标签：#ics

How the joule thief circuit “steals” energy from seemingly depleted power cells.

One advertisement featured a pornographic video with a deepfake closely resembling a prominent US politician. Apple removed the app from the App Store after an inquiry from WIRED.

ABC is suing the Federal Communications Commission over claims the agency "waged a retaliatory campaign" against its networks over the content they broadcast. In a lawsuit filed in federal court on Tuesday, ABC and its parent company Disney accuse the FCC of "punishing ABC for its speech" by threatening its broadcast licenses. It's the latest […]

You typed it. Claude rewrote it. Then it watermarked its version and shipped it to your reader without telling either of you. Last week, Anthropic's text adulteration watermarking became the most-discussed AI topic on Hacker News. Daring Fireball called it a perversion of writing. The thread hit 762 points and 673 comments. The same week, Anthropic reported 65 billion dollars in annualized revenue, and a separate debate over Claude's war on open-source AI added 133 points to the conversation. I write technical articles on Medium, Dev.to, and LinkedIn. I use Claude as an editing assistant. When I read the watermarking coverage, I realized this is not a technical debate about whether watermarking works. It is a fight over who owns the words you write with AI help. Here is what is actually happening, why writers and developers are angry, and what it means for anyone who publishes online. What the watermark actually does Anthropic's documentation (watermarking and attribution) explains the feature this way: text adulteration watermarking adds invisible signals to Claude's output so that services can detect whether text was generated by AI. The watermark survives copy-paste, paraphrasing, and light editing. If a platform integrates Anthropic's detection tool, it can flag AI-generated content even after the text has been modified. The controversy is not that watermarking exists. The controversy is that Claude applies this watermark to content the user wrote themselves, if that content passes through Claude's interface. Here is the scenario from the angry commenters: You write an email in a text editor. You paste it into Claude and ask: Clean this up, make it shorter, fix the grammar. Claude rewrites your email, applies the watermark, and returns the text. You copy that text into your email client and hit send. The recipient's email system, if it uses Anthropic's detection, flags your email as AI-generated. You wrote the original words. You directed the rewrite. You approv

In August 2026, Congo's Council of Ministers approved a collaboration convention worth about $1.26 billion to rehabilitate the Dilolo–Sakania line: roughly a thousand kilometres of track running from the Angolan border, across the Congolese copper belt, to the Zambian border. It is good news. Copper and cobalt from Katanga have spent decades travelling thousands of kilometres by road to ports in South Africa, Tanzania and Mozambique. Trucking is slow, expensive, and exposed to every border queue between the mine and the ship. A working railway to the Atlantic cuts that journey from something like forty-five days to under ten. I want to sit with a smaller detail. That line is the Congolese leg of the Lobito Corridor, and the corridor's spine is the Benguela Railway. The Benguela was chartered in 1902, when the Portuguese government granted a ninety-nine-year concession to Sir Robert Williams, a Scottish mining magnate and an associate of Cecil Rhodes. Construction started in 1903. The line reached the Belgian Congo border in 1929. So the flagship infrastructure project of Africa's 2026 critical-minerals moment is a rehabilitation of a route designed in 1902 to move Katanga copper to a European-facing port. The route was correct then, for the people who commissioned it. The question worth asking is whether it is still the route we would draw today, and what it means that we are mostly repairing rather than redrawing. The test Here is a test you can run on any colonial-era African railway, using nothing but a map. Find the two endpoints. One of them will be a mine, an oil field, or a plantation belt. The other will be a port. Draw the line between them and you will notice it runs more or less perpendicular to the coast — inland to seaward — and that it does not stop anywhere particularly useful along the way except to pick up more of the same cargo. Then look for what is missing. Look for lines running parallel to the coast, connecting one colony to its neighbour. Look

A week cleaning 290 booking records taught me more about silent failure than any error message ever has Last week I cleaned a deliberately messy dataset; 290 booking records from Safari Connect, Nairobi bus platform, 21 columns, 23 catalogued data problems. Class exercise, but the data was built from real failure modes. The problems I'd been warned about took an afternoon. The ones that cost me were the five that ran perfectly, returned plausible output, and were wrong. Every one of these produced a result. None produced an error. 1. The date heuristic that silently dropped five bookings The dataset had three date formats in one column: 2024-09-15 , 15/09/2024 ,and 09-25-2024 . Two of those are ambiguous - 01-18-2024 is unmistakably MM-DD-YYYY because there's no month 18, but 04-10-2024 could be either. The supplied guide handled it like this: UPDATE bookings_staging SET departure_date = TO_DATE ( departure_date , 'MM-DD-YYYY' ):: TEXT WHERE departure_date LIKE '%-%' AND LENGTH ( departure_date ) = 10 AND SPLIT_PART ( departure_date , '-' , 2 ):: INTEGER > 12 ; Read that last condition. If the second component is too large to be a month,this must be month-first. Reasonable logic - and it only fires when the day happens to be 13 or higher. Five rows had days between 1 and 12. They never converted. Then the next step filtered on ISO format: INSERT INTO bookings SELECT ... FROM bookings_staging WHERE departure_date SIMILAR TO '[0-9]{4}-[0-9]{2}-[0-9]{2}' ; ...and dropped them. No error. No warning. Five completed bookings and KES 3,840 of revenue gone from every downstream total. The guide's expected row count was written as "~280+", which is loose enough to hide it. The fix is to match on shape, not to infer from values: WHERE departure_date ~ '^ \d {2}- \d {2}- \d {4}$' Anchored patterns are mutually exclusive, so you can classify every row before touching any of it: SELECT CASE WHEN departure_date ~ '^ \d {4}- \d {2}- \d {2}$' THEN 'ISO' WHEN departure_date ~ '^ \d

About 22 miles south of the former mining town of Patagonia, Arizona, down a winding, unpaved mountain road that passes cows grazing on open range, stands a cottonwood tree believed to be at least 200 years old. The "grandmother" tree, as some locals call it, is imperiled and already showing signs of stress, almost as […]

Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops. By Hemant Kumar Mahato, Łukasz Sieczkowski, Vijayasenthilkumar Kuppusamy

From threats to the safety of poll workers to voters who can’t distinguish between odds and results, prediction markets are already scrambling the political process.

Observatories have been built in mines, deep under the Antarctic ice sheet, and elsewhere in an effort to detect the fleeting particles.

It turns out that the economics of rocket reuse are pretty, pretty good.

One of the most common assumptions in engineering leadership is that a rising number of reported incidents signals declining system reliability. However, a recent article from Great Circle argues that the opposite is often true: an increase in incident counts may actually indicate that an organization's incident management culture is improving. By Craig Risi

The agency signed agreements to not enforce parts of three federal court orders against auto dealers accused of discrimination—and didn’t notify judges or at least one of its coplaintiffs.

Human-AI marriages are not currently recognized by US law. Some Republican state policymakers are drafting legislation to keep it that way.

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

The Trump administration is launching a new program that will allow private firms to perform cyberattacks against foreign criminals, as reported earlier by Bloomberg. The private firms would operate "under the control and oversight" of the federal government, giving them permission to surveil and disrupt criminal networks, according to a presidential memorandum published on Wednesday. […]

Surveillance tech company Flock is rolling out updates to address reports of cops across the country abusing its tools to stalk ex-romantic partners and others. CEO Garrett Langley is delivering a mea culpa, and in an interview with The Verge, says he's changed his mind on what responsibility Flock bears for how law enforcement uses […]

“It’s the strongest evidence yet that particles dominated by a glueball component can exist in nature.”

Open models may soon be added to an updated AI framework, sources tell WIRED, as the White House continues to grapple with how to regulate a technology it has tried not to regulate.

The Mission Robotic Vehicle is making the first attempt to attach a new thruster to an aging satellite.