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The code is being produced faster than I can confidently review, validate, and ship it. Old workflow vs. new workflow The biggest change in my workflow is not simply that AI writes code faster. It is that my role is gradually moving away from manually implementing every detail and toward designing, orchestrating, reviewing, and validating the overall result . That sounds like a small shift, but it changes where I spend most of my engineering effort. Coding is no longer the slowest part Working with coding agents has changed how I think about development productivity. I can define a task, let an agent explore the codebase, implement the change, add tests, and return a working diff much faster than I could build everything manually. But implementation is only one stage of software delivery. flowchart LR A[Requirement] --> B[Design] --> C[Implementation] --> D[Review] --> E[Test] --> F[Deploy] AI can compress the implementation stage dramatically, but review, testing, integration, security, and deployment still have their own limits. When those stages cannot keep up, faster coding does not remove the bottleneck. It simply moves it downstream. This is also the point Red Hat recently raised in Why faster coding isn't making delivery any faster : generating code and delivering reliable software are not the same thing. I have started to notice this more clearly in my own workflow. An agent can produce a fairly large change while I am still building the mental model needed to judge whether that change is actually good. More code is not the same as more productivity Suppose I used to complete two meaningful changes in a day and AI now helps me produce six. Calling that a 3x productivity increase sounds reasonable at first, but only if the rest of the engineering system can absorb those six changes. They still need to be understood, reviewed, tested, integrated, and eventually operated in production. If review capacity or CI becomes the constraint, I have not created three ti

One thing I’ve been thinking about something that sounds simple but is actually a little tricky: what do we do after AI fixes a failed test and it passes again? There are already some interesting approaches to this. mabl looks at adaptive healing, Testim focuses on smarter locators and maintenance, and Applitools approaches changes from the visual validation side. I don’t think there’s one perfect way to handle test maintenance, it really depends on why the test failed in the first place. While exploring X360 AI Tech, this made me look at the problem a little differently. Getting a test back to green is useful, but I’m more interested in what happened along the way. Looking at the failure details, previous execution, and the actual flow can help answer a basic question: did we really fix the test, or did we just find another way to make it pass? So, after an AI fix, I’d still want to check a few things: Is the test checking the same thing as before? Does it still match the original requirement? Was the failure actually caused by a UI change? And does the fix continue to work in the next few runs? I’m starting to feel that the real value of AI self-healing isn’t just fixing tests faster. It’s helping QA spend less time fixing tests blindly and more time deciding whether the fix actually makes sense. What’s the first thing you would check after an AI-healed test turns green?

Promises in JavaScript When JavaScript performs an operation that takes some time, such as fetching data from a server, it does not want to wait and block the rest of the program. Instead, JavaScript can handle the operation asynchronously. A Promise is an object that represents the eventual result of an asynchronous operation. In simple words, a Promise means "I don't have the result right now, but I will give you the result later." Creating a Promise We can create a Promise using the built-in Promise constructor: const result = new Promise (( resolve , reject ) => { const age = 10 ; setTimeout (() => { if ( age >= 18 ) { resolve ( " You are eligible to vote " ); } else { reject ( " You are not eligible to vote " ); } }, 3000 ); }); Here, Promise is a built-in JavaScript constructor, and new Promise() creates a new Promise object. The function passed to new Promise() is called the executor function : ( resolve , reject ) => { // code } resolve and reject are parameters of this executor function. The Promise constructor provides functions as arguments for these parameters. We call resolve() when the operation is successful and reject() when the operation fails. resolve ( " You are eligible to vote " ); means the operation was successful. reject ( " You are not eligible to vote " ); means the operation failed. Promise States A Promise has three possible states: State Meaning Pending The operation is still in progress Fulfilled The operation completed successfully Rejected The operation failed In our example, when the Promise is created, it is initially pending . After 3 seconds, the age is checked. Since the age is 10 , the condition is false and reject() is called. So the Promise changes from: Pending ↓ Rejected If the age were 18 or above, resolve() would be called instead: Pending ↓ Fulfilled Handling a Promise After creating the Promise, we can use .then() to handle a successful result and .catch() to handle an error. const result = new Promise (( resolve , rejec

Original Article published on ZeroLabs . Designing Production-Grade OpenClaw Skills: Schemas, Tool Calling, and Dynamic Dispatch Key Takeaway: A deep engineering walkthrough on creating modular, reusable skills for OpenClaw agents with strict JSON schemas, fallback execution paths, and error telemetry. Structured verification, strict boundaries, and deterministic tooling prevent production failure. Implemented directly across the ZeroLabs and OpenClaw platform architecture. Image credit: labs.zeroshot.studio Why this matters: Engineering reliable systems requires moving past unstructured prompts into hardened execution contracts. Contents What is an OpenClaw skill? How do you structure the SKILL.md specification? How do you implement reliable Python tool scripts? What is dynamic dispatch and context management? FAQ What is an OpenClaw skill? In OpenClaw, a skill is a self-contained directory containing instructions, configuration schemas, and executable scripts. Instead of writing monolithic prompts that describe every possible task, skills allow agents to discover, load, and execute specialized capabilities on demand. flowchart TD A[User Request] --> B[OpenClaw Router Agent] B -->|Matches Capability| C[Load skill: domain-seo-audit] C --> D[Read SKILL.md Frontmatter & Rules] D --> E[Execute Scoped Python Script / Tool] E --> F[Return Formatted Output to Context] How do you structure the SKILL.md specification? Every skill must reside in its own subdirectory under skills/<skill-name>/ with a root SKILL.md file: --- name : domain-seo-audit description : " Scans a target URL for Core Web Vitals, OpenGraph tags, and indexability issues." version : 1.0.0 parameters : type : object properties : url : type : string format : uri description : " The full target URL to audit (including https://)." check_mobile : type : boolean default : true description : " Whether to emulate mobile viewport checks." required : - url --- # Domain SEO Audit Skill ## Overview Use this skill whe

Original Article published on ZeroLabs . Taming Vibe-Coded Technical Debt: Automated Test Harnesses for AI-Generated Repos Key Takeaway: A pragmatic strategy for refactoring AI-generated codebases, eliminating dead boilerplate, and establishing regression test harnesses before shipping to production. Structured verification, strict boundaries, and deterministic tooling prevent production failure. Implemented directly across the ZeroLabs and OpenClaw platform architecture. Image credit: labs.zeroshot.studio Why this matters: Engineering reliable systems requires moving past unstructured prompts into hardened execution contracts. Contents What causes vibe-coded technical debt? How do you build a safety test harness? What is the 4-step refactoring loop for AI code? How do you clean dead dependencies and boilerplate? FAQ What causes vibe-coded technical debt? AI coding models are optimized to satisfy the user's immediate prompt. When asked to add a feature, models often take the path of least resistance: Copy-Pasting Logic : Duplicating utility functions across multiple files rather than importing shared modules. Swallowing Errors : Wrapping fragile database or network calls in broad try/except: pass blocks. Dependency Sprawl : Installing heavy npm packages or Python libraries for trivial single-line operations. flowchart TD A[Vibe Coded Prototype] --> B[Generate Smoke & Contract Tests] B --> C[Run Static Analysis & Linters] C --> D[Identify Duplication & Dead Imports] D --> E[Scoped AI Refactor on Single Module] E --> F[Run Test Suite] F -->|Pass| G[Commit Refactor] F -->|Fail| E How do you build a safety test harness? Before asking an AI agent to clean up or refactor an existing repository, you must write automated smoke tests that verify critical user journeys. If you don't have tests, ask the agent to write tests before modifying any implementation code: # tests/test_smoke_endpoints.py import pytest import httpx BASE_URL = ' http://localhost:3000 ' def test_homepage

Original Article published on ZeroLabs . Context Engineering with Claude Code: The Spec-First Pipeline for Production Codebases Key Takeaway: How to structure markdown specification files, linting contracts, and context boundaries to eliminate hallucinated refactors when coding with Claude Code and modern CLI agents. Structured verification, strict boundaries, and deterministic tooling prevent production failure. Implemented directly across the ZeroLabs and OpenClaw platform architecture. Image credit: labs.zeroshot.studio Why this matters: Engineering reliable systems requires moving past unstructured prompts into hardened execution contracts. Contents What is the problem with unstructured conversational prompting? How does the Spec-First Pipeline work? What belongs in a production feature spec? How do you enforce automated verification loops? FAQ What is the problem with unstructured conversational prompting? When developers ask CLI coding agents to 'Fix the user profile page' or 'Refactor our database queries' , the model must guess which files to edit, what interfaces to preserve, and how to verify correctness. This ambiguity leads to three common failure modes: Collateral Damage : The agent modifies unrelated utility functions, introducing silent regressions across the codebase. Context Saturation : The agent reads dozens of unnecessary files, exhausting its context window and forgetting the primary objective. Premature Completion : The agent claims a task is complete without running linters, compilers, or test suites. flowchart TD A[Feature Request / Bug] --> B[Draft SPEC.md in Repo] B --> C[Review Interface & Target Files] C --> D[Feed Spec to Claude Code / CLI Agent] D --> E[Agent Edits Code in Target Files] E --> F[Run Deterministic Test Suite] F -->|Tests Fail| E F -->|Tests Pass| G[Commit & Open PR] How does the Spec-First Pipeline work? The Spec-First Pipeline replaces open-ended chatting with a deterministic three-stage workflow: Stage Artifact Action O

Google said it would bring an Iowa nuclear power plant back from the dead. Now, the plant's owner is getting a $1.9B loan from the U.S. Energy Department.

No, the Zelda anniversary edition Switch 2 doesn't come with a copy of Ocarina of Time.

Pusheen is getting her first game, and it will appear exclusively on Apple Arcade. Launching October 1st, Pusheen's Place lets you collect and care for more than 100 Pusheens as you play minigames and decorate rooms for Pusheenicorn, Pancake Pusheen, and other variations of the cartoon cat. In the mix of minigames, you'll "sort color […]

Google is speeding up Chrome’s release schedule to ship security patches and new features faster.

A lot of people are split between getting a nice all-in-one soundbar or spending more for an option that includes rear satellites for immersive surround sound. JBL’s Bar 700 Mark 2 system is one of the most unique options available, shipping with two modular speakers that charge when docked to the soundbar. When detached, the […]

We're heading back to Link's original 3D adventure ahead of next year's live action Zelda movie.

The Switch 2 is newer and more affordable than the Steam Deck, but Valve's console still has serious power to draw on.

Link's next adventure now has a release date. During a Legend of Zelda stream for the franchise's 40th anniversary, Nintendo announced that the upcoming Ocarina of Time remake for the Switch 2 is launching on November 5th. The release date makes Ocarina of Time one of just a few big-budget titles launching in the same […]

The French AI lab has raised €3 billion at a €21 billion valuation in a Series D round led by Samsung, Scaleup Europe and PSG Equity.

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The latest heist is one of the largest thefts of cryptocurrency to date.

Bluecore Energy announced Tuesday an oversubscribed $50 million seed round — just months after raising a $10 million pre-seed and coming out of stealth.

Oh, this thing? It's just a crossbody strap. It's a fun and youthful way to carry your phone. It's all the rage with Europeans and young people. I love it, and I definitely don't feel weird and self-conscious using it. Plus, it's great to have a $700 investment dangling near your waist while you walk […]

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