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A number of Apple products got more expensive last month, so we’re happy to find deals wherever and whenever we can. If you’re searching for a high-end iPad, one of the more notable deals currently happening is on the 13-inch iPad Pro M5 with 512GB of storage. Normally $1,699, it’s $150 off at Amazon, costing […]
You spend weeks tuning an AI chatbot. Answers are accurate. Stakeholders sign off, and you ship it. Three months later, the system is confidently wrong about a third of what users ask. Nobody changed the model, and nobody touched the prompts. The world moved, pricing changed, a policy updated, a product spec shipped a new version, and the underlying knowledge store didn't move with it. This is not a hypothetical. It's one of the most common production failure modes in enterprise AI right now, and most data engineering teams don't have the right tooling to catch it, regardless of how the AI system retrieves the data. The failure that doesn't look like a failure An AI application doesn't care whether it's retrieving from a vector store, a document index, or an API call. Whatever the mechanism, nothing in a standard retrieval pipeline checks whether what it's serving is still correct. A stale pricing document retrieves just as confidently as a current one, because the system is scoring relevance or availability, not correctness. A record with a silently missing field passes through just as cleanly as a complete one, for the same reason. So the failure is invisible by design. Outdated or incomplete data still scores high on relevance, or passes every check a data pipeline was built to run. The model answers with full confidence because the retrieved context looks authoritative. Every dashboard you're watching stays green. The system looks like it's working. It's just wrong. I’ve watched a similar version of this happen outside the AI context, in a fintech pipeline. An upstream system changed a field without notifying downstream users. The pipeline did not fail; it simply propagated bad values into dashboards because the system only checked whether the job completed, not whether the data was still correct. The issue surfaced only when a customer noticed something inconsistent. By then, the bad data had already moved downstream. Whether it's a document that's gone stale or a field that's gone silently missing, the failure shape is the same: the absence of an error is not the presence of correctness, and without building proper validation layers, nothing in the pipeline could identify the problem. Why this is a data engineering problem Teams that hit this failure tend to misdiagnose it, and they tend to do it twice. Blaming the model: The first instinct is to blame the model, try a different LLM, adjust the prompt. The real problem lies further upstream, at the data engineering layer, the same instinct behind the fintech failure above: monitoring built for the pipeline, not the data. Blaming the retrieval layer: Once the model's ruled out, the next instinct is to blame the retrieval or context layer instead and buy a better one. The timing isn't a coincidence: as enterprises push these systems into the real production world, this gap is exactly what's starting to surface, and the vendor response has been everywhere. AWS just entered the "context layer" race with a knowledge graph that learns from agent usage. Snowflake's new Horizon Context and Cortex Sense target the exact symptom this piece opened with : agents giving confident wrong answers because nothing governs the business logic underneath them. Both are real responses to a real problem, but they sit one layer above it; a knowledge graph still depends on whatever feeds it. The real problem lies further upstream, at the data engineering layer. Teams check whether a job ran, not whether the data it moved is still true, an instinct that predates AI by years. Monitoring is built for the pipeline, not for the data. What's actually missing: Data observability Data observability is a well-known concept that doesn't get enough attention in how it's actually implemented. The relevant metric isn't a percentage — it's coverage: what fraction of critical datasets have lineage that's actually queryable, versus only living in someone's head. Uber built a dedicated data quality and observability platform long before retrieval-augmented generation existed. Their Unified Data Quality platform supports more than 2,000 critical datasets and detects around 90% of data quality incidents before they reach downstream consumers. Netflix solved a different piece of the same problem, building a company-wide data lineage system so anyone could answer where a dataset came from and what touched it along the way. It maps dependencies across Kafka topics, ML models, and experimentation, not just warehouse tables. Similar to Uber, the platform was built for humans and now it has become more important with the rise in AI/LLM applications. Between them, Uber and Netflix cover two of the four things worth building for. In practice, I think about it as four dimensions, each measurable on its own terms. Correctness: Does each record conform to the shape and rules it's supposed to, right field types, no unexpected nulls, values in range. Tools like Great Expectations and Soda handle this well: automated row and column-level validation instead of manual checks after something breaks. Track percentage of records passing validation per run. Freshness: Is the data still current relative to its source, not just current as of its last check. Track time since last successful update per source, with an SLA per dataset rather than one blanket threshold, since some sources need hourly refresh and others don't. Consistency: Does the same fact read the same way everywhere it's stored or indexed. This fails silently, it only shows up when two systems fed by the same source start disagreeing. A periodic cross-check between downstream destinations, flagging mismatch rate above a threshold, is enough to catch it early. Lineage: Can you trace any output back to its source and every transform it passed through, the same question Netflix built its system to answer. None of this requires infrastructure most data teams don't already have. I know because I've built it, not just argued for it. At Socure , client data arrived in whatever shape the client felt like sending it, and occasionally, quietly wrong. The challenge was building a system where incorrect data could be identified before it propagated downstream. The same principles applied: Validate what arrived, understand where it came from, and prevent bad data from becoming someone else's problem. Great Expectations became part of that foundation: schema and range validation at ingestion, per-source SLAs for freshness, cross-system checks for consistency, and file-level lineage. All of it sat behind a write-audit-publish pattern, where data landed in staging, was validated, and only moved downstream if it passed the required checks. The result showed up downstream: better accuracy across the board, in reporting, in the ML models, and in AI retrieval built on top of that same data. What to do Monday morning If you're running retrieval-based AI systems in production, the diagnostic question isn't which model to try next or which retrieval architecture to migrate to. It's four narrower questions: Is the underlying data validated against the standards required by its consumers? What's the oldest piece of content currently being served with high confidence? Would two chunks of the same source ever disagree with each other in the same retrieval result? Could you trace where it came from if it turned out to be wrong? If you can't answer those questions, then the gap lies in the pipeline between your source systems and whatever your agent reads from. That’s a data engineering fix, not a model swap or a vendor migration. Whether you're building reporting pipelines, ML systems, or AI agents, correctness, freshness, consistency, and lineage are what make data trustworthy. AI simply exposes weaknesses that have existed in data engineering all along.
Code found in an iOS 27 beta would allow Apple to put a financed iPhone in "Restricted Mode" if it detects any missed payments, 9to5Mac reports. The finding follows a story from Bloomberg earlier this week claiming Apple is preparing to launch a new "Apple Upgrade" financing program for leasing new devices. iPhones in "Restricted […]
OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible.
The nuclear agreement was first reported Tuesday and Trump is expected to sign it Wednesday.
The Chiefs released their first drawings and details of their new stadium. We break down the positives and negatives of those details.
Claims from a 15-year-old boy were set to go to trial next week in Los Angeles, but the case has now been dropped.
Karen Carter was found with stab wounds in the Dordogne village she had lived in for over a decade.
As access to Anthropic’s and OpenAI’s frontier models becomes more restricted, Chinese labs are pitching their open-source alternatives as stable, accessible, and increasingly capable.
On Disney Plus, you can catch some great miniseries, including Devs, WandaVision, Silver Surfer, Andor, Star Wars: Clone Wars and Muppets Mayhem
Copilot now bills usage at listed API rates. Compare direct model access with the coding workflow, policy, and harness work around it. The post Copilot vs. raw API access: What are you actually paying for? appeared first on The GitHub Blog .
Posted by Fahd Imtiaz, Senior Product Manager and Miguel Montemayor, Developer Relations Engineer, Android Developer Experience Today at Galaxy Unpacked, Samsung unveiled its latest lineup of foldable and wearable devices. For developers, this means that the variety of form factors, screen sizes, and device postures your app needs to support is expanding once again. With devices like the Galaxy Z Fold8, the ecosystem is expanding to include hardware with a landscape-first natural orientation and a wider aspect ratio in its main display state. Whether a user is unfolding a large display, flipping open a cover screen, or glancing at their wrist, users expect a flawless experience. To help you meet this moment, we’re sharing actionable guidance and new tooling updates to enable you to build adaptively proactively. Rethink layout architecture for dynamic displays, including ultra-wide foldables Building for the latest foldables means dropping assumptions about display orientation and size. This is especially true for the Galaxy Z Fold8, which adopts an ultra-wide display, adding to the variety of aspect ratios to account for. Devices with this landscape-first natural orientation show the limitations of hardcoded layout rules when users unfold the device. That’s why we’ve introduced dedicated guidance for building for landscape foldables and trifolds. To build a responsive UI that handles these physics seamlessly, focus on the following core pillars: Build fluid, adaptive layouts: Wide aspect ratios and compact vertical heights require fluid UIs that scale responsively. Our updated adaptive design guidance advises considering the window class width first to determine layout changes, then adjusting for height. To let individual components fluidly adapt to the grid, structure your layout using flexible containers that allow your content to automatically wrap, span, and reflow. For design inspiration browse our adaptive sample app and dual-screen design galleries. Track actual app space: Your app's display space rarely matches the physical device size, especially on an ultra-wide screen during multi-window, split-screen, or multitasking states. Sometimes even the orientations differ. Leverage Window Size Classes using the Jetpack Window Manager library to calculate the exact space your app occupies. Leverage the latest Jetpack Compose Update: Start by adopting the stable Jetpack Compose April '26 release ( Compose BOM version 2026.04.01 ).Take advantage of the new structural layout tools to manage complex architectures. The new Grid API allows you to define dynamic tracks and column spans without the performance overhead of a lazy list. Pair Grid with the new FlexBox layout API to easily handle multi-axis alignment and dynamic item wrapping. You can also use the new MediaQuery API to adapt your UI to its environment, using conditions to detect signals like device posture, window size, and keyboard types. Make your app fold aware: Use the Jetpack WindowManager library, which provides an API surface for foldable device window features such as folds and hinges. When your app is fold aware , it can adapt its layout to avoid placing important content in the area of folds or hinges and use folds and hinges as natural separators. Maintain app continuity: Avoid breaking the user journey when the device configuration shifts. Retain your UI state using ViewModel to ensure smooth transitions when a user folds or unfolds their device. Ensure seamless camera capture on foldable devices Camera implementation on foldables brings unique hardware quirks. Moving from a compact outer display to an expanded inner display introduces distinct layout aspect ratios while device rotation remains unchanged. If an app assumes a fixed portrait relationship between the camera sensor and the device layout, the app will likely suffer from sideways, stretched, or cropped previews during these folding transitions. When optimizing your app's media pipeline, migrate your capture experiences to CameraX using the CameraX migration skill . The library’s PreviewView automatically handles sensor orientation, device rotation, and scaling behind the scenes. This guarantees a clean, stable preview regardless of how the user holds or positions the device. If you are maintaining an existing Camera2 codebase, integrate the CameraViewfinder library to apply these complex aspect ratio and rotation transformations automatically without needing a total architecture overhaul. Extend glanceable interactions to Wear OS 7 The opportunity to build for this new generation of devices extends right to the wrist. Launching with Wear OS 7, Wear Widgets give you a fresh surface to provide users with instant, glanceable access to their essential updates. You can build these highly expressive experiences using Jetpack Glance and RemoteCompose . Crucially, Widgets built with this framework can now populate multi-widget tiles that were previously reserved for first-party widgets. Build intelligent features Gemini intelligence already completes tasks on users’ behalf, and you can experiment with the intelligence system by sharing your apps capabilities. Samsung’s new foldable devices come with Gemini Nano 4, our latest on-device model. Nano 4 provides support for over 140 languages, better multimodal understanding, and much more . Use ML Kit’s Prompt API with advanced features like s tructured output and thinking mode to build intelligent features on-device. Start optimizing today The tools and frameworks are ready to help you optimize your app for all screen sizes. Begin by exploring our guidance for building adaptive apps to learn more about core adaptive design principles. To dive deeper, check out our comprehensive YouTube playlist . Finally, ensure your app delivers a flawless, premium experience on the newest form factors by reviewing our dedicated quality guidelines for trifolds and landscape foldables and WearOS . Unfold the future today!
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“GPT-5.5's API pricing is reshaping how startups build AI products”
