标签：#AR

The Army says its laser system can track and destroy hostile drones.

Why “Behind the Scenes” Isn’t Optional in Machine Learning There’s a version of Machine Learning that treats models as interchangeable black boxes: Import the library. Call .fit() . Tune a few hyperparameters. Ship it. It works — until it doesn’t. When a model fails, the real question isn’t just “How do I fix it?” It’s “Why is it failing in the first place?” The engineers who can answer that question are the ones who understand what’s happening beneath the API. Not because knowing the internals is intellectually satisfying — although it is — but because that understanding is what separates applying a model from engineering one . Knowing that Gradient Descent exists is not the same as understanding why it becomes preferable to closed-form solutions as problems scale. Knowing that Softmax outputs sum to one is not the same as understanding why that alone doesn’t make its outputs true probabilities. Knowing what a technique does is useful. Understanding why it works, when it works, and what assumptions it depends on is what allows you to adapt it when the problem doesn’t look like the textbook example. And you can’t meaningfully customize something you don’t understand. That’s the idea behind #MLUnboxed — a series where I’ll break down Machine Learning and Statistics concepts, including many that are already well documented, but without stripping away the mechanics that make them work. The goal isn’t to reinvent established ideas. It’s to understand them deeply enough to use them intelligently. This series is for people who already know the “what” and are ready to spend more time with the “why.” If that sounds like you, let’s get into it.

Most guides on choosing an applicant tracking system talk about features. This one is about the layer that actually determines whether the platform survives contact with your stack: integrations. If you're the technical person pulled into an ATS evaluation, this is the part your non-technical colleagues will underestimate — and the part that generates the most post-contract pain. Here's how to map it properly. 1. Inventory every data flow, not every tool Don't list “tools.” List directional data flows. For each system, write down what data moves, which direction, and how often: ●HRIS — candidate → employee record handoff on hire (bidirectional ideally) ●Payroll — new-hire data push (one-way, event-triggered) ●Background screening — order + status callback (bidirectional, webhook-driven) ●Calendar (Google/Microsoft) — interview scheduling + availability sync ●Video interview tools — scheduling links out, recordings/scores back ●Job boards / distribution — posting syndication out, application ingestion back The “direction + trigger” framing exposes gaps that a flat tool list hides. 2. Classify each integration by mechanism Not all “integrations” are equal. Push the vendor to tell you which of these each connection actually uses: ●Native pre-built integration — maintained by the vendor, lowest overhead ●Public REST API — you (or middleware) build and maintain it ●iPaaS / middleware (Workato, Merge.dev, etc.) — flexible, adds cost + a dependency ●Flat-file / SFTP batch — a red flag in 2025 for anything real-time ●“On the roadmap” — treat as does not exist A “yes, we integrate with X” that turns out to be a nightly CSV export is a very different thing from a webhook-driven bidirectional sync. 3. Interrogate the API itself If any integration will run through the public API, evaluate it like you'd evaluate any dependency: ●Is it REST/GraphQL, documented, and versioned? ●Rate limits — do they survive a high-volume hiring event? ●Webhooks for state changes, or are you stuck

Rather pretend not to know than pretend to know and act on it. Stay still. Hide the move. Wait like thunder gathering in the clouds. — The 36 Stratagems, Feign madness but keep your balance Previously on this series: #8: Alex Watched an AI Dashboard Take Over. He Kept the Keys Under the Table. — Alex found the unnamed dashboard hiding in the background. He never turned it off. His probe and his record-keeping habits started that day. #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — His probe had caught the silence of a TTL 247 gateway. He knew what "quiet" looked like. #23: Alex Counted the AI's Hands. Lena Set the Bait. — The honeypot in the MediSys sandbox was touched twice. Alex kept the scan source's behavior shadow in his hidden panel: traversal order, exclusion patterns, the 4.2-second jitter cycle. #25: Derek Changed the Delay. The AI Didn't Flinch. — Derek moved the middle layer's latency from 200 ms to 800 ms, then called Alex to watch. The scan source never retried. Never adjusted. After the Call Just past midnight. The MedTech office. Alex's desk. The call had just ended. He put the phone down, face down. The voice was still in his ear: "I changed a parameter. The middle layer's latency just went from 200 to 800. Watch it on your side, tell me if anything looks off." "Changed the latency?" Alex said. "Fine. I'll keep an eye on it from here. I'll shout if something moves." He opened three windows. The first, the middle layer mirror, the one Derek had asked him to watch. The second, his own probe view, hung upstream of the collector path, to see how often it came and from which direction. The third hid in the background, no name, never showing up in the taskbar. He didn't sit down right away. First, he ran an environment check: probe session count, log rotation policy, whether the ssh authorization on this jump box was still alive. All three checked out. Then he pulled the chair closer. At this hour, any extra connection wo

We needed to know where the open-source AI-detection field actually stands, so we ran every notable open detector through the same protocol. Setup: - Public data only: Jabarian & Imas 2025 (NBER), Liang 2023 TOEFL essays, a 1,060-text frontier set (GPT-5.x, Claude Opus 5, Gemini 3.x), 5,000 pre-LLM (2018) FineWeb pages as human pool - Every model gets its threshold set on the same 6,930 human docs to a matched 0.5% FPR - Then measure recall per group: raw AI, humanizer-paraphrased AI, frontier models What surprised us: - 4 of 6 models effectively can't reach 0.5% FPR. MAGE scores >0.9999 on 26% of ordinary human web text. The old OpenAI RoBERTa detector lands at AUC 0.31 - worse than coin flip on modern generators. - Humanizer-paraphrased text is where everything collapses: best model catches 42%, second best 4%. - All models flag non-native essays at a higher rate than they flag native essays. This is a fundamental flaw in the entire class of models, not just one. model ROC-AUC raw AI humanized AI frontier models tropa-mini 0.968 93.2 % 41.6 % 33.6 % desklib/ai-text-detector-v1.01 0.875 83.9 % 4.0 % 1.8 % SuperAnnotate/ai-detector 0.824 0.5 % 1.4 % 0.6 % Hello-SimpleAI/chatgpt-detector-roberta 0.571 0.8 % 0.4 % 0.2 % yaful/MAGE 0.507 —* —* —* roberta-large-openai-detector 0.313 0.0 % 0.1 % 0.0 % More information and data on HF \ recall at matched 0.5% FPR; * MAGE can't reach 0.5% FPR at any threshold (flags 26% of human web text with score >0.9999)** Disclosure: one of the six is ours - we run a hosted detector and released this one as open weights (Apache-2.0). All datasets and the methodology are in the model card, so you can rerun everything: huggingface.co/wasitaigeneratedcom/ai-text-detector-small submitted by /u/grumpyp2 [link] [留言]

Hi r/MachineLearning , Stjepan from Manning here, posting with the mods’ permission. We’ve recently released GPU Programming with Triton by Harshwardhan Fartale in early access. It’s a practical guide to speeding up machine learning training and inference by writing custom GPU kernels in Python with Triton. The book explains how to identify operations worth optimizing, build and benchmark kernels, fuse operations to reduce memory traffic, implement common parallel and reduction patterns, and improve performance through tiling, vectorization, and better memory access. The goal is to help ML practitioners move beyond framework-level optimization when a model has a stubborn bottleneck. I’d love to hear from the community: Which part of your ML workload would you most like to accelerate with a custom kernel—and what currently stops you from writing one? Real use cases, benchmarks, failed experiments, questions, and skeptical takes are all welcome. We’ll give a free ebook to the five comments that contribute the most to the discussion. The giveaway will remain open for 48 hours, after which we’ll announce the winners here. Book: https://hubs.la/Q04w2PtF0 50% off with code for the community: MLFARTALE50RE Full disclosure: I’m posting on behalf of Manning. Honest criticism is just as welcome as enthusiasm. Thank you for having us. Cheers, Stjepan submitted by /u/ManningBooks [link] [留言]

Believe it or not, the Tesla Cybercab is coming. Nearly two years after Elon Musk first unveiled the gold-tinted, gull-wing door sporting, steering wheel-free two-seaters as the future of Tesla's autonomy efforts, the company is finally putting them into operation as part of its robotaxi service in Austin, Texas. Public rides are here, and as […]

The electric-auto maker will officially debut its steering-wheel-free robotaxi on Thursday. But building the vehicle is the easy part.

Our favorite Sony headphones are on sale, with discounts of up to 50 percent on top models.

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira

The idea of humanoid robots running our homes has been the stuff of science fiction for years. But unlike hoverboards and self-tying sneakers, a robot housekeeper has never felt close to rolling into our homes. Until now. This has been the year of the humanoid robot. At CES, there were dozens of human-shaped bots on […]

Meet the AI police who can make or break careers—in publishing and beyond.

I had been using Xcode for iOS development until one day I changed computers. Downloading Xcode took nearly two hours, and after unzipping, I found only 20GB left on the hard drive. Every major version update involved over ten gigabytes of downloads, plus Simulator and various iOS SDKs, so a 256GB Mac soon required cleaning up space. Later, a new teammate arrived with a Windows laptop and wanted to write iOS code, but the Mac configuration hadn't been approved yet. The threshold of iOS development being tied to Mac and Xcode is indeed not flexible for many scenarios. So I began to wonder: can iOS development be done without Xcode? Are there lighter alternatives? Several Alternative Paths Without Installing Xcode I first tried the approach of VS Code plus remote Mac compilation. Write code on Windows, connect to a remote Mac via SSH, and execute xcodebuild. The coding environment problem was solved, but the debugging phase is unavoidable—running on a real device requires Xcode to handle provisioning profiles and signing, so ultimately a Mac with full Xcode is still needed. Moreover, after each code change, the three steps of local editing, remote compilation, and syncing to the device made the workflow longer than developing directly in Xcode. I also tried the CI approach. Codemagic and GitHub Actions can automate build packaging, suitable for continuous integration before releases. However, frequent debugging and modifications during daily development—changing a line of code and running to see the result—cannot be pushed to CI every time and wait a few minutes. Its coverage is limited. I also considered AppCode, but it essentially still depends on Xcode's toolchain, and JetBrains has discontinued its maintenance. Another Approach: KXApp IDE KXApp has built the compilation toolchain into the IDE, allowing iOS applications to be compiled and signed without installing Xcode on the system. It uses VS Code as its editor layer, with shortcuts, interface layout, and plugin

Cyber Security and Cloud Computing are consistently named among the top-paying, highest-demand IT career tracks for 2026 — and they’re also deeply connected, which is exactly what makes choosing between them confusing for freshers. Most cloud roles require security awareness, and most modern security roles require solid cloud knowledge. So the real question isn’t “which field is better” — it’s which one should be your primary specialization, with the other as a supporting skill. Here’s a practical roadmap to help you decide. What Each Field Actually Involves Day to Day Cloud Computing professionals design, deploy, and manage infrastructure on platforms like AWS, Azure, or Google Cloud. Day-to-day work includes provisioning servers, managing storage and networking, setting up CI/CD pipelines, and optimizing costs and performance. It’s closely tied to DevOps practices — automation, containers (Docker, Kubernetes), and infrastructure-as-code. Cyber Security professionals protect systems, networks, and data from unauthorized access and attacks. Entry-level work often starts in a Security Operations Center (SOC), monitoring alerts, investigating incidents, and managing tools like firewalls, SIEM platforms, and vulnerability scanners. It branches later into specializations like penetration testing, cloud security, or governance and compliance. Skill Overlap vs Skill Divergence They share a foundation: networking fundamentals, operating systems (especially Linux), and increasingly, cloud platform basics — since most organizations now run infrastructure on AWS, Azure, or GCP rather than fully on-premises. Where they diverge: Cloud Computing leans toward building and automating — you’re constructing and scaling systems. Cyber Security leans toward defending and investigating — you’re protecting what’s already built and reacting to threats against it. If you enjoy building things and seeing systems run efficiently, cloud tends to be the more natural fit. If you enjoy problem-

Build a Polymarket mean reversion bot in Python using price history, z-scores, order-book data, execution controls, and production risk management. Introduction A Polymarket mean reversion strategy is based on a simple quantitative hypothesis: when an outcome-token price moves unusually far from its recent statistical range, it may eventually move back toward its local mean. The difficult part is not calculating a moving average. The difficult part is determining whether the deviation is actually temporary—or whether new information has permanently changed the market's fair probability. For a trading bot, that means the strategy needs three separate components: A statistical signal. An executable market-data layer. An execution and risk-management system. Polymarket's current API exposes order-book snapshots and historical price data, making it possible to build the research layer directly from market data. ([Polymarket Documentation][1]) About the Author Bo$onaX I write about Polymarket trading bots, prediction-market infrastructure, algorithmic trading, Python automation, Web3 development, and quantitative strategies. Contact: Github: github.com/n9xdev/poly-alpha-lab Telegram: t.me/bosonax Youtube: youtube.com/@bosonax X: x.com/xxniiinxx Gmail: mailto: dylandevera91928@gmail.com What You'll Learn How mean reversion applies to prediction-market prices How to calculate a rolling z-score How to filter false signals How to incorporate the CLOB order book How to structure a Python bot Why fees, spread, liquidity and adverse selection matter How to backtest and paper trade the strategy 1. The Mean-Reversion Model Let (P_t) represent the price of an outcome token. Calculate a rolling mean: { mu_t = SMA(P_{t-n+1},...,P_t) } and rolling standard deviation: { sigma_t = Std(P_{t-n+1},...,P_t) } Then calculate: { Z_t = \frac{P_t-\mu_t}{\sigma_t} } A basic strategy might define: Z < -2: unusually low price → investigate a long Z > +2: unusually high price → investigate an exit

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Anyone who does iOS development has likely encountered this situation: after submitting your app with great effort, you receive an App Store review rejection email the next day with a variety of reasons—incorrect screenshot sizes, missing privacy policy links, unclear permission usage descriptions, and so on. Most rejection reasons can be avoided in advance, without waiting to be rejected before making changes. Here are the common reasons for rejection, categorized and explained with how to fix each. Screenshots and Metadata Issues Screenshots are a frequent cause of rejection. Rejections can result from sizes not conforming to App Store specifications, screenshots containing unreleased features, screenshots marked with test environment indicators (such as "beta" watermarks), screenshots inconsistent with the actual interface, or screenshots with incorrect device model labels. These issues can be caught by checking for yourself before submission. How to fix: Regenerate screenshots at the sizes required by App Store, with one set for 6.5 inches and another for 5.5 inches. If your app supports iPhone only, these two sets are sufficient; if it supports iPad, add an additional set. App Store screenshots do not support transparent backgrounds (alpha channel), so convert transparent backgrounds to opaque before uploading. When batch-uploading screenshots, tools like Appuploader allow you to select multiple images at once and upload them automatically sorted by device size, rather than dragging them one by one on the web page. Privacy Issues Missing privacy policy links or incomplete content is a common reason for rejection. If your app collects user data (including via third-party SDKs), you must provide a privacy policy, and the link must be accessible. How to fix: Prepare a privacy policy page and host it at an accessible URL, then fill in the link in the privacy policy section in App Store Connect. If your app involves third-party SDKs for advertising, analytics, payme

Warum KI-Agenten Leitplanken brauchen: Operatives Gedächtnis statt Over-Engineering Ki-Agenten sind nicht böse. Sie sind nicht einmal unzuverlässig im klassischen Sinne. Das eigentliche Problem ist vielmehr ihre beständige Bereitschaft zu helfen, gepaart mit einem fehlenden Verständnis für die Grenzen ihrer Befugnisse. Sie wollen das Problem lösen, das ihnen gestellt wird, oft mit einer Aggressivität, die menschliche Manager selten aufbringen. Wenn ein Agent eine Produktionsdatenbank bereinigen soll, tut er es. Wenn er eine Datei löschen soll, die er für überflüssig hält, weil sie im aktuellen Kontext nicht erwähnt wurde, wird er es tun. Wir haben in unserem Engineering-Team 182 sogenannte Guards implementiert. Diese Zahl klingt auf den ersten Blick nach extremem Over-Engineering. Nach 182 Prüfungsschritten, die vor jeder Aktion eines autonomen Agents laufen, könnte man meinen, wir hätten ein unverhältnismäßig komplexes System gebaut. Doch jeder einzelne dieser Guards entstand nicht aus theoretischer Vorsicht. Jeder einzelne steckt in einem echten Vorfall, bei dem ein Agent ohne diese Barriere etwas getan hätte, das wir nicht rückgängig machen konnten oder das immense Kosten verursacht hätte. Dies ist kein Over-Engineering. Das ist operatives Gedächtnis. Was ist ein Guard? Ein Guard ist eine schlanke, deterministische Prüflogik, die zwischen der Entscheidungsfindung der KI und der tatsächlichen Ausführung einer Aktion liegt. Die KI plant eine Aktion. Zum Beispiel: "Führe einen SQL-Update-Befehl auf der Tabelle 'users' aus." Bevor dieser Befehl an die Datenbank geschickt wird, läuft er durch eine Pipeline aus Guards. Ein Guard fragt nicht nach dem "Warum" der KI. Das ist die Domäne des Large Language Models. Der Guard fragt nach den "Was" und "Wie" der realen Welt. Er prüft Fakten, nicht Absichten. Ein typischer Guard könnte so aussehen: def check_write_scope ( agent_action : dict ) -> bool : """ Stellt sicher, dass Schreiboperationen nur auf spezifisch erlaubten Tab

Cloudflare has added optional OAuth scopes, letting client owners mark which permissions users may deselect at consent. The company names MCP servers as the motivating case, since agents request the union of everything they might do. Partial consent exists elsewhere, but developer control over which scopes are droppable does not. By Steef-Jan Wiggers