Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Deal pricing and availability subject to change after time of publication. Get six courses and six hours of Claude & ChatGPT project management training for $19.99 (reg. AI is becoming another tool in the project manager's toolbox, but knowing how to use it effectively is a different matter.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I tried the new ChatGPT Desktop App for Linux - but I'll stick to my browser for now This preview release of ChatGPT Desktop for Linux supports Ubuntu, Debian, and Fedora is here. The application is proprietary, not open source. I'll keep using ChatGPT in the browser. OpenAI has released a preview of its ChatGPT desktop application for Linux . With this, OpenAI now has a native client on all major desktop operating systems and is placing its coding agent, Codex, alongside ChatGPT and its Work environment.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Three out of four of the 138 developers I surveyed use Claude Code. Here's what they say matters in daily AI coding workflows. Claude Code dominates, but Codex has crucial advantages. Whichever AI you choose, human review remains essential. I explained how I use both tools and provided some suggestions for exploring them.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Deal pricing and availability subject to change after time of publication. Sometimes the fastest way to get your ideas across is by speaking, but anyone who's relied on speech-to-text tools knows they aren't always accurate. Contextii Pro Plus puts an end to that, turning your natural speech into clean, formatted text.

Total Solar Eclipse 2024 Look Up Mashable Selects Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable All Series The following content is brought to you by Mashable partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. Deal pricing and availability subject to change after time of publication. It's 2026, and it's time to design smarter, not harder. If you work with 2D designs, you'll want to check out this AI-powered tool that makes it easier than ever to create technical drawings and documentation with industry-standard DWG compatibility.

Discover four foundational elements of AI architecture that will endure as models continue to advance: data quality, context engineering, governance, and human expertise. With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture--the structural framework required for deploying and managing reliable, integrated AI systems at scale--allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems. The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves. Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.

A ranking of 101 agent tasks reveals where workflows are trending and where connected intelligence is critical. Enterprise investment in AI is booming. Gartner is calling 2026 an " inflection year " for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey . And in the last 18 months, tech teams--the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations' infrastructure and applications--are clearly putting agents to work.

Designing effective black-box optimizers is hampered by limited problem-specific knowledge and manual control that spans months for almost every detail. In this paper, we present DesignX, the first automated algorithm design framework that generates an effective optimizer specific to a given black-box optimization problem within seconds. Rooted in the first principles, we identify two key sub-tasks: 1) algorithm structure generation and 2) hyperparameter control. To enable systematic construction, a comprehensive modular algorithmic space is first built, embracing hundreds of algorithm components collected from decades of research. We then introduce a dual-agent reinforcement learning system that collaborates on structural and parametric design through a novel cooperative training objective, enabling large-scale meta-training across 10k diverse instances. Remarkably, through days of autonomous learning, the DesignX-generated optimizers continuously surpass human-crafted optimizers by orders of magnitude, either on synthetic testbed or on realistic optimization scenarios such as Protein-docking, AutoML and UAV path planning. Further in-depth analysis reveals DesignX's capability to discover non-trivial algorithm patterns beyond expert intuition, which, conversely, provides valuable design insights for the optimization community.

Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate research ideas, and even conduct computational experiments. Ultimately, our goal is for these to help scientists derive novel scientific insights. In many areas of science, such insights often arise from processing and visualizing data to understand its patterns. However, evaluating whether an LLM-mediated scientific workflow produces outputs conveying the correct scientific insights is challenging to evaluate and has not been addressed in past work. We introduce ASTROVISBENCH, the first benchmark for both scientific computing and visualization in the astronomy domain. ASTROVISBENCH judges a language model's ability to both (1) create astronomy-specific workflows to process and analyze data and (2) visualize the results of these workflows through complex plots.

Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even editing steps, leading to significant improvements in image quality. However, their effective design requires substantial expertise. Recent approaches have shown promise in automating this process through large language models (LLMs), but they suffer from two critical limitations: extensive computational requirements from generating images with hundreds of predefined pipelines, and poor generalization beyond memorized training examples. We introduce a novel reinforcement learning-based framework that addresses these inefficiencies.