标签：#ci

Brad Grantham discusses how software engineers and architects can transition from individual contributors to influential technical leaders. Brad shares actionable insights on expanding skills into business and legal domains, adapting communication styles for non-technical stakeholders, moving past ego to empower teams, and navigating complex organizational dynamics to maximize engineering impact. By Brad Grantham

Research shows heat amplifies the dangers of pesticides.

AI companies like Anthropic and OpenAI regularly publish reports on how people are using products like Claude and ChatGPT, but they only release the data they want us to see, AI researchers say. “There is no independent source to corroborate it,” says Anka Reuel, a Computer Science PhD candidate at the Stanford Trustworthy AI Research…

Successfully onboarding an automated test suite to Harness CI requires configuring infrastructure placeholders, secrets, pipelines, and branch protection rules. Here is a 10-step checklist to help you onboard your end-to-end (E2E) automation pipelines seamlessly. Step 1: Replace Infrastructure Placeholders Ensure your pipeline YAML definitions (e.g., .harness/e2e-poc.yaml and .harness/e2e-regression-parallel.yaml) contain your specific environment values: ORG_ID: Harness Organization Identifier PROJECT_ID: Harness Project Identifier GIT_CONNECTOR: Harness Git Connector for GitHub Enterprise access APP_REPO_NAME: Target repository in owner/repo format K8S_CONNECTOR: Kubernetes connector for build infrastructure K8S_NAMESPACE: Kubernetes namespace where build pods run Step 2: Configure Environment Secrets In Harness, set up the following runtime secrets: CONNECT_URL CONNECT_USERNAME CONNECT_PASSWORD Step 3: Setup PR Validation Pipeline Import your short-run pipeline YAML into Harness. Save it as your PR Validation Pipeline. Run a manual validation test using runtime overrides: TargetEnv = qa cucumberTags = @smoke Step 4: Verify Artifact Generation Confirm that the initial execution correctly generates and uploads all required outputs: JUnit Report: reports/junit-report.xml Test Reports: reports/** Failure Artifacts: test-results/** (screenshots, traces) Step 5: Setup Nightly Parallel Pipeline Import your parallel pipeline YAML into Harness. Save it as your Nightly Regression Pipeline. Run a manual validation test with target concurrency parameters: TargetEnv = qa cucumberTags = @regression cucumberParallel = 4 Step 6: Configure Automated Triggers & Branch Protection PR Trigger: Configured on pull requests with cucumberTags= @smoke . Nightly Schedule Trigger: Configured on a nightly cron schedule with cucumberTags=@regression and cucumberParallel=4. GitHub Branch Protection: Enable branch protection on target branches requiring the Harness PR pipeline status check to p

Integrating end-to-end (E2E) automation suites into enterprise CI/CD pipelines requires robust reporting, dynamic execution controls, and seamless artifact management. Here is a guide on setting up a Node.js + Playwright + Cucumber.js test suite using Harness CI , configured with dual-repository dependencies, parallel execution capabilities, and dashboard-ready reporting. Key Architectural Setup Two-Repo Architecture: Repository A (Application Automation Repo): Contains application-specific feature files, page objects, and pipeline definitions. Repository B (Shared Framework Repo): Hosts core framework utilities, custom assertions, and base drivers consumed as a pinned dependency. Tech Stack: Node.js, Playwright, Cucumber.js, Allure/JUnit reporting. Step 1: Configure Harness Connectors & Secrets Set up these foundational resources within your Harness account: Connectors: GIT_CONNECTOR: Grants access to both application and framework GitHub repositories. K8S_CONNECTOR: Manages the Kubernetes build infrastructure. Secrets: CONNECT_URL, CONNECT_USERNAME, and CONNECT_PASSWORD (and proxy settings if required). Step 2: Configure Pipelines Import your execution configurations using YAML files inside .harness/: Standard Run (.harness/e2e-poc.yaml): Used for fast PR checks. Parallel Regression (.harness/e2e-regression-parallel.yaml): Used for scheduled, high-volume regression runs. Replace placeholders such as , , and to map to your cluster environment. Step 3: Define Pipeline Triggers Set up two primary execution workflows: Pull Request (PR) Trigger: Event: Pull Request to main/POC branch. Runtime Variables: cucumberTags= @smoke Scheduled Nightly Trigger: Event: Scheduled Cron. Runtime Variables: cucumberTags=@regression, cucumberParallel=4 Step 4: Test Report & Artifact Collection To ensure test metrics display properly on the Harness dashboard, configure both JUnit parsing and raw artifact archiving. Generated Outputs: reports/junit-report.xml (parsed by Harness for test

The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict that what researchers call recursive self-improvement is on the horizon. …

Z.ai’s latest AI model release could help companies secure their systems—or find its way into the hands of hackers.

Getting started with machine learning WEKA for Beginners: A Practical Introduction to Machine Learning Without Code Getting started with machine learning often means learning Python, libraries, datasets, and a lot of new terminology at the same time. WEKA offers a different approach. WEKA (Waikato Environment for Knowledge Analysis) is a machine-learning and data-mining workbench that lets you explore datasets and experiment with algorithms through a graphical interface. It is particularly useful for students and beginners who want to understand the machine-learning workflow before writing everything from scratch in code. What Can You Do With WEKA? WEKA provides tools for several common machine-learning tasks: Data preprocessing Classification Regression Clustering Association-rule mining Attribute selection Model evaluation Data visualization The Explorer interface is usually the best place for beginners to start. A typical workflow looks like: Dataset ↓ Preprocessing ↓ Feature Selection ↓ Algorithm ↓ Model Evaluation ↓ Interpretation Step 1: Load Your Dataset WEKA commonly works with ARFF (Attribute-Relation File Format) files, although it can also work with formats such as CSV. A simple ARFF dataset might look like: @relation students @attribute study_hours numeric @attribute attendance numeric @attribute passed {yes,no} @data 5,90,yes 2,60,no 8,95,yes 3,70,no The header describes the attributes, while the data section contains the individual instances. Understanding the structure of your dataset is important before applying any algorithm. Step 2: Preprocess the Data After loading the dataset, use WEKA's Preprocess section to inspect and prepare the data. You can examine: Attributes Number of instances Missing values Class distribution Attribute types WEKA also provides filters for operations such as removing attributes, handling missing values, normalization, and other transformations. Good preprocessing can have a significant impact on model performance. Step 3

Practices shifted from individuals buried in coffins to reusing sites for later mummy interments.

Again, the CDC did not publish a full report and instead simply put the data online.

This year’s El Niño is shaping up to be the strongest on record. This is what impacts to expect and how bad it could get.

Here's what it was like watching a total solar eclipse 90 minutes north of Madrid.

Cybersecurity experts who investigate spyware attacks say the number of people who received a recent threat notification from Apple is unusually high.

The company wants users to be able to watch and listen to conversations.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Flock, the police-tech giant known for its network of some 120,000 automatic license plate readers around the US, announced some changes to its platform last Thursday. The updates are meant to prevent…

Reddit is beginning to test video and audio versions of popular posts, allowing users to watch or listen to Reddit stories instead of just reading them.

SpaceXAI has introduced Grok Bot, a system of persistent AI agents that operate on dedicated cloud computers and can interact with websites, applications, inboxes, and other tools. By Daniel Dominguez

title: Why I left Warehouse out of our Fabric deployment scope published: true tags: microsoftfabric, datawarehouse, cicd, devops Our Fabric deployment pipeline handles sixteen item types. Warehouse is not one of them, and that was deliberate. DEFAULT_ITEM_TYPES = [ " DataPipeline " , " Lakehouse " , " Notebook " , " SemanticModel " , # "Warehouse" is intentionally excluded. Warehouse schema deployment must # be handled separately to avoid schema reset risk during publish. " Environment " , " Eventhouse " , ... ] The reason Publishing a warehouse through this path can reset its schema. Not "might behave unexpectedly". The failure mode is that a deployment intended to be additive removes structure, and the thing that removes it is the same routine that successfully deploys the other sixteen types. The choice that follows Two options once you know that. Include it and hope nobody deploys a warehouse without reading the docs. The pipeline supports everything, and one day someone promotes a change on a Friday and finds out. Or exclude it, document why, and handle warehouse deployment as its own problem with its own tooling. I took the second. An automation that covers most cases and silently corrupts the rest is worse than one that covers most cases and refuses the rest. The refusal is visible. The corruption is not. Making the exclusion loud An exclusion is only useful if someone notices it. Three things help: The comment sits inside the list , not in a doc nobody opens. Anyone reading the item types sees the gap and the reason in the same glance. It is in the README under known limitations, next to the other things the framework does not do. There is a test. It asserts Warehouse is absent from the default scope: def test_warehouse_stays_excluded ( self ): """ Warehouse publish can reset schema, so it is handled separately. """ self . assertNotIn ( " Warehouse " , deploy . DEFAULT_ITEM_TYPES ) That test looks silly. It is asserting that a string is missing from a list.

Dozens of states accuse the company of violating the Children's Online Privacy Protection Act.

Grab is using AI agents to automate analytics workflows, cutting mechanical analyst work from 44% in February to 30% in June. Its approach combines agent autonomy, certified data, context management and human oversight, with self service analytics increasingly handling metric, data and SQL requests without analyst intervention. By Leela Kumili