标签：#ics

And they looked absolutely ridiculous doing it.

How a tiny little electronic switch sparked the computer revolution.

Contractor rates are falling, contract durations are shrinking, and the freelance labor market is flooding with senior talent — and the problem isn't the market, it's that contractors keep letting companies define the terms. A senior backend engineer — eight years of production experience, solid Go and Kubernetes chops, three reference clients — recently told a recruiter she was looking for £650 a day. The recruiter called back two days later to say the client had found someone at £450. The counter-offer was presented as good news. That's the state of independent tech work right now. Not a crisis, not a correction — something more mundane and more insidious: a slow, structural re-anchoring of what contractor labor is worth, driven less by any single market force than by the compound effect of layoff volumes, budget caution, and platform-mediated price visibility. Rates are going down. Engagements are getting shorter. And the freelancers accepting this are — not entirely without blame — helping it stick. Here's the uncomfortable claim: the ongoing compression of tech contractor rates is as much a self-inflicted wound as a market inevitability. The conditions that caused it are real. But the capitulation that maintains it is a choice. How We Got Here: The Supply Side Exploded The overrecruitment of 2021 and 2022 didn't just hurt the permanent hiring market when the hangover hit. Software developer jobs saw the biggest boom and bust in vacancies of any sector. No other segment saw hiring more than double in 2022, and hiring has since fallen faster in software development than anywhere else. The engineers who got caught in that bust didn't all disappear. Many turned to contracting. More than 100,000 people were laid off in the technology industry in 2024 alone, and at least some of them are not heading back into exclusively full-time work. LinkedIn's Services Marketplace, launched in 2021 to catch exactly this cohort, saw 10 million people create pages on the platform,

Short version of a post on gex.live/research ; the full write-up, definitions and reproduce block live there. Most published dealer-gamma numbers are built from open interest : yesterday's outstanding contracts, multiplied by a convention about who holds which side. Whether the convention is right is a separate question. The prior question is simpler: how much of what trades today was already in that book this morning — and how much of tomorrow's book is being created today? Open interest and volume are enough to answer it, with no assumption about who bought. Sample: SPX and SPXW, 2022-04-14 to 2026-08-14, 1,081 trading days, every expiry within about a month (0DTE plus the 21 nearest), 8.6 million contract-days, 4.3 million with volume. Definitions Per contract (expiry, strike, right) and session D: OI(D) is open interest at the start of D, OI(D+1) at the start of the next session, ΔOI = OI(D+1) − OI(D) , vol the day's volume in that contract. |ΔOI| / vol is a lower bound on how one-sided the day's trading in that contract was — 1.0 means every lot opened (or every lot closed), 0 means opens and closes cancelled. Contracts expiring on D have no next-day OI and drop out of the ΔOI statistics; 4.1% of rows (3.8% of volume) show |ΔOI| > vol, which is impossible (OI snapshot timing) and are excluded. The book grows by 40% of what trades, every day days to expiry on D net ΔOI / volume |ΔOI| / volume (lower bound on one-sidedness) share of volume in contracts whose OI rose contract-days 1–5 37.8% 41.8% 90.3% 813,013 6–21 42.7% 53.3% 81.1% 2,206,446 22+ 42.8% 57.6% 76.5% 831,896 Across the whole book, net ΔOI is 39.9% of the day's volume on the median session (IQR 36.2–44.0%), positive in every year and every expiry bucket: the SPX book is always being built faster than it is unwound, until expiry does the unwinding. Far expiries are open-and-hold (a day's trading in a 22+ DTE contract is at least 58% one-sided); the nearest expiries churn (42% at 1–5 DTE). Per contract-

The US Department of Justice announced on Friday that TikTok will pay $400 million to settle a lawsuit filed in 2024 over allegedly violating the Children's Online Privacy Protection Act (COPPA). In the lawsuit, the DOJ alleged that TikTok collected data from children without notifying parents or obtaining consent and did not delete the accounts […]

Tina Peters was convicted of seven counts related to election interference in 2024. Now, she’s fielding an offer from a county that’s become a hotbed of voting-related conspiracy theories.

Sources tell WIRED that Elon Musk is expected to spend up to $200 million in the midterms. It could be a big boost for GOP Senate candidate Ken Paxton, who’s struggled to raise cash.

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.

Star, moving at 8% the speed of light, may let us measure the black hole's rotation.

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.