Google is testing a new security feature as part of Android Advanced Protection Mode (AAPM) that prevents certain kinds of apps from using the accessibility services API. The change, incorporated in Android 17 Beta 2, was first reported by Android Authority last week. AAPM was introduced by Google in Android 16, released last year. When enabled, it causes the device to enter a heightened

• The Hacker News
• 3 gün, 1 saat, 27 dakika

Amazon AU has officially kicked off its first big sale event for 2026 with the Big Smile Sale, and there are some great tech upgrades for under AU$50 — here are the ones that I’m personally eyeing off.

• TechRadar
• 3 gün, 1 saat, 33 dakika

Sarah Perez / TechCrunch : Spotify announces Taste Profile editing in beta, the first time it lets users fine-tune the recommendation algorithm, starting with Premium users in New Zealand —  At the SXSW conference on Friday, Spotify co-CEO Gustav Söderström announced a new feature, launching in beta …

• Techmeme
• 3 gün, 1 saat, 35 dakika

Zac Bowden / Windows Central : Sources: Microsoft sidelined some Copilot-branded AI features on Windows 11 after the Recall delay, and shipped some promised features without the Copilot name —  Originally announced in 2024, Microsoft's plan to integrate Copilot across various areas of the Windows 11 shell has been shelved …

• Techmeme
• 3 gün, 1 saat, 45 dakika

Timmy Shen / The Block : Reliz Ltd., which operates crypto lender BlockFills, files for Chapter 11 bankruptcy, and reports $50M-$100M in estimated assets and $100M-$500M in liabilities —  Quick Take  — Chicago-based BlockFills has filed for Chapter 11 bankruptcy in the U.S. Bankruptcy Court for the District of Delaware.

• Techmeme
• 3 gün, 2 saat

This sale spans almost an entire week, and it’s full of fake “deals.” We hunted to find truly good prices on gear we’ve tested.

• WIRED
• 3 gün, 2 saat, 9 dakika

Hints and answers to today's Hurdle all in one place.

• Mashable
• 3 gün, 2 saat, 10 dakika

For more than two decades, digital businesses have relied on a simple assumption: When someone interacts with a website, that activity reflects a human making a conscious choice. Clicks are treated as signals of interest. Time on page is assumed to indicate engagement. Movement through a funnel is interpreted as intent. Entire growth strategies, marketing budgets, and product decisions have been built on this premise. Today, that assumption is quietly beginning to erode. As AI-powered tools increasingly interact with the web on behalf of users, many of the signals organizations depend on are becoming harder to interpret. The data itself is still accurate — pages are viewed, buttons are clicked, actions are recorded — but the meaning behind those actions is changing. This shift isn’t theoretical or limited to edge cases. It’s already influencing how leaders read dashboards, forecast demand, and evaluate performance. The challenge ahead isn’t stopping AI-driven interactions. It’s learning how to interpret digital behavior in a world where human and automated activity increasingly overlap. A changing assumption about web traffic For decades, the foundation of the internet rested on a quiet, human-centric model. Behind every scroll, form submission, or purchase flow was a person acting out of curiosity, need, or intent. Analytics platforms evolved to capture these behaviors. Security systems focused on separating “legitimate users” from clearly scripted automation. Even digital advertising economics assumed that engagement equaled human attention. Over the last few years, that model has begun to shift. Advances in large language models (LLMs), browser automation, and AI-driven agents have made it possible for software systems to navigate the web in ways that feel fluid and context-aware. Pages are explored, options are compared, workflows are completed — often without obvious signs of automation. This doesn’t mean the web is becoming less human. Instead, it’s becoming more hybrid. AI systems are increasingly embedded in everyday workflows, acting as research assistants, comparison tools, or task completers on behalf of people. As a result, the line between a human interacting directly with a site and software acting for them is becoming less distinct. The challenge isn’t automation itself. It’s the ambiguity this overlap introduces into the signals businesses rely on. What do we mean by AI-generated traffic? When people hear “automated traffic,” they often think of the bots of the past — rigid scripts that followed predefined paths and broke the moment an interface changed. Those systems were repetitive, predictable, and relatively easy to identify. AI-generated traffic is different. Modern AI agents combine machine learning (ML) with automated browsing capabilities. They can interpret page layouts, adapt to interface changes, and complete multi-step tasks. In many cases, language models guide decision-making, allowing these systems to adjust behavior based on context rather than fixed rules. The result is interaction that appears far more natural than earlier automation. Importantly, this kind of traffic is not inherently problematic. Automation has long played a productive role on the web, from search indexing and accessibility tools to testing frameworks and integrations. Newer AI agents simply extend this evolution — helping users summarize content, compare products, or gather information across multiple sites. The issue is not intent, but interpretation. When AI agents interact with a site successfully on behalf of users, traditional engagement metrics may no longer reflect the same meaning they once did. Why AI-generated traffic is becoming harder to distinguish Historically, detecting automated activity relied on spotting technical irregularities. Systems flagged behavior that moved too fast, followed perfectly consistent paths, or lacked standard browser features. Automation exposed “tells” that made classification straightforward. AI-driven systems change this dynamic. They operate through standard browsers. They pause, scroll, and navigate non-linearly. They vary timing and interaction sequences. Because these agents are designed to interact with the web as it was built — for humans — their behavior increasingly blends into normal usage patterns. As a result, the challenge shifts from identifying errors to interpreting behavior. The question becomes less about whether an interaction is automated and more about how it unfolds over time. Many of the signals that once separated humans from software are converging, making binary classification less effective. When engagement stops meaning what we think Consider a common e-commerce scenario. A retail team notices a sustained increase in product views and “add to cart” actions. Historically, this would be a clear signal of growing demand, prompting increased ad spend or inventory expansion. Now imagine that a portion of this activity is generated by AI agents performing price monitoring or product comparison on behalf of users. The interactions occurred. The metrics are accurate. But the underlying intent is different. The funnel no longer represents a straightforward path toward purchase. Nothing is “wrong” with the data — but the meaning has shifted. Similar patterns are appearing across industries: Digital publishers see spikes in article engagement without corresponding ad revenue. SaaS companies observe heavy feature exploration with limited conversion. Travel platforms record increased search activity that doesn’t translate into bookings. In each case, organizations risk optimizing for activity rather than value. Why this is a data and analytics problem At its core, AI-generated traffic introduces ambiguity into the assumptions underlying analytics and modeling. Many systems assume that observed behavior maps cleanly to human intent. When automated interactions are mixed into datasets, that assumption weakens. Behavioral data may now include: Exploration without purchase intent Research-driven navigation Task completion without conversion Repeated patterns driven by automation goals For analytics teams, this introduces noise into labels, weakens proxy metrics, and increases the risk of feedback loops. Models trained on mixed signals may learn to optimize for volume rather than outcomes that matter to the business. This doesn’t invalidate analytics. It raises the bar for interpretation. Data integrity in a machine-to-machine world As behavioral data increasingly feeds ML systems that shape user experience, the composition of that data matters. If a growing share of interactions comes from automated agents, platforms may begin to optimize for machine navigation rather than human experience. Over time, this can subtly reshape the web. Interfaces may become efficient for extraction and summarization while losing the irregularities that make them intuitive or engaging for people. Preserving a meaningful human signal requires moving beyond raw volume and focusing on interaction context. From exclusion to interpretation For years, the default response to automation was exclusion. CAPTCHAs, rate limits, and static thresholds worked well when automated behavior was clearly distinct. That approach is becoming less effective. AI-driven agents often provide real value to users, and blanket blocking can degrade user experience without improving outcomes. As a result, many organizations are shifting from exclusion toward interpretation. Rather than asking how to keep automation out, teams are asking how to understand different types of traffic and respond appropriately — serving purpose-aligned experiences without assuming a single definition of legitimacy. Behavioral context as a complementary signal One promising approach is focusing on behavioral context. Instead of centering analysis on identity, systems examine how interactions unfold over time. Human behavior is inconsistent and inefficient. People hesitate, backtrack, and explore unpredictably. Automated agents, even when adaptive, tend to exhibit a more structured internal logic. By observing navigation flow, timing variability, and interaction sequencing, teams can infer intent probabilistically rather than categorically. This allows organizations to remain open while gaining a more nuanced understanding of activity. Ethics, privacy, and responsible interpretation As analysis becomes more sophisticated, ethical boundaries become more important. Understanding interaction patterns is not the same as tracking individuals. The most resilient approaches rely on aggregated, anonymized signals and transparent practices. The goal is to protect platform integrity while respecting user expectations. Trust remains a foundational requirement, not an afterthought. The future: A spectrum of agency Looking ahead, web interactions increasingly fall along a spectrum. On one end humans are browsing directly, in the middle users are assisted by AI tools, on the other end agents are acting independently on a user’s behalf. This evolution reflects a maturing digital ecosystem. It also demands a shift in how success is measured. Simple counts of clicks or visits are no longer sufficient. Value must be assessed in context. What business leaders should focus on now AI-generated traffic is not a problem to eliminate — it’s a reality to understand. Leaders who adapt successfully will: Reevaluate how engagement metrics are interpreted Separate activity from intent in analytics reviews Invest in contextual and probabilistic measurement approaches Preserve data quality as AI participation grows Treat trust and privacy as design principles The web has evolved before, and it will evolve again. The question is whether organizations are prepared to evolve how they read the signals it produces. Shashwat Jain is a senior software engineer at Amazon.

• VentureBeat
• 3 gün, 2 saat, 10 dakika

Presented by AudioEye While most organizations recognize the importance of accessibility from a theoretical angle, a stark gap exists between that awareness and actual execution. Companies can't just give a nod to accessibility -- and it can't just be a nice-to-have. The chasm between knowing and doing is not only exposing businesses to significant legal risk, it's also costing them actual business and growth opportunities. According to AudioEye’s newly released 2026 Accessibility Advantage Report , 59% of business leaders say their organization would face legal risk due to accessibility failure if audited today, and more than half have already encountered accessibility-related lawsuits or threats. That’s unsurprising, because today the average web page still contains 297 accessibility issues, based on an analysis of over 15,000 websites in AudioEye’s 2025 Digital Accessibility Index . The report, which surveyed more than 400 business leaders across the C-suite, VPs, and directors, reveals that organizations understand accessibility matters, but most lack the systems, expertise, and operational infrastructure to deliver it consistently, says Chad Sollis, CMO at AudioEye. “What the data makes clear is that accessibility hasn’t stalled because people don’t care,” Sollis says. “It’s stalled because fragmented ownership and reactive workflows make it hard to sustain as digital experiences evolve. Leaders know accessibility matters, but their organizations aren’t set up to deliver it consistently.” Why digital accessibility delivers a measurable business advantage With regulations like the European Accessibility Act now in effect and enforcement intensifying globally, the benefits extend far beyond avoiding lawsuits. Over half of leaders now cite accessibility as a business growth opportunity, recognizing that accessible digital experiences drive better user outcomes across the board. “Organizations that treat accessibility purely as a compliance exercise miss the opportunity to improve performance, reach new audiences, and build stronger digital experiences for everyone,” Sollis says. “Accessibility is a growth lever hiding in plain sight.” In fact, accessible design doesn’t just serve users with disabilities; it creates faster, more intuitive experiences for everyone. Organizations leading in accessibility are seeing it as a performance multiplier that: • improves site discoverability through better structure and cleaner code • reduces friction in the customer journey • strengthens brand loyalty by demonstrating inclusion in action “The leaders making the smartest decisions aren’t asking, ‘What’s the fastest fix?’” Sollis adds. “They’re asking, ‘What gives us durable protection while improving experience?’” Where digital accessibility breaks down in execution Despite widespread recognition of accessibility’s importance, implementation remains inconsistent. The report identifies what AudioEye calls “The Yet Problem,” or the gap between good intentions and actual execution. While many business leaders say they actively champion accessibility, the same percentage cite low budgets and limited expertise as barriers. Developers, designers, and content creators want to build accessible experiences. But when accessibility isn’t integrated into their everyday tools and processes, it creates additional complexity — with extra steps, extra time, and extra cost added to already heavy workloads and tight deadlines. The result is what the report calls “patchwork accessibility,” or programs that appear compliant on paper but fail users in practice. Many organizations treat accessibility as a project to complete rather than a practice to maintain, pursuing compliance milestones or quick fixes without building sustainable systems. “Accessibility doesn’t fail because companies aren’t trying; it fails because it’s treated as a single-layer problem,” Sollis says. “Real accessibility spans code, content, design, and ongoing change.” This pattern reveals a fundamental truth: accessibility is failing because the systems supporting it weren’t built for the people doing the work. Until accessibility is easier to design, build, and track alongside other priorities, it will continue to be deprioritized. The limits of fully in-house digital accessibility programs Even when leaders secure better tools and a larger budget, progress often stalls because of the misconception that accessibility must be tackled entirely in-house. AudioEye calls this “the in-house illusion,” or the assumption that internal responsibility automatically translates to organizational ability. “There’s a growing gap between ownership and capability,” Sollis explains. “Managing accessibility within the company can create the illusion of control, but without the right expertise and support, progress often stalls.” In fact, while nearly half of organizations manage accessibility with their own teams, 50% admit those teams lack internal accessibility expertise, and 43% cite competing priorities as major barriers. Only 47% describe their programs as proactive, while the rest operate reactively or meet only bare minimums. The illusion persists because many organizations equate ownership with control, and control with efficiency. In reality, accessibility is a specialized, evolving discipline. Without cross-functional expertise and external guidance, well-intentioned teams end up doing more work for less impact and more cost. True ownership doesn’t mean doing everything yourself, but knowing where to partner, automate, and delegate. The organizations advancing fastest are rethinking ownership altogether, treating accessibility as a system to orchestrate rather than a silo to control. Building a sustainable digital accessibility program The report’s findings point toward a clear path forward: organizations must move accessibility from aspiration to operational habit. This requires giving teams what they need to implement, maintain, and measure accessibility efficiently. Leading companies are building scalable systems that make accessibility part of everyday work. Plus, they’re elevating it from a compliance cost to a growth opportunity, in order to secure adequate budget and internal resources. And they’re quantifying the impact of the work, to demonstrate that accessibility improvements drive traffic, reduce abandonment, and expand total addressable market. Most importantly, they’re recognizing that sustainability often requires partnership. “The organizations making the most progress are the ones treating accessibility as an always-on system rather than a one-time project,” Sollis says. “That means using automation to handle scale, pairing it with expert review for complex, high-risk issues, and backing it all with protection that actually holds up when legal claims arise.” Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .

• VentureBeat
• 3 gün, 2 saat, 10 dakika

Apple's original film "F1: The Movie" yesterday won an Oscar for Best Sound at the 98th Academy Awards. The film, produced by Jerry Bruckheimer and directed by Joseph Kosinski, received four Oscar nominations in total, including Best Picture. "F1" has already picked up multiple honors across the industry, including Best Editing and Best Sound at the Critics Choice Awards and Best Sound at the BAFTA Film Awards. The film stars Brad Pitt as a once-promising Formula 1 driver whose career was nearly ended by a crash in the 1990s. Decades later, he returns to the sport after being recruited by his former teammate to help save a struggling team, partnering with an ambitious rookie driver. In 2022, Apple's "CODA" became the first streaming film to win Best Picture, with Troy Kotsur winning Best Supporting Actor and Siân Heder winning Best Adapted Screenplay. Apple later won Best Animated Short Film for "The Boy, the Mole, the Fox and the Horse." More recently, "Killers of the Flower Moon" received several Oscar nominations, including Best Actress for Lily Gladstone. "F1" is now available to stream globally on Apple TV . Apple previously said it is the highest-grossing sports feature film of all time. Tag: Apple TV Service This article, " Apple Original Film 'F1' Wins Oscar for Best Sound " first appeared on MacRumors.com Discuss this article in our forums

• MacRumors.com
• 3 gün, 2 saat, 16 dakika

In 2010, Warren Buffett and Bill Gates launched a disarmingly simple campaign to get the richest people on earth to promise, publicly, to give most of their money away. The moment seemed to call for it. Tech was minting billionaires faster than any industry in history, and the question of how those fortunes would impact […]

• TechCrunch
• 3 gün, 2 saat, 29 dakika

Jagmeet Singh / TechCrunch : Google's and Accel's joint AI startup accelerator Atoms picked five Indian startups from 4K+ applications, saying ~70% of rejected applicants were AI “wrappers” —  Many artificial intelligence startup ideas are still little more than superficial “wrappers” built on top of existing models.

• Techmeme
• 3 gün, 2 saat, 30 dakika

The 2026 Oscars had everything: historic wins, K-pop light sticks, political statements, and a few bits that overstayed their welcome.

• Mashable
• 3 gün, 2 saat, 39 dakika

Kenrick Cai / Reuters : President Trump accuses Iran of using AI as a “disinformation weapon” to make fake images of wartime successes against the US, saying “AI can be very dangerous” —  U.S. President Donald Trump on Sunday accused Iran of using artificial intelligence as a “disinformation weapon” …

• Techmeme
• 3 gün, 3 saat, 5 dakika

Under the updated approach, Shopify merchants’ products will still appear inside ChatGPT conversations, but buyers will typically complete purchases on the merchant’s own online storefront.

• Digiday
• 3 gün, 3 saat, 9 dakika

Linden Lane Films combines Hollywood talent, YouTube mega creators, and audience data with hopes of reshaping monetization and content.

• Digiday
• 3 gün, 3 saat, 9 dakika