标签：#AR

Pet owners, rescue agencies, and wildlife groups are calling for new safeguards as AI makes it harder to tell whether animals, from polar bears to house cats, are real or fake.

Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.

Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise. By Alex Palcuie

HelloFresh’s organic meal kit Green Chef offers transparent sourcing, layered cooking, and trustworthy gluten-free dishes.

Xbox and Ikea collab alert! This unique nine-piece collection for gamers arrives this fall and has a slew of Easter eggs.

A technically perfect app can still fail. It can have clean code, modern architecture, powerful APIs, and impressive features—and still leave users uninstalling it, abandoning transactions, or switching to a competitor. Because users don't experience your code.They experience the product. That is why developers and businesses need to look beyond functionality and ask a more important question: “Does this software make the user’s life easier?” The Real Cost of a Poor Digital Experience Customer expectations are rising quickly. According to PwC’s 2025 Customer Experience Survey, 70% of executives say customer expectations are evolving faster than their companies can adapt. Even more importantly, 29% of consumers said they stopped using or buying from a brand because of poor customer experience. That means a frustrating digital experience isn't simply a UX problem. It can become a business problem . A confusing checkout flow, slow screen, unnecessary registration step, broken search function, or poorly designed notification can turn a potential customer into a lost customer. And users rarely tell you exactly what went wrong. They simply leave. More Features Don't Always Mean More Value One of the biggest mistakes in software development is assuming that adding more features automatically makes a product better. It doesn't. Imagine an app with: 30+ features AI integration Multiple dashboards Complex personalization Advanced analytics …but users struggle to complete the one task they downloaded the app for. That's not innovation. That's friction. A better development approach starts with identifying the core user problem and then building around it. Before adding a feature, ask: What problem does this solve? If the answer isn't clear, the feature may not belong in the product. Performance Is Part of User Experience Developers often separate performance from UX. Users don't. To them, a slow API, delayed screen, frozen button, or failed transaction is simply a bad experien

Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur…

When my oldest child was born, I immediately set up Gmail and Twitter accounts in her name. I broadly announced her birth online and proceeded to plaster her photo across all sorts of platforms. In short, I began creating her digital footprint long before she could stand on her own two feet. Fast-forward a couple…

I've been working on an open-source project called Millwright , an attempt to explore what an end-to-end machine learning workflow could look like in Rust. https://millwright-rs.dev/ This started while I was learning and building ML tooling in Rust. I kept finding capable individual libraries, but also gaps between them. Training a model was rarely the problem. Building the workflow around it — preprocessing, model selection, evaluation, explainability, deployment and monitoring — often meant integrating several unrelated crates and data representations. I initially started implementing some of those missing pieces as smaller independent crates. Eventually I realized I was more interested in the integration problem itself. That became Millwright. The current idea is to cover the classical ML lifecycle: ingest → explore → preprocess → select → fit → assess → explain → export → serve → monitor without trying to reimplement every ML algorithm. Instead, Millwright provides a common abstraction layer over existing Rust libraries and uses adapters for different ML backends. One architectural decision I'm experimenting with is having the framework own a small 2D data boundary ( Frame ) rather than exposing a particular backend's ndarray/dataframe representation throughout the API. That allows models and components backed by different libraries to participate in the same pipeline, at the cost of conversions at backend boundaries. The project currently includes work around: preprocessing and composable pipelines cross-validation and hyperparameter optimization multiple ML backends ensembles regression diagnostics SHAP-based explainability ONNX export model serving and registry drift monitoring time-series workflows incremental learning AutoML There are also Python bindings. I'm not building this on the assumption that Rust should replace Python for ML. Python's ecosystem is enormously more mature, and there would be little value in simply recreating scikit-learn in another l

Because "we'll be in touch" is not an observable state. I submitted a job application recently. It...

It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in…

Diagrid Catalyst 2.0 applies Dapr-based recovery, signed workflow history and execution attestation across several agent frameworks. Architects should compare it with framework-native durability and established workflow engines, while evaluating benchmark evidence and operational trade-offs. By Mark Silvester

In today’s fast-paced digital world, organizations face a growing number of challenges in managing their enterprise architectures. Complex systems, rapid technological advancements, and evolving business needs make it difficult to maintain a balance between risk management and cost efficiency. This is where Risk and Cost Driven Architecture (RCDA) plays a pivotal role. What is RCDA? RCDA, or Risk Cost Domain Architecture, is a framework that helps organizations make informed architectural decisions by weighing the trade-offs between risk and cost. This approach enables architects to develop sustainable, resilient, and cost-effective solutions that align with business goals and technical requirements. By breaking down architecture into domains of risk and cost, RCDA provides a structured methodology to address uncertainties while optimizing investments. Why RCDA Matters Every architectural decision carries a degree of risk, whether it be technical, financial, or operational. These risks, if not properly managed, can lead to project delays, increased costs, and even system failures. Traditional methods of architecture design often focus on functionality and performance, leaving risk management as an afterthought. RCDA flips this approach by putting risk management and cost at the center of decision-making, ensuring that every aspect of the architecture is thoroughly evaluated from these two perspectives. RCDA is particularly beneficial in large-scale, complex systems where the stakes are high, and decisions must be made carefully. It allows architects to balance innovation with risk tolerance, ensuring that projects are not only delivered on time and within budget but are also resilient and adaptable to future needs. The Core Principles of RCDA Risk-Driven Decision Making: RCDA emphasizes identifying and assessing risks early in the architectural design process. These risks can include security vulnerabilities, performance bottlenecks, scalability issues, and more. By

2024 was the year AI was everywhere. The ads, the praise, the feeling that something had already been decided without me. I am in my fifties. I had to decide whether to watch or take part. I decided to take part. I started with VS Code, on a paid plan. I did not have to think about what to build. Something had been sitting in my head for years: an automated trading system. I have lived fifty years, and while raising children the money got tighter, not looser. Financial freedom was moving away from me, not toward me. So I wanted to make money with automated trading. I think the idea first arrived in my mid-forties. That is why the decision to take part came so quickly. Whatever I said out loud would simply get built. That was the hope I walked in on. I do not really know how to code. But the AI would handle that part, so I trusted it. Where the illusion first cracked A year inside VS Code taught me that two names mattered: GPT and Claude. I used them in turn. I used them one at a time. Two problems. First, even on a paid plan the usage ran out fast. Faster than I expected. The road ahead was long and I was sitting still, waiting for a quota to reset. The second one was worse. The explanations were excellent. The results were not. That is where the illusion cracked for the first time. I still could not let go, so I paid for more. Adding Cursor bought me some headroom. And a different problem showed up immediately. Switch the model and it wants to start over Change the model, and it wants to rewrite everything from the beginning. Handed code written by a different AI, it would rather replace the whole thing than edit it. That is when I understood that switching AI mid-project is a bad idea. Everyone talks about pricing. Almost nobody talks about this one. And this is the one that actually held me back. I spent a lot of time fighting the tool. In the end I paid for Claude's hundred-dollar plan, and from then on I worked with Claude. What I gained: the job nobody wanted

I got banned from SoloLearn for trying to help beginners. Here's what happened. Yesterday, I was on a mission. I'm Harun, a 12-year-old solo dev who built KODA , an AI coding mentor, entirely on my Android phone. I noticed hundreds of beginners on SoloLearn asking: "How do I start?" , "Help me with loops!" , "I'm stuck!" So I did what any helpful founder would do: I answered their questions, gave them code solutions, and added a small P.S.: "P.S. I built a free tool called KODA to help with this. Try it here: [Link]." I thought I was being helpful. SoloLearn's algorithm thought I was spamming. Within hours, my account was blocked. 🚫 The Moment of Panic When I saw "Your account is blocked," my first thought was: "Oh no, I messed up. My marketing is over." But then, my CEO brain kicked in. I realized: If an automated bot thought my helpful comments were "spam," maybe I was doing something right. Maybe I was being too effective. The Lesson: Marketing vs. Helping Here is what I learned in 24 hours: Algorithms hate links. Even helpful ones. If you paste a URL 10 times, the bot doesn't care about your intent; it sees a pattern. Trust takes time. You can't force users; you have to earn them. The Story > The Link. People don't click links because they are forced to. They click because they connect with the story . The Pivot So, am I quitting? No. I'm pivoting. I'm turning this ban into this article. I'm going to focus on Dev.to , where the community values "Build in Public" stories. I'm going to ask my friends (Renuka, Dharaneesh) to be my first real users, face-to-face. And maybe, one day, I'll go back to SoloLearn with a smarter strategy: No links in comments. Just value in the bio. To Other Founders If you get blocked, rejected, or told "no" today: Don't stop. Turn that hurdle into content. Turn that rejection into a lesson. Turn that "Blocked" screen into your next viral post. Because while others see a wall, I see a story. And stories build empires. Try KODA anyway (no

sklearn 1.9 fixed a bug in how BayesianRidge computes its uncertainty. We traced predict on 1.8 and 1.9 and compared the two formulas it actually computes, see if you can spot what changed before the notebook tells you. https://github.com/aadya940/scikit-verify/blob/master/examples/sklearn_bug_hunting.ipynb submitted by /u/Lost-Dragonfruit-663 [link] [留言]

As of August 26, 2026, GitHub reports that 398 merged pull requests carry my name on the author line. Twenty nine of those live inside repositories that I own myself, so I removed them from this count on purpose. What remains is the number that actually matters to me: 369 pull requests merged into other people's repositories , across 33 external projects , maintained by strangers who had zero reason to trust my code. Every single one of those 369 merges is real, dated, and linked in this post. Nothing here is rounded up and nothing is claimed without proof. If you want to skip my writing entirely, open the search query in section two and run it yourself. That is the whole point of this article. You should never have to take a stranger's word about their own stats. Jump To Any Section How I Verified These Numbers | The Complete Scoreboard | 2024 The Year Of Volume | 2025 Fewer Pull Requests Higher Quality | 2026 The Year Production Code Got Merged | What 369 Merges Taught Me | Frequently Asked Questions | Where To Find Me How I Verified These Numbers I did not count these by hand. I queried the GitHub search API directly, which means the numbers come from GitHub itself, not from my memory or my ego. You can reproduce everything in this post with one click: https://github.com/search?q=is%3Apr+is%3Amerged+author%3Aaniruddhaadak80&type=pullrequests Or if you have the GitHub CLI installed: gh api -X GET search/issues -f q = "is:pr is:merged author:aniruddhaadak80" That query currently returns 398 results. I then filtered out every repository under my own account, which left exactly 369 external merges. The math is boring on purpose: 398 minus 29 own repository merges equals 369. One more honest number before we go further. I have submitted 918 pull requests in my lifetime so far. That means fewer than half of everything I ever sent got merged. Rejection is not the exception in open source, it is the price of admission, and anyone who shows you a 100 percent merge rate is

The strongest candidate I ever voted no on solved the problem in eleven minutes. Clean. Optimal. Caught the edge case I normally have to hint at twice. Then I opened my notes to write the scorecard and found one line: "Solved it. I have no idea how." That is the short version of why strong engineers fail coding interviews. Not because they can't code. Because nothing they did survived the trip from the room to the scorecard. The interview is not the thing being graded. The document I write forty minutes later is the thing being graded, and you are not in the room when it gets read. TL;DR Strong engineers fail coding interviews mostly on signal density , not correctness. A silent correct answer scores lower than a narrated near-miss. Interviewers score 3-4 rubric axes (problem solving, coding, communication, and for senior roles, judgment) and each axis needs quotable evidence , not vibes. The decision happens in the debrief , where ambiguity defaults to no. "Lean hire" across the board is a rejection at most companies. The most common senior failure is solving a senior problem like a junior : no scoping, no tradeoffs, no failure modes, no tests. Fix it by talking in sentences your interviewer can transcribe verbatim: assumption, tradeoff, complexity, test. What do interviewers actually score in a coding interview? Not "did you get the answer." Almost every structured loop I've been part of scores a fixed rubric, and correctness is one box inside one axis. Here is roughly what the form looks like: Axis What it's really asking What lands on the scorecard Problem solving Did you scope before you built? "Asked whether input fits in memory before choosing an approach." Coding Would this survive code review? "Named things well, extracted a helper, no off-by-one." Communication Could I follow you in real time? "Told me the plan first, then coded the plan." Judgment (senior+) Do you know what breaks in prod? "Unprompted, called out the retry storm risk." Notice what every r

Customizing cell backgrounds in Excel is one of the fastest ways to transform a plain data dump into a professional, scannable report. Whether you need to highlight headers, flag key metrics, or add visual polish to dashboards, the Free Spire.XLS for .NET library makes it easy to apply solid fills , texture patterns , and gradient effects programmatically. In this guide, you'll learn how to implement each style with concise C# examples. Prerequisites Install the library via NuGet Package Manager: Install-Package FreeSpire.XLS Then add the required namespaces to your project: using Spire.Xls ; using System.Drawing ; 1. Solid Fill (Flat Background) The solid fill is the most common background type—ideal for headers, totals, or status-based highlighting. using ( Workbook workbook = new Workbook ()) { Worksheet sheet = workbook . Worksheets [ 0 ]; CellRange cell = sheet . Range [ "B2" ]; cell . Text = "Solid Background" ; // Solid fill requires the pattern to be explicitly set to Solid cell . Style . FillPattern = ExcelPatternType . Solid ; cell . Style . Color = Color . LightGreen ; workbook . SaveToFile ( "CellSolidColor.xlsx" , ExcelVersion . Version2016 ); } ⚠️ Crucial : Always set FillPattern to ExcelPatternType.Solid before assigning a color. If omitted, the color change will be ignored. 2. Texture Fill (Pattern Overlay) Texture fills overlay a repeating pattern (e.g., brick, checker, or angle) over a base color. They're perfect for subtly distinguishing data categories without overwhelming the reader. using ( Workbook workbook = new Workbook ()) { Worksheet sheet = workbook . Worksheets [ 0 ]; CellRange cell = sheet . Range [ "B2" ]; cell . Text = "Texture Background" ; // Angle texture pattern cell . Style . FillPattern = ExcelPatternType . Angle ; cell . Style . Color = Color . LightGray ; // Base background color cell . Style . PatternColor = Color . Beige ; // Pattern overlay color workbook . SaveToFile ( "CellPattern.xlsx" , ExcelVersion . Version2016 ); } N

Why are AI scientists so adamant that frontier model LLMs are NOT conscious or sentient? submitted by /u/DoublePassRadiator [link] [留言]