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OpenAI has launched a new feature called “Record Mode” for ChatGPT, currently available to Pro, Enterprise, and Education users on the macOS desktop app. This voice-powered tool allows users to record meetings, brainstorming sessions, or voice notes directly within the ChatGPT app. The AI then automatically transcribes the audio, highlights key points, and can generate summaries, follow-up tasks, action items, or even code snippets based on the conversation.

Record Mode aims to transform how users take notes and capture ideas in real time, effectively offloading the note-taking burden and preserving important information from spoken discussions. The transcriptions are saved as editable summaries, integrated into the chat history as canvases, making past conversations searchable and referenceable for later queries. This feature represents a shift from AI simply responding to queries to AI that “remembers” and provides context-aware assistance, enhancing productivity and reducing knowledge loss in business settings.

At launch, Record Mode is free for eligible users and limited to the macOS desktop app, with potential future expansion to mobile platforms and free accounts anticipated. OpenAI emphasizes responsible use, advising users to obtain proper consents before recording others, as legal requirements may vary by location.

OpenAI’s Record Mode is a significant step toward memory-enabled AI that listens, remembers, and acts on spoken content, aiming to improve meeting efficiency and knowledge retention in professional and educational environments.

Mistral AI has launched Magistral, its first AI model specifically designed for advanced reasoning tasks, challenging big tech offerings with a focus on transparency, domain expertise, and multilingual support. Magistral comes in two versions: Magistral Small, a 24-billion-parameter open-source model available for public experimentation, and Magistral Medium, a more powerful enterprise-grade model accessible via API and cloud platforms like Amazon SageMaker, with upcoming support on IBM WatsonX, Azure, and Google Cloud.

Magistral distinguishes itself by emulating the non-linear, intricate way humans think—integrating logic, insight, uncertainty, and discovery—while providing traceable reasoning steps. This transparency is crucial for regulated industries such as law, finance, healthcare, and government, where users must understand the rationale behind AI-generated conclusions rather than accepting opaque outputs. The model also supports robust multilingual reasoning, addressing a common limitation where AI performance drops outside English, thus enhancing equity and compliance with local AI regulations.

In practical use, Magistral offers two modes: a fast “Flash Answers” mode for quick responses without detailed reasoning, and a “Think” mode that takes more time but reveals each logical step, enabling users to verify the AI’s thought process. The model excels in structured thinking tasks valuable to software developers for project planning, architecture design, and data engineering, and it also performs well in creative domains like writing and storytelling.

Magistral’s open-source availability under the Apache 2.0 license invites the AI community to innovate collaboratively, while its enterprise edition positions Mistral AI as a European leader in sovereign AI technology, supported by initiatives like France’s national AI data center project. Overall, Magistral sets a new standard for reasoning AI by combining open transparency, domain depth, multilingual capabilities, and versatility across professional and creative applications.

Kimi-Researcher is an advanced autonomous AI agent developed by Moonshot AI that excels in multi-turn search and complex reasoning tasks. It performs an average of 23 reasoning steps and explores over 200 URLs per task, achieving state-of-the-art results such as a Pass@1 score of 26.9% on the challenging Humanity’s Last Exam benchmark, significantly improving from an initial 8.6% score through end-to-end reinforcement learning (RL).

The model is built on an internal Kimi k-series foundation and trained entirely via end-to-end agentic RL, which allows it to learn planning, perception, and tool use holistically without relying on hand-crafted rules. It uses three main tools: a parallel, real-time internal search engine, a text-based browser for interactive web tasks, and a coding tool for automated code execution. This enables Kimi-Researcher to solve complex problems requiring multi-step planning and tool orchestration effectively.

Kimi-Researcher has demonstrated strong performance across multiple real-world benchmarks, including 69% pass@1 on xbench-DeepSearch, outperforming other models with search capabilities. It also excels in multi-turn search reasoning and factual question answering tasks.To train the agent, Moonshot AI developed a large, diverse, and high-quality dataset emphasizing tool-centric and reasoning-intensive tasks, generated through a fully automated pipeline ensuring accuracy and diversity. The training uses the REINFORCE algorithm with outcome rewards and gamma-decay to enhance stability and efficiency.

Currently, Kimi-Researcher is being gradually rolled out to users, enabling deep, comprehensive research on any topic within the platform. Moonshot AI plans to expand the agent’s capabilities and open-source both the base pretrained and reinforcement-learned models soon, aiming to evolve Kimi-Researcher into a versatile general-purpose agent capable of solving a wide range of complex tasks.

In summary, Kimi-Researcher represents a cutting-edge AI agent that combines powerful multi-step reasoning, extensive tool use, and end-to-end reinforcement learning to deliver state-of-the-art autonomous research and problem-solving capabilities.

The Gemini 2.5 update from Google DeepMind introduces significant enhancements with the Gemini 2.5 Flash and Pro models now stable and production-ready, alongside the preview launch of Gemini 2.5 Flash-Lite, which is designed to be the fastest and most cost-efficient in the series.

Key features of Gemini 2.5 Flash and Pro:

Both models are faster, more stable, and fine-tuned for real-world applications.Gemini 2.5 Pro is the most advanced, excelling in complex reasoning, code generation, problem-solving, and multimodal input processing (text, images, audio, video, documents).It supports an extensive context window of about one million tokens, with plans to expand to two million.Incorporates structured reasoning and a “Deep Think” capability for parallel processing of complex reasoning steps.Demonstrates top-tier performance in coding, scientific reasoning, and mathematics benchmarks.Used in production by companies like Snap, SmartBear, Spline, and Rooms.

About Gemini 2.5 Flash:

Optimized for high-throughput, cost-efficient performance without sacrificing strength in general tasks.Includes reasoning capabilities by default, adjustable via API.Improved token efficiency with reduced operational costs (input cost increased slightly by $0.15, but output cost reduced by $1.00).Suitable for real-time, high-volume AI workloads.

Introducing Gemini 2.5 Flash-Lite:

Preview model designed for ultra-low latency and minimal cost.Ideal for high-volume tasks such as classification and summarization at scale.Reasoning (“thinking”) is off by default to prioritize speed and cost but can be dynamically controlled.Maintains core Gemini power with a 1 million-token context window and multimodal input handling.Offers built-in tools like Google Search and code execution integration.

Overall, the Gemini 2.5 update delivers a suite of AI models tailored for diverse developer needs—from complex reasoning and coding with Pro, to efficient, scalable real-time tasks with Flash and Flash-Lite—making it a versatile and powerful AI platform for production use.

Nvidia and Foxconn are collaborating to deploy humanoid robots in the production of Nvidia’s AI servers at a new Foxconn factory in Houston, Texas, expected to begin operations by early 2026. This initiative marks the first time Nvidia products will be assembled with the assistance of humanoid robots and Foxconn’s first use of such robots on an AI server production line.

The humanoid robots are being trained to perform tasks traditionally done by humans, such as precision cable insertion, component placement, picking and placing objects, and assembly work. Foxconn is developing two types of robots for this purpose: a legged humanoid robot designed for complex tasks and a more cost-effective wheeled autonomous mobile robot (AMR) for repetitive logistics tasks. The Houston factory’s new and spacious design facilitates the flexible deployment of these robots, enabling scalable automation without disrupting existing operations.

This collaboration represents a significant milestone in manufacturing automation, signaling a shift toward robotic automation in high-tech production. It also aligns with Nvidia’s broader push into humanoid robotics, as the company already provides platforms for humanoid robot development. The deployment of these robots is anticipated to start by the first quarter of 2026, coinciding with the factory’s ramp-up in producing Nvidia’s GB300 AI servers.

Overall, the Nvidia-Foxconn partnership pioneers the integration of humanoid robots in AI chip manufacturing, aiming to revolutionize production efficiency and set a new standard in the AI infrastructure market.

Mattel, the maker of iconic toy brands such as Barbie, Hot Wheels, and American Girl, has formed a strategic partnership with OpenAI to develop AI-powered toys and interactive experiences. This collaboration aims to integrate OpenAI’s advanced generative AI technology, including ChatGPT, into Mattel’s products to create toys that can learn, talk, and evolve with each child, offering personalized, educational, and engaging playtime experiences while prioritizing safety and privacy.

The first AI-enhanced product from this partnership is expected to launch by the end of 2025, targeting users aged 13 and up due to age restrictions on AI use. Beyond physical toys, Mattel plans to incorporate AI into digital games and content, including storytelling and interactive play, enhancing fan engagement and creativity across its brands.

Internally, Mattel will also deploy ChatGPT Enterprise to support its design, marketing, and research and development teams, accelerating innovation and streamlining product development processes. OpenAI’s COO Brad Lightcap described the collaboration as a “company-wide transformation” that will empower Mattel employees with advanced AI tools.

This partnership represents a significant evolution in the toy industry, blending fun with cutting-edge technology to redefine how children and families interact with toys. Mattel emphasizes that all AI-driven products will be developed with a strong focus on age-appropriateness, privacy, and safety. The move also aligns with Mattel’s broader strategy to expand digital and AI-enhanced experiences amid challenges in the traditional toy market.

In summary, the Mattel-OpenAI partnership is set to revolutionize playtime by introducing AI-powered toys that are interactive, personalized, and educational, while also transforming Mattel’s internal innovation capabilities through AI integration.

In today’s world of artificial intelligence, language models are rapidly evolving. One of the most exciting developments in this field is “Self-Adapting Language Models (SEAL)”, that is, language models that adapt themselves. So, what is SEAL and how are they different from other models?

SEAL, as the name suggests, are “language models that continuously improve their learning ability and adapt to changing environments”. Traditional models are trained on a specific dataset and often need to be retrained to adapt to new data. SEAL models, on the other hand, can continuously absorb new information and integrate it with their existing knowledge. In this way, they become more flexible and adaptable for different tasks.

Advantages of SEAL:

Flexibility: They can easily adapt to different data types and tasks.
Less Training Needed: They save resources by reducing the need for constant retraining.
Better Performance: Thanks to rapid adaptation to new data, they show higher performance in different tasks.

Difference from Other Models:

While traditional language models have a static structure, SEAL models have a “dynamic structure”. This allows SEAL to adapt to changing information and environments more quickly and effectively. This dynamic structure makes SEAL an important candidate for “future language models”. However, the development and improvement of these models is still an ongoing process.