Custom GPTs in ChatGPT will likely be retired on December 11 "Migrate your GPTs by December 11 - GPTs that aren’t migrated won’t be available after December 11. Migrate yours so people can keep using them as plugins." "Create a plugin instead - Plugins are replacing GPTs as the new home for custom tools. Create a plugin now, or continue creating a GPT during the two-week transition."

Creating and publishing new custom GPTs is no longer available on personal ChatGPT accounts (including Free, Go, Plus, and Pro plans) OpenAI also appears to be working on a way to convert existing GPTs into Skills

It does feel like every year OpenAI releases a variation on the same third-party ecosystem only to semi-abandon it: Plugins in 2023, GPTs and the GPT Store in 2023-2024, Apps in 2025, and now Plugins (same name, different thing than before) in 2026.

With OpenAI's 2026 DevDay coming up, let's pay an ode to DevDay 2023 - OpenAI's first DevDay, where they announced GPT-4 Turbo, GPTs and the GPT Store, and broader access to GPT-4's vision Sam was fired as OpenAI CEO by its Board of Directors just 11 days later. Time flies

Reverse splits effective July 15, 2026. 4 Leverage Shares ETPs have been consolidated, trading under new ISINs from July 16. FUT3 (3x Long Futu): 1 for 87 GPTS (-3x Short AI): 1 for 149 STAI (-3x Short Taiwan): 1 for 190 AMDS (-1x AMD): 1 for 84

Top 25 questions the world's largest C-Suite executives ask us re: AI transformation in the enterprise: 1. Are AI transformation firms more like McKinsey or Palantir? 2. We launched Copilot for everyone to use, but why hasn't it changed anyone's week? 3. We've done training, custom GPTs, enablement. What's next? 4. Where do we even start with AI? 5. How do we choose the first workflow to AI-enable? 6. Should AI be owned by IT or the business? 7. Do we really have to train all 10,000 people in the company? 8. How do we enable everyone to build agents, without ending up with agent sprawl? 9. Why are our token costs increasing? 10. How do we know if using AI is really cheaper than just hiring another person? 11. Which steps should remain human? 12. How do we communicate AI won't replace our team's jobs? (Will it?) 13. Will we own the agents you build for us? 14. What does maintenance look like in 6 months? 15. Will our data leave our environment? 16. What models should we be using? What do we do when they're deprecated? 17. Can we run these agents inside our ERP, or will people have to use a new tool? 18. Do we need to clean up our data first? 19. How long until we start seeing ROI? 20. When you talk about ROI, do you mean headcount? 21. How do we keep growing our business without increasing headcount? 22. Why did our last pilot project die in POC purgatory? 23. Our process documentation doesn't match what our team actually does – how do you know what to automate? 24. How do we keep up with how quickly AI is changing? 25. If AGI becomes a reality tomorrow, where should we point it? We solve these every day at Varick. Work with us below:

Georgia Tech prof. @animesh_garg predicts the next 2 years of robotics will be "the return of the RL" as the field shifts from collecting data to learning from it: "Pillar one: how do you represent the environment? Should the input be objects? Should the input be completely latent? Should the input be somehow processed with a VLM or LLM? There is debate in the field." "Pillar two is fundamentally what people now call models of the world. World models or critic models or reward models, some sort of world models that allow a very good prior on what to do." "Robotics is classically a reinforcement learning problem, so what we call post-training or recursive improvement or continued learning, those are exactly the problems that will show up in robotics in maybe a year or a year and a half." "In the same way we saw the jump from early GPTs to GPT-3 and ChatGPT, but ChatGPT by itself did not end up becoming enterprise useful. It took another two or three years from there to becoming the Claude moment." "The next two years of robotics will basically be, now that we have the data, what do we do in RL? As a lot of people call it fondly, the return of the RL." @GeorgiaTech

NVIDIA's Rev Lebaredian breaks down Jensen Huang's 5-layer cake of AI: "Jensen aptly describes AI in layers. We call it the five-layer cake. The bottom layer of AI is energy. Everything starts from that. The maximum amount of AI you can have is dependent on how much energy you have" "Next layer above that is chips. This is the machine that converts the energy into tokens, into intelligence. Then you have infrastructure above that, which takes these chips and scales it out and makes it so we can really take it out to scale" "On top of that, we have the models. This is normally what we think of as AI. These are the GPTs and Claudes and Groks of the world" "The layer above that is arguably the most important, which is the applications. This is where we take all the AI, all this work we do below that, and we turn it into value that makes a difference in people's lives" @nvidia @JensenHuang