Google Just Dropped Mangle - The Most Powerful AI Reasoning Language

Google is making significant advancements in AI, introducing a new programming language called Mangle designed to process messy and scattered data, a mysterious image model known as Nano Banana that's generating buzz for its quality, and five AI agents aimed at automating developer workflows. Mangle, built on data log, allows AI to query diverse data sources as a unified system, crucial for tasks like security analysis and software supply chain management. Nano Banana, believed to be a Google project due to subtle clues, is impressing users with its image generation capabilities and potential for local device integration. The new AI agents, including BigQuery, notebook, Looker code, database migration, and GitHub agents, aim to streamline development by handling tasks such as data pipeline creation, data analysis, dashboard generation, database modernization, and repository management, signaling a shift towards autonomous developer agents.

• Google has launched Mangle, a new programming language to help AI manage scattered and messy data by treating disparate sources as a unified logical system.
• A mysterious image model named Nano Banana has appeared online, with strong indications pointing to it being a Google project due to its impressive image generation quality and subtle online hints.
• Google Cloud has introduced five new AI agents: BigQuery data agent, notebook agent, Looker code assistant, database migration agent, and GitHub agent (Gemini CLI).
• These AI agents are designed to automate time-consuming developer tasks, including building data pipelines, conducting data analysis, generating visualizations, migrating databases, and managing code repositories.
• The overall trend suggests Google is moving towards autonomous developer agents that can handle entire workflows rather than just code suggestions.

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Mangle: AI for Messy Data [1:14]

• Modern software systems are overwhelmed with data from various sources like dependency files, security reports, configuration data, and logs, often in different formats.
• Manually connecting this scattered information is time-consuming and difficult for both humans and AI systems that require reliable facts for reasoning.
• Mangle is Google's solution, extending the logic-based language Data log to practically handle today's data chaos.
• It allows AI tools to treat data from files, APIs, and databases as a single logical system, enabling precise queries across all sources simultaneously.
• A key feature is recursive rules, enabling Mangle to trace complex chains of connections, such as dependency chains in software, to identify risks like known vulnerabilities.
• This structured reasoning is vital for AI agents to act as verifiable security assistants, providing logic instead of just statistical guesses.
• Mangle can also be used for software supply chains, scanning Software Bills of Materials (SBOMs) to enforce version rules and highlight outdated or risky libraries.
• It facilitates the representation and querying of knowledge graphs, providing AI with a structured foundation for reasoning over interconnected data.
• Mangle is implemented as a Go library, allowing developers to integrate it directly into their applications and AI pipelines, making it practical rather than just a research concept.
• It supports aggregations (counts, sums) and external function calls, allowing for a mix of symbolic reasoning with real computations and custom business logic.
• Mangle aims to provide AI systems like Gemini with a crucial logic layer for more reliable, explainable, and useful outputs when dealing with complex data.

Nano Banana: The Mysterious Image Model [4:57]

• A new, mysterious image model with the unusual name "Nano Banana" has surfaced online, drawing significant attention.
• Users on LMA Arena, a platform for comparing AI models, noticed Nano Banana's impressive performance, generating sharper and more creative images.
• The model also excelled at image editing based on instructions, outperforming many existing models.
• Its appearance led to widespread speculation on Reddit and X, with Google being the leading suspected developer.
• Several subtle clues support this theory: Google had previously teased image-related advancements, Logan Kilpatrick (Head of Product, Google AI Studio) posted a banana emoji, and a DeepMind product manager posted a photo referencing Maurizio Cattelan's duct-taped banana artwork.
• The name "Nano Banana" might indicate a smaller, lightweight model designed for local device operation, similar to Google's previous "Nano" models.
• The potential release of such a model at an upcoming Google device event is a point of speculation.
• While still facing challenges like spelling within images, the overall quality of Nano Banana's outputs is considered top-tier.
• Although unconfirmed, the evidence strongly suggests Nano Banana is a secret Google project, potentially marking their next major step in image generation technology.

Google Cloud AI Agents for Developers [8:39]

• Google Cloud has introduced five new AI agents designed to automate repetitive and time-consuming tasks for developers.
• These agents go beyond code suggestions to handle entire workflows and tasks that typically require hours of manual effort.
• The BigQuery data agent automates data pipeline creation from sources like Google Cloud Storage, ensuring data consistency and adapting to schema changes using natural language descriptions.
• The notebook agent, integrated with Notebook LM for enterprise, transforms BigQuery notebooks into AI-powered research and modeling tools, capable of performing exploratory data analysis, generating machine learning features, and building models on the fly. It can also summarize findings and create knowledge bases.
• The Looker code assistant allows users to generate charts, queries, or Python code by typing plain English questions directly within Looker, explaining results and suggesting next steps. It ensures accuracy by connecting to Looker's semantic layer.
• The database migration agent simplifies the process of moving data from legacy databases to modern cloud systems like Spanner or AlloyDB by automatically converting schemas, stored procedures, and functions, and setting up continuous replication for minimal downtime. It also provides side-by-side code comparisons and explanations for validation.
• The GitHub agent, also known as Gemini CLI GitHub Actions, operates within GitHub to automate repository management tasks such as triaging issues, labeling, reviewing pull requests, and generating tests. It is open-source and customizable.
• These agents represent a move towards autonomous developer agents that manage full workflows, allowing developers to focus on core coding tasks.