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There is a quiet tax many engineering teams pay after their automated test suite starts to matter. The tests live in code. They run in CI. They already know the branch, commit, environment, failure message, stack trace, screenshot, trace file, retry status, and build URL. Then someone asks: Can we put these tests in Jira too? That request usually comes from a good place. Jira is where work is planned. Product, QA, engineering, and release stakeholders already use it. If quality signal is invisible there, people end up asking for screenshots from CI, links to failed builds, or spreadsheet summaries before every release. The mistake is assuming the answer is to recreate every automated test as a manual Jira test case. For many teams, that creates a second source of truth that starts decaying immediately. Automated tests are not manual test cases Manual test cases and automated tests have different jobs. A manual test case is a human-readable procedure. It often describes a workflow, expected result, and maybe some preconditions. It is useful when a person needs to execute or review a scenario. An automated test is executable behavior. It is versioned with the code, refactored with the product, reviewed in pull requests, and run repeatedly by machines. When teams try to manage automated tests by copying them into a test-case inventory, they usually create a translation problem: The test name changes in code, but the Jira case does not. The test is deleted or split, but the manual record still exists. The CI failure has rich evidence, but the test case only says "failed." The test belongs to a branch or commit, but the copied case does not. The release team sees a static inventory instead of the latest run signal. The test case becomes a label for a thing that actually lives somewhere else. The better question Instead of asking, "How do we turn all automated tests into Jira test cases?", ask: What does Jira need to know from each automated run? That changes the shape of

As a backend developer, I sometimes help companies evaluate candidates by reviewing their recorded technical interviews. However, over time, I’ve noticed a deeply ingrained misconception. When discussing memory management or data streaming, many developers explicitly state: "Iterators in Python are inherently eager. If you want true lazy loading or lazy evaluation, you have to use generators and the yield keyword." This misconception is common. Many popular bootcamps and online courses introduce lazy evaluation exclusively through generators . Custom class-based iterators are usually skipped or dismissed as boilerplate-heavy OOP theory rarely used in production Python. This confusion is further reinforced by two common educational simplifications : The List vs. Generator Expression Analogy: Beginners are taught that square brackets [...] (list comprehensions) are eager and take up memory, while parentheses (...) (generator expressions) are lazy. This often creates a false binary mental model: "generators = lazy, everything else = eager." Standard "Textbook" Examples: When courses demonstrate a custom iterator, they usually write a basic class that accepts an already fully loaded list in its __init__ and simply increments an index in __next__ . While this is valid for in-memory data, it leads developers to assume that custom iterators inherently require loading all data upfront. In reality, generators are a specialized language feature designed to implement the iterator protocol automatically . They comply with the exact same interface ( __iter__ and __next__ ). A generator is lazy not because of some magical property of the yield keyword, but simply because it adheres to this underlying contract. To show that custom iterators can be lazy without using any generators or yield keywords, I’ve put together a lightweight and reproducible benchmark. 🧪 The Experiment: Proving Lazy Loading with Custom Iterators Suppose we need to read a database export file ( test_users_db.

submitted by /u/High-Impact-2025 [link] [留言]

The Canadian pension giant will acquire an 8.2% stake in CtrlS, a tech giant that operates more than 15 data centers across India.

Must Listen! She believes the engineering bottleneck is removed with AI. submitted by /u/ShivamOujlayan [link] [留言]

We’ve trawled the depths of Amazon to find the best deals on gear we’ve tested.

Uber recently described an internal architecture for propagating identity across multi-agent AI workflows. The design aims to perserve user context, agent provenance, and scoped access as agents delegate work and call internal tools. The case study aligns with Auth0’s view that AI agents need permissions based on delegated authority, scoped credentials, and explicit human approval boundaries. By Eran Stiller

With this acquisition, DeepL is opening an office in San Francisco to expand its U.S. business.

submitted by /u/Malwarebeasts [link] [留言]

They’re much more recyclable than the current plastic ones, and they will still probably be locked away behind that anti-theft plexiglass.

Let's be real: There's no shortage of Zelda-style games to play right now. That's true even if you've exhausted all there is to see in Tears of the Kingdom or Echoes of Wisdom. Indie developers keep finding new ways to refresh the formula, whether it's fusing it with a Soulslike, making it cozier, or simply […]

Matter, the smart home interoperability standard, might finally get a feature that should have been there from day one: a single shared Matter network managed by multiple ecosystems. With this feature, called Joint Fabric, smart devices added to the network will be controllable by any authorized platform - Apple Home, Google Home, Amazon Alexa, and […]

I’ve been reviewing laptops for over a decade, and this is my advice on how to find the right laptop for you.

I'll admit it. I was wrong. Wildly colorful lighting is delightful for your smart home - well, outdoors, at least. Smart lighting is one of my favorite features of the smart home - it combines convenience with ambiance, letting you change the entire look of your room with just a press of a button. But, […]

On 14 April, the Trump administration quietly acknowledged the widespread use of AI to automate government processes. The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government. The list has ballooned by 70% from the one published in the final year of the Biden administration, and includes many disturbing-seeming plans to hand over sensitive governmental functions to AI. Scanning this list, many readers may find many causes for alarm. It represents a transfer of decision processes from human to machine on a massive scale over matters of individual freedom, public health and well-being, nuclear reactor safety and more...

Aditya Kumarakrishnan explains how to move past the "amnesia phase" of AI. He shares a blueprint for engineering leaders to build modular agent frameworks using CoALA, leverage decades of process science for scalable workflows, and "terraform" legacy environments into robust, event-sourced artifacts capable of handling unpredictable, cross-functional agent demands. By Aditya Kumarakrishnan

Pinterest has launched 'Ask Pinterest,' an experimental AI-powered shopping app that lets users seek recommendations and inspiration through a conversational interface.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Temperatures have climbed up to 45 degrees Fahrenheit above normal, stopping ice from forming in the dead of Antarctic winter.

From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state.