Chrome’s AI features may be hogging 4GB of your computer storage

Google Chrome can automatically download a local AI model that takes up to 4 gigabytes of hard drive space on a computer when certain AI features are enabled, according to The Verge. The file, called weights.bin, is used by Google’s Gemini Nano AI model to provide writing assistance, autocomplete, and fraud protection directly on the device. (Nano has been around since Gemini was introduced in late 2023.) Since the model runs locally, the AI data is stored on the computer instead of in the cloud, which can provide better privacy, but also takes up storage space. Users can check whether the file is present by looking for the OptGuideOnDeviceModel folder in Chrome’s system files. To free up the space, users need to disable the on-device feature in Chrome’s settings under Settings > System.

In 2021, I was developing software for an aerospace manufacturer and met with our machine learning team to discuss innovative approaches for tracking FOD (free-orbiting debris), a major security and operational concern in the industry. What struck me wasn’t the algorithms or tracking equipment, but the terabytes of data (up to petabytes) that were being produced. Old-school problems of limited hardware resources and inefficient data compression were bottlenecking cutting-edge visual learning models and traditional tracking solutions alike. The team was smart and could fine-tune quickly, but the real challenge was making sure our infrastructure could scale with them. In aerospace, performance hinges on how fast systems can absorb and interpret massive telemetry streams, and storage is often the silent limiter. When you’re generating terabytes to petabytes of data in a single test cycle, even a brief stall in the storage layer becomes a bottleneck. A few milliseconds of delay between wha

Let’s be honest about what’s happening in the market: Public cloud has become the easy button for AI. It offers immediate access to compute, storage, managed services, foundation model ecosystems, automation tools, and global reach. For enterprises that want to launch quickly, it is hard to argue against it. You do not need to spend years standing up infrastructure, hiring specialized operations teams, or engineering your own scalable environment before you can test your first use case. This is exactly why adoption continues even as confidence in cloud resilience becomes more complicated. This article about the expanding cloud market makes the point clearly. Enterprises are not pulling back from hyperscale clouds despite numerous outages. They continue to move forward because the benefits of agility, scalability, and rapid deployment are too valuable to ignore. The cloud remains deeply embedded in business operations, and for many organizations, stepping away would undo years, often de

OpenAI's Privacy Filter Is a 1.5B-Parameter PII Detector Built on a Distilled Decoder — And It Runs in Your Browser The post OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters appeared first on MarkTechPost.

With every passing year, local AI models get smaller, more efficient, and more comparable in power with their higher-end, cloud-hosted counterparts. You can run many of the same inference jobs on your own hardware, without needing an internet connection or even a particularly powerful GPU. The hard part has been standing up the infrastructure to do it. Applications like ComfyUI and LM Studio offer ways to run models locally, but they’re big third-party apps that still require their own setup and maintenance. Wouldn’t it be great to run local AI models right in the browser? Google Chrome and Microsoft Edge now offer that as a feature, by way of an experimental API set. With Chrome and Edge, you can perform a slew of AI-powered tasks, like summarizing a document, translating text between languages, or generating text from a prompt. All of these are accomplished with models downloaded and run locally on demand. In this article I’ll show a simple example of Chrome and Edge’s experimental l

AI agents struggle with tasks that require interacting with the live web — fetching a competitor’s pricing page, extracting structured data from a JavaScript-heavy dashboard, or automating a multi-step workflow on a real site. The tooling has been fragmented, requiring teams to stitch together separate providers for search, browser automation, and content retrieval. TinyFish, a […] The post TinyFish AI Releases Full Web Infrastructure Platform for AI Agents: Search, Fetch, Browser, and Agent Under One API Key appeared first on MarkTechPost.

AI agents struggle with tasks that require interacting with the live web — fetching a competitor’s pricing page, extracting structured data from a JavaScript-heavy dashboard, or automating a multi-step workflow on a real site. The tooling has been fragmented, requiring teams to stitch together separate providers for search, browser automation, and content retrieval. TinyFish, a […] The post TinyFish Launches Full Web Infrastructure Platform for AI Agents — Search, Fetch, Browser, and Agent Under One API Key appeared first on MarkTechPost.

The premade Skills available through the Gemini sidebar in Chrome include ways to maximize protein in recipes or summarize YouTube videos.