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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.

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5,677,923 просмотров • 1 месяц назад •via X (Twitter)

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wow... GPT 5.6 Sol Passed the Replit Benchmark. In one prompt 5.6 on Codex created a full Replit clone with DB, Sandboxes, and an agent that builds and edits full web apps. Only Fable and GPT 5.6 have passed this benchmark. 5.6 is much better at designing iOS apps than 5.5 as well.

wow... GPT 5.6 Sol Passed the Replit Benchmark. In one prompt 5.6 on Codex created a full Replit clone with DB, Sandboxes, and an agent that builds and edits full web apps. Only Fable and GPT 5.6 have passed this benchmark. 5.6 is much better at designing iOS apps than 5.5 as well.

Riley Brown

123,242 просмотров • 1 месяц назад

i recreated this with GPT 5.6 Sol & it costed me ~$5 only & 2 prompts >GPT 5.6 Sol Ultra as the main agent, admin & orchestrator >GPT 5.6 Luna Max as sub-agents >used the Codex app >used about 20% of my weekly limit on $100 plan ($100/4 * 20%) some bugs, and improvements would take another prompt but pretty good for $5!

i recreated this with GPT 5.6 Sol & it costed me ~$5 only & 2 prompts >GPT 5.6 Sol Ultra as the main agent, admin & orchestrator >GPT 5.6 Luna Max as sub-agents >used the Codex app >used about 20% of my weekly limit on $100 plan ($100/4 * 20%) some bugs, and improvements would take another prompt but pretty good for $5!

anul agarwal

15,718 просмотров • 10 дней назад

An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.

An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.

OpenAI

1,783,105 просмотров • 9 дней назад

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

Hyperagent

304,027 просмотров • 1 месяц назад

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.

OpenAI

16,026,335 просмотров • 1 месяц назад

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

34,188 просмотров • 1 месяц назад

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

TBPN

639,180 просмотров • 6 месяцев назад

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.

Chubby♨️

111,412 просмотров • 1 год назад

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):

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):

Matt Dancho (Business Science)

35,452 просмотров • 1 год назад

Opus 5 is a modest upgrade, but it has tough competition in GPT-5.6 Sol Initial thoughts from our early access: > Clearer writing than Fable's dense communication style > Still gets too verbose compared to GPT models (even when explicitly instructed to keep text purposeful) > Strong on browser use inside Hyperagent > Strong on reviewing complex data to justify a decision > Less likely to take creative risks. More of a "rule follower" personality. Costs shown for five test cases (GPT-5.6 Sol was consistently much cheaper) Explore and judge the actual work for yourself in thread 👇

Opus 5 is a modest upgrade, but it has tough competition in GPT-5.6 Sol Initial thoughts from our early access: > Clearer writing than Fable's dense communication style > Still gets too verbose compared to GPT models (even when explicitly instructed to keep text purposeful) > Strong on browser use inside Hyperagent > Strong on reviewing complex data to justify a decision > Less likely to take creative risks. More of a "rule follower" personality. Costs shown for five test cases (GPT-5.6 Sol was consistently much cheaper) Explore and judge the actual work for yourself in thread 👇

Hyperagent

150,871 просмотров • 19 дней назад

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

Chris

250,405 просмотров • 1 месяц назад

Forget autocomplete — this is agent-complete. On Episode 6 of the OpenAI Podcast, Greg Brockman and Codex lead Thibault Sottiaux join @andrewmayne 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 @andrewmayne 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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Matt Dancho (Business Science)

242,084 просмотров • 1 год назад

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

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H

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Introducing Agent Plugins 🚨 Plugins help you save tokens on expensive models like GPT-5.6 Sol and Claude Fable, while getting tasks done more accurately. Some examples that will work out of the box: 👩‍💼 SMB back-office automation 💵 Track your personal finances; or 🎒 Manage your kid’s school schedule I love tokenmaxxing, but not everything needs more prompting.

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107,495 просмотров • 10 месяцев назад

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

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been building an agent-native, node-based, real-time environment. as a test, I gave gpt-5.6 sol a prompt to plan out its own setup and go into a loop of creative exploration until it felt that it was done. it invented every node, every data type, every connection and all of it comes out modular and human-readable. i could open any piece and steer while it built

been building an agent-native, node-based, real-time environment. as a test, I gave gpt-5.6 sol a prompt to plan out its own setup and go into a loop of creative exploration until it felt that it was done. it invented every node, every data type, every connection and all of it comes out modular and human-readable. i could open any piece and steer while it built

Spencer Sterling

22,217 просмотров • 20 дней назад

OpenAI just killed the idea of one best model. GPT-5.6 is three different jobs: - Sol for hard reasoning and long agent runs - Terra for everyday work - Luna for cheap high-volume tasks Most people will send every prompt to Sol, then complain about the cost. The edge is not having the flagship. It is knowing when you do not need it.

OpenAI just killed the idea of one best model. GPT-5.6 is three different jobs: - Sol for hard reasoning and long agent runs - Terra for everyday work - Luna for cheap high-volume tasks Most people will send every prompt to Sol, then complain about the cost. The edge is not having the flagship. It is knowing when you do not need it.

Fluixo

12,641 просмотров • 1 месяц назад

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Matt Dancho (Business Science)

31,079 просмотров • 1 год назад