标签：#Science

If you clip short-form video for money, you know Whop Content Rewards: hundreds of live campaigns paying $0.15–$20 per 1,000 views. The discover page lets you sort by budget. That sort is quietly costing you nights of work. Here's the number that changed how I pick campaigns: on the live board right now, 21% of active campaigns have never paid out a single cent. Big banner budget, $0 actually spent. A "$30,000 budget" campaign that has paid nobody in three weeks is not a $30,000 opportunity — it's a landing page. The problem: the board doesn't show you payout speed. You can see budget and budget left , but not how fast the money is actually moving — and that's the only number that separates a campaign that pays from a campaign that poses. The trick: the page already contains everything you need Every campaign card on Whop publishes three things: when it was funded, how much has been spent, and how many creators joined. From one snapshot — no monitoring, no state between runs — you can derive: dailyBurnUsd = budgetSpent / daysSinceFunded → is money moving? estimatedDaysLeft = budgetLeft / dailyBurnUsd → will it still be there? payoutPerCreatorUsd = budgetSpent / creators → what did the average clipper earn? budgetPace = "draining" | "healthy" | "slow" | "stalled" That last field is the shortcut. On today's board of 456 campaigns: pace meaning what to do draining <3 days of budget left skip — gone before your clip gains traction healthy 3–60 days this is where you clip slow 60–180 days fine, but budget may outlive the campaign stalled >180 days at current burn the "big budget" mirage — money posted, almost nobody paid null zero paid out so far unproven; could be brand new, could be dead Real example from today: two campaigns, both showing ~$30K budget. One burns $255/day and has paid the average creator $75 . The other burns $19/day — at that rate its budget lasts four years , which is a polite way of saying nobody is getting paid. On the default board they look ident

“That product is off the market, so I think there’s absolutely lower risk for cyclospora in terms of eating lettuce,” one professor of food safety tells WIRED.

The Perseids will peak between August 12 and 13. Here’s what time—and where—you can get the best view, and why 2026 will be an exceptional year.

Hot hours of summer could triple in the coming decades.

New research finds that by making the fossil fuel industry more productive, AI could help increase carbon emissions by up to nearly 5 percent—vastly outpacing the impact of data centers.

Startups and Big Ag are using Crispr gene editing to create crops that taste better and grow on a hotter planet. But will they find a market?

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

Imagine working as a Data Analyst in a healthcare Non-Governmental Organization (NGO) implementing HIV and AIDS programmes across several communities. The organization has limited resources. There may not be enough funding, healthcare workers, testing kits, transport, outreach teams, or community programmes to serve every community at the same intensity. This creates an important question: How can we use data and machine learning to direct limited programme resources to communities with the greatest need? This is where Machine Learning (ML) can become valuable. Rather than distributing resources equally across all communities, an NGO can use historical programme data to identify communities experiencing greater HIV-related service gaps or higher levels of need. Resources can then be prioritized based on evidence. What Is Machine Learning? Machine Learning is a branch of Artificial Intelligence that enables computers to learn patterns from data and use those patterns to make predictions or support decisions. Instead of manually creating rules for every situation, you provide the algorithm with historical data and allow it to identify relationships within that data. For example, the NGO could have this information about different communities: Community HIV Testing Coverage ART Coverage Missed Appointments Outreach Activities Community A 85% 90% 5% High Community B 52% 61% 25% Low Community C 70% 75% 15% Medium Community D 40% 55% 32% Low Looking at this data, Community D appears to have greater programme gaps than Community A. However, in a real programme, the decision should not be based on one indicator alone. Machine learning can analyse many variables simultaneously to identify communities that may require greater attention. Why Resource Allocation Matters in HIV Programmes HIV programmes operate in environments where resources are often limited. An NGO may have: A limited number of community health workers A fixed outreach budget Limited HIV testing supplies Limi

Recent findings provide one of the most detailed pictures to date of the genetic architecture of schizophrenia, opening up new avenues for research into the disorder.

Context: I'm a data scientist/analyst (in Python and R) learning development from scratch. Inevitably, I am learning these through the lens of what I already know. If you have a similar background and are a beginner developer, I hope these analogies help! Any comments, especially if you spot any misunderstanding, are appreciated. Commenting is caring <3 Motivation: I was building a mock data layer for a fitness social app — simulating what happens when users fetch new posts from a feed. The function needs to return mock posts after a delay, simulating a real network request. Working Code: `function fakeFetchPosts() { return new Promise((resolve) => { setTimeout(() => { resolve(posts); }, 2000); }); } async function main() { console.log("Fetching..."); const fetchedPosts = await fakeFetchPosts(); console.log("Fetched posts:", fetchedPosts); } main(); console.log("Sync code ran");` What do you expect to see as an output? I first confused the logic with blocking. For example, in webscraping, something like time.sleep() or Selenium's WebDriverWait(driver, 10).until(EC.presence_of_element_located(...)) . In this case, output will be Fetching..., Fetched posts: ..., then Sync code ran. However, the output gives Fetching..., Sync code ran, and then Fetched posts. In the former, the whole script (single thread) pauses and does nothing else until the wait ends or the condition is met. The latter is different in that the rest of your program keeps running during the wait, and thus the output where Sync code ran is printed first before the fetchedPosts. By the way, posts are arrays. const posts = [{ author: "j1wonkim", text: "Testing Physical", likes: 100, }, {author: "onewc0218", text: "Love love", likes: 55, }, {author: "gakbca", text: "You are good", likes: 10, } ];

Three semantic layers, three architectures, three very different bills. All three will define what a metric means. None of them proves an AI agent is allowed to run it. Quick orientation dbt Semantic Layer Cube AtScale Core idea Metrics as version-controlled code Headless API in front of metrics OLAP-style aggregate acceleration Strongest when You want engineering discipline Many apps consume the same numbers Heavy, stable aggregate workloads Modelling Hand-authored YAML Hand-authored data model Hand-authored cubes Cost driver Plan tier + query volume Pre-aggregation builds + compute Quote-based licence + compute Governance Upstream, in the warehouse In front of the API On the cube Each is competent at what it was built for. If your consumers are dashboards and analysts, any of the three will serve you. The question none of them answers An agent doesn't arrive with a metric name. It arrives with an intent in English and has to work out which entities, which grain, which joins, and whether it's entitled to any of it. That exposes two gaps every one of these shares: Undefined intent has no answer. Coverage is whatever someone remembered to model. Business questions don't respect that boundary. Authorisation is checked around the query, not inside it. A filter applied after execution means the data already moved. What to actually evaluate on Ignore feature matrices and score these five: Answer a question nobody modelled, on your schema Show why one join path was chosen over two others Same question, two users with different entitlements — show both SQL statements Ask something ambiguous. Refusal or guess? Reproduce a number from six months ago with the definitions then in force Most evaluations stop at 1. Numbers 3 and 5 are the ones that decide whether the thing ships in a regulated business. The full breakdown — architecture-by-architecture comparison, cost profiles, and the migration implications of each — is here: 👉 dbt Semantic Layer vs Cube vs AtScale: Choosing a

fMRI scans show happiness, fear, anger, and sadness have distinct brain activity patterns in doggy brains.

As research and AI-assisted papers surge, volunteer reviewers struggle to keep up.

The approach is like a Costco for heat pumps, and it’s helping make installations more accessible as heat waves worsen.

Solar energy, at any time of day or night - that's the dream of space mirror projects. Futurists have been imagining satellite mirrors that could reflect the sun's light onto the Earth's surface for over a century. Russian scientists experimented with the concept in the '90s with the Znamya project. The idea is to put […]

Two-thirds of an octopus’s neurons are in its arms—each operating independently—including the one it uses to have sex.

A device currently being used to help octogenarians do self-administered eye exams could be part of future space missions to monitor astronauts’ ocular health.

The massive Toba eruption seems to have had little climate impact.

To power its new West Texas data center, Amazon is investing in the construction of a new power plant that could be one of the largest single producers of greenhouse gases in the US, according to the New York Times. The new gas-burning plant in Pecos County, Texas has received significant investment from Amazon and, […]

The photograph, taken in visible light, made it possible to observe for the first time the tiny plasma vortices that had been predicted for more than a century.