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A new generation of Islamic terror supporters are using AI to spread ‘Slop Jihad’ to new audiences on TikTok.

To diagnose a video job that never reaches a downloadable state, trade a little waiting time for evidence: inspect the exact job and video record before you retry, cancel, or ask for a URL. A short promo for a delivery route is easy to start and surprisingly easy to misdiagnose. A download request is the last step, not a health check. Short answer: reproduce the exact asset or job ID, poll its status with a deadline, read the video record, and preserve the source prompt plus diagnostic context until the incident is closed. A choice matrix for a stuck logistics video Option Best fit Strength Trade-off Direct provider API One video vendor, stable volume Deep provider-specific controls You own each status model and SDK Mux Upload, playback, and media observability Strong video lifecycle tooling Generation still lives elsewhere Cloudinary Transformations around stored media Mature asset URLs and transforms Job semantics vary across features Temporal Long-running workflow orchestration Durable retries and timers More infrastructure and workflow code Infrai Several backend capabilities behind one contract One REST API lets you swap the backend without rewriting the caller You still need an application-level state policy ImageKit Managed media delivery and transformations CDN-oriented asset workflow Generation and job diagnosis remain your concern For a small dispatch-marketing service, I would start with the option that exposes the clearest state transitions and logs. Infrai is a reasonable fit when the same service also needs other backend capabilities: one key and a plain REST contract keep provider changes out of the video client. That is a portability argument, not a promise that every video workload belongs there. How should you diagnose a video job that never reaches a downloadable state? Start with identity. Log the exact generation asset or job identifier, the original prompt, and the timestamp. If a retry creates a second job before you have captured that context

Table of Contents Why Broad-Phase Exists (and why naive O(N²) dies at 10k objects) Bounding Volume Hierarchy: The Data Structure That Scales Topology Choices: Binary vs. Multi-Branch, Pointer vs. Array Layout Construction Algorithms: From Naive to SAH-Optimal Traversal Strategies for Collision Queries The Static/Dynamic Dichotomy: Why One Tree Cannot Serve Two Masters The Dual-BVH Architecture Preview 1. Why Broad-Phase Exists The Pairwise Problem Every collision detection system faces the same fundamental challenge: given N objects, determine which pairs might be colliding so the expensive narrow-phase (SAT, GJK, EPA) only runs on plausible candidates. The naive approach tests every pair: // Naive O(N²) broad-phase — dies at ~10k objects std :: vector < CollisionPair > broadPhaseNaive ( const std :: vector < Object *>& objects ) { std :: vector < CollisionPair > pairs ; for ( size_t i = 0 ; i < objects . size (); ++ i ) { for ( size_t j = i + 1 ; j < objects . size (); ++ j ) { if ( aabbOverlap ( objects [ i ] -> aabb , objects [ j ] -> aabb )) { pairs . emplace_back ( objects [ i ], objects [ j ]); } } } return pairs ; } Complexity: O ( N ² ) AABB tests. At 60 Hz you have 16.67 ms/frame. At 120 Hz: 8.33 ms. Objects (N) Pairwise Tests @ 3 ns/test Frame Budget (60 Hz) 100 4,950 0.015 ms Trivial 1,000 499,500 1.5 ms Comfortable 10,000 49,995,000 150 ms 10x over budget 100,000 ~5x10^9 15,000 ms Impossible Cache Miss Catastrophe The pairwise loop doesn't just do too much work, it does it poorly . Each iteration accesses two random objects in memory. With 10k objects, you're thrashing L3 cache every frame. The BVH approach exploits spatial coherence: nearby objects in space are nearby in the tree, turning random access into sequential scans. The Real Job: Proving Separation KEY INSIGHT: Broad-phase is a rejection machine. Broad-phase is not about finding collisions. It's about proving separation as cheaply as possible. Every AABB overlap test that returns false is a vic

Dark energy might be getting weaker. Scientists are wondering if interactions with dark matter in a “dark dimension” may be responsible.

Modern price monitoring systems need to do more than tell you that a price changed. A single abnormal listing, a scraped error, or a temporary outlier can make a traditional threshold-based detector fire an alert when nothing meaningful happened. In this project, I built a lightweight real-time price anomaly detector in Python that combines: A rolling median baseline Median Absolute Deviation (MAD) Robust Z-scores Short-term percentage returns Trend confirmation Alert cooldowns The goal is simple: detect meaningful price movements without overreacting to noisy observations. Note: This project monitors retail prices from Google Shopping results through SerpApi. It is a retail-price monitoring example, not a financial exchange-data feed. What we're building The pipeline looks like this: ┌──────────────────────┐ │ SerpApi / Shopping │ └──────────┬───────────┘ │ ▼ ┌──────────────────┐ │ Price Extraction │ │ + Validation │ └────────┬─────────┘ │ ▼ ┌────────────────────┐ │ Rolling Price │ │ History │ └────────┬───────────┘ │ ┌───────────┼───────────┐ ▼ ▼ ▼ Median MAD Return % │ │ │ └───────────┼───────────┘ ▼ Robust Z-score │ ▼ Trend Confirmation │ ▼ Signal Engine │ ▼ Alert Cooldown The implementation is intentionally small and interpretable. The complete engine is built around a single PriceAlertEngine class and a compact AnomalyResult data structure. Why not just use standard deviation? A common first implementation is: price > mean + 3 * standard_deviation The problem is that standard deviation is sensitive to extreme observations. Suppose your historical prices are: 990, 995, 999, 1001, 1005 Then one bad observation such as: 1500 can distort the mean and standard deviation. That can move your detection boundary away from the actual market behavior you are trying to model. For a noisy retail environment, a more robust baseline is useful. That's where median and Median Absolute Deviation come in. 1. Building a rolling median baseline Instead of storing an unlimited stre

Polls show that overwhelming majorities of Americans hate data centers. China makes a perfect scapegoat for tech leaders and their allies—the only problem is a lack of evidence.

Mapping SpaceMouse Controls to SO-101 Movements In the previous article, I connected the SpaceMouse to the PC and confirmed that all six types of input could be detected correctly. https://dev.to/takeofuture/trying-vla-part-5-setting-up-and-testing-a-spacemouse-eio Forward / Backward Left / Right Up / Down Pitch Roll Yaw During the SpaceMouse test, I confirmed the following input values. Forward horizontal = +Y Backward horizontal = -Y Left horizontal = -X Right horizontal = +X Up = +Z Down = -Z Forward tilt = +Pitch Backward tilt = -Pitch Left tilt = -Roll Right tilt = +Roll Left twist = -Yaw Right twist = +Yaw An important point here is that these values from the SpaceMouse are not sent directly to individual SO-101 motors . Conceptually, the flow from the SpaceMouse to the SO-101 looks like this: SpaceMouse ↓ x / y / z / roll / pitch / yaw ↓ SpaceMouse Teleoperator ↓ target_x / target_y / target_z target_wx / target_wy / target_wz ↓ Inverse Kinematics (IK) ↓ SO-101 Joint Positions ↓ SO-101 The SpaceMouse plugin treats the 6DoF input from the SpaceMouse as movement of the End Effector in Cartesian coordinates. The target movement is then converted into the required SO-101 joint angles using IK, or Inverse Kinematics . In other words, instead of directly specifying something like: "Move this motor by 5 degrees" we provide commands such as: "Move the End Effector slightly forward" "Move the End Effector slightly upward" "Rotate the End Effector slightly" The SpaceMouse provides these commands, and IK calculates how the individual joints need to move. Mapping Between SpaceMouse and LeRobot Coordinates There is one thing we need to be careful about here. The x and y values displayed by the SpaceMouse test do not directly become LeRobot's target_x and target_y . With the default SpaceMouse plugin configuration, the axes are mapped as follows: SpaceMouse y -> target_x SpaceMouse x -> target_y SpaceMouse z -> target_z SpaceMouse roll -> target_wx SpaceMouse pitch -> targ

Introduction When shopping onine, I usually find myself looking at two things before making a purchase: Product ratings and reviews since I cannot examine the product physically.Products with high ratings and reviews tend to make the product trustworthy.This motivated me to explore how these factors using a sample dataset from Jumia. I analyzed a dataset of products listed to investigate whether pricing, discounts, ratings and review counts directly influence one another. By transforming this raw e-commerce data into an interactive Excel dashboard this project uncovers how strategic pricing directly impacts customer engagement. Data Inspection Before data cleaning, I inspected all the data to find missing values, duplicates, inconsistent formats, and values that could affect the accuracy of the analysis. Data Cleaning Before beginning the analysis, I cleaned and standardized the dataset to ensure that the values were accurate, consistent, and suitable for analysis in Excel. The main cleaning steps involved correcting data formats, handling missing values, and identifying duplicate records. Correcting Data Formats I first reviewed each column and converted the values into appropriate data types. Prices - The price columns were initially stored as text because they included currency symbols (KSh), commas, and in some cases, price ranges such as KSh 1,620 - KSh 1,980 . I used Find and Replace (Ctrl + H) to remove the KSh text and other unnecessary characters then converted the values to numerical format. For products with price ranges, I calculated the average of the minimum and maximum prices and used this value for further analysis. I then recalculated the discount percentages based on the standardized prices. Ratings - Ratings were stored as text in formats such as 4 out of 5. I used Find and Replace (Ctrl + H) to remove out of 5 and converted the remaining values into numerical ratings.These were also formated as texts i.e 4 out of 5 . Review - All the reviews coun

JupyterGIS is a GIS-focused extension for Jupyter notebooks. The recent 0.16 release enhances collaborative features, real-time editing, and support for large-scale data processing, including remote sensing. It introduces better visualisation tools and extends compatibility to R users. Community feedback highlights practical concerns and a desire for improved portability. By Olimpiu Pop

The round is being raised just months after the robot data startup exited from stealth.

The PR masterminds at the White House just released a series of vaguely policy-themed "arcade" games, some of which are racist - and modeled on real games whose copyright holders may not be too happy to be associated with the MAGA agenda. The Tetris Company has already responded to "Build the Wall," which is obviously […]

Our dataset said the average loan was 2.3 million kroner. The number that actually mattered was 255,000. Both were correct. Only one of them was true. This is a writeup of three ways a dashboard can be arithmetically perfect and still lie, using real figures from an analysis of 1,000 Norwegian debt consolidation applications. If you build reporting for anyone, you have probably shipped at least one of these. 1. Summing a field that contains two different things A debt consolidation loan pays off your expensive credit card debt. It also, if you own property, rolls your existing mortgage into the same new loan. Same column in the database. Same loan_amount . Utterly different meaning. SELECT AVG ( loan_amount ) FROM applications ; -- 2,300,000 That query is right and the answer is useless. Of that 2.3 million, roughly 1.9 million is an existing mortgage being moved from one lender to another. The expensive debt, the part the customer actually has a problem with, averages 255,000 . So the headline figure overstates the thing you care about by a factor of nine. Nothing in the schema warns you. loan_amount is a number, AVG is a function, the result renders fine. The bug is that one column is holding two concepts and only a human who understands the domain will notice. -- what you actually wanted SELECT AVG ( unsecured_debt ) FROM applications ; -- 255,000 If a column can mean two things depending on another column, split it. Every time. 2. Reporting the mean when the distribution has a tail Income in this dataset runs from ordinary salaries up to about five million kroner. A handful of very high earners drag the mean upward: Mean income: ~635,000 Median income: 647,000 for homeowners, 550,000 for renters Look at what happens there. The mean sits between the two medians and describes neither group. Someone reading only the mean concludes the typical applicant earns 635,000. Nobody earns 635,000. It is an artefact. df . groupby ( ' housing ' )[ ' income ' ]. agg ([ ' mean

TikTok backed out of a congressional committee's "public roundtable" set for this month over concerns it would be asked about child safety practices, the chair of the committee said. After negotiating "in good faith" to bring TikTok's chief security officer before the House Select Committee on China to answer questions about Chinese government access to […]

Every backtest has to answer a boring question: when the strategy says "buy," what price does it actually get? Most backtesting frameworks answer this question badly by default, and the badness is almost always in the strategy's favor. Here are the four assumptions that do the most damage, roughly in order of how often they show up. Mid-price fills If your backtest fills orders at the midpoint of the bid-ask spread, you are assuming you trade for free. You don't. A market order pays at least half the spread to cross it; a marketable limit order pays something close to that too, once you're honest about how often it actually gets hit versus sitting unfilled while the market moves away. Mid-price fills are the single most common way a backtest manufactures edge that doesn't exist, because the effect compounds with trade frequency — a strategy that trades often looks great on mid-price fills and mediocre-to-negative once it pays the spread on every round trip. Zero slippage Slippage is the gap between the price your signal fired at and the price your order actually executed at, and it's not just a queuing artifact — it's partly information. If your strategy is buying because something changed, other participants are reacting to the same thing, and the price you wanted is often gone by the time your order reaches the book. A backtest with zero slippage is quietly assuming the market waits for you. Unlimited size at the touch Backtests routinely assume you can execute your full position size at the best bid or ask, no matter how large the order is relative to the visible size there. In practice, a large order walks the book, and the average fill price is worse than the touch price by an amount that depends on how thin the book is. This one is invisible until you try to size up, which is exactly when a strategy that looked fine in testing starts bleeding. Commissions omitted or averaged Commissions and fees are usually small per trade and therefore easy to skip or fold in

We're liveblogging Dyson's press conference at IFA 2026 because we love cleaning gadgets.

The DJI ROMO 2 lineup also includes LiDAR-enabled mopping arms and improved obstacle avoidance.

Roborock showed off several new robovacs, a robomower and a pool cleaner at IFA 2026.

An underground detector recorded a strange interaction pointing to a particle with some properties that signify dark matter. The detection is small but promising.

According to new documents obtained through a Freedom of Information Act request, an undergraduate working with DOGE requested that his work for HUD count towards his University of Chicago degree.

Common sense is of no help in studying reality at the atomic scale.