标签：#ci

Beluga whales were in danger of getting euthanized after a Canadian theme park went bust. WIRED spoke with some of the scientists behind the groundbreaking rescue mission.

Sometimes, compelling stories beat medical evidence.

Starting with Spotify, Snapchat users will be able to link their accounts, choose who can see their listening activity, and see what their friends are listening to in real time.

I keep a shelf. Rules I haven't earned the pain for yet go on it — because my own rule says a rule is born from an incident, not from someone else's "best practice." Import a rule you haven't bled for, and you'll be the first one to route around it. On the shelf sat a rule with its trigger condition written down, word for word: The first merged PR with a green DoD checklist and a flow that doesn't actually work. I put it there a couple of weeks ago, thinking "this'll come in handy someday." It came in handy two days after I published an article about this very method. The trigger fired. Word for word. What happened The PR merged. CI green. Every DoD box checked. And the flow didn't work — not for one second, not in a single real stack. Three bugs in a cascade, and every one of them invisible to CI by construction. One. A module read a JSON registry from a shared/ folder at import time, on app startup. Works in CI — full checkout there, shared/ is present. But the production image is built from a narrow context that doesn't include that folder. The container crash-looped on its very first start. And you know the best part? CI never ran the image at all. It ran the tests on the host. Green. Two. Two migrations merged the same day and got the same version. And the version is the primary key in the applied-migrations table. A local db reset died on the second row: duplicate key . Columns never got created. CI didn't see this one either — it runs migrations through a bare psql loop, no duplicate check. Three was just a consequence: no columns, endpoints return 500. Every check was honestly green. All three bugs would've been caught by one attempt from a live human to hit the endpoint on a running stand. One. The lesson, one paragraph Deterministic checks catch structure: the test file exists, the status is set, migrations are listed, the linter is clean. What they can't see, by construction, is whether the flow works in the stack where the product actually lives. Green C

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate…

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…

For the enterprise, the promise of agentic AI is much more than just a better chatbot. It is software agents that execute business tasks end-to-end across people, business workflows, data, and systems. The platform best-suited to run agents is built with proper CPU capacity, resilient data access, policy-aware tool use, observability, memory management, and the…

As the US sees its highest number of measles cases in decades and vaccination rates fall, researchers are developing drugs to help those who contract the virus or who are particularly vulnerable.

Black holes eject powerful energy jets that blow away the surrounding gas to great distances. So how can they continue to grow?

Regression and regularization are both important concepts in machine learning and statistics, but they solve different problems. Regression is primarily used to model relationships and make predictions. Regularization is used to improve a model's ability to generalize by controlling its complexity. Regression This is a statistical and machine learning technique used to predict a continuous numerical outcome based on one or more input variables. For example, we might want to predict: A house's price based on its size and location A student's exam score based on study hours A company's sales based on advertising spending Simple Linear Regression In simple linear regression, we model the relationship between an input variable (x) and an output (y): $$ y = \beta_0 + \beta_1x + \epsilon $$ Where: (y) is the predicted outcome (\beta_0) is the intercept (\beta_1) is the coefficient or slope (x) is the input variable (\epsilon) represents the error The model learns values for (\beta_0) and (\beta_1) that make its predictions as close as possible to the actual values. Multiple Linear Regression In multiple linear regression, several predictors are used: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \cdots + \beta_px_p + \epsilon $$ The goal is typically to minimize the sum of squared errors (SSE) : $$ \text{SSE} = \sum_{i=1}^{n}(y_i - \hat{y}_i)^2 $$ This approach is known as Ordinary Least Squares (OLS) . Regularization Regularization is a technique used to prevent a machine learning model from becoming too complex. A model can perform extremely well on training data but poorly on new, unseen data. This problem is called overfitting . Regularization addresses overfitting by adding a penalty for large model coefficients to the model's objective function. Instead of minimizing only the prediction error, the model minimizes: $$ \text{Prediction Error} + \text{Complexity Penalty} $$ The penalty discourages the model from relying too heavily on individual features. The Main Types o

More electric vehicles are becoming capable of providing backup power at a time when the US is increasingly in need of more electrons.

"What if satellite data could help protect the livelihoods of millions who depend on Africa's largest lake?" Our team JONAM had the privilege of participating in the Kijani Space Hackathon, where we proudly secured 3rd place while tackling Challenge 2: Sustainable Fisheries & Blue Economy. Rather than building another dashboard, we wanted to solve a real problem affecting millions of people around Lake Victoria: declining fish stocks caused by worsening water quality. Lake Victoria supports millions of people through fishing, transportation, agriculture, and tourism. However, over the years the lake has experienced: Increasing water pollution Poor water quality Frequent algal blooms Reduced fish breeding habitats Declining fish populations For fishing communities, these are not just environmental issues—they directly affect livelihoods, food security, and local economies. Our question became: Can Earth observation data help communities understand where water conditions are becoming unsuitable for fish before the problem becomes critical? Our Solution: JONAM JONAM is an AI-powered web application that combines satellite-derived environmental data with machine learning to monitor water quality and provide insights into conditions that may contribute to declining fish stocks. Instead of relying solely on manual sampling—which is expensive and only covers small areas—our platform continuously analyses satellite observations covering the entire lake. Why Copernicus? To build JONAM, we integrated the KijaniBox API, which provides access to environmental datasets from the Copernicus Programme. Copernicus is the European Union's Earth observation programme. It uses a constellation of Sentinel satellites together with in-situ observations to monitor Earth's atmosphere, land, and oceans. For our project, we focused specifically on live water telemetry variables available through the KijaniBox platform. Water Temperature Satellites measure the thermal radiation emitted from th

The latest findings could come with a fine of up to six percent of TikTok's annual revenue.

SpaceX will likely attempt to catch Starship back at the launch pad on its next flight.

A test failure that takes 20 minutes to surface, buries the error in 3000 lines of log output, and gives no context about what changed is nearly useless. Good test reporting transforms raw pass/fail data into actionable signals. Failing fast — stopping the pipeline the moment you have enough information to make a decision — keeps feedback loops tight and respects developer time. These two concerns are deeply connected: you can only fail fast confidently when your reporting is good enough that a fast failure still gives you everything you need to fix the problem. What Good Test Reporting Looks Like Before discussing implementation, it's worth being precise about what "good" means here: Immediate visibility — failures are surfaced at the PR/commit level, not buried in logs Failure context — what failed, with what input, producing what output, and in which file/line Historical comparison — is this a new failure or a pre-existing one? Trend data — is this test getting flakier? Is the suite getting slower? Actionability — the report points to a fix, not just a symptom Most teams get #1 and stop. The teams that nail all five have fundamentally different debugging velocity. JUnit XML: The Universal Format JUnit XML is the lingua franca of CI test reporting. Almost every test framework can emit it, and almost every CI platform can ingest it. Understanding the format helps you produce better reports. <?xml version="1.0" encoding="UTF-8"?> <testsuites name= "My Test Suite" tests= "42" failures= "2" errors= "0" time= "8.432" > <testsuite name= "UserService" tests= "15" failures= "1" time= "2.1" > <testcase name= "should create user with valid email" classname= "UserService" time= "0.234" > <!-- Empty = passed --> </testcase> <testcase name= "should reject duplicate email" classname= "UserService" time= "0.089" > <failure message= "Expected 409, got 200" type= "AssertionError" > Expected status code 409 but received 200 Request: POST /api/users Body: {"email": "existing@example

Museums are embracing data-driven curation and a shifting technology landscape.

Researchers say TikTok, X, and Meta aren't providing data they're legally required to.

Electrolyte powders promise better hydration, energy, and recovery. But unless you’re losing serious fluid, water and food probably have you covered.

Scientists identified the first known cancer transmissible among freshwater fish in a lake that spans the US and Canada.

The engineering team at Zalando recently described the design and implementation of an in-process, client-side load balancer for a high-throughput API handling around 1 million requests per second. The result was more predictable latency, a drop in infrastructure costs, and better visibility into where failures actually originate. By Renato Losio