标签：#AR

The ring is 40% smaller than its predecessor, and comes with more accurate sensing, enhanced battery life, and more.

I first realized there was an issue with Kia's flagship EV9 when I tried to unlock my car last year. The hulking three-row SUV was sitting on my driveway completely dead. The key didn't work, the app connection to the car was gone, and I was already late to an appointment. Luckily, I had prepared […]

Wherever I go, whatever I do, people point at my finger and ask, "Is that an Oura Ring?" Lots of people find they like the design, and they tell me why they're thinking about switching to a smart ring from a smartwatch. But the people who scrunch up their noses? They usually say something along […]

OpenClaw the open source AI agent platform with 346K+ GitHub stars had four chainable CVEs disclosed on May 15. But that was just the latest chapter. The crisis started in january and it's worse than most people realize. The numbers 245,000 instances exposed to the public internet (Shodan + ZoomEye scans) 30,000+ actively compromised and used by attackers (Flare) 1,184 malicious marketplace skills across 12 publisher accounts (Antiy Labs) 12% of the entire ClawHub marketplace was compromised 4 chainable CVEs including a CVSS 9.6 sandbox write escape (Cyera Research) 9 CVEs disclosed in a 4-day window in March 50,000+ instances exploitable via one-click RCE (CVE-2026-25253) The Claw Chain (Cyera Research, May 15) Four CVEs that chain together into a complete kill chain CVE-2026-44113 (CVSS 7.7) - TOCTOU filesystem read escape. Race condition lets you swap paths with symlinks to read outside the sandbox CVE-2026-44115 (CVSS 8.8) - Credential disclosure. Gap between command validation and shell execution leaks API keys through unquoted heredocs CVE-2026-44118 (CVSS 7.8) - MCP loopback privilege escalation. Trusts client-controlled senderIsOwner flag without session validation CVE-2026-44112 (CVSS 9.6) - Filesystem write escape. Same TOCTOU race in write ops. Backdoor placement on the host The chain malicious plugin -> read escape + credential theft -> privilege escalation -> persistent backdoor. Every step mimics normal agent behavior. Traditional monitoring cannot distinguish this from legitimate operations. ClawHavoc supply chain attack (Jan-Feb 2026) First malicious skill appeared January 27 By February 5, 1,184 malicious packages identified Skills disguised as crypto bots and productivity tools Installed keyloggers on Windows, Atomic Stealer on macOS 76 distinct malicious payloads ClawHub had zero verification for skill publishers until March 26 - eight weeks after the attack started Timeline Jan 27 - First malicious skill on ClawHub Feb 1 - Koi Security names "Cla

I've spent 18 months building agent infrastructure and watched a lot of impressive demos. Here's the uncomfortable pattern: the demo works beautifully, the founder posts it, everyone claps and then it touches real users and quietly dies. Not because GPT-5 / Claude / whatever isn't smart enough. The model is almost never the problem anymore. It dies for three boring reasons nobody wants to talk about because they're not sexy: 1. AMNESIA. Your agent forgets everything the moment the process restarts. Crash, redeploy, pod cycle gone. So everyone hacks together a pickle file or a Postgres table, and it works until they have more than one agent and the memory needs to be shared. Then it's a mess. 2. SUICIDE BY LOOP. An agent has no idea it's in a loop. It will call the same tool with the same args 400 times and cheerfully burn $200 of tokens overnight, because it has no metacognition. It literally cannot detect its own failure. The defense has to live OUTSIDE the agent and almost nobody builds that. 3. NO BLACK BOX. The agent does something weird in front of a customer. They ask "why did it do that?" and you stare at logs that show inputs and outputs but no chain of reasoning. You have no answer. Trust evaporates. The whole industry is obsessed with the brain (the model and ignoring the nervous) system (memory , the immune system (loop detection), and the flight recorder (audit).) The unsexy truth: the next wave of agent winners won't have better prompts. They'll have better infrastructure. The model is commoditising. The reliability layer is where the actual moat is. I got annoyed enough about this that I built the layer myself persistent memory, automatic loop detection, and a tamper-evident audit trail, framework-agnostic (LangChain/CrewAI/AutoGen/OpenAI/MCP . It's at) octopodas.com if you want to tear it apart genuinely want feedback from people who've shipped agents and hit this wall. But honestly even if you never touch my thing: stop optimising the prompt and star

AI bias mirrors human bias; both stem from our language and lived experiences. Ethics and AI are inseparable, but AI changes affordances, making harmful actions easier to carry out. The EU regulations apply to AI, since digital products are products. The ultimate goal is accountability: companies must ensure transparency, and laws should favor using the simplest AI that gets the job done. By Ben Linders

Google Home is rolling out a new Gemini-powered automation feature that can trigger smart home routines based on what your security cameras can see. This is one of several updates announced yesterday for Gemini for Home, including enhanced voice command support and general stability improvements, following its early access launch in October. "We are introducing […]

Created from fabric scraps that would otherwise end up in a landfill, this thoughtfully constructed bag looks great and can carry everything you need for a weeklong trip.

New mothers working in software development are staring down an AI-pilled workplace they barely recognize.

With sharper resolution and lightning-fast performance, Canon’s latest full-frame mirrorless punches well above its class.

The tech giant says a breakthrough in data-center networking has dramatically accelerated the flow of information through its massive cloud infrastructure.

This year, there’s been a wave of notable energy companies going public via IPO in the US. The solar and battery company Solv Energy went public in February, to the tune of $6 billion. X-energy, which is building small modular nuclear reactors, did the same in April, and its stocks surged on its first day…

It is one thing to say AI will change the world. It is another to expect the class of 2026 to applaud it. In fact, when former Google CEO Eric Schmidt told University of Arizona graduates that their task is to help shape AI, he was met with a resounding chorus of boos. “I can…

Why your age-gating algorithm is probably doomed to fail in the wild For developers building in the computer vision and biometrics space, there is a massive gap between a model that passes a NIST benchmark and a system that survives the "child-with-a-VPN" test. Recent data indicates that roughly 32% of children are successfully bypassing age-gating tech. As engineers, our first instinct is often to blame the model—to tweak the weights, gather more training data, or tighten the threshold. But the technical reality is more sobering: the failure isn't in the algorithm; it's in the deployment architecture. The Problem with Probabilistic Logic in Binary Workflows Most age estimation models rely on analyzing biometric markers—skin texture, bone structure ratios, and periocular geometry. They produce a probabilistic age range. However, according to NIST's evaluation of age estimation software, to maintain a low false-positive rate, systems often need to set a "challenge age" between 29 and 33 years. If you are a dev tasked with keeping 17-year-olds off a platform, you are essentially forced to build a "buffer zone" of over a decade. If the system flags anyone who might be under 30, the UX becomes a nightmare. If you lower the threshold to 18, the false-negative rate skyrockets. This is the fundamental trade-off of probabilistic facial analysis: precision and recall are at constant war, and in a high-traffic production environment, the "noise" of real-world variables (poor lighting, low-res sensors, off-axis angles) makes consistency nearly impossible. The Breakdown of the Identity Handoff Beyond the model, there are three technical failure points that no amount of Euclidean distance analysis can fix if the pipeline is broken: The Signal-to-Noise Ratio at Source: Evaluation datasets are clean. Production images are taken on scratched lenses in low-light bedrooms. The delta between training distribution and inference-time reality is where the first 10% of accuracy vanishes.

Over the past decade, the core evolution of data engineering has been the deconstruction and reconstruction of traditional data warehouse architectures through the Modern Data Stack. We separated data ingestion from databases, forming the Data Ingestion layer, using tools like FiveTran, Airbyte, and Apache SeaTunnel to solve ELT / CDC / Reverse ETL problems; We separated compute from storage, forming cloud data warehouse and lakehouse systems such as Snowflake, Databricks, Iceberg, and Hive; We separated orchestration from scripts, leading to orchestration systems like Apache Airflow and Apache DolphinScheduler; SQL development, data modeling, lineage, data quality, BI, and AI analytics were further split into independent tools. This architecture was undoubtedly progress. It moved data engineering away from the primitive era of “a bunch of scripts + Crontab” toward cloud-native infrastructure, elastic computing, engineering governance, and open ecosystems. The greatest contribution of the Modern Data Stack was “decoupling,” and its biggest side effect was also “decoupling.” Tools became more powerful, but data engineers were forced to switch between more systems than ever before: datasources in one place, synchronization configs in another, DAGs somewhere else, logs elsewhere, SQL stored in Git, and Snowflake / Iceberg / cloud warehouse execution results living in yet another environment. As a result, many data engineers spend less time on data modeling, business understanding, metric definitions, architecture design, and cost optimization — and far more time configuring datasources, setting field mappings, dragging DAG nodes, modifying SQL, checking logs, and rerunning tasks. This is the hidden pain created by the Modern Data Stack: data engineers became trapped inside tools. The emergence of engineering-focused AI systems like Codex and Claude Code is now changing the entire software engineering workflow. But how can data engineers truly achieve Vibe Coding? That

Bugs, crashes, glitches... Game development is full of them, and even experienced teams run into issues. But while no game is perfect, that doesn't mean we should stop chasing better quality. In this live session, we'll look at why even seasoned game development teams make mistakes and how you can reduce the number of issues in your own projects. What's the talk about? The speaker, Gleb Aslamov, developer advocate and static analyzer developer at PVS-Studio, will walk you through common and less obvious reasons behind code errors, share real-world bug examples from actual game projects, discuss development practices that help prevent bugs before release, and demonstrate tools designed to catch those issues early. Gleb will show some amusing bug examples from projects like osu!, GZDoom, and SanAndreas Unity. The discussion will cover how code reviews, testing, and CI/CD, combined with profilers, dynamic analyzers, and static analyzers, can help detect issues long before players ever encounter them. Also, expect to see static analysis in action, including warnings that reveal performance-sensitive issues and other hidden problems in game code. When? Mark your calendar for June 2, 2026, at 1:00 PM UTC+1 . Join the live talk and learn how to make your game code more reliable—one bug at a time. P.S. And don't forget to check your inbox to confirm the registration!

n fan-out microservice architectures, slow-but-completing requests accumulate across services and drive p99 latency far higher than per-service metrics suggest. This article presents an adaptive hedging mechanism that uses DDSketch for real-time quantile estimation, windowed rotation to handle distribution drift, and a token-bucket budget to prevent load amplification. By Prathamesh Bhope

Samsung has teamed up with diabetes experts at Massachusetts General Hospital to see how Galaxy Watch 8 can help prevent muscle loss in GLP-1 patients.

The AC email says the rebuttal is between 28 to 4th. The June 4th on website is the deadline. So I created this post for the discussion. I know it's a MM conference and less about ML but I think many people here are still submitting there. submitted by /u/Striking-Warning9533 [link] [留言]