PCWorld reports that Mozilla Firefox is introducing comprehensive AI Controls in its browser, allowing users to completely disable or selectively manage AI features. This development matters as other major browsers like Chrome and Edge integrate AI extensively without easy opt-out options for users. The feature debuts in Firefox Nightly builds and will reach most users within two months, with settings persisting through updates. Mozilla Firefox has always seemed like the "cool kid" browser option, for the power users who don't want to deal with Google or Microsoft. So perhaps filling it up with "AI" features -- you know, the things people are kind of tired of seeing from Google and Apple -- might not be winning people over.

I Let Google's'Auto Browse' AI Agent Take Over Chrome. Auto Browse can shop for clothes, plan a trip, and buy tickets for you. So, while testing Google's new "Auto Browse" feature for Chrome, I was filled with a strange sense of loss as I watched the AI agent open browser tabs and attempt to complete digital tasks with automated clicks. Sure, I felt some loss of control as the bot tapped away on my laptop screen. But also a kind of preemptive nostalgia for how the internet currently works, flaws and all, considering Google's plans to fundamentally alter the user experience.

Google's latest addition to its Chrome browser puts generative AI behind the wheel and you in the passenger seat. Google debuted a new "Auto Browse" feature for Chrome on Wednesday. The tool, powered by Google's current Gemini 3 generative AI model, is an AI agent designed to take over your Chrome browser to help complete online tasks like booking flights, finding apartments, and filing expenses. The release of Auto Browse is part of Google's continued integration of AI features into Chrome. Last year, Google dropped the "Gemini in Chrome" mode to answer questions about what's on web pages and synthesize details from multiple open tabs.

Google Chrome is launching agentic AI "autobrowsing" that automates web tasks like form filling, reservations, and license renewals for paid Google AI subscribers. PCWorld reports that a new Gemini AI sidebar will be available to all Chrome users, accessing personal data from Gmail and other apps.

'No reasons to own': Software stocks sink on fear of new AI tool The new year was supposed to bring opportunities for beaten-down software stocks. Instead, the group is off to its worst start in years. The release of a new artificial intelligence tool from startup Anthropic on Jan. 12 rekindled fears about disruption that weighed on software makers in 2025. TurboTax owner Intuit tumbled 16% last week, its worst since 2022, while Adobe and Salesforce, which makes customer relationship management software, both sank more than 11%. All told, a group of software-as-a-service stocks tracked by Morgan Stanley is down 15% so far this year, following a drop of 11% in 2025.

We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly selecting micro-actions, our agent first generates a latent plan in natural language, which is then executed by a separate model. We introduce a challenging real-time strategy game environment in which the actions of a large number of units must be coordinated across long time scales. We gather a dataset of 76 thousand pairs of instructions and executions from human play, and train instructor and executor models. Experiments show that models using natural language as a latent variable significantly outperform models that directly imitate human actions. The compositional structure of language proves crucial to its effectiveness for action representation. We also release our code, models and data.

AI coding is now everywhere. But not everyone is convinced. Developers are navigating confusing gaps between expectation and reality. So are the rest of us. Depending who you ask, AI-powered coding is either giving software developers an unprecedented productivity boost or churning out masses of poorly designed code that saps their attention and sets software projects up for serious long term-maintenance problems. The problem is right now, it's not easy to know which is true. As tech giants pour billions into large language models (LLMs), coding has been touted as the technology's killer app. Both Microsoft CEO Satya Nadella and Google CEO Sundar Pichai have claimed that around a quarter of their companies' code is now AI-generated. And in March, Anthropic's CEO, Dario Amodei, predicted that within six months 90% of all code would be written by AI.

The 300-person startup hopes bringing designers aboard will give it an edge in an increasingly competitive AI software market. Cursor, the wildly popular AI coding startup, is launching a new feature that lets people design the look and feel of web applications with AI. The tool, Visual Editor, is essentially a vibe-coding product for designers, giving them access to the same fine-grained controls they'd expect from professional design software. But in addition to making changes manually, the tool lets them request edits from Cursor's AI agent using natural language. Cursor is best known for its AI coding platform, but with Visual Editor, the startup wants to capture other parts of the software creation process.

Junior indie game developers in distributed, part-time teams lack production frameworks suited to their specific context, as traditional methodologies are often inaccessible. This study introduces the CIGDI (Co-Intelligence Game Development Ideation) Framework, an alternative approach for integrating AI tools to address persistent challenges of technical debt, coordination, and burnout. The framework emerged from a three-month reflective practice and autoethnographic study of a three-person distributed team developing the 2D narrative game "The Worm's Memoirs". Based on analysis of development data (N=157 Jira tasks, N=333 GitHub commits, N=13+ Miro boards, N=8 reflection sessions), CIGDI is proposed as a seven-stage iterative process structured around human-in-the-loop decision points (Priority Criteria and Timeboxing). While AI support democratized knowledge access and reduced cognitive load, our analysis identified a significant challenge: "comprehension debt." We define this as a novel form of technical debt where AI helps teams build systems more sophisticated than their independent skill level can create or maintain. This paradox (possessing functional systems the team incompletely understands) creates fragility and AI dependency, distinct from traditional code quality debt. This work contributes a practical production framework for resource-constrained teams and identifies critical questions about whether AI assistance constitutes a learning ladder or a dependency trap for developer skill.

--Processing-in-Memory (PIM) architectures offer promising solutions for efficiently handling AI applications in energy-constrained edge environments. While traditional PIM designs enhance performance and energy efficiency by reducing data movement between memory and processing units, they are limited in edge devices due to continuous power demands and the storage requirements of large neural network weights in SRAM and DRAM. Hybrid PIM architectures, incorporating nonvolatile memories like MRAM and ReRAM, mitigate these limitations but struggle with a mismatch between fixed computing resources and dynamically changing inference workloads. T o address these challenges, this study introduces a Heterogeneous-Hybrid PIM ( HH-PIM) architecture, comprising high-performance MRAM-SRAM PIM modules and low-power MRAM-SRAM PIM modules. We further propose a data placement optimization algorithm that dynamically allocates data based on computational demand, maximizing energy efficiency. FPGA prototyping and power simulations with processors featuring HH-PIM and other PIM types demonstrate that the proposed HH-PIM achieves up to 60.43% average energy savings over conventional PIMs while meeting application latency requirements. These results confirm HH-PIM's suitability for adaptive, energy-efficient AI processing in edge devices. With the advent of artificial intelligence (AI), real-world applications are rapidly expanding, fueling a trend to embed AI capabilities into IoT devices across diverse fields. However, traditional server-centric data processing, such as cloud computing, faces significant energy and latency challenges due to processing and communication overloads.