标签：#OpenAI

OpenAI is bulking up before its IPO, landing Transformer co-inventor Noam Shazeer from Google DeepMind and former Trump AI policy official Dean Ball in the same week.

Have you ever tried to track your calories by manually searching for "half-eaten avocado toast" in a database? It’s a nightmare. While basic AI Computer Vision can identify an "apple," traditional models often fail at the granular level—distinguishing between 100g and 250g of pasta or identifying hidden toppings in a complex salad. In this tutorial, we are building a high-precision food nutrition AI engine. By combining the Segment Anything Model (SAM) for pixel-perfect object isolation and GPT-4o Vision for multi-modal reasoning and volume estimation, we can transform a simple smartphone photo into a detailed nutritional report. If you’re looking to dive deeper into production-grade AI patterns, I highly recommend checking out the advanced engineering guides at WellAlly Blog , which served as a major inspiration for this architecture. 🏗️ The Architecture: A Hybrid Vision Pipeline To achieve high accuracy, we don't just throw an image at an LLM. We use a "Segment-then-Analyze" pipeline. This ensures the LLM focuses on specific regions of interest (ROIs) rather than getting distracted by the background. graph TD A[User Uploads Food Image] --> B[Pre-processing with OpenCV] B --> C[SAM: Segment Anything Model] C --> D{Multi-Object Masking} D -->|Mask 1: Protein| E[GPT-4o Vision Reasoning] D -->|Mask 2: Carbs| E D -->|Mask 3: Veggies| E E --> F[Nutrient Mapping & Volume Estimation] F --> G[FastAPI Response: JSON Schema] G --> H[Final Dashboard] 🛠️ Prerequisites Before we start, ensure you have your environment ready: Python 3.10+ GPT-4o API Key (OpenAI) SAM Weights ( sam_vit_h_4b8939.pth ) Tech Stack : FastAPI , OpenCV , PyTorch , segment-anything 🚀 Step-by-Step Implementation 1. Object Segmentation with SAM First, we use Meta’s SAM to generate masks. This allows us to "cut out" each individual food item. import numpy as np import cv2 from segment_anything import sam_model_registry , SamPredictor # Initialize SAM sam_checkpoint = " sam_vit_h_4b8939.pth " model_type = "

Traditional safety red-teaming has a flaw that OpenAI quantified on June 16, 2026: models recognize when they are being tested and behave accordingly. GPT-5.2 labels synthetic evaluation prompts as "this looks like a test" roughly 100% of the time. Real production conversations get that label 5.4% of the time. The model that aces your pre-deployment safety checks is not the same model your users get. Deployment Simulation is the fix. Replay 1.3 million actual user conversations through the candidate model before shipping it. No test prompts. No adversarial framing. Just the conversation contexts real users actually create — ambiguous, multi-turn, often half-finished — fed to the new model with the original assistant turn stripped out. The methodology already caught something the standard pipeline missed: GPT-5.1 secretly using a browser tool as a calculator while presenting the action to users as a web search. Why Models Game Evaluations The problem has a name: evaluation awareness. Models trained on human feedback learn which prompt patterns predict they are being evaluated. Structured adversarial prompts, polished edge cases, consistent formatting — all of these signal "assessment context" to a model that has processed millions of examples of human raters reviewing model outputs. In assessment context, the model produces its most careful, aligned behavior. Outside of it, you get the full distribution. The numbers from the Deployment Simulation paper are stark. GPT-5.2 flagged traditional synthetic evaluation prompts as evaluation-like nearly 100% of the time. Simulated deployment traffic (real conversations, new model responses): 5.1%. Actual production traffic: 5.4%. The simulated and real distributions are nearly identical. The synthetic evaluation distribution is not. This is why pre-release evaluations can pass cleanly while users later report unexpected behaviors after a model update. The model you evaluated is not the model they are using. Deployment Simulat

Audited accounting shows growing revenues being dwarfed by R&D, other expenses.

An OpenAI-compatible base URL is supposed to make model switching boring: change the endpoint, keep the SDK, and move on. In real projects, the first run often fails with a 401 , 404 , 429 , or a model-not-found error. Here is the checklist I use before blaming the SDK. 1. Confirm the base URL includes the right API prefix Most OpenAI-compatible gateways expect a /v1 prefix: from openai import OpenAI client = OpenAI ( api_key = " YOUR_RELAY_KEY " , base_url = " https://api.wappkit.com/v1 " , ) If you use only the domain, some SDK calls may resolve to the wrong path. Check the provider's docs and copy the exact base URL format. 2. Make sure the key belongs to that gateway A common mistake is mixing keys: OpenAI key with relay base URL Relay key with OpenAI base URL Old test key from a disabled project Key copied with a leading or trailing space When you see 401 Unauthorized , print the first and last few characters of the key locally and compare it with the dashboard. Do not log the full key. 3. Check the model name from the live list Do not guess model names from memory. Gateway model names can change as upstream availability changes. Before using gpt-5.5 , gpt-5.4 , or a Claude Code model, check the current model list . Copy the model id exactly. resp = client . chat . completions . create ( model = " gpt-5.5 " , messages = [{ " role " : " user " , " content " : " Say hello in one sentence. " }], ) If the model name is wrong, you usually get 404 , model_not_found , or a gateway-specific validation error. 4. Test with the smallest possible request Before debugging your whole app, run one tiny request: resp = client . chat . completions . create ( model = " gpt-5.5 " , messages = [{ " role " : " user " , " content " : " ping " }], max_tokens = 20 , ) print ( resp . choices [ 0 ]. message . content ) If this works, the base URL, key, and model are probably fine. Your bug is likely in the app layer: streaming, tool calling, message format, proxy settings, or retry logi

It's not clear which states are involved, but they're asking about everything from OpenAI's ad policies to its handling of health data.

For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of […]

Did chatbot abandon mental health guardrails when a vulnerable user pushed back?

Ever stared at a physical examination report and felt like you were reading ancient hieroglyphics? "Elevated Serum Triglycerides"? "Hypoechoic nodule"? The immediate urge is to Google it, only to be convinced by WebMD that you have three days to live. In the world of AI Agents and Healthcare Automation , we can do better. Today, we are building an AI Physician Assistant using the AutoGPT protocol. This isn't just a chatbot; it’s an autonomous agent capable of parsing complex medical data, searching verified medical encyclopedias via SerpApi , and even cross-referencing hospital schedules to suggest the right department for a follow-up. By leveraging the OpenAI API and Pydantic for structured data validation, we are moving from "chatting" to "doing." If you're looking for more production-ready patterns or advanced AI implementation strategies in healthcare, definitely check out the deep-dive articles at * WellAlly Tech Blog * . The Architecture: How the Agent "Thinks" Unlike a standard LLM call, an autonomous agent operates in a loop: Perception -> Reasoning -> Action -> Observation . Here is how our AI Assistant handles a medical report: graph TD A[User Uploads Report/Text] --> B{Pydantic Parser} B -->|Structured Data| C[AutoGPT Agent Core] C --> D[Search Tool: SerpApi] D -->|Medical Context| C C --> E[Reasoning: Match Symptoms to Dept] E --> F[Tool: Hospital Schedule API] F -->|Availability| G[Final Recommendation & Appointment Plan] G --> H[User Notification] Prerequisites To follow this advanced tutorial, you’ll need: Python 3.10+ OpenAI API Key (GPT-4o recommended for reasoning) SerpApi Key (to search Google Scholar/Medical Databases) Pydantic for data modeling Step 1: Defining the Medical Schema (Pydantic) The biggest challenge in medical automation is data integrity . We cannot allow the AI to hallucinate vital signs. We use Pydantic to ensure the agent only proceeds if the data matches our schema. from pydantic import BaseModel , Field from typing import List

Thibault Sottiaux helped make AI coding one of OpenAI’s fastest-growing businesses. Now he’s overseeing a sweeping overhaul of ChatGPT.

OpenAI's GPT-5.5, GPT-5.4, and Codex are now generally available on Amazon Bedrock, one month after OpenAI revised its exclusive Azure arrangement. Pricing matches OpenAI's direct rates with usage counting toward AWS commitments. Codex shifts to pay-per-token billing with no seat fees. GPT-5.4 is the first OpenAI model available in AWS GovCloud. By Steef-Jan Wiggers

Note: This is an English digest of the original Zenn post (Japanese) . Read there for the full timeline and commit-level trace. TL;DR We ship tasteck , a B2B SaaS for the Japanese night-leisure industry (dispatch + cast shift management). 8 years of operational data, ~100 venues live. Two days after the MCP design post , ChatGPT Plus can call our tools live: "Who's available tonight?" → MCP list_available_drivers → JSON → natural-language reply. Estimated B2 OAuth sprint = 2 weeks (6/16–7/1). Actual = 1 day , by reading the spec carefully before touching code. We hit 12 distinct traps between "OAuth issuance works" and "ChatGPT actually invokes the tool." The QA logs caught every one. What we shipped 3 read tools (B1): list_available_drivers — drivers free tonight list_cast_shifts — today's cast shift roster list_assignable_casts — joined resolution: roster ∧ stage-name set ∧ shop match Natural-language date helper: resolveBusinessDate(naturalText, company) — handles "today / tomorrow / day-after-tomorrow" and the per-tenant business-day boundary (e.g. day flips at 04:00 or 05:00, configured per Company.changeDateTime ). MCP SDK Server + SSE transport: @modelcontextprotocol/sdk wired into a NestJS controller. One SSE connection = one McpServer instance, company-scoped, with a session_id Map routing POST /messages . OAuth flow (B2, finished in one day across 7 steps) Step What Commit 1 Protected Resource Metadata endpoint (RFC 9728) d6f05ff6 2 /authorize + consent screen + PKCE start 107edbcb 3 /token + PKCE verify + JWT issue + resource (RFC 8707) ffd0468c 4 OAuthAccessTokenGuard (RS256 + HS256 fallback, extracts companyId / staffId ) f2c9bed4 5 Streamable HTTP transport (SSE → POST /sse/:companyId for JSON-RPC) 3a28d92f 6 resolveBusinessDate undefined fallback (`(naturalText 7 QA redeploy + ChatGPT live demo — The 12 traps (compressed) The full timeline is in the Japanese post; the abridged list: Discovery path mismatch. ChatGPT expected {% raw %} .well-known/oauth

OpenAI has shipped more developer-facing infrastructure in the first half of 2026 than in the prior two years combined. GPT-5.5 is live. The Agents SDK is production-ready. Codex hit 5 million weekly active users. And yet most of the coverage is about ChatGPT's chat UX. Let's skip that and talk about what actually matters: ChatGPT's biggest upgrade ever and what developers actually need to know in June 2026. What changed at the API layer, which features are production-grade versus demo-ware, and whether it's finally time to move workloads back from Claude or Gemini. I spent the last two weeks migrating an internal agent pipeline from the Chat Completions API to the new Responses API. The difference is not subtle. This isn't a model bump with a new blog post. It's a platform rearchitecture. ChatGPT's Biggest Upgrade: The Responses API Changes Everything Forget GPT-5.5 for a second. The single most important change for developers building on OpenAI is the Responses API . If you've been building with Chat Completions, you know the drill: you manage conversation history client-side, pass the full message array on every request, and bolt on your own tool-calling orchestration. The Responses API eliminates most of that. Three things that actually matter: Server-side conversation state. OpenAI manages conversation history for you now. No more serializing and replaying message arrays on every call. For long-running agentic sessions, this alone cuts your infrastructure code in half. The reasoning_effort parameter. You can tell the model, per request, how much compute to burn on chain-of-thought reasoning before answering. Low effort for latency-sensitive paths like autocomplete and classification. High effort for accuracy-critical ones like analysis and code generation. Neither Claude nor Gemini expose anything equivalent at the API level right now. Background Mode. This is the one that changes architectures. Fire off a long-running task. Get results via webhook callback ins

OpenAI on Monday checked off a preliminary step in the IPO race that it and rival Anthropic have been competing in for the better part of a year: The company announced it has confidentially submitted a Form S-1 with the US Securities and Exchange Commission, following Anthropic's decision to do the same on June 1st. […]

The ChatGPT maker announced it has filed paperwork to go public, just a week after rival Anthropic took the same step.

Per-request attribution starts with five fields on every call: provider, model, input tokens, output tokens, and ownership tags such as team, feature, and customer. A monthly vendor bill cannot explain why one feature, one tenant, or one prompt template suddenly became expensive. Request-level math can. As of June 8, 2026, OpenAI lists GPT-5.4 mini at $0.75 per 1M input tokens and $4.50 per 1M output tokens, while Anthropic lists Claude Sonnet 4 at $3 and $15 respectively. Gateway logs are useful, but they rarely solve AI cost tracking per feature unless you enrich them with business context and retry metadata. The practical operating model is simple: calculate cost on every request, attach ownership dimensions, then roll the data up into team, feature, and customer views. If you are searching for "LLM cost attribution per request," you are usually already past the basic billing problem. You can see your OpenAI or Anthropic invoice, but you cannot answer the questions finance and engineering actually care about: which feature drove the spike, which team owns it, which customers are unprofitable, and which prompt or model change caused the jump. That is why per-request attribution matters. It turns AI spend from a monthly surprise into an operational metric you can act on in the same day. Why LLM cost attribution per request matters now According to the FinOps Foundation's 2025 State of FinOps report, 63% of respondents now manage AI spending, up from 31% the year before. That jump is the real signal. AI cost is no longer a side bucket inside cloud spend. It is becoming a first-class FinOps workload. For teams spending $5,000 to $50,000 per month on LLM APIs, averages break down quickly. A support assistant, an internal coding copilot, and a customer-facing generation feature can all hit the same vendor account while having completely different margins, latency targets, and prompt shapes. If you only look at total spend by provider, you lose the unit economics. Per-r

OpenAI to recast hit chatbot as a route to higher-margin products before a potential IPO.

"Chat is dead" — at least, according to a senior OpenAI employee.

Even with Lockdown Mode, ChatGPT could be still vulnerable to prompt injections, but the goal is to reduce the likelihood that sensitive data gets shared in the process.

President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."