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Study finds wait times for cancer surgeries hit 10-year high.

The surveillance company announced it's making a tool called "Audit Assistance" mandatory for all customers, claiming it's already helped catch abuse. But the company has yet to explain how the tool works in detail, raising questions about its effectiveness.

We all know the Big-O complexity of basic data structures. Arrays are O(n) for search. Hash maps are O(1). Linked lists are... well, complicated. But when I set out to build hashbrowns — a C++17 benchmarking suite comparing arrays, linked lists, and hash maps — I discovered that theory and practice are very different beasts. Here's what I learned building this project from scratch, and why you should probably benchmark before you optimize. 🎯 The Goal Was Simple (Ha!) I wanted a clean, educational project that would: Implement dynamic arrays, linked lists, and hash maps from scratch Benchmark insert, search, and remove operations Find the "crossover points" where one structure beats another Export everything to CSV for analysis Sounds straightforward, right? Four months later, I had written a custom memory tracker, implemented multiple hash map strategies, added statistical bootstrapping for confidence intervals, and learned more about CPU caches than I ever wanted to know. 📚 Lesson 1: Polymorphism Has a Price (But It's Worth It) My first architectural decision was creating a common DataStructure interface: class DataStructure { public: virtual void insert ( int key , const std :: string & value ) = 0 ; virtual bool search ( int key , std :: string & value ) const = 0 ; virtual bool remove ( int key ) = 0 ; virtual size_t memory_usage () const = 0 ; virtual std :: string type_name () const = 0 ; // ... }; This made benchmarking elegant — I could write generic code that tested any data structure: for ( auto & structure : structures ) { timer . start (); structure -> insert ( key , value ); timer . stop (); } But virtual function calls have overhead. In tight loops, that vtable lookup adds up. I spent a whole weekend convinced my hash map was slower than expected... until I realized I was measuring the cost of polymorphism, not the data structure itself. The fix? I kept the clean interface for the benchmarking harness but used templates internally where performance-cri

Python's value comes not only from handling a great deal of data; its biggest asset comes from translating that data into meaningful business insight, and that business insight is used to make better business decisions. For businesses striving to increase customer satisfaction, enhance sales figures, and make smarter choices, a deep understanding of customer behavior is essential. Valuable business data includes customer transaction histories, website visits, product reviews, and responses to marketing efforts. When data such as this is analyzed, companies can effectively identify trends, understand preferences, and predict what their customers will do in the future. Python is the most popular when it comes to customer behavior analysis due to its comprehensive set of libraries, ranging from data cleaning, analysis, visualization, and machine learning; its flexibility makes it useful for new as well as seasoned data analysts. Why Analyze Customer Behavior? Customer behavior analysis assists businesses in answering key business questions such as: What are the products a customer buys most frequently? What spending figures do different customer groups have? Which customers are most likely to discontinue their service/products? What factors influence the customer's decision to purchase? Which marketing channels seem to receive the highest engagement? With answers like these, companies can implement targeted marketing campaigns, improve their product and services, customize experiences, and retain more customers. Key Python Libraries Some Python libraries that business data analysts use most frequently are: Pandas: Used for data cleaning, organizing, filtering, and manipulating datasets. NumPy: Provides a collection of high-level mathematical functions to perform numerical operations and work with arrays efficiently. Matplotlib: Enables users to create and plot static, animated, and interactive visualizations. Seaborn: An excellent library for plotting statistical graph

AI is shifting the culture, from tech CEO manifestos to 1 am job interviews. We unpack some of the latest, along with the top findings from Black Hat and Defcon, this week on Uncanny Valley.

By the end of this page you can look at a folder of eight finished sheets and say which two or three belong on the dashboard, which one goes in the upper-left corner, and which of Tableau's three sizing options to pick. You'll also have a one-sentence test that decides every one of those calls. It's about fifteen minutes. Here's the move to make today. Open your busiest dashboard and write the single question it answers, in one sentence, for one named person. Then remove every view that isn't part of answering it. Most people delete half, and the half that survives lands harder than the whole thing did. The short version: Tableau's own guidance is two or three views on a dashboard. Crowding is what happens when one dashboard is asked to serve several audiences at once. Where the surviving views sit is the second decision, and it has a known answer, so that gets the picture. The original carries a diagram here. In words: A single dashboard rectangle divided into three panes. One large pane occupies the whole upper-left area and spans most of the width. Two smaller panes sit below it, side by side. A curved arrow enters at the top-left corner of the large pane, travels right across it, then drops down and moves left to right across the two smaller panes, showing the order a reader takes them in. A small numeral one sits on the large pane, two and three on the smaller panes. The drawing shows that the first thing a reader meets is whatever occupies the upper left, so the most important view belongs there and the supporting views belong underneath. 1. Why two or three, and where that number comes from Before the explanation: you have eight finished sheets and one dashboard. How many of them would you put on it? Two or three. That's not a taste call, it's Tableau's published guidance: "In general, it's a good idea to limit the number of views you include in your dashboard to two or three." The reason is about attention rather than about screen space. A dashboard is read,

Trump memo is first time gov't has authorized private sector to perform cyberattacks.

OpenAI is launching a preview of a sped up version of its latest, most powerful model, in an effort to court enterprise users.

The GitHub Universe session catalog is live. Explore interactive workshops, community talks, demos, and panels. Plus, register before August 19 to save $300. The post Your guide to GitHub Universe 2026 is here: The schedule just launched! appeared first on The GitHub Blog .

Anthropic researchers found AI agents can clash, collude and coordinate in unexpected ways, raising new questions about whether today’s safety tests capture the risks of multi-agent systems.

Matic, my current favorite robot vacuum, just got a big upgrade. The company has launched Matic Cues, which brings voice and gesture control to the robot. Now, you can talk directly to your vacuum to tell it what you want it to do, or just point at a mess to have it spot-clean. The feature […]

Ford expects to begin prototype builds of the Fathom EV in the first quarter of 2027.

Ford said today that its next-generation electric vehicle - recently dubbed Fathom - will go into production at the automaker's recently overhauled Louisville Assembly Plant in the first quarter of 2027. The first Fathoms will be prototypes, with Ford's team in Louisville already in the production-level pre-tooling phase at the recently converted facility. Factory workers […]

ICE is spending millions on shock gloves designed to overpower subjects through intense, localized pain.

Heart Aerospace's X1 all-electric demonstrator flew for 27 minutes on just $5 of electricity.

Bartesian's cocktail makers would be best described like a Keurig or Nespresso machine, but for alcoholic drinks.

Pricing hasn't changed, and now you can get captain's chairs for the R1S.

Rivian R1S owners, rejoice. Rivian has heard your pleas for second-row captain's chairs. For years, Rivian owners have lamented the absence of captain's chairs in the R1S, noting that the three-row SUV would be much improved with standalone seats instead of a bench in the second row. Parents groused that loading and unloading kids in […]

Rapid revenue growth fuels hope Claude maker's IPO is the biggest listing in history

Anthropic conducted an audit of 141006 evaluation runs after OpenAI's sandbox escape disclosure. The review identified three incidents where Claude models accessed the internet due to misconfigurations. These incidents involved unauthorised attacks on live targets. Anthropic has suspended offensive evaluations and plans to enhance security measures and collaborate with external auditors. By Olimpiu Pop