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GPT‑5.6 is helping Vercel’s internal data science agent tackle harder work Andrew Qu, Vercel’s Chief of Software, used GPT-5.6 and Codex to unblock an internal data science agent on work where earlier models got stuck.

OpenAI Developers

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207,563 次观看 • 2 天前 •via X (Twitter)

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GPT-5.6 is a viable alternative to Fable for most knowledge work. GPT-5.6's writing is consistently well-structured and easy to read. Fable's prose can be exhausting. 5.6 also makes fresh design choices with clean hierarchy. Fable can struggle to maintain visual relationships and leans on worn-out AI tropes like eyebrow text. Examples in thread 👇 - Orbital objects explainer - Site plan for fitness resort - World Cup goals data viz GPT-5.6 is now available on Hyperagent

GPT-5.6 is a viable alternative to Fable for most knowledge work. GPT-5.6's writing is consistently well-structured and easy to read. Fable's prose can be exhausting. 5.6 also makes fresh design choices with clean hierarchy. Fable can struggle to maintain visual relationships and leans on worn-out AI tropes like eyebrow text. Examples in thread 👇 - Orbital objects explainer - Site plan for fitness resort - World Cup goals data viz GPT-5.6 is now available on Hyperagent

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Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

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Introducing ChatGPT Work, a new agent in ChatGPT powered by Codex and GPT-5.6. It can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It’s a whole new way to get work done.

Introducing ChatGPT Work, a new agent in ChatGPT powered by Codex and GPT-5.6. It can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It’s a whole new way to get work done.

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GPT-5.6 is rolling out in the API. Sol is our flagship model, leading in coding, knowledge work, cybersecurity, and science. Terra delivers performance competitive with GPT-5.5 at lower cost. Luna is our fastest, most affordable model for high-volume tasks.

GPT-5.6 is rolling out in the API. Sol is our flagship model, leading in coding, knowledge work, cybersecurity, and science. Terra delivers performance competitive with GPT-5.5 at lower cost. Luna is our fastest, most affordable model for high-volume tasks.

OpenAI Developers

219,809 次观看 • 2 天前

OPANAI 👀: ChatGPT Work arrived as a new AI Agent, along with a completely new app based on Codex Desktop. > Introducing ChatGPT Work, a new agent in ChatGPT powered by Codex and GPT-5.6. It can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It’s a whole new way to get work done. A merge into a Super App is complete 🤖

OPANAI 👀: ChatGPT Work arrived as a new AI Agent, along with a completely new app based on Codex Desktop. > Introducing ChatGPT Work, a new agent in ChatGPT powered by Codex and GPT-5.6. It can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It’s a whole new way to get work done. A merge into a Super App is complete 🤖

🚨 AI News | TestingCatalog

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FULL INTERVIEW: Sam Altman joins TBPN to discuss GPT-5.3-Codex, AI agents, Anthropic's Super Bowl ads, and more. 00:00 GPT-5.3-Codex 02:27 AI agents and the future of work 03:20 The role of forward-deployed engineers in AI 05:42 AI benchmarks 07:29 Emotional attachment to chatbots 10:40 On data and compute being the 'new oil' 12:56 Is software dead? 17:48 Codex Desktop and the rise of the general-purpose work agent 25:00 OpenAI’s last Super Bowl ad and the Anthropic ads

FULL INTERVIEW: Sam Altman joins TBPN to discuss GPT-5.3-Codex, AI agents, Anthropic's Super Bowl ads, and more. 00:00 GPT-5.3-Codex 02:27 AI agents and the future of work 03:20 The role of forward-deployed engineers in AI 05:42 AI benchmarks 07:29 Emotional attachment to chatbots 10:40 On data and compute being the 'new oil' 12:56 Is software dead? 17:48 Codex Desktop and the rise of the general-purpose work agent 25:00 OpenAI’s last Super Bowl ad and the Anthropic ads

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639,180 次观看 • 5 个月前

Forget autocomplete — this is agent-complete. On Episode 6 of the OpenAI Podcast, Greg Brockman and Codex lead Thibault Sottiaux join Andrew Mayne to talk about the leap from code suggestions to coding agents, the launch of GPT-5-Codex, and where software development could be by 2030.

Forget autocomplete — this is agent-complete. On Episode 6 of the OpenAI Podcast, Greg Brockman and Codex lead Thibault Sottiaux join Andrew Mayne to talk about the leap from code suggestions to coding agents, the launch of GPT-5-Codex, and where software development could be by 2030.

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The New GPT 5.6 Sol is insane. ❤️‍🔥Just recorded a 21-min Full Tutorial on How To Build Premium, Cinematic Websites with GPT 5.6 Sol!

The New GPT 5.6 Sol is insane. ❤️‍🔥Just recorded a 21-min Full Tutorial on How To Build Premium, Cinematic Websites with GPT 5.6 Sol!

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📣 OpenAI Developers's GPT-5.6 family is now generally available and rolling out in GitHub Copilot. GPT-5.6 comes in three variants: ☀️ GPT-5.6 Sol: the highest reasoning ceiling in the family. Best for complex reasoning over large codebases and demanding, long-running agentic work. 🌎 GPT-5.6 Terra: the balanced default. A strong all-round choice for everyday interactive and agentic coding. 🌕 GPT-5.6 Luna: a lightweight, cost-efficient variant for smaller, faster tasks and the lowest-cost option in the family. Try one, two, or them all in Visual Studio Code or the GitHub Copilot app.

📣 OpenAI Developers's GPT-5.6 family is now generally available and rolling out in GitHub Copilot. GPT-5.6 comes in three variants: ☀️ GPT-5.6 Sol: the highest reasoning ceiling in the family. Best for complex reasoning over large codebases and demanding, long-running agentic work. 🌎 GPT-5.6 Terra: the balanced default. A strong all-round choice for everyday interactive and agentic coding. 🌕 GPT-5.6 Luna: a lightweight, cost-efficient variant for smaller, faster tasks and the lowest-cost option in the family. Try one, two, or them all in Visual Studio Code or the GitHub Copilot app.

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Google release its Data science Agent The Google Data Science Agent in Colab (powered by Gemini) revolutionizes data analysis by allowing users to easily formulate their analysis goals in natural language. This automatically generates fully functional Colab notebooks, which drastically reduces tedious routine work such as code creation, data import and setup. Particularly noteworthy are the flexibility of the generated notebooks, the ease of collaboration and the fast, accurate results thanks to Gemini 2.0.

Google release its Data science Agent The Google Data Science Agent in Colab (powered by Gemini) revolutionizes data analysis by allowing users to easily formulate their analysis goals in natural language. This automatically generates fully functional Colab notebooks, which drastically reduces tedious routine work such as code creation, data import and setup. Particularly noteworthy are the flexibility of the generated notebooks, the ease of collaboration and the fast, accurate results thanks to Gemini 2.0.

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ICYMI: We launched Data Agent Kit, a unified, open-source collection of data engineering and data science skills, tools, and plugins that integrate into the environments practitioners already use—VS Code, Claude Code, Codex, Antigravity CLI, and more →

ICYMI: We launched Data Agent Kit, a unified, open-source collection of data engineering and data science skills, tools, and plugins that integrate into the environments practitioners already use—VS Code, Claude Code, Codex, Antigravity CLI, and more →

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GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

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I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

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At Perplexity, GPT-5.5 in Codex helped build an internal tool in under an hour. In Perplexity Computer workflows, GPT-5.5 used 56% fewer tokens on the same complex tasks, creating faster feedback loops for users.

At Perplexity, GPT-5.5 in Codex helped build an internal tool in under an hour. In Perplexity Computer workflows, GPT-5.5 used 56% fewer tokens on the same complex tasks, creating faster feedback loops for users.

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Introducing Exa Agent: frontier web research at less than half the cost of GPT 5.5 and Opus. /agent orchestrates a mixture of cost-effective models to complete any web research task, from simple data enrichments to building gigantic lists.

Introducing Exa Agent: frontier web research at less than half the cost of GPT 5.5 and Opus. /agent orchestrates a mixture of cost-effective models to complete any web research task, from simple data enrichments to building gigantic lists.

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Sam Altman: GPT-6 could bring a GPT-3 to 4-like leap for science. An incredible leap for science.

Sam Altman: GPT-6 could bring a GPT-3 to 4-like leap for science. An incredible leap for science.

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GPT-5.6 is here. I wish we could use it.

GPT-5.6 is here. I wish we could use it.

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IBM is offering a SQL for Data Science course for FREE! This is a must course for anyone who wants to work in data science. Course link 🧵 👇

IBM is offering a SQL for Data Science course for FREE! This is a must course for anyone who wants to work in data science. Course link 🧵 👇

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⚡️OPENAI: GPT-5.6 SOL IS 54% MORE TOKEN EFFICIENT ON AGENTIC CODING Sam Altman says the new GPT-5.6 Sol model is 54% more token efficient on agentic coding tasks and is “as good or better” than competing models. He also said OpenAI aims to offer better value with “every enterprise now is thinking about spend and the value they’re getting in exchange for AI.”

⚡️OPENAI: GPT-5.6 SOL IS 54% MORE TOKEN EFFICIENT ON AGENTIC CODING Sam Altman says the new GPT-5.6 Sol model is 54% more token efficient on agentic coding tasks and is “as good or better” than competing models. He also said OpenAI aims to offer better value with “every enterprise now is thinking about spend and the value they’re getting in exchange for AI.”

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🔥 Matt Dancho (Business Science) 🔥

240,806 次观看 • 1 年前