MAROKO133 Hot ai: Google's new vibe coding AI Studio experience lets anyone build, de

📌 MAROKO133 Update ai: Google's new vibe coding AI Studio experience lets any

Google AI Studio has gotten a big vibe coding upgrade with a new interface, buttons, suggestions and community features that allow anyone with an idea for an app — even complete novices, laypeople, or non-developers like yours truly — to bring it into existence and deploy it live, on the web, for anyone to use, within minutes.

The updated Build tab is available now at ai.studio/build, and it’s free to start.

Users can experiment with building applications without needing to enter payment information upfront, though certain advanced features like Veo 3.1 and Cloud Run deployment require a paid API key.

The new features appear to me to make Google's AI models and offerings even more competitive, perhaps preferred, for many general users to dedicated AI startup rivals like Anthropic's Claude Code and OpenAI's Codex, respectively, two "vibe coding" focused products that are beloved by developers — but seem to have a higher barrier to entry or may require more technical know-how.

A Fresh Start: Redesigned Build Mode

The updated Build tab serves as the entry point to vibe coding. It introduces a new layout and workflow where users can select from Google’s suite of AI models and features to power their applications. The default is Gemini 2.5 Pro, which is great for most cases.

Once selections are made, users simply describe what they want to build, and the system automatically assembles the necessary components using Gemini’s APIs.

This mode supports mixing capabilities like Nano Banana (a lightweight AI model), Veo (for video understanding), Imagine (for image generation), Flashlight (for performance-optimized inference), and Google Search.

Patrick Löber, Developer Relations at Google DeepMind, highlighted that the experience is meant to help users “supercharge your apps with AI” using a simple prompt-to-app pipeline.

In a video demo he posted on X and LinedIn, he showed how just a few clicks led to the automatic generation of a garden planning assistant app, complete with layouts, visuals, and a conversational interface.

From Prompt to Production: Building and Editing in Real Time

Once an app is generated, users land in a fully interactive editor. On the left, there’s a traditional code-assist interface where developers can chat with the AI model for help or suggestions. On the right, a code editor displays the full source of the app.

Each component—such as React entry points, API calls, or styling files—can be edited directly. Tooltips help users understand what each file does, which is especially useful for those less familiar with TypeScript or frontend frameworks.

Apps can be saved to GitHub, downloaded locally, or shared directly. Deployment is possible within the Studio environment or via Cloud Run if advanced scaling or hosting is needed.

Inspiration on Demand: The ‘I’m Feeling Lucky’ Button

One standout feature in this update is the “I’m Feeling Lucky” button. Designed for users who need a creative jumpstart, it generates randomized app concepts and configures the app setup accordingly. Each press yields a different idea, complete with suggested AI features and components.

Examples produced during demos include:

• An interactive map-based chatbot powered by Google Search and conversational AI.

• A dream garden designer using image generation and advanced planning tools.

• A trivia game app with an AI host whose personality users can define, integrating both Imagine and Flashlight with Gemini 2.5 Pro for conversation and reasoning.

Logan Kilpatrick, Lead of Product for Google AI Studio and Gemini AI, noted in a demo video of his own that this feature encourages discovery and experimentation.

“You get some really, really cool, different experiences,” he said, emphasizing its role in helping users find novel ideas quickly.

Hands-On Test: From Prompt to App in 65 Seconds

To test the new workflow, I prompted Gemini with:

A randomized dice rolling web application where the user can select between common dice sizes (6 sides, 10 sides, etc) and then see an animated die rolling and choose the color of their die as well.

Within 65 seconds (just over a minute) AI Studio returned a fully working web app featuring:

• Dice size selector (d4, d6, d8, d10, d12, d20)

• Color customization options for the die

• Animated rolling effect with randomized results

• Clean, modern UI built with React, TypeScript, and Tailwind CSS

The platform also generated a complete set of structured files, including App.tsx, constants.ts, and separate components for dice logic and controls.

After generation, it was easy to iterate: adding sound effects for each interaction (rolling, choosing a die, changing color) required only a single follow-up prompt to the built-in assistant. This was also suggested by Gemini, too, by the way.

From there, the app can be previewed live or exported using built-in controls to:

• Save to GitHub

• Download the full codebase

• Copy the project for remixing

• Deploy via integrated tools

My brief, hands-on test showed just how quickly even small utility apps can go from idea to interactive prototype—without leaving the browser or writing boilerplate code manually.

AI-Suggested Enhancements and Feature Refinement

In addition to code generation, Google AI Studio now offers context-aware feature suggestions. These recommendations, generated by Gemini’s Flashlight capability, analyze the current app and propose relevant improvements.

In one example, the system suggested implementing a feature that displays the history of previously generated images in an image studio tab. These iterative enhancements allow builders to expand app functionality over time without starting from scratch.

Kilpatrick emphasized that users can continue to refine their projects as they go, combining both automatic generation and manual adjustments. “You can go in and continue to edit and sort of refine the experience that you want iteratively,” he said.

Free to Start, Flexible to Grow

The new experience is available at no cost for users who want to experiment, prototype, or build lightweight apps. There’s no requirement to enter credit card information to begin using vibe coding.

However, more powerful capabilities — such as using models like Veo 3.1 or deploying through Cloud Run — do require switching to a paid API key.

This pricing structure is intended to lower the barrier to entry for experimentation while providing a clear path to scale when needed.

Built for All Skill Levels

One of the central goals of the vibe coding launch is to make AI app development accessible to more people. The system supports both high-level visual builders and low-level code editing, creating a workflow that works for developers across experience levels.

Kilpatrick mentioned that while he’s more familiar with Python than TypeScript, he still found the editor useful because of the helpful file descriptions and intuitive layout.

This focus on usability could make AI Studio a compelling option for developers exploring AI for the first time.

More to Come: A Week of Launches

The launch of vibe coding is the first in a series of announcements expected throughout the week. While specific future features haven’t been revealed yet, both Kilpatrick and Löber hinted that additional updates are on the way.

With this update, Google AI Studio positions itself as a flexible, user-friendly environment for building AI-powered ap…

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🔗 Sumber: venturebeat.com

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📌 MAROKO133 Update ai: MIT Researchers Unveil “SEAL”: A New Step Towards Self-Impr

The concept of AI self-improvement has been a hot topic in recent research circles, with a flurry of papers emerging and prominent figures like OpenAI CEO Sam Altman weighing in on the future of self-evolving intelligent systems. Now, a new paper from MIT, titled “Self-Adapting Language Models,” introduces SEAL (Self-Adapting LLMs), a novel framework that allows large language models (LLMs) to update their own weights. This development is seen as another significant step towards the realization of truly self-evolving AI.

The research paper, published yesterday, has already ignited considerable discussion, including on Hacker News. SEAL proposes a method where an LLM can generate its own training data through “self-editing” and subsequently update its weights based on new inputs. Crucially, this self-editing process is learned via reinforcement learning, with the reward mechanism tied to the updated model’s downstream performance.

The timing of this paper is particularly notable given the recent surge in interest surrounding AI self-evolution. Earlier this month, several other research efforts garnered attention, including Sakana AI and the University of British Columbia’s “Darwin-Gödel Machine (DGM),” CMU’s “Self-Rewarding Training (SRT),” Shanghai Jiao Tong University’s “MM-UPT” framework for continuous self-improvement in multimodal large models, and the “UI-Genie” self-improvement framework from The Chinese University of Hong Kong in collaboration with vivo.

Adding to the buzz, OpenAI CEO Sam Altman recently shared his vision of a future with self-improving AI and robots in his blog post, “The Gentle Singularity.” He posited that while the initial millions of humanoid robots would need traditional manufacturing, they would then be able to “operate the entire supply chain to build more robots, which can in turn build more chip fabrication facilities, data centers, and so on.” This was quickly followed by a tweet from @VraserX, claiming an OpenAI insider revealed the company was already running recursively self-improving AI internally, a claim that sparked widespread debate about its veracity.

Regardless of the specifics of internal OpenAI developments, the MIT paper on SEAL provides concrete evidence of AI’s progression towards self-evolution.

Understanding SEAL: Self-Adapting Language Models

The core idea behind SEAL is to enable language models to improve themselves when encountering new data by generating their own synthetic data and optimizing their parameters through self-editing. The model’s training objective is to directly generate these self-edits (SEs) using data provided within the model’s context.

The generation of these self-edits is learned through reinforcement learning. The model is rewarded when the generated self-edits, once applied, lead to improved performance on the target task. Therefore, SEAL can be conceptualized as an algorithm with two nested loops: an outer reinforcement learning (RL) loop that optimizes the generation of self-edits, and an inner update loop that uses the generated self-edits to update the model via gradient descent.

This method can be viewed as an instance of meta-learning, where the focus is on how to generate effective self-edits in a meta-learning fashion.

A General Framework

SEAL operates on a single task instance (C,τ), where C is context information relevant to the task, and τ defines the downstream evaluation for assessing the model’s adaptation. For example, in a knowledge integration task, C might be a passage to be integrated into the model’s internal knowledge, and τ a set of questions about that passage.

Given C, the model generates a self-edit SE, which then updates its parameters through supervised fine-tuning: θ′←SFT(θ,SE). Reinforcement learning is used to optimize this self-edit generation: the model performs an action (generates SE), receives a reward r based on LMθ′’s performance on τ, and updates its policy to maximize the expected reward.

The researchers found that traditional online policy methods like GRPO and PPO led to unstable training. They ultimately opted for ReST^EM, a simpler, filtering-based behavioral cloning approach from a DeepMind paper. This method can be viewed as an Expectation-Maximization (EM) process, where the E-step samples candidate outputs from the current model policy, and the M-step reinforces only those samples that yield a positive reward through supervised fine-tuning.

The paper also notes that while the current implementation uses a single model to generate and learn from self-edits, these roles could be separated in a “teacher-student” setup.

Instantiating SEAL in Specific Domains

The MIT team instantiated SEAL in two specific domains: knowledge integration and few-shot learning.

• Knowledge Integration: The goal here is to effectively integrate information from articles into the model’s weights.
• Few-Shot Learning: This involves the model adapting to new tasks with very few examples.

Experimental Results

The experimental results for both few-shot learning and knowledge integration demonstrate the effectiveness of the SEAL framework.

In few-shot learning, using a Llama-3.2-1B-Instruct model, SEAL significantly improved adaptation success rates, achieving 72.5% compared to 20% for models using basic self-edits without RL training, and 0% without adaptation. While still below “Oracle TTT” (an idealized baseline), this indicates substantial progress.

For knowledge integration, using a larger Qwen2.5-7B model to integrate new facts from SQuAD articles, SEAL consistently outperformed baseline methods. Training with synthetically generated data from the base Qwen-2.5-7B model already showed notable improvements, and subsequent reinforcement learning further boosted performance. The accuracy also showed rapid improvement over external RL iterations, often surpassing setups using GPT-4.1 generated data within just two iterations.

Qualitative examples from the paper illustrate how reinforcement learning leads to the generation of more detailed self-edits, resulting in improved performance.

While promising, the researchers also acknowledge some limitations of the SEAL framework, including aspects related to catastrophic forgetting, computational overhead, and context-dependent evaluation. These are discussed in detail in the original paper.

Original Paper: https://arxiv.org/pdf/2506.10943

Project Site: https://jyopari.github.io/posts/seal

Github Repo: https://github.com/Continual-Intelligence/SEAL

The post MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI first appeared on Synced.

🔗 Sumber: syncedreview.com

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