It wasnt until recently that tablets shed off their media consumption device identity and stepped into productivity and content creation. The larger screen of the iPad Pro and its accompanying Apple Pencil proved that you can do a lot more on such a device. Over the years, however, this market has somewhat stagnated again, but the rise of E Ink tablets like Bigme, BOOX, Supernote, and reMarkable are once again shaking up the industry.The only problem with the latter group of devices is that, even if they displayed colors, their displays were more muted and pixelated, better suited for reading text and nothing else. There are now a few tablets that are trying to offer the best of both worlds, delivering true colors and fast screen refresh rates for regular digital content while being friendlier on the eyes. One of those comes from a rather unlikely source, a brand thats better known for color-accurate pen displays and drawing tablets for artists and designers.Designer: XPPenTo be clear, the XPPen Magic Note Pad doesnt use a Color E Ink screen or even a transflective LCD screen. It has a rather standard 19201200 display with a 90Hz refresh rate, 95% sRGB gamut, and support for 16.7 million colors. Its special sauce, however, is the three modes you can set for the display, depending on your activity or preference.Nature Color Mode gives you access to the whole spectrum of hues that the screen can support and is best for content consumption, making digital art, designing, and other creative works. Light Color Mode dials the saturation down to reduce eye fatigue, making it more suited for taking notes, reading comics or books with illustrations, or even browsing the web. Finally, theres Ink Paper Mode that removes all color for a more relaxing paper-like reading experience.Beyond this, the XPPen Magic Note Pad is like a general-purpose version of its Magic Drawing Pad, both of which are Android tablets. In terms of specs, the Magic Note Pad leans more towards the mid-range tablet market, so dont expect impressive gaming capabilities from this slate. That said, probably the only reason youd even be taking a glance at this device is the integration of XPPens stylus technology, one of Wacoms bigger rivals in this space.The XPPen Magic Note Pad probably wont satisfy more discerning e-book readers, as the black-and-white mode doesnt exactly reduce eye fatigue as much as a true E Ink tablet would. But for someone who constantly switches between sketching, note-taking, and reading, the device is more versatile than any e-book reader, and also a little more affordable than the likes of the Galaxy Tab S with a price tag of $395.99 only.The post XPPen Magic Note Pad tablet aims for designers, note takers, and book lovers first appeared on Yanko Design.

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When generative AI (genAI) suddenly burst onto the tech scene with the arrival in late 2022 of OpenAIs ChatGPT, companies quickly embraced its potential for automating tasks such answering customer inquiries, handling support tickets, and generating content.A slew of rival chatbots followed ChatGPT. But they tended to be static tools; they didnt learn from user interactions or application integrations. Only their foundational large language models (LLMs) could be trained.Enter agentic AI. By leveraging technologies such as machine learning, natural language processing (NLP), and contextual understanding, AI agents can operate independently, even partnering with other agents to perform complex tasks.Think virtual coworkers able to complete complex workflows, McKinsey & Co. explained in a report. The technology promises a new wave of productivity and innovation.According to the 2025 Connectivity Benchmark Report from Mulesoft and Deloitte, 93% of IT leaders plan to introduce autonomous AI agents within two years and nearly half have already implemented them.Like chatbots, AI agents have existed since the 1960s. However, it wasnt until advances in AI, ML, deep learning, and transformer models (such as GPT-3 and ChatGPT) that they became capable of adapting to tasks and learning from data. That dramatically expanded their use cases.Agentic AI systems typically use a transformer-based LLM as the core, enhanced with reasoning, memory, reinforcement learning, and tool integrations. The LLMs understanding of language allows it to interpret instructions and generate responses.In the simplest terms,an AI agentis the combination of an LLM and a traditional software application that can act independently to complete a task. They can operate autonomously, make decisions, plan, and take actions to achieve specific goals without constant human oversight.This is a way to deliver business value, and I think that is where the focus should be, to think about how youre going to disrupt the business process, said Samta Kapoor, a principal on Ernst & Youngs tech consulting team.For example, if an employee requests vacation time, an AI agent can automate the process of entering the dates into the HR system and ensuring all other systems are aware that employee will be away for the specified time. If the employee changes his or her mind and enters new dates, the agent can reschedule everything in the HR system autonomously. All it takes is a simple set of commands and away the AI agent goes, Kapoor said.AI agents can also autonomously write software code and offer that base code to a developer, who can then review it for accuracy and modify it if necessary. But there are also agents that can perform the code review, as well. And, best of all, it can all be done in seconds, not hours or days.AI-assisted code generation tools are increasingly prevalent in software engineering and, somewhat unexpectedly, have become low-hanging fruit for most organizations experimenting with generative AI (genAI) tools. Adoption rates are skyrocketing, because even if they only suggest a baseline of code for a new application, automation tools can eliminate hours that otherwise would have been devoted to manual code creation and updates.By 2027, 70% of professional developers are expected to be using AI-powered coding tools, up from less than 10% in September 2023, according to Gartner Research. And within three years, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering tool chain a significant increase from approximately 15% early last year, Gartner said.Beyond coding, AI agents are designed to perceive their surroundings, make decisions based on that information, take actions, and sometimes learn and adapt over time to perform tasks autonomously. Reinforcement learning is key to agentic AIs ability to continue to grow in sophistication when performing tasks.If youre playing a game, you either win or lose. If you lose, you go back and evaluate why, and then play again but do it differently, Kapoor said. With agentic AI, there are a very defined set of KPIs that youre asking it to meet, so it would know whether it has met them or not. And then it goes back and it reinforces itself to do this task differently.For agentic AI, decision-making is structured around autonomy and goal-orientation. There is a reward system within agentic AI and this is frequently based on reinforcement learning, where the AI learns to maximize rewards through interactions with its environment, said Arun Gururajan, NetApps vice president of research and data science.The sense-think-act process and agent typesAgentic AI, Gururajan said, follows a cyclical sense-think-act process, which has the following steps:Perception: The system gathers input from its environment and/or the user.Reasoning and Planning: The central brain of the agent, typically a powerful LLM, reasons through the task and generates and evaluates possible actions.Decision-making: Reinforcement learning strategies, often supplemented by human feedback as well as the memory of past interactions, help select the optimal action.Execution: The chosen action is carried out, possibly by calling on internal/external tools via API integrations.Feedback loop: Outcomes are assessed and used to refine future decisions, creating a continual learning process.There are several types of AI agents that can be employed based on the complexity of the task. They include:Reactive agents: These only respond to their environment based on predefined rules. They dont store history or learn from it (e.g., simple game AI). The most basic of agents, theyre used in customer service bots or smart home devices that can adjust themselves automatically.Deliberative agents: These use an internal model and reasoning to make informed, long-term decisions. Theyre used in applications such as autonomous vehicles, supply chain management, and medical decision systems.Hybrid agents: These combine reactive and deliberative approaches for more efficient decision-making. For instance, a robot might react to immediate obstacles and plan its path to a goal simultaneously. Hybrid AI is used in automating business tasks, where reactive agents handle routine actions (e.g., responding to emails) while deliberative agents plan and optimize workflows for efficiency over time.In short, hybrid agents integrate both immediate reaction and thoughtful planning in their decision-making.Traditional AI or predictive AI is often tuned to solve a narrow and specific problem for example, predicting drive failure in storage systems, Gururajan said. Agentic AI is more dynamic; It can adapt, reason and strategize.Imagine, for example, agents managing a data storage system by monitoring dashboards, identifying bottlenecks, predicting failures, and proactively taking action to prevent errors, ensuring system SLAs are met.NetApp, for instance, sets up reward models based on objectives (such as maximizing uptime or minimizing energy use) that combine human preferences, real-time data, and instructions, enabling AI to optimize behavior and improve performance over time, according to Gururajan.Reasoning techniques like Chain-of-Thought prompting, which mirrors human thought, or ReAct prompting help break down tasks and plan actions. Memory modules store context and intermediate results for tasks requiring continuity. Reinforcement learning with human feedback fine-tunes the systems outputs to align with human values. Additionally, tool integrations enable the AI to perform complex tasks beyond text generation, such as web search and interacting with APIs.The growing use of API integrationsAPI integrations with AI agents are currently the pinnacle of use cases. In agentic AI, tools via API integrations allow agents to interact with the real world. When a task requires external information, the agent generates an API call, formats parameters, authenticates, and processes the response to complete the task or take further action.When an agent needs to perform a task that requires external information, such as searching a database, sending an email, executing another ML model, Gururajan said, it generates an API call based on its understanding of the task and the APIs documentation.Executing on an API involves formatting aspects with the correct parameters and authenticating with the API, which in turn, returns data (or performs actions); the agent processes the response and completes the task or takes subsequent actions if needed, Gururajan explained.Looking ahead, there are still improvements needed for agentic AI to mature, such as addressing challenges with API discoverability and adaptation, and dealing with issues such as a lack of standardization and documentation, Gururajan said.Change management also makes it difficult for agents to select the right APIs. And API security and authentication remain crucial, requiring robust protocols and access control to protect sensitive data. Implementing service-level credentials could provide more granular control, such as restricting agents to read-only access or specific actions.There is emerging research involving agents, such as multi-objective optimization, which focuses on solving conflicting task goals using goal-based programming. Additionally, system-level heuristics can be created as general rules reflecting core principles, constraints, or safety measures.Heuristics can be incorporated into the agentic framework by: (a) filtering goals (such as removing goals requiring restricted data), (b) modifying objectives (insuring safety overrides efficiency), and (c) integrating reinforcement learning to weight goals.Looking ahead, theres a need for agents to autonomously create their own APIs for tasks, as most agents currently rely on pre-existing ones. This would be a positive step towards Artificial General Intelligence or AGI, Gururajan said.

Are tariffs good for U.S. workers?Open mic nights + cowgirl wisdom (Issue #294)Published inThe Medium BlogSent as aNewsletter4 min readJust now--A tariff is a tax placed on goods when they cross national borders. Example: the The Chicken Tax, a term that confusingly describes tit-for-tat tariffs that partially remain in place today. In the early 1960s, European countries levied a tariff on U.S. chickens, so that European farmers werent priced out of the chicken market. In response, President Johnson levied a tariff on European exports, such as potato starch, brandy, and automobiles. (And that, kids, is how the Chicken War started.)Trump recently decided to impose tariffs on several countries, then paused some, then suspended some, and then promised that reciprocal tariffs are coming in April. The talking head class mobilized, either barking that tariffs are an effective and underused economic and policy tool, or that they will destroy life as we know it. So I decided to figure out whats up, narrowing my focus to one seemingly simple question: Are tariffs good for U.S. workers?Roger Martin, strategy advisor and former dean of Torontos Rotman School of Management, suggests they are, citing Harvard Kennedy School Professor Dani Rodrik for the proposition that freer trade massively damages the economic prospects of a large swath of workers primarily low-medium skilled workers in tradeable goods industries from which they never recover. Everyone else in the economy gets the benefit of cheaper goods while these workers are sacrificed. In short, without tariffs, manufacturers will always choose foreign cheap labor to maximize profit.While not, on its face, related to the benefit or disadvantage to the U.S. worker, an administration might take the gamble anyway because tariffs can be used to make other countries support our domestic policy goals. As mathematician and data scientist Laurel W writes, the Trump administrations threatened tariffs against Canada and Mexico are designed to force both countries to increase border security. (If they comply, the U.S. will ease up on those threats.) Tariffs, she writes, are not merely economic instruments; they are geopolitical tools of coercion.But these potential economic and policy wins are outweighed by the unavoidable fact that supply chains are dizzyingly complex foreign thingamabobs are often necessary to make domestic thingamajigs and therefore U.S. manufacturing may become more expensive with tariffs, not less. This would further incentivize manufacturers to find cheap foreign labor, potentially leading to a decrease in jobs rather than an increase.Reynolds Taylor, a human rights lawyer, articulates this point well, explaining that even U.S. manufacturers who label their wares made in U.S.A. frequently import materials. (For instance, Honda has 12 manufacturing plants in the U.S. but some of its engines are made in Thailand and Japan.) If manufacturers search for cheap foreign labor in response to tariffs, it could result in the lowest international standards for health and safety, labor rights, or decent wages. Even if you dont care about exploiting foreign workers, there is no economic benefit to the exploitation anyway, because there is no quid pro quo: A 25% tariff on foreign goods will not result in a 25% increase in funds available to the American public, she points out, because any profit would be offset by slower consumption and shrinking markets.My conclusion? I understand why people feel that tariffs could be an effective tool. But after reading Taylors piece, it doesnt seem like the juice is worth the squeeze. What else were readingIn a world increasingly governed by algorithms and AI, Richard Owen Collins reminds us that there is still open mic night, where freedom, spontaneity, and imperfection flourish.A top highlight on Medium last month, via our February roundup: When did we forget how to do something purely for its own sake? (JA Westenberg in You Dont Have to Monetize the Things You Love)Poetry from Ismael S Rodriguez Jr about a humble (yet deeply important) family game of dominoes:When shuffling, tell stories about your primas new boyfriendwho doesnt even know how to make pegao.Let the tiles speak their own chisme as they scrapeacross formica worn smooth by three generationsof elbows and arguments Your daily dose of practical wisdomMy friends and I are horse-obsessed and we often remind each other of this quote from 1861, attributed to cowgirl Marie Lords: a cowgirl gets up in the morning, decides what she wants to do, and does it. I couldnt find any real proof on the internet that Lords actually existed or even said this, but even potentially fake wisdom can pack a punch.

SpecialEffect's GameBlast25 raises over 130,000 | News-in-briefThe charity event has raised over 1.8 million over the past 12 years, with GameBlast26 announced across February 17 to March 1 next yearImage credit: SpecialEffect News by Sophie McEvoy Staff Writer Published on March 24, 2025 This is a News-in-brief article, our short format linking to an official source for more information. Read more about this story by following the link below:SpecialEffect's GameBlast25 raises over 130,000

Silent Hill F has not been banned in AustraliaUpcoming game was previously refused classification, but has since been removed from the Australian Classification Board's websiteImage credit: Konami News by Sophie McEvoy Staff Writer Published on March 24, 2025 Silent Hill F has not been refused classification in Australia, as confirmed by the country's Classification Board.In a statement provided to independent games site Stevivor, the entry for Silent Hill F published on March 14, 2025 has since been removed."Silent Hill F is not currently classified as 'Refused Classification' in Australia," the Australian Classification Board said."The entry on the National Classification Database has been removed. A classification decision will be published to the National Classification Database ahead of the game's release."Stevivor notes that this entry may have been due to an error with a self-reporting tool used by the ACB. GamesIndustry.biz has reached out to the ACB for further clarification.As explained by IGN, this tool is managed by the International Age Rating Coalition, "which is a classification system designed for mobile and digitally delivered games."The IARC tool requires developers to complete an online questionnaire about the game, then the tool automatically provides a rating for participating territories. According to IGN, this rating is published "automatically" on the ACB's National Classification Database.Earlier this month, Konami released a new cinematic trailer for Silent Hill F. The game has since been rated 18+ in Japan, making it the first title in the franchise to receive this rating.Last year, Hunter x Hunter Nen x Impact was refused classification by the ACB due to "implied sexual violence."

The company says it will remain in operation during the bankruptcy process. | Image: Justin Sullivan/Getty ImagesDNA testing firm 23andMe has filed for Chapter 11 bankruptcy protection to help the company sell itself after years of financial difficulties. Anne Wojcicki, 23andMes co-founder and CEO, announced on X that she has resigned with immediate effect to become an independent bidder for the company, with chief financial and accounting officer Joe Selsavage stepping in as interim CEO.After a thorough evaluation of strategic alternatives, we have determined that a court-supervised sale process is the best path forward to maximize the value of the business, Mark Jensen, chair and member of the Special Committee of the Board of Directors, said in a statement. We expect the court-supervised process will advance our efforts to address the operational and financial challenges we face, including further cost reductions and the resolution of legal and leasehold liabilities.23andMe is best known for its at-home gene testing kits that use a customers saliva to dig into their ancestry. The company was briefly valued at $6 billion after going public in 2021, but has since plummeted to $50 million after failing to turn a profit. It settled a lawsuit for $30 million in September that alleged the firm had failed to protect the data of almost seven million customers during a data breach in 2023, and announced in November that it was cutting 40 percent of its workforce.23andMe says that it intends to continue operating its business throughout the bankruptcy proceedings and that customers access to data and subscriptions wont be affected.We have had many successes but I equally take accountability for the challenges we have today, Wojcicki said. There is no doubt that the challenges faced by 23andMe through an evolving business model have been real, but my belief in the company and its future is unwavering.