Google Gemini 2.5 Deep Think highlights the rollout of this advanced AI model designed to enhance reasoning and problem-solving abilities by engaging in extended, parallel thinking. Gemini 2.5 Deep Think uses multiple AI agents working simultaneously to explore and evaluate various ideas before arriving at an answer, significantly improving the quality and depth of responses. This model integrates tools such as code execution and Google Search to support complex tasks like coding, advanced mathematics, and data analysis.

The Deep Think feature, available to Google’s $250-per-month Ultra subscribers via the Gemini app, improves multi-step reasoning and creativity by allowing the AI more “thinking time” and iterative refinement, closer to human-style problem-solving. It can generate longer, more detailed responses and has demonstrated superior performance on challenging benchmarks like the International Math Olympiad and coding competitions, outperforming competitors including OpenAI and xAI models.

Google emphasizes safety and content moderation improvements in this release and is actively seeking academic feedback for further refinement. The company may broaden access to Deep Think after initial testing phases. Overall, Gemini 2.5 Deep Think represents a significant leap in AI reasoning capacity, boosting capabilities across scientific research, programming, and problem-solving domains.

Microsoft and OpenAI are currently in advanced negotiations to finalize a new partnership agreement that would allow Microsoft to maintain continuous access to OpenAI’s technology even after OpenAI achieves artificial general intelligence (AGI), a milestone at which AI attains human-level cognitive abilities across diverse tasks.

Here is the key points about the deal include:

• Current Contract Limitation: Under the existing agreement, Microsoft would lose rights to new OpenAI technology once OpenAI’s board officially determines that AGI has been reached, which poses a significant barrier for Microsoft’s AI strategy, especially as its products like Azure, Microsoft 365 Copilot, and GitHub Copilot heavily depend on OpenAI’s models.
• Equity and Financial Terms: Microsoft is seeking to increase its equity stake in OpenAI’s restructured company, aiming for a low- to mid-30% range, while renegotiating revenue sharing and IP rights as OpenAI shifts from a nonprofit to a for-profit structure. OpenAI’s planned $40 billion funding round, with $20 billion from SoftBank, hinges partly on these governance changes.
• Definitions and Licensing: The talks also involve clarifying what exactly constitutes the AGI milestone, how Microsoft can have ongoing licensed access to advanced AI systems beyond AGI, and embedding oversight and safety mechanisms related to the use of the technology.
• Strategic Significance: Securing this deal is crucial for Microsoft to preserve its competitive edge in AI, particularly for its enterprise software and cloud products worth billions. It also clears a major hurdle for OpenAI’s transition into a more commercial enterprise model, enabling both to capitalize on the evolving AI landscape.
• Potential Obstacles: Despite positive progress, challenges remain including potential regulatory scrutiny and a lawsuit by Elon Musk challenging OpenAI’s for-profit transition and governance changes.

The negotiations have been ongoing for several months with frequent meetings and could conclude within weeks. OpenAI has not publicly commented, and Microsoft has likewise withheld comment on the specifics of the talks.

Microsoft aims to secure a long-term deal granting it continuous access to OpenAI’s cutting-edge AI technologies through and beyond the achievement of AGI, restructuring their partnership to reflect new commercial realities and safeguard Microsoft’s AI-driven product ecosystem. This agreement will shape the future control and commercialization of transformative AI technologies.

OpenAI recently removed a controversial ChatGPT feature that allowed users to make their private conversations publicly discoverable and searchable via search engines like Google. This feature was an opt-in “Make this chat discoverable” checkbox included in the chat’s sharing option. Users who selected this made their conversations indexable by search engines, which then showed up publicly in search results.

The removal happened after widespread reports emerged of private, sensitive, and even confidential conversations appearing publicly on Google Search. Despite requiring explicit user consent, many unintentionally shared private information by ticking the checkbox, often without fully understanding the risks. Examples found online included personal topics, corporate data, emotional reflections, and even confessions.

OpenAI’s Chief Information Security Officer Dane Stuckey said this was a “short-lived experiment” intended to help people discover useful conversations, but it “introduced too many opportunities for folks to accidentally share things they didn’t intend to.” The feature was quickly disabled and OpenAI is actively working to remove previously indexed content from search engines. Conversations made public were anonymized but could still reveal identifiable information if users mentioned names or specific details.

OpenAI pulled the ChatGPT discoverability feature to address significant privacy concerns after private chats became unexpectedly public through search indexing.

Reddit holds several competitive advantages over Google (and Bing) in the search landscape, especially in terms of content authenticity, user engagement, and the nature of its information. So Reddit has become a major SEO powerhouse and a critical platform for AI-driven search visibility, especially relevant for B2B SaaS marketers.

Here is the Key points include:

• Reddit ranks as the #2 most-visited site via Google searches in the US, driven by Google’s algorithm favoring authentic, user-generated content (UGC) that Reddit excels at providing.

• Google’s 2023 partnership with Reddit, reportedly worth $60 million annually, gives Google real-time access to Reddit’s dynamic content via an API to train AI models (like Google’s Vertex AI). This partnership also means Reddit content gets significantly higher visibility in Google search results (up to 400% increase), making Reddit threads dominate top search slots.

• AI search engines and large language models (LLMs) frequently cite Reddit as a trusted source, impacting the answers generated by assistants like ChatGPT and Google Bard.

• For marketers, leveraging Reddit SEO by engaging authentically within relevant subreddits can boost organic Google traffic, improve brand visibility, and influence AI-generated search outcomes.

• The guide lays out a detailed Reddit SEO and LLM optimization playbook including creating a dedicated brand Reddit account, selecting the right subreddits, doing Reddit-specific keyword research, contributing helpful and authentic content, planning consistent posting, using paid Reddit ads to amplify good content, and monitoring performance metrics.

• Reddit acts not only as a traffic source but also as an “always-on focus group” for real customer insights and engagement.

• The intersection of Reddit and LLM search means that shaping positive conversations on Reddit can translate into favorable AI output about brands or industries.

• The overall message advises marketers not to ignore Reddit in 2025 SEO strategies, as it is a key driver for both traditional search and AI answer generation.

In essence, the playbook emphasizes integrating Reddit participation with AI optimization to win visibility on both search engines and AI-powered assistants, underscoring Reddit’s transformed role in the modern search ecosystem.

Kombai is characterized as the first AI agent purpose-built specifically for real-world frontend development tasks. It specializes in converting UX designs from sources like Figma, images, or text prompts into high-fidelity, clean frontend code such as HTML, CSS, and React components. Kombai significantly outperforms generic coding AI agents and the latest frontier models in building user interfaces from design specs. It understands your existing codebase, works inside your integrated development environment (IDE), and delivers backend-agnostic code optimized for frontend stacks and repositories.

Key capabilities include deep-learning models tailored for frontend fidelity, specialized tools for indexing and searching frontend codebases to accurately and efficiently reuse code, and the ability to generate editable, task-optimized plans with previews before code changes. It supports projects of all sizes and complexities, from small components to entire app UIs. Importantly, Kombai does not alter database or backend logic, isolating its focus to frontend development.

For enterprise customers, Kombai offers custom context engines to accommodate complex technology stacks. It is SOC 2 certified, ensuring data security and that user data is not used for further model training or improvements.

Overall, Kombai fills a unique niche as the first domain-specific AI coding agent built exclusively for the frontend development domain, delivering unmatched code quality, developer velocity, and accuracy compared to generalist AI coding tools.

Ollama’s new app, released on July 30, 2025, delivers a user-friendly way to interact with large language models on both macOS and Windows. Here’s an overview of the standout features and capabilities:

Here is the key Core Features:

• Download and Chat with Models Locally: The app provides an intuitive interface to download, run, and chat with a wide range of AI models, including advanced options like Google DeepMind’s Gemma 3.

• Chat with Files: Users can easily drag and drop text or PDF files into the chat. The app processes the file’s contents, enabling meaningful conversations or question answering about the document. For handling large documents, you can increase the context length in the app’s settings, though higher values require more system memory.

• Multimodal Support : Thanks to Ollama’s new multimodal engine, the app lets you send images to models that are able to process them, such as Google’s Gemma 3. This enables use cases like image analysis and visual question answering, alongside typical text-based interactions.  Gemma 3 in particular boasts a context window of up to 128,000 characters and can process both text and images in over 140 languages.

• Documentation Writing and Code Understanding: The app enables you to submit code files for analysis by the models, making it easier to generate documentation or understand complex code snippets. Developers can automate workflows such as summarizing codebases or generating documentation directly from source files.

Additional Improvements

• Optimized for Desktop : The latest macOS and Windows versions feature improved performance, reduced installation footprint, and a model directory that users can change to save disk space or use external storage.

• Network Access & Automation: Ollama can be accessed over the network, allowing headless operation or connecting to the app from other devices. Through Python and CLI support, users can easily integrate Ollama-powered AI features into their own workflows or automation scripts.

As a summary :
Drag-and-drop files –> Chat with text/PDFs; increase context for large documents
Multimodal support –> Send images to vision-capable models e.g., Gemma 3
Documentation writing –> Analyze code, generate documentation
Model downloads –> Choose and run large selection of LLMs locally
Network/API access –> Expose Ollama for remote or automated workflows

The International Olympiad in Artificial Intelligence (IOAI) is an international science competition focused on artificial intelligence, designed for high school students. Each participating country or territory can send up to two teams, with each team consisting of up to four students supported by one leader.

The International Olympiad in Artificial Intelligence (IOAI) 2025 will be held in Beijing, China, from August 2nd to August 9th, 2025. This will be the second edition of the IOAI, following the inaugural event in 2024.

The competition has two main rounds:

• Scientific round: This comprises an at-home portion, which has a smaller weight, giving participants a month to solve problems, and an on-site portion with an 8-hour time limit. The scientific round tests theoretical and problem-solving knowledge in AI.
• Practical round: Conducted on-site, lasting four hours, where participants solve two tasks related to AI applications like image and video generation, using existing AI tools to produce results.

Awards are distributed with roughly half the participants receiving distinctions in gold, silver, and bronze medals in a 1:2:3 ratio for the scientific round and corresponding awards in the practical round. The top 3 teams receive honorary trophies.

Beyond competition, IOAI fosters discussion on ethical AI issues and aims to engage the broader community, with activities such as involving local celebrities for promotion and hosting conferences where teams attend lectures and practical sessions on current AI topics.

IOAI is a growing global event, with over 85 countries involved as of 2025, and the 2025 edition is scheduled to take place in Beijing, China. The Olympiad encourages international collaboration and talent development, supporting educational initiatives and national AI Olympiads through the Global AI Talent Empowerment (GAITE) program to promote equal participation worldwide.

The official syllabus covers both theoretical foundations (“how it works”) and practical coding skills (“what it does and how to implement”), focusing on areas such as machine learning, natural language processing, and computer vision, ensuring students develop a balanced understanding and proficiency in AI.

Zhipu AI (also known as Z.ai or 智谱AI) is a leading Chinese AI company specializing in large language models and other artificial intelligence technologies. Originating from Tsinghua University, Zhipu AI has attracted major investment from top Chinese tech firms and international backers. By 2024, it was regarded as one of the “AI Tiger” companies in China and is a significant player in the global AI landscape. The company is known for rapidly developing innovative LLMs, releasing open-source models, and building tools focused on agentic and reasoning capabilities.

GLM-4.5 and GLM-4.5 Air: Overview

Both GLM-4.5 and its compact sibling, GLM-4.5 Air, are foundation large language models designed for advanced reasoning, coding, and agentic tasks. They mark Zhipu AI’s push to unify general cognitive capabilities and serve as powerful backbones for intelligent agent applications.

GLM-4.5

• Size: 355 billion total parameters, 32 billion active parameters at runtime.

• Core Features:

• • Hybrid Reasoning: Supports a “thinking mode” for tool use and multi-step reasoning (e.g., solving math, code, and logical problems) and a “non-thinking mode” for instant responses.
  • Agent Readiness: Designed for agent-centric workflows, integrating tool-calling natively for seamless automation and coding.
  • Performance:
    • Ranks in top three across many industry benchmarks, comparable to leading models such as Claude 4 Opus and Gemini 2.5 Pro.
    • Particularly excels in mathematics, coding, data analysis, and scientific reasoning—achieving near or at state-of-the-art results in tests like MMLU Pro and AIME24.
    • Demonstrates a high tool-calling success rate (90.6%) and strong coding benchmark performance.
• Context Window: 128,000 tokens.
• Open source: Weights and implementation available for research and commercial use (MIT license condition).

GLM-4.5 Air

• Size: 106 billion total parameters, 12 billion active parameters during inference.
• Design: Lightweight, mixture-of-experts architecture for optimal efficiency and deployment flexibility, including running locally on consumer-grade hardware.
• Same 128K context window as GLM-4.5.

• Hybrid Reasoning & Agentic Capabilities:

• • Maintains strong reasoning and tool-use abilities, a hallmark of the GLM-4.5 family.
  • Offers a balance of performance and resource consumption, making it well suited to cost-sensitive and high-throughput applications.
  • On benchmarks, it scores competitively with other industry-leading models while using far fewer compute resources.

• Use cases: Efficient deployment for enterprise AI assistants, automation, coding support, customer service, and affordable large-scale deployments.

Performance and Accessibility

• Competitive Pricing: API costs are among the lowest on the market, reflecting Zhipu AI’s strategy to undercut competitors and democratize access to advanced AI.
• Open Source Access: Both models are available for free testing and deployment through multiple platforms like Hugging Face, Zhipu AI Open Platform, and third-party APIs.
• Community and Ecosystem: Zhipu AI encourages developer and research engagement, providing technical blogs, documentation, and standard model APIs.

In Summary

• Zhipu AI is a dominant force in China’s rapidly growing AI industry, focusing on high-performance, open-source language models.
• GLM-4.5 is a very large LLM targeting top-tier reasoning, agentic, and coding abilities.
• GLM-4.5 Air offers similar power but much higher efficiency for wider, cost-effective deployment.

These models are part of a new wave of AI technologies enabling more accessible, adaptable, and powerful agentic applications in both research and enterprise settings.