标签：#AR

If you have ever tried to win a domain at auction, you probably assumed the game works like eBay: watch the clock, wait for the last eight seconds, fire your bid, walk away with the name. On Namecheap, that does not work. Not "works badly". Does not work. Namecheap's expiring and marketplace auctions close in a daily batch at 11:00 AM ET. Every auction ending that day ends at roughly the same moment, which means there is no quiet corner of the day where you and one other bidder are paying attention. And if a bid lands in the closing window, the auction extends. So the buzzer-beater you were planning gets absorbed and the clock keeps running. The winner is not the fastest click. The winner is whoever set the smartest proxy maximum, on a name they found before anyone else was looking at it. I have been building PounceDomains around that one fact for months, and it is the reason the product looks the way it does. The edge moved from timing to discovery If speed is not the lever, the levers left are: find the good names earlier, and know what they are actually worth before you commit a number. So the engine scans the Namecheap aftermarket around the clock rather than at the bell. You describe the domains you want in plain English, something like "pronounceable 5-letter .com brandables under $50, no numbers or hyphens", and it builds a tuned config you can edit. If your config is too broad, it tells you and tightens it. There are seven scoring lenses you can stack: pronounceable, brandable, exact-match keyword, short premium, dictionary word, two-word combo, and free-text custom criteria. Fast programmatic filters run first, then AI scores what survives, and only domains that clear your threshold become matches. It has graded over 340,000 domains so far. The second lever is the one I care about more. Every match arrives with its receipts The failure mode in domain investing is not missing a name. It is paying $400 for something worth $80 because a free appraisal tool pri

GrapheneOS 2027 Lands on Flagship Phones: Real‑World Privacy for Premium Android Users Introduction When GrapheneOS announced official support for Motorola, OnePlus and Sony’s top‑tier phones in early 2027, the tech community stopped scrolling. Within days the phrase “GrapheneOS Motorola” spiked 250 % on Google Trends and sparked a firestorm on Hacker News. Why the hype? Because for the first time a hardened, auditable Android fork is available on devices that don’t compromise on performance, camera quality, or design. In this guide you’ll get a hands‑on look at what GrapheneOS 2027 actually does, how to install it, and which commands and configuration tweaks let you turn a flagship phone into a privacy‑first workstation. Quick‑Start Checklist ✅ Item 1 Verify device compatibility (locked bootloader, Snapdragon 8 Gen 3, TEE) 2 Backup current ROM (e.g., adb backup -apk -shared -all -f backup.ab ) 3 Unlock bootloader ( fastboot oem unlock ) – note this wipes data 4 Flash GrapheneOS boot and system images (see “Flashing the ROM”) 5 Enable verified boot ( fastboot flashing lock ) 6 Install the optional Play Store Compatibility Layer (PSCL) if needed Supported Premium Devices (2027) Manufacturer Model Key Security HW Motorola Edge 30 Ultra Snapdragon 8 Gen 3, TEE, Secure Enclave OnePlus 12 Pro Snapdragon 8 Gen 3, TEE, Secure Enclave Sony Xperia 1 V Snapdragon 8 Gen 3, TEE, Secure Enclave Google Pixel 9 (reference) Snapdragon 8 Gen 3, Titan M2 All listed phones meet GrapheneOS’s Hardware Security Module (HSM) requirements: locked bootloader, hardware‑backed keystore, and a modern Trusted Execution Environment. How GrapheneOS Differs from Stock Android Feature Stock Android GrapheneOS 2027 Google Play Services Core system component, heavy telemetry Replaced by a sandboxed Play Store Compatibility Layer (PSCL) Kernel Standard Linux kernel with optional vendor patches Memory‑safe, mitigates Spectre/Meltdown, SELinux Enforcing by default App Sandbox Permissions granted per‑app

I tested the top-of-the-line Ecovacs Winbot W2S Omni window-cleaning robot on my Victorian home, and it was an unmitigated disaster.

Sam Newman discusses the concept of progressive collapse in civil engineering and how it applies to distributed systems. Using real-world examples - from the 1968 Ronan Point tower failure to AWS outages - he shares crucial resilience engineering strategies for software leaders. Learn how to strengthen components, isolate failures, and reduce interconnections to prevent catastrophic cascades. By Sam Newman

The replica watch industry is in its “super clone” era. Following tips from murky internet forums, we bought three budget fakes that were good enough to pass as real—but ultimately disposable.

The car world is filled with designers still obsessed with shoehorning sex into their rides. Slate went a different route, and it’s why people are smitten with the charming electric pickup.

The Dell XPS 13 and MacBook Neo are both premium-feeling laptops with only 8 GB of RAM. Which $700 laptop is the better buy?

📈 Building the Future of Trading: An AI-Powered Platform for Smarter Investments The Introduction: Empowering Every Investor Hello, Builders and tech enthusiasts! I'm thrilled to share my journey as part of the "Meet The Builders" campaign, where innovators are leveraging Google AI to tackle real-world challenges. My project is an ambitious endeavor to democratize effective stock trading through an intuitive, AI-enabled platform. Inspired by industry leaders like Zerodha, I set out to create a comprehensive website that not only facilitates trading but also acts as a smart, AI-powered guide, helping users navigate the often-complex world of stock markets more effectively. This project is my story, a testament to how technology, especially AI, can empower individuals to make more informed investment decisions. The Deep Dive: Why Investors Need a Guiding Hand The stock market can be a daunting place. For many retail investors, it's a whirlwind of data, conflicting advice, and emotional decision-making that can lead to missed opportunities or significant losses. From understanding market trends and analyzing complex financial reports to knowing when to buy or sell, the sheer volume of information can be overwhelming. Many feel like they're trading blind, lacking the expertise and analytical tools available to professional institutions. I believe there's a significant gap here – a need for a personal, intelligent assistant that can cut through the noise, provide actionable insights, and guide users towards more strategic trading choices. This conviction fueled the inception of my project. The Solution: Stock Trading Platform – Intelligent Trading, Engineered for Success My project, Stock Trading Platform, is a robust web-based platform designed to simplify stock trading with the power of artificial intelligence. While currently in its final polishing stages on my local machine and version-controlled with Git and hosted on GitHub, the core functionality revolves around a

Designing Reliable APIs for Production Applications: Lessons From Building Real-World Digital Products APIs are often described as the “bridge” between different parts of an application, but building a production-ready API involves much more than sending data from a frontend to a backend. Through my experience building full-stack applications, I've learned that a good API needs to be designed around reliability, security, maintainability and the actual needs of its users. Here are some of the principles I now consider when designing APIs: Design around resources, not screens An API shouldn't simply mirror the frontend interface. It should expose meaningful resources and operations that can evolve independently from the UI. Validate everything at the API boundary Data coming from a client should never be trusted automatically. Request validation, type checking and clear error responses help prevent invalid data from propagating through the system. Authentication is only the beginning An authenticated user should not automatically have access to every resource. APIs need appropriate authorisation and access-control rules for sensitive operations. Design predictable errors A useful API doesn't just return “something went wrong.” Clients need consistent status codes and structured error responses so that applications can respond appropriately. Think about idempotency This becomes particularly important when an API handles operations such as payments, orders or other actions that shouldn't accidentally happen twice because of a network retry. Don't expose unnecessary data APIs should return what the client needs rather than exposing entire database records. This reduces unnecessary data transfer and can also reduce the risk of accidentally exposing sensitive information. Logging and observability matter An API can appear perfect during development and still fail in production. Good logging and monitoring make it possible to understand what happened when requests fail, la

Most speech-to-text is benchmarked on audio that looks nothing like a WhatsApp voice note. The standard evaluation sets are read speech, broadcast news, or recorded interviews: single speaker, decent microphone, one language, quiet room, speaker aware they are being recorded. A WhatsApp voice note is close to the opposite on every axis. I have spent a while building around this, and the gap turned out to be wider than I expected. Acoustics Phone held at arm's length while walking, in a car, in a kitchen, on a street. Distance-to-mic varies wildly within a single recording , which breaks a lot of assumptions about consistent gain. Then there is the codec. Voice notes are Opus at low bitrate — efficient, but it discards exactly the high-frequency detail that helps disambiguate fricatives. /s/ versus /f/ versus /th/ get genuinely harder, and those distinctions carry real meaning. Register Conversational, not read. False starts, self-corrections, filler, trailing off mid-sentence, and long pauses that are not sentence boundaries — someone thinking, or getting distracted. Punctuation inference is much harder here than on read speech. And punctuation is most of what makes a transcript skimmable rather than a wall of text. A perfectly accurate word sequence with no paragraph breaks is close to useless if the point was to let someone read it faster than listening. Language This is the one that surprised me most. Voice notes are heavily code-switched. People drop English technical terms into Urdu, Hindi, Arabic, Spanish sentences constantly — not as an edge case, as the default register for a huge number of speakers. If you force a single language selection up front, you mangle every mixed utterance. Auto-detection is not a convenience feature in this domain. It is a correctness requirement. Length distribution Most notes are 5–45 seconds. Very little context to work with, and per-request overhead dominates if you architected for long files. Batching strategies that make sen

Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.

Amazon Web Services has extended Amazon Bedrock AgentCore with runtime instances, a new compute option that gives AI agents persistent infrastructure purpose-built for complex long-running workflows and multi-agent coordination. By Matt Saunders

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

The 65% mechanical keyboard has become a popular format for people who want a compact keyboard without giving up the dedicated arrow keys. Compared with a 60% keyboard, a typical 65% layout adds an arrow-key cluster and usually includes a small navigation area. Compared with a TKL keyboard, it removes the dedicated function row and reduces the overall footprint. For keyboard designers, however, reducing the physical size of the keyboard does not simply mean removing a few keys. The PCB has to accommodate the switch matrix, diodes, controller, USB or wireless circuitry, RGB lighting, mounting features, and sometimes hot-swap sockets within a relatively constrained outline. That makes the PCB one of the most important parts of a 65% keyboard design. What Is a 65% Mechanical Keyboard PCB? A 65% mechanical keyboard PCB is the circuit board designed specifically for a 65% keyboard layout. The exact key count and physical arrangement can vary between designs, so the term "65%" describes a form factor rather than one universal PCB specification. A typical board may contain: Mechanical switch footprints A switch matrix One diode per switch position A microcontroller USB connectivity or wireless circuitry Reset and boot controls Indicator LEDs Per-key RGB or underglow lighting Hot-swap sockets, when supported Mounting holes and mechanical cutouts The electrical design and physical design have to work together. A PCB can have a perfectly functional schematic and still fail to fit the intended keyboard case if the mounting holes, switch positions, USB opening, stabilizer locations, or board outline are not correct. Why the PCB Layout Matters So Much Keyboard PCBs are unusual compared with many conventional electronics boards because the PCB also defines part of the physical typing experience. The location of switch footprints determines the key positions. The mounting system affects how the PCB interacts with the case. Flex cuts can change the mechanical response of different

Why plain k-fold silently overfits your trading model — and the 4-line fix that stops it. The Problem With k-Fold in Time Series Financial data is sequential. k-fold shuffles rows, so a training row from 2 PM Tuesday sits next to a test row from 10 AM Monday. Worse: triple-barrier labels overlap . A label at bar t looks 6 bars into the future; a training row at t+2 "knows" part of that future. The model leaks. V1's history is full of "HIGH overfit" verdicts — train AUC high, test AUC flat. Plain TimeSeriesSplit is only marginally better; it still lets adjacent windows bleed into each other. Purged + Embargoed CV For each test window [t0, t1] : Purge any train row whose label window overlaps the test window. Embargo max_training_horizon bars after the test window — drop those too. Overlapping labels are not i.i.d. Purging + embargoing makes the split honest. def purged_embargo_split ( n , n_splits = 5 , embargo_frac = 0.02 ): idx = np . arange ( n ) fold = np . array_split ( idx , n_splits ) splits = [] for i in range ( n_splits ): test = fold [ i ] emb = int ( len ( test ) * embargo_frac ) lo , hi = max ( 0 , test [ 0 ] - emb ), min ( n , test [ - 1 ] + emb + 1 ) train_mask = np . ones ( n , bool ); train_mask [ lo : hi ] = False splits . append (( idx [ train_mask ], test )) return splits Tune Only When You Have Enough Optuna once "won" a validation set with only 4 decisive rows — statistically meaningless. Rule: never tune when the decisive (non-abstained) validation rows are below ~30–50. Widen the date range or symbol basket first; don't trust the trial. Three-Way Split, Always train (fit) → validation (early stop + HP select) → disjoint calibration set (sigmoid/ isotonic) → test (untouched, final score only). V1 sometimes conflated validation and calibration. Keep them separate. The Promotion Gate Log every trial's train/val/test gap, not just the winner's test score. Promote only if replay AND shadow (≥1 live session) both beat baseline on buyer metrics : 1.5x

The Quest Begins (The "Why") I still remember the first time our API started choking under a sudden traffic spike. It was a Friday afternoon, the kind where you’re just about to log off, and the monitoring dashboard lit up like a Christmas tree. Requests were piling up, latency shot through the roof, and our users began seeing those dreaded “429 Too Many Requests” errors. We had a naive rate limiter in place—a simple fixed‑window counter that reset every minute. It worked fine when traffic was steady, but as soon as a burst hit, the counter would either let too many through (because we hadn’t hit the limit yet) or block everything for the whole minute (because we’d already exhausted the quota). It felt like trying to hold back a tsunami with a sandbag. Honestly, I was frustrated. I knew there had to be a smarter way to smooth out those bursts without penalizing honest users or over‑protecting the system. That’s when I dove into the world of rate‑limiting algorithms, and the token bucket caught my eye like a shiny loot drop in a dungeon. The Revelation (The Insight) The token bucket is deceptively simple, yet it solves the exact pain points we were experiencing. Imagine a bucket that holds a fixed number of tokens. Tokens drip into the bucket at a steady rate (say, 10 tokens per second). Each incoming request consumes a token. If the bucket is empty, the request is denied or delayed; if there’s a token, the request proceeds and the token is removed. Why does this beat the fixed‑window counter? Burst tolerance – The bucket can store up to its capacity, allowing a short burst of requests up to that limit without waiting for the next window. Smooth throttling – Because tokens are added continuously, the limiter adapts to the actual request rate rather than resetting abruptly at arbitrary intervals. Memory‑light – We only need to track two numbers: the current token count and the last time we refilled the bucket. No arrays of timestamps per key. Here’s a quick ASCII sket

Real integration notes for wiring NIFTY ML models to live broker data via Dhan. Research/ paper-trading context — not a live-trading recommendation. Why Dhan Dhan's API exposes direct option-chain access — exactly what an options-ML system needs: POST /optionchain — full chain for an underlying POST /optionchain/expirylist — available expiries Fields: security_id , last_price , volume , oi , previous_oi , implied_volatility , top_bid_price , top_ask_price , and greeks (delta/theta/gamma/vega) Security IDs are stable: NIFTY = 13 (IDX_I) , BANKNIFTY = 10001 (IDX_I) . The Pipeline Shape A research dashboard pulls live chain + underlying, runs the trained XGBoost model on each new 15-minute bar, and displays: side score (CE/PE alignment) gate state (entry ready / blocked) contract quality scores a doctrine/backtest report Keep the inference path separate from the execution path . The dashboard shows; a permissioned, human-approved module places orders. Paper Trade First The DhanLiveTrader pattern: load the model, predict on each new bar, place long orders with configurable SL/TP (default 1.0 ATR SL, 2.0 ATR TP), and run in paper mode first . Only after stable out-of-sample + paper evidence should any execution module even be considered. { "client_id" : "YOUR_DHAN_CLIENT_ID" , "access_token" : "YOUR_DHAN_ACCESS_TOKEN" , "is_paper_trade" : true , "nifty_symbol" : "NIFTY" , "quantity" : 50 , "max_trades_per_day" : 3 , "sl_atr_mult" : 1.0 , "tp_atr_mult" : 2.0 } The Hard Part: Stops A known footgun: using a Stop-Loss Limit (SL-L) order with price = sl − 0.05 means it won't fill if price crashes through the stop. Prefer SL-Market for the protective stop. Execution quality is its own research topic — don't bolt it on at the end. Honest Status The ML side of this stack showed real directional skill (60.5% top-decile accuracy) but the fixed-SL backtest was still unprofitable (PF 0.53). A dashboard that displays an honest "RESEARCH / PAPER" status is worth more than one that hid

Lessons from a real rebuild of an options-buyer prediction system. No profit claims — just the architecture that fixes the chronic bugs of V1. The Core Mistake in V1 V1 asked one XGBoost model one big fuzzy question: "CE ya PE?" — directly from raw CE/PE premium data. Premium is a transformed signal (underlying move × delta × gamma × IV × theta × spread × strike distance × liquidity). The model learned noise as much as signal. Concrete evidence from the research logs: Balanced accuracy stuck at 51–61% for months — hyperparameters were never tuned ( lr=0.02, depth=3 defaults used throughout; Optuna existed but was never run). A partition bug ( iv_change_1d shift inside single-row groups) silently zeroed a whole feature for the entire history. A rollup config flag compressed 15-minute bars into 1 row/day, destroying 760× of training volume (387 sequences instead of 295K+). Live paper trading: 31.6% win rate, −₹90.3k PnL , entry confidences only 55–64%. V2 Principle: Split the Question underlying mechanics --> side, range, ETA, invalidation option chain scanner --> is the buyer contract worth paying for? XGBoost (many heads) --> thin calibrated learner on clean mechanics Rule: underlying decides side; option contract decides execution eligibility. CE/PE premium is validated against, never learned as, direction. Many Shallow Heads, Not One Deep Model Instead of one CE/PE answer, V2 trains separate narrow heads: underlying_up/down_touch_{15,30,60}m ce_1p3x / ce_1p5x / ce_2p0x and pe_1p3x / pe_1p5x / pe_2p0x (SEPARATE CE and PE) no_trade_quality This single change removes most of the CE/PE confusion V1 fought for months. The Shallow Regularized Grid (the actual fix for overfit) learning_rate = 0.015 – 0.035 n_estimators = 800 – 2000 ( early stop ) max_depth = 2 – 3 min_child_weight = 12 – 40 gamma = 0.1 – 2.0 subsample = 0.65 – 0.90 colsample_bytree = 0.55 – 0.85 reg_alpha = 0.5 – 3.0 reg_lambda = 6.0 – 20.0 scale_pos_weight = min ( neg / pos , 8.0 ) V1's intraday head ha

Preparing for a Python developer interview often starts with coding problems. You practice arrays, strings, dictionaries, functions, and algorithms. Then you solve a few more problems and feel like you're ready. But an actual Python developer interview can test much more than whether you can write working code. You may need to explain your decisions, debug an unfamiliar piece of code, discuss Python concepts, or describe how you would approach a real development problem. Here are the areas I'd focus on before an interview. 1.Don't Just Solve Python Problems—Explain Them It's possible to solve a coding problem correctly and still struggle in an interview. Interviewers often want to know: Why did you choose this approach? What is the time complexity? What happens with edge cases? Is there another way to solve it ? How would you improve the solution? Try explaining your solution aloud after solving it. If you can't explain why your code works, you probably don't understand the solution as well as you think. 2. Know Python Beyond the Basics Don't stop at syntax. Review concepts such as: Lists, tuples, sets, and dictionaries Mutable vs immutable objects *args and **kwargs Exception handling Iterators and generators Decorators List comprehensions Context managers Object-oriented programming Memory management You don't need to memorize every Python feature. Focus on understanding concepts well enough to explain when and why you'd use them. 3. Practice Debugging Real developers don't spend all day writing code from scratch. A large part of the job involves understanding existing code and fixing problems. Take a small Python program with a bug and practice: Reproducing the problem. Reading the error carefully. Finding the likely cause. Testing your assumption. Fixing the issue. Explaining why it happened. This is also useful interview practice because debugging reveals how you think when the answer isn't immediately obvious. 4.Be Ready for Real-World Questions Depending on t

Hoi hoi! I’m @nyaomaru, a frontend engineer who dislikes crowded places, so I'm planning to take a...