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Baek, a 35-year-old manager at the South Korean semiconductor titan SK Hynix, was enrolled in Sunoo, a matchmaking company based in Seoul, a year ago. In a move typical of anxious South Korean parents, his mother signed him up, hoping to find a good wife for her son. Lately, says Baek (who asked to be…

Even Realities, an ex-Apple team building camera-free smart glasses, raised $150M from Meituan and Tencent at a $1B valuation.

Distributed backups, cyber resilience, and a half-million records are preserving Palestinian history beyond any single building or border.

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Many people who want Apple's rumored folding iPhone 'Ultra' may not be able to get it at first

The Model Context Protocol team has promoted its Enterprise-Managed Authorisation extension to stable status, adding a centralised way for organisations to control access to MCP servers through their identity provider. The project states the aim is to replace per-server consent prompts with a zero-touch flow in which users sign in once and then access approved servers without further setup. By Matt Saunders

GitHub热门项目 | A Python library for building AI agents that leverage the full power of Google Antigravity. | Stars: 2,250 | 21 stars today | 语言: Python

When a bug is fixed, most teams retest the exact failure path once and move on. That is understandable, but it leaves a gap: the team learned something from a real failure, then failed to turn that learning into reusable regression coverage. Here is a lightweight template I use for turning resolved bugs into regression test cases that can be copied into a spreadsheet, Jira, TestRail, Qase, Xray, Zephyr, or any other QA workflow. The CSV fields For a bug fix regression test, I like these columns: Test ID Bug ID Feature Area Regression Scenario Original Failure Preconditions Test Data Steps Expected Result Negative Check Priority Regression Risk Test Type Automation Candidate Notes This is enough structure to make the test reusable without turning every bug fix into a heavyweight test plan. Example bug Bug ID: BUG-1842 Bug title: Non-admin users could resend workspace invitations. Original failure: A workspace member could open Pending Invitations and click Resend, even though only owners and admins should be allowed to resend invitation emails. Fix summary: The resend invitation action now checks the user's workspace role before sending the email. Example regression test case Test ID: REG-BUG-1842-001 Feature Area: Workspace invitations Regression Scenario: Workspace member cannot resend a pending invitation. Preconditions: Workspace has at least one pending invitation. Test user is a workspace member, not an owner or admin. User is logged in. Steps: Log in as the workspace member. Open Workspace Settings. Go to Pending Invitations. Locate the pending invitation. Check whether the Resend action is visible or available. If the action can be triggered through the API, attempt the resend request. Expected Result: The member cannot resend the pending invitation. The UI hides or disables the action, and the API rejects unauthorized resend attempts. Negative Check: Confirm that an owner or admin can still resend the invitation if product rules allow it. Priority: High Regr

With a Real CI Automation Example Loop Engineering is suddenly everywhere, and honestly, I wanted to understand it properly instead of just repeating the buzzword. The simplest way I can explain Loop Engineering is this: it replaces me as the person constantly prompting the agent. Instead of me manually noticing a problem, deciding what it means, writing the next prompt, and pushing the process forward, I design a system that keeps moving on its own until it reaches the outcome I want. That is the whole point of Loop Engineering. I stop acting like the operator and start acting like the system designer. To make that idea concrete, I built a practical software engineering workflow around CI failures. Whenever a GitHub Actions CI run fails, the system automatically classifies the failure, creates a Jira bug for real issues, sends a Slack notification, and records the outcome so it does not process the same failure twice. What Loop Engineering actually means Early AI workflows were mostly linear. I would give a prompt, the model would return an answer, and if the answer was incomplete or wrong, I would jump back in and prompt again. That worked, but it kept me trapped inside the process. Loop Engineering changes that dynamic. I am no longer the person babysitting each step. I build an autonomous loop that can observe, decide, act, and persist state. The system keeps iterating until the task is done, without needing me to micromanage it. That distinction matters. In a normal prompt based workflow, the human is still the glue. In Loop Engineering, the human creates the machine, and the machine runs the loop. The five building blocks of Loop Engineering When I break down Loop Engineering, I think of it as five core building blocks working together. 1. Automations These are the event driven triggers that start the whole system. They are the heartbeat of the loop. Something happens, and the automation fires. Without this, nothing starts. 2. Skills Skills give the agent stru

In my previous articles, I’ve consistently emphasized a core architectural principle: once the render layer no longer dictates the entire data flow, the boundaries between State, Derived State, and Effects become critical. When we fall into the habit of stuffing every UI-affecting variable into generic "state," the system quickly loses its semantic structure. In modern frontend applications, this architectural gap becomes most glaring when dealing with asynchronous work. Async data is never merely "a value that will appear in the future." It carries complex semantics regarding its source, temporal validity, cancellation, error recovery, and invalidation. If these semantics aren't modeled explicitly, they inevitably get pushed down into the UI framework’s lifecycle—indirectly patched together through component mounts, effect dependencies, and callback guards. This brings us to the core question of this article: What does a system lose when the correctness of async work is forced to depend on the UI lifecycle? We are all incredibly familiar with this pattern: const data = await fetchSomething () setState ( data ) Or, using a standard UI framework hook: useEffect (() => { let cancelled = false fetchSomething (). then ( result => { if ( ! cancelled ) { setData ( result ) } }) return () => { cancelled = true } }, []) There is nothing inherently wrong with this code for simple use cases. It’s intuitive and perfectly aligns with how Promises are designed to work: trigger the operation, wait for the resolution, and write the result back into state. However, this mental model has a subtle downside. It encourages us to think of async work as simply calling setState after a Promise resolves. That may hold up for simple screens, but as an application grows, the model starts to expose structural problems. Promise Only Describes Completion, Not Ownership A Promise solves a very specific problem: A piece of work will complete in the future, and it will either succeed or fail. It c

A few months ago I didn't know much about coding. Today I have a full stack marketplace running with a real API, a live database, user authentication, image uploads, messaging and a React frontend. The Idea Kisumu has a huge secondhand clothes market. Mitumba is everywhere — Kibuye market, roadside stalls, WhatsApp groups. But there is no dedicated digital platform for it. If you want to sell a jacket in Kondele you have no easy way to reach buyers in your area. If you want to find size L shoes in CBD you have to physically go and look. I wanted to build something that solved a real local problem. Not another todo app. Not another weather app. Something that could actually help people in my city. That is how Kisumu Marketplace was born. The Tech Stack I Chose I built the backend in Go using the Gin framework. The database is PostgreSQL hosted on Neon.tech — a free cloud database that saved me more than once when my laptop broke. Authentication uses JWT tokens and bcrypt for password hashing. Images are uploaded to Cloudinary. The frontend is React with Tailwind CSS. I chose Go because Zone 01 teaches it and I wanted to go deep on one language rather than shallow on many. I chose PostgreSQL because it is the industry standard and learning it properly matters. I chose React because it is the most in-demand frontend framework and I wanted to build something real with it. What I Learned I learned Go from scratch while building this. I learned React from scratch while building this. I learned PostgreSQL, JWT, bcrypt, Cloudinary, Tailwind, axios, React Router and more — all by needing them for this project. The most valuable thing I learned is that you understand something properly only when you build with it. Reading about JWT is nothing like debugging a 401 Unauthorized error at midnight. I also learned that documentation is a skill. Writing this article, explaining how things work, is making me understand my own project better.