OpenAI has announced a preview of the GPT-5.6 family, introducing three models designed for different levels of capability, speed, and cost. The New Models GPT-5.6 Sol - Flagship model with the strongest reasoning capabilities - Introduces a new Ultra Mode that leverages sub-agents for complex tasks - State-of-the-art performance in coding, biology, and cybersecurity benchmarks GPT-5.6 Terra - Balanced model for everyday work - Delivers performance comparable to GPT-5.5 - Costs 2x less than GPT-5.5 GPT-5.6 Luna - Fast and affordable model - Designed for speed and cost efficiency while maintaining strong capabilities Key Updates - Improved long-horizon reasoning and agentic workflows - Stronger performance in coding and cybersecurity tasks - Enhanced safety systems with extensive automated and human red-teaming - New pricing and prompt caching improvements for developers - New naming system: generation number (5.6) + capability tier (Sol, Terra, Luna) Availability Current Status: Limited preview for a small group of trusted partners through the API and Codex. General Release: OpenAI plans to make GPT-5.6 Sol, Terra, and Luna available in ChatGPT, Codex, and the API in the coming weeks. The GPT-5.6 launch is not just about introducing more powerful models-it is about giving users and developers clearer choices across intelligence, speed, and cost. Which model would best fit your use case: Sol, Terra, or Luna?

I spent some time testing it today and created two videos using simple prompts. My first impression: I generated the same video using a simple prompt in both HyperFrames and Remotion. Personally, I liked the HyperFrames output more for this particular test. I'm still experimenting, so I'd love to hear from others who have tried it. Which one do you prefer: HyperFrames or Remotion? 👇

After temporarily blocking the release of Anthropic's advanced AI model due to cybersecurity concerns, the US government has now approved limited access for a small group of trusted partners. This decision shows that AI regulation is entering a new phase - where innovation, security, and national interests must all be balanced. The conversation around who should have access to powerful AI models is only getting started. Should access to powerful AI models be restricted to trusted organizations, or should they be available to everyone? What do you think?

A few days ago, I made a prediction: «The globalization of frontier AI is slowly coming to an end.» Back then, it was just an observation. Today, it feels a little less hypothetical. First, we saw export restrictions affecting the latest AI chips and frontier models. Now, OpenAI's GPT-5.6 is launching through a limited preview requested by the U.S. government before becoming more broadly available. This isn't a criticism of any company. It's an observation about where the industry is heading. For decades, the market decided who got access to the latest technology. Now, governments are increasingly influencing that decision. That raises an interesting question... Nuclear technology didn't remain a purely commercial technology. Over time, international treaties, export controls, and national security policies determined who could develop or access the most sensitive capabilities. Could frontier AI follow a similar path? Not necessarily with identical rules—but with increasing government oversight over who can build, train, export, or deploy the most advanced models. A year ago, that idea sounded extreme. Today, it doesn't sound impossible. The AI race is no longer just about building the smartest model. It's also about who controls access to it. What do you think? Are we witnessing the end of truly global AI, or is this just a temporary phase?

A fascinating example of AI augmenting human expertise. Immunologist Dr. Derya Unutmaz had been investigating how glucose affects the development of T cells- immune cells that play a critical role in fighting cancer, infections, and autoimmune diseases. His team ran an experiment in 2022 but couldn't explain the results, so the research was put aside. Years later, they revisited the data using GPT-5 Pro. After analyzing the experimental results, GPT-5 suggested a mechanism involving a protein called IL-2 that could explain the unexpected behavior of the T cells. According to Dr. Unutmaz, the explanation "made perfect sense" and revealed a connection that neither he nor his lab had considered. The team also asked GPT-5 to predict the outcome of another unpublished experiment involving cancer-fighting T cells. The model correctly predicted the results. This isn't about AI replacing scientists. It's about AI becoming a powerful research collaborator that can: - Analyze complex datasets - Generate new hypotheses - Simulate experiments - Identify patterns humans may overlook - Accelerate scientific discovery Researchers still need domain expertise to validate and interpret these insights, but AI is increasingly becoming a tool that can significantly speed up scientific progress. From cancer research to immunology and precision medicine, AI is beginning to change how discoveries are made. The future of science may not be human or AI-it may be human and AI working together.