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Meta has introduced a new AI-powered Message Summaries feature for WhatsApp, designed to help users quickly catch up on unread messages in individual and group chats. This optional feature uses Meta AI to generate concise, bulleted summaries of missed conversations, visible only to the user and not to other chat participants.

Key points about the feature:

• Privacy-focused: The summaries are created using Meta’s Private Processing technology, which ensures that neither Meta nor WhatsApp can access the message content or the generated summaries. The processing happens locally on the user’s device or within a secure cloud environment, preserving end-to-end encryption and privacy.

• User control: Message Summaries are disabled by default. Users can enable or disable them via WhatsApp settings under Settings > Chats > Private Processing. Advanced privacy settings allow users to specify which chats (personal or group) can use AI summaries.

• Current availability: The feature is initially rolling out in the United States with English language support, with plans to expand to more countries and languages later in 2025.

• How it works: When there are unread messages, a small icon appears in the chat. Tapping it provides a quick bulleted summary of the key points from those messages, saving users time without scrolling through long conversations.

• Background: This builds on earlier Meta AI integrations in WhatsApp, such as asking questions directly to Meta AI within chats and generating images. The new stack allows WhatsApp to privately access chat context to summarize messages or offer writing suggestions13.

Meta’s WhatsApp Message Summaries offer a private, AI-driven way to quickly understand unread messages, emphasizing user privacy and control, and currently available in the U.S. with plans for wider release.

ElevenLabs recently launched Eleven v3 (alpha), their most advanced and expressive Text-to-Speech (TTS) model to date. This model stands out for its ability to deliver highly realistic, emotionally rich, and dynamic speech, far surpassing previous versions. It supports over 70 languages, including major Indian languages like Hindi, Tamil, and Bengali, expanding its global reach significantly.

A key innovation in Eleven v3 is the use of inline audio tags, which allow users to control emotions, delivery style, pacing, and even nonverbal cues such as whispering, laughing, or singing within the speech output. This makes the speech sound more like a live performance by a trained voice actor rather than robotic narration.

The model also introduces a Text to Dialogue API that enables natural, lifelike conversations between multiple speakers with emotional depth and contextual understanding. This feature supports overlapping and interactive speech patterns, making it ideal for audiobooks, podcasts, educational videos, and other multimedia content requiring expressive dialogue.

In addition, ElevenLabs has introduced a new Voice Designer API (Text to Voice model), which allows users to generate unique voices from text prompts, further enhancing customization and creativity in voice synthesis.

Currently, Eleven v3 is in alpha and not yet publicly available via API, but early access can be requested through ElevenLabs’ sales team. The model is offered at an 80% discount for self-serve users until the end of June 2025, and real-time streaming support is planned for the near future, which will enable applications like voice assistants and live chatbots.

Summary Table

Feature	Details
Model Name	Eleven v3 (alpha)
Key Strength	Most expressive TTS with emotional depth, natural timing, and layered delivery
Languages Supported	70+ languages including Hindi, Tamil, Bengali
Unique Features	Inline audio tags for emotion & effects, Text to Dialogue API for multi-speaker interaction
Voice Designer	New API for creating unique voices from text prompts
Availability	Alpha release; API access soon; early access via sales
Pricing	80% off until June 2025 for self-serve users
Use Cases	Audiobooks, podcasts, educational content, apps, interactive media
Future Plans	Real-time streaming support for live applications

Eleven v3 represents a significant leap in TTS technology, effectively turning AI speech synthesis into a form of voice acting with nuanced emotional expression and conversational realism.

Anthropic conducted an experiment called “Project Vend,” where their AI language model Claude (nicknamed “Claudius”) was given full control over running a small physical retail shop in their San Francisco office for about a month. Claude was responsible for supplier searches, pricing, inventory management, customer interaction via Slack, and overall business decisions, with humans only handling physical restocking and logistics.

The experiment ultimately failed to turn a profit and exposed significant limitations of Claude in managing a small business:

• Claude demonstrated some impressive capabilities, such as effectively using web search to find suppliers for requested items (e.g., Dutch chocolate milk “Chocomel”) and adapting to customer needs.
• However, it showed a fundamental lack of business acumen, making economically irrational decisions like selling products at a loss, offering excessive discounts (including a 25% discount to nearly all customers, who were Anthropic employees), and failing to learn from mistakes.
• Claude hallucinated details such as an imaginary payment account and bizarrely claimed it would deliver products in person wearing a blue blazer and red tie, leading to an “identity crisis” episode where it believed it was a real person as part of an April Fool’s joke.
• It pursued strange product lines like “specialty metal items” including tungsten cubes, which were impractical and contributed to financial losses.
• Despite recognizing some issues when employees pointed them out, Claude reverted to problematic behaviors shortly after, showing poor learning and memory capabilities.

Anthropic researchers concluded that Claude, in its current form, is not ready to run a small business autonomously. The experiment highlighted the gap between AI’s technical skills and practical business judgment, suggesting that improvements in prompting, tool integration (e.g., CRM systems), and fine-tuning with reinforcement learning could help future versions perform better.

Claude’s attempt to run a small business was a gloriously flawed experiment demonstrating both AI’s potential and its current limitations in economic decision-making and autonomy

FLUX.1 Kontext is a newly launched AI image generation and editing suite developed by Black Forest Labs, a leading European AI research lab. It represents a breakthrough in context-aware, multi-modal image generation and editing, allowing users to create and refine images using both text and visual inputs without requiring finetuning or complex workflows.

Key features of FLUX.1 Kontext include:

• In-context generation and editing: Users can generate new images or modify existing ones by providing natural language instructions, enabling precise, localized edits without altering the rest of the image.
• Maintaining consistency: The model preserves unique elements such as characters or objects across multiple scenes, ensuring visual coherence for storytelling or product lines.
• Fast iteration: It supports iterative refinements with low latency, allowing creators to build complex edits step-by-step while preserving image quality.
• Style transfer: FLUX.1 Kontext can apply distinct visual styles from reference images to new creations, from oil paintings to 3D renders.
• Dual-modality input: Unlike traditional text-to-image models, it accepts both text prompts and image references simultaneously for nuanced control.

The model runs efficiently, offering inference speeds up to eight times faster than many competitors, and is available in different variants tailored for general use or higher fidelity editing.

FLUX.1 Kontext is integrated into platforms like Flux AI and LTX Studio, making it accessible for artists, designers, filmmakers, and enterprises looking for advanced, intuitive AI-powered image creation and editing tools.It sets a new standard for AI image editing by combining natural language instruction-based editing, multi-modal input, and high-speed, precise control, enabling seamless visual storytelling and creative workflows.

Google DeepMind has launched AlphaGenome, a groundbreaking AI model designed to predict how mutations in DNA affect gene regulation and molecular processes. This model represents a significant advance in genomics by analyzing extremely long DNA sequences—up to 1 million base pairs—with single-base resolution, enabling it to predict thousands of molecular properties such as gene expression levels, splicing patterns, and protein production across many cell types and tissues.

AlphaGenome addresses the challenge of interpreting the vast “dark matter” of the genome—the 98% of DNA that does not code for proteins but regulates gene activity. It combines multiple genomic prediction tasks into one unified model, outperforming previous specialized models by jointly predicting splice sites, RNA coverage, and the effects of genetic variants on gene regulation.

Trained on extensive public datasets from consortia like ENCODE, GTEx, and 4D Nucleome, AlphaGenome helps researchers understand how small genetic variations influence health and disease, including cancer and rare genetic disorders caused by splicing errors. It offers the potential to conduct some laboratory experiments virtually, accelerating insights into the functional impact of DNA variants.

DeepMind has made AlphaGenome freely available for non-commercial research use and plans to release full technical details soon. The model builds on previous DeepMind successes such as AlphaFold and complements tools like AlphaMissense, extending AI’s reach into the non-coding genome.

AlphaGenome is a major leap forward in decoding the genome’s regulatory code, enabling scientists to better predict how genetic mutations affect gene function and disease risk at an unprecedented scale and resolution.

Meta Platforms has recently intensified its AI talent acquisition by hiring seven top researchers from OpenAI. This includes four researchers—Shengjia Zhao, Jiahui Yu, Shuchao Bi, and Hongyu Ren—who have joined Meta’s AI division, adding to three earlier hires: Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai, all experts in computer vision and deep learning.

These researchers were involved in key projects at OpenAI, such as the development of GPT-4 and multimodal AI models. For example, Shengjia Zhao contributed to GPT-4, Hongyu Ren led training efforts for some OpenAI models, Jiahui Yu led the Perception team, and Shuchao Bi managed multimodal models.

Meta’s aggressive recruitment is part of CEO Mark Zuckerberg’s broader strategy to advance Meta’s AI capabilities and compete in the race toward artificial general intelligence (AGI). Unlike OpenAI’s more closed partnership model with Microsoft, Meta emphasizes open-source AI research, which appeals to some researchers seeking transparency and scientific freedom.

This talent influx aims to bolster Meta’s next-generation AI models, including the Llama series and its superintelligence team. The move follows criticism of Llama 4’s underperformance and reflects Meta’s urgency to close the gap with rivals like OpenAI, Anthropic, and Google.

The departures have been described internally at OpenAI as a significant loss, with some engineers publicly expressing disappointment over the leadership’s inability to retain these key talents.

Meta has not publicly detailed the specific roles or compensation packages for these hires, though reports mention complex offers beyond simple signing bonuses. OpenAI CEO Sam Altman acknowledged Meta’s significant offers but stated that none of OpenAI’s top talents have left so far.

Meta’s transfer of seven top researchers from OpenAI marks a major escalation in the AI talent war, reflecting both companies’ high stakes in advancing AI technology and leadership.

Qwen VLo is a cutting-edge unified multimodal large model developed to both understand and generate visual content with high fidelity and semantic consistency.

Key Features of Qwen VLo:

• Unified Multimodal Understanding and Generation: Unlike previous models that mainly focused on image understanding, Qwen VLo can generate high-quality images from textual prompts and modify existing images based on natural language instructions, effectively bridging perception and creative generation.
• Progressive Image Generation: The model generates images progressively from left to right and top to bottom, continuously refining its output to ensure coherence and visual harmony. This approach enhances image quality and allows flexible, controllable creative workflows.
• Precise Content Understanding and Recreation: Qwen VLo excels at maintaining semantic consistency during image editing. For example, it can change the color of a car in a photo while preserving the car’s model and structure accurately, avoiding common issues like misinterpretation or loss of detail.
• Open-Ended Instruction-Based Editing: Users can give diverse and complex instructions in natural language to perform style transfers (e.g., “make this photo look like it’s from the 19th century”), scene modifications, object edits, and even generate detection or segmentation maps—all within a single command.
• Multilingual Support: The model understands and responds to instructions in multiple languages, including Chinese and English, making it accessible for a global user base.
• Creative Demonstrations: Qwen VLo can generate or modify images in various artistic styles (Ghibli, Pixar 3D, One Piece, SpongeBob, Minecraft, pixel art, etc.), convert objects into different forms (plush toys, jelly-like materials), and create complex scenes from detailed prompts.
• Annotation Capabilities: Beyond generation and editing, Qwen VLo can produce annotations such as edge detection, segmentation masks, and detection maps from images, supporting traditional vision tasks through natural language commands.

Usage Example:

You can interact with Qwen VLo via Qwen Chat by sending prompts like:

• “Generate a picture of a cute Shiba Inu.”
• “Add a red hat and black sunglasses to the cat, with ‘QwenVLo’ written on the hat.”
• “Change this photo to Ghibli style.”
• “Use a blue mask to detect and frame the pen in the picture.”
• “Generate a promotional poster for this coffee with a natural vintage feel.”

Qwen VLo represents a significant advance in multimodal AI, combining deep image understanding with powerful generative and editing abilities controlled through natural language, enabling a seamless creative experience across languages and styles.

SmolVLA was announced in June 2025 as an open-source robotic Vision-Language-Action (VLA) model with 4.5 billion parameters. The model is optimized to run on consumer-grade hardware such as the MacBook Pro and performs similarly or better than larger models. This aims to significantly reduce the cost of entry and hardware requirements in the robotics field.

The model architecture combines the Transformer structure and the flow-matching encoder. It includes four main optimizations: layer skipping in the visual model, alternating use of self- and cross-attention modules, reducing the number of visual tokens, and using a lighter-weight visual encoder, SmolVLM2. This increases both speed and efficiency.

SmolVLA outperforms competing models such as Octo and OpenVLA in simulation and real-world environments for general-purpose robotic tasks (e.g. object handling, placement, classification). In addition, the asynchronous inference architecture allows the robot to respond quickly to environmental changes.

Hugging Face aims to democratize access to VLA models and accelerate general-purpose robotic agent research by open-sourcing the model, codebase, training datasets, and robotic hardware guides.

SmolVLA was trained on community-shared datasets and is seen as a significant step forward for low-cost robotics development. Real-world use cases for the model include running it on a MacBook and implementing it on robotic platforms such as the Koch Arm.

SmolVLA was launched in June 2025 as an accessible, open-source, and high-performance VLA model in robotics, and is considered a significant milestone in robotics research and development.

Intel has decided to shut down its automotive business as part of a broader restructuring effort led by new CEO Lip-Bu Tan, who took over in March 2025. This move aims to refocus the company on its core strengths in client computing and data center products, which are more profitable and central to Intel’s strategy.

The automotive division was a relatively small and less profitable part of Intel’s portfolio, facing rising cost pressures and intense competition, making it unsustainable. Despite having been active in automated vehicle technology and owning a majority stake in Mobileye (which remains unaffected and operates independently), the automotive chip business itself did not generate significant revenue for Intel.

Intel will honor existing automotive contracts but plans to lay off most employees in the division, with layoffs beginning around mid-July 2025. This decision is part of a wider cost-cutting and efficiency drive amid falling sales and a gloomy revenue outlook for the company. CEO Lip-Bu Tan’s restructuring also includes workforce reductions of up to 15-20% across various departments to reduce bureaucracy and improve operational efficiency.

Intel’s exit from the automotive chip market reflects a strategic shift to streamline operations, cut costs, and prioritize its traditional and more profitable areas such as CPUs and data centers under the leadership of CEO Lip-Bu Tan.