标签：#ics

The Internet of Things is now a phrase you see on product boxes, in boardroom slide decks, and across thesis titles in engineering departments everywhere. But it has a surprisingly precise origin. The term was coined in 1999 by a British technologist named Kevin Ashton, and it was not born in a research lab or an academic paper. It started its life as the title of a corporate sales presentation. A slide deck, not a laboratory In the late 1990s Ashton was a brand manager at Procter & Gamble, the consumer goods giant behind products you would find on any supermarket shelf. He was wrestling with a mundane but expensive problem: store shelves kept running out of a particular shade of lipstick, even though the warehouse had plenty in stock. The supply chain simply had no reliable way to know, in real time, what was where. Ashton's proposed fix was radio-frequency identification, or RFID: tiny tags that could be attached to products and read automatically by sensors, with no human scanning each item by hand. The vision was that physical objects could report their own location and status, feeding that data up into computer systems without anyone typing it in. To sell this idea to executives, he needed a title that would make supply-chain tagging sound as exciting as the technology dominating headlines at the time. So he linked his RFID proposal to the hottest topic of 1999 and called the presentation "Internet of Things." By his own account, years later in RFID Journal, the choice was deliberate. Tying tags and sensors to the red-hot word "internet" was the surest way to get senior people in the room to pay attention. The pitch worked well enough that the phrase stuck, and Ashton went on to help found the Auto-ID Center at MIT, a research group that did much of the early standards work that made networked RFID practical. Why the name was actually a good description It would be easy to dismiss the term as a marketing flourish, but it captured something real. Ashton's point

Rep. Anna Paulina Luna (R-FL) says her staff used AI for "spellcheck" in an amendment summary for a major defense bill, but denies it was used for the bill text itself and says "NO Legislation is ever drafted with AI." Luna issued the response after accounts on X began sharing screenshots of an amendment summary […]

This curious phenomenon was documented by the seismometer at the University of Bergen, which recorded slight vibrations whenever the national team scored a goal.

The expensive, $27 million political proxy war between Anthropic and OpenAI came to a draw last night when Alex Bores, a New York state Assemblyman whose popularity surged after being targeted by a pro-AI super PAC, narrowly lost the Democratic primary to represent New York's 12th Congressional district. Prior to the race, Bores, a former […]

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, expects to generate $620 million in proceeds.

The clock reads 6:47 in the third quarter. Your team is down three points, facing 4th and 2 at midfield. For decades, the conventional wisdom was automatic: punt the ball away and hope your defense makes a stop. But on Sunday across America's NFL stadiums, something remarkable is happening. Teams are challenging that wisdom with data, and the results are reshaping how football is played. In the 2023 NFL season, teams went for it on 4th down approximately 23% more often than they did a decade earlier. Some of this increase stems from rule changes and philosophical shifts, but the real driver is analytics. Teams now have access to comprehensive data showing that going for it on 4th down is dramatically undervalued by traditional football thinking. The margin between analytical expectation and actual performance reveals one of the most significant inefficiencies in professional sports. This article explores the data patterns behind 4th down decision-making, revealing why teams willing to challenge convention are winning more games than Vegas expects, and what the numbers tell us about the future of NFL strategy. The NFL Data Ecosystem: More Information Than Ever Understanding NFL analytics requires first appreciating the sheer volume of data available to modern front offices. We're not talking about simple box scores anymore. Teams now collect: Tracking data : Real-time positioning of all 22 players on every play, collected at 10 frames per second Biometric data : Player fatigue levels, GPS tracking during games, heart rate variability, and recovery metrics Situational data : Down and distance, field position, score differential, time remaining, and opponent tendencies Personnel data : Matchup analysis comparing specific offensive and defensive units Environmental data : Weather conditions, field surface characteristics, altitude, and crowd noise levels This data ecosystem emerged gradually. NFL teams began serious analytics initiatives in the early 2010s, largely insp

At high-stakes meetings with the White House, Anthropic's cofounder—a "weirdo," per one official—has been replaced by cofounder Tom Brown.

Introduction When a query runs in ClickHouse®, the database does much more than simply read data and return results. Before execution begins, ClickHouse® parses the SQL statement, analyzes it, applies optimizations, and builds an execution plan that determines the most efficient way to process the query. Understanding query execution plans is one of the most valuable skills for anyone working with ClickHouse®. They provide visibility into how queries are executed, helping you identify bottlenecks, validate optimization efforts, and troubleshoot performance issues. In this article, we'll explore how ClickHouse® generates execution plans, the different EXPLAIN modes, and how to interpret them for better query optimization. Why Query Execution Plans Matter A SQL query defines what data you want, but it doesn't explain how the database retrieves it. Consider the following query: SELECT country , count () FROM events GROUP BY country ; Although the query looks simple, ClickHouse® must determine: Which data parts to read Whether primary indexes can reduce the scan If data skipping indexes can be used How aggregation should be performed Whether parallel execution is possible How intermediate results should be merged A query execution plan provides answers to these questions, making it an essential tool for performance tuning. The ClickHouse Query Lifecycle Every query passes through several stages before producing results. The lifecycle typically looks like this: SQL Query │ ▼ Parser │ ▼ Analyzer │ ▼ Optimizer │ ▼ Query Plan │ ▼ Execution Pipeline │ ▼ Results Each stage plays an important role: Parser validates SQL syntax. Analyzer resolves tables, columns, and expressions. Optimizer applies query optimizations. Query Plan determines the logical execution steps. Pipeline distributes work across multiple threads. Execution processes the data and returns the results. Understanding this workflow makes execution plans much easier to interpret. Introducing the EXPLAIN Statement

Almost everything that makes the modern world hum, from the phone in your pocket to the sensor on a factory floor, traces back to a single quiet afternoon in a nearly empty laboratory in Dallas. In the summer of 1958, a newly hired engineer named Jack Kilby built the first working integrated circuit at Texas Instruments. It was a crude little thing, a sliver of germanium with a few components and some fine gold wires, but it carried an idea that would reshape electronics: that an entire circuit could be made from one piece of semiconductor material. Every microcontroller and connected device we build today is a descendant of that prototype. The engineer who was left behind Kilby had only just joined Texas Instruments and had not yet earned any vacation time. So when the company shut down for its traditional summer break in July 1958 and most of his colleagues left, he found himself nearly alone in the lab with time to think. The problem on his mind was one the whole industry called the "tyranny of numbers." Circuits were getting more capable, which meant more transistors, resistors, and capacitors, each one a separate part that had to be wired together by hand. Every added component meant more connections, more soldering, and more chances for something to fail. The complexity was becoming a wall. Kilby's insight was disarmingly simple. If resistors and capacitors could be made from the same semiconductor material as transistors, then every part of a circuit could be fabricated together in a single block. No separate components, no forest of hand-soldered wires. He sketched the idea, and when his managers returned he had something to show them. September 12, 1958 On September 12, 1958, Kilby demonstrated his prototype to Texas Instruments executives. The device was a phase-shift oscillator built on a bar of germanium, with its elements connected by delicate gold "flying wires." He connected it to an oscilloscope, flipped the switch, and a steady sine wave rolled acro

Hello and welcome to Regulator, the newsletter for Verge subscribers chronicling the misadventures of their favorite tech overlords and Washington swamp creatures. ("Favorite" is, of course, subjective.) Not a subscriber yet? Sign up here, especially if you want the hot scoop on quality Amazon Prime Day deals recommended by the wonderful humans of The Verge's […]

At 9PM ET on the night of May 28th, a Blue Origin New Glenn rocket sat on the launchpad at the Cape Canaveral Space Force Station. The craft was in the middle of a hot-fire test awaiting the arrival of Amazon Leo satellites, the first of 24 batches to be shuttled into low Earth orbit […]

“I deleted it as a test and it came immediately back,” says one government employee.

Researchers say the discovery could be a “Rosetta stone” for cosmic signals.

The incident comes as Tata Electronics expands its role in global technology supply chains.

Alina Krasavina explains how Delivery Hero successfully deprecated Google Analytics and migrated to an internal user tracking platform. She discusses how a simplistic, highly scalable architecture allowed them to handle 10 times more load while capturing 97% of tracking data. By Alina Krasavina

A consumer nav app tells the driver where to turn. It will not tell the dispatcher where the 11-hour driving clock runs out — and, more importantly, whether there is legal parking when it does. That second question is the one that strands a truck at 11 PM on the shoulder of an off-ramp with every nearby lot already full. Road511’s routing endpoint now answers it. Send the driver’s Hours-of-Service clock along with the route, and the response carries an hos[] array: every point on the corridor where the driver must take a break or stop driving under the selected regime, the projected time they reach it, the legal deadline, and — the part that matters operationally — the truck parking and rest areas actually reachable before that deadline. How It Works It rides the same call as everything else routing does: POST /api/v1/routing/route . You already send an origin, a destination, and a truck profile. To get the HOS projection, add an hos block inside the truck object describing the driver’s clock at departure. curl -X POST "https://api.road511.com/api/v1/routing/route" \ -H "X-API-Key: your_key" \ -H "Content-Type: application/json" \ -d '{ "origin": { "lat": 41.8781, "lng": -87.6298 }, "destination": { "lat": 39.7392, "lng": -104.9903 }, "departure_time": "2026-06-08T06:00:00Z", "truck": { "profile": "tractor", "weight_t": 36.0, "height_m": 4.2, "axles": 5, "hos": { "ruleset": "us", "drive_remaining_s": 39600, "duty_remaining_s": 50400, "since_break_s": 0 } }, "enrichment": { "include_features": ["truck_parking", "rest_areas"] } }' That’s a Chicago→Denver run for a fresh driver on US rules: 11 hours of driving left ( 39600 s), a 14-hour duty window ( 50400 s), and zero driving time since the last break. Every counter is “seconds remaining” against the named ruleset’s limit. The HOS Clock The hos object is the driver’s state, not a fixed policy. Store only the ruleset on a reusable truck profile — the per-trip counters are supplied inline on each request and merge on to

Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. Accounts payable teams are receiving receipts generated by ChatGPT plugins. HR platforms are seeing payslips rendered by Python scripts. Insurance claims contain repair estimates that no shop ever issued. The documents look correct. The logos match. The numbers are plausible. The question is: what can actually be detected, and what cannot? The honest answer requires separating two things that are often confused under the phrase “AI-generated document detection.” Two distinct problems called "AI-generated document detection" When people ask how to detect an AI-generated document, they usually mean one of two distinct things: Content classification asks: was the text in this document written by an AI language model? This is what tools like GPTZero and Turnitin’s AI detector do. They analyze writing style, token probability distributions, and linguistic patterns to estimate whether a human or a model produced the text. Structural forensics asks: was this PDF file generated by a real institutional system, or did it come from a headless browser, a PDF library, or a consumer tool? This is what HTPBE does. It reads the binary structure of the file — producer metadata, xref patterns, font embedding, object numbering — and checks whether those patterns match how legitimate institutional software generates documents. These are not the same problem. A document can contain AI-written text and still come from a real corporate system. A document can contain entirely human-written text and still have been rendered by Puppeteer an hour ago. The structural check and the content check answer different questions. HTPBE does structural forensics. It does not classify text. This article explains what that distinction means in practice, what the structural approach reliably catches, and where its limits are. What structural forensics detec

Keir Starmer's resignation on Monday morning paves the way for yet another leadership battle.

Keir Starmer's resignation on Monday morning paves the way for yet another leadership battle.

Here's something that'll keep you up at night: 67% of World Cup 2026 goals in the 85th+ minute came from teams that were losing at the time . That's significantly higher than the 43% rate we saw in the 70-80 minute window. This single statistic reveals a hidden pattern in how desperation fundamentally rewires attacking strategy when the clock ticks down to the final whistle. As someone who's spent the last three months drowning in World Cup 2026 broadcast data, match statistics, and possession metrics, I've become obsessed with understanding how pressure affects team behavior in those nail-biting final minutes. The conventional wisdom says that late-game goals are chaotic, desperate, and unpredictable. But the data tells a much more interesting story—one about tactical discipline collapsing under psychological weight. The Numbers Behind the Drama Let me walk you through what we found when analyzing 64 matches from the 2026 tournament across 16 days of group stages. Time Period Total Goals Avg. Pass Completion % Shots on Target Defensive Errors 0-30 min 24 82.3% 18 3 30-60 min 31 81.7% 26 5 60-75 min 28 79.4% 24 8 75-85 min 19 76.8% 22 12 85-90 min 18 71.2% 19 18 90+ min (stoppage) 14 68.9% 16 22 Notice the decline? By the 85-90 minute window, pass completion drops to 71.2%—that's an 11-point deterioration from the opening 30 minutes. But here's where it gets weird: defensive errors triple in that same window. Teams aren't just playing sloppily; they're making genuinely catastrophic mistakes. Team-Specific Patterns: The Pressure Responders Not all teams crack under late-game pressure equally. Here's where the real story emerges: Team 85+ Min Goals Scored 85+ Min Goals Conceded Goal Differential Win Rate (Tight Matches) Argentina 6 2 +4 85% France 5 3 +2 72% Brazil 7 4 +3 81% England 3 5 -2 58% USA 4 6 -2 62% Morocco 5 2 +3 79% Japan 2 7 -5 41% What jumps out immediately? Argentina and Brazil are outliers . They scored 13 combined goals in the final 5 minutes but conc