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Today’s Topics Are:

- Learn in Newer, Deeper Ways with Gemini
- OpenAI’s Codex Joins New Wave of Autonomous Coding Tools

Learn in Newer, Deeper Ways with Gemini

Quick Summary
Google is advancing learning with Gemini 2.5, now infused with LearnLM—a set of AI models fine-tuned for effective learning. Announced at Google I/O 2025, this upgrade makes AI-powered education more interactive, personalized, and multimodal, transforming how users engage with knowledge across Google’s products.

Key Points

• LearnLM is integrated into Gemini 2.5, the leading AI model for learning.

• Gemini 2.5 excels in pedagogical principles, offering explanations beyond simple answers.

• Multimodal learning tools support audio, video, images, and text formats.

• NotebookLM allows personalized research with Audio and upcoming Video Overviews.

• AI Mode in Google Search offers advanced reasoning, multimodal input, and soon, real-time “Search Live.”

• Students globally gain free access to Google AI Pro plan with study aids and custom quizzes.

• New Labs experiments like Sparkify and Project Astra prototype animated videos and conversational tutors.

The Story
Google’s mission to make knowledge accessible is now turbocharged by Gemini 2.5, powered by LearnLM, which focuses on proven learning science principles. Unlike traditional AI, Gemini helps users understand how to reach answers, unraveling complex topics with clear explanations. At I/O 2025, Google showcased how these capabilities enhance products like NotebookLM—an intelligent research assistant that transforms uploaded documents into rich learning experiences with Audio Overviews and soon Video Overviews.

Google Search’s AI Mode now offers deeper exploration via multimodal queries, links to trustworthy sources, and upcoming deep research features. Search Live, coming soon, lets users ask questions about the real world in real-time using their camera.

For students worldwide, Google offers free access to advanced AI tools, including custom quiz creation that adapts interactively to learning needs. Meanwhile, Labs experiments explore new ways to learn—like turning ideas into animated videos or using conversational tutors to guide problem-solving.

Conclusion
By embedding LearnLM into Gemini 2.5 and expanding multimodal, personalized tools, Google is reshaping learning for the modern era. These innovations make education more engaging, interactive, and accessible, empowering learners to master any subject in ways that best suit them.

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OpenAI’s Codex Joins New Wave of Autonomous Coding Tools

Quick Summary
OpenAI has launched Codex, a next-generation AI coding system designed to perform complex programming tasks autonomously, marking a shift toward agentic coding tools that manage software development without constant human oversight. While promising, these systems still face significant challenges in reliability and error management.

Key Points

• Codex enables natural language programming commands to handle complex coding tasks.

• Agentic coding tools aim to work independently, managing tasks through workplace systems without user interaction with code.

• Current AI assistants mostly act as advanced autocomplete rather than full autonomous coders.

• Human supervision remains essential due to frequent errors and AI hallucinations.

• SWE-Bench benchmarks show Codex achieving a 72.1% problem-solving rate, the highest claimed so far.

• Reliability and trust remain major hurdles for widespread adoption.

The Story
OpenAI’s new Codex system represents a leap forward from traditional AI coding assistants like GitHub Copilot, moving toward agentic tools that act more like engineering managers than mere autocomplete aids. Instead of developers writing or even reviewing every line of code, these agents are designed to receive high-level tasks—like bug reports via platforms such as Slack—and independently deliver solutions. This vision aligns with a natural progression toward greater automation in software development.

However, the technology is still nascent. Early adopters of similar tools like Devin have reported frequent errors requiring as much oversight as manual coding. OpenHands CEO Robert Brennan highlights persistent challenges, including AI hallucinations—where models invent plausible but false information—and stresses the need for human code reviews. While Codex’s underlying models demonstrate strong benchmark performance, solving nearly three-quarters of tested issues, real-world application demands much higher reliability.

Conclusion
OpenAI’s Codex and other agentic coding tools are pioneering a future where AI takes on more autonomous programming roles, but current limitations mean human developers must remain deeply involved. Continued progress in model accuracy and error mitigation will be key to transforming these promising agents into trusted, hands-off partners in software creation.

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