. The Architecture Shift: Agentic-First, Not Just Smarter GPT-5.5 was trained as a reasoning model with agent capabilities added on top. GPT-5.6 is reportedly designed in the opposite order. The primary optimization target during training was not MMLU or GPQA benchmark scores — it was token efficiency on long-horizon agentic t