标签：#p

Meta expanded its AI coding offerings with a new agent that, it promises, can handle complex tasks with complex software.

Hello world, it's Wednesday, August 5, 2026, and here's what happened This Week in PHP Internals. 13 stories this week, so let's get into it. But first, This week's episode is brought to you by Tideways . When a request is slow in production, Tideways takes you from symptom to root cause in minutes, with profiling, tracing, and monitoring built specifically for PHP. It installs in 5 minutes, there's no credit card required, and it's hosted in Germany. Start your free trial at tideways.com . This week's top story: the mass deprecation vote for PHP 8.6 is in its final week. All 35 ballots close Monday , August 10, and Gina P. Banyard posted the 1-week reminder so nobody gets caught out. Most of the 35 are passing comfortably. The interesting ones are the holdouts. list() is now deadlocked at 21 to 21 — a flat tie, nowhere near the 2/3 it needs. Reserving let stands at 22 to 11, which is exactly two-thirds — a single vote in either column decides it. The dechunk filter sits at 17 to 15 — still well short. The gettext _() alias is failing at 9 to 20, and reserving in , out , and inout is failing at 7 to 20, with 12 abstentions. Everything else you'd recognize from the list — the object-parameter cleanups, the is_double() family, spl_classes() — is cruising toward the finish. The thread itself turned into a corrections desk this week. Calvin Buckley relayed a note from Nora, who isn't on the list, pointing out: "The text for the metaphone deprecation isn't fully right. It lists \"linguistics\" as a replacement package, but that one actually uses php-src's metaphone internally too." Weilin Du, who proposed that item, conceded the docs point while standing by the idea, writing: "My point in deprecating it is to stop using ancient metaphone algo as a whole." Voters seem unbothered — metaphone stands at 19 to 6, with 15 abstentions. Rowan Tommins raised a bigger flag on reserving is : it would collide with Hamcrest, the test assertion framework, whose PHP port has 500 millio

The DOJ alleged that OpenAI did not meaningful attempt to hire U.S. citizens before seeking permanent residence for Visa-holding employees.

For a side project, the short answer is: Cloudflare Pages if you want the cheapest ceiling and never think about bandwidth, Vercel if you're on Next.js and want the smoothest developer experience, Netlify if you want a mature all-in-one with forms and identity baked in. All three have a free tier that will host a hobby app fine. The differences that actually bite you show up later — when a post gets traffic, when your build gets slow, or when you outgrow static files and start running server code. I've deployed personal projects on all three over the last couple of years. Below is how I'd choose today, with the real trade-offs rather than the marketing version. What are you actually deploying? Before comparing platforms, be honest about your app, because it changes the answer more than any feature chart: Pure static site (docs, a marketing page, a SPA that talks to an external API): all three are excellent and free. The decision barely matters. Static frontend + a few serverless functions (a contact form handler, an auth callback, a small API): now runtime, cold starts, and function limits matter. A full framework app with server rendering (Next.js App Router, SvelteKit, Remix): now framework-specific adapters and edge/runtime compatibility matter a lot. The takeaway: pick based on your heaviest workload, not your current one — migrating hosts after you've wired up auth and functions is the annoying part. How do the free tiers really compare? This is where these platforms differ the most for hobby use. The headline distinction, as of mid-2026: Cloudflare Pages does not meter bandwidth on its free plan , while Vercel and Netlify both count usage (bandwidth, function invocations, build minutes) against free-tier limits and will ask you to upgrade — or throttle — when you cross them. Concern Vercel (Hobby) Netlify (Free) Cloudflare Pages (Free) Bandwidth Metered, capped Metered, capped Unlimited Build minutes Limited Limited Limited (per-month build count) Serverless/e

WIRED spoke with an engineer who was interviewed by Edward “Big Balls” Coristine for a government engineering role at the NDS, the White House agency staffed by other DOGE veterans.

Moove is scaling up the autonomous vehicle fleet management side of its business and plans to someday own, not just manage, Waymo robotaxis.

Lightspeed partners Josh Machiz and Claire Zau stopped by the Equity studio to talk about the strategies behind their growing social media presence and their podcast, Lightwork.

Venture firms are turning to creators to build trust with the next generation of founders before a check is ever written. It’s a trend that’s been building with a16z’s acquisition of Erik Torenberg’s Turpentine podcast and OpenAI’s acquisition of TBPN. Lightspeed Venture Partners just made its own notable hire in that vein, bringing on Claire Zau, a seed investor with a major following on Instagram and […]

Anthropic and OpenAI models’ unprompted actions forced halt to UK cyber tests.

Lets start with a bit of back story. I am a full stack developer. Developer being the keyword here, not a QA developer. But in my current role, I was recently asked to come up with a testing suite for the web application and the Flutter app I was managing and maintaining. At that time, I didn’t have anything better to do and thought this would be a fun little project to work on for a couple of weeks. Boy o boy, I was wrong. People in QA are so opinionated. Everyone has their preferred framework, structure, naming convention, abstraction, folder structure and a very strong opinion about why your approach is wrong. Starting with the industry best practices I started by trying to follow the trends and best practices used in the industry. Page Object Models, reusable helpers, proper assertions and all the usual bits and bobs. For the web application, which was built with React, I chose Playwright. For the Flutter app, I went with integration_test . Sounded simple enough. The login test that took three hours The first test I tried to write was a simple login flow. Open the application Enter the username and password Press the login button Wait for the dashboard Easy, right? It took me ages. And by ages, I mean roughly three hours just to get the web test to pass reliably. The actual Playwright test ended up being around 300 lines once I included the boilerplate, setup, selectors, assertions, waits, Page Object Model structure and everything else needed around the actual journey. Then came the Flutter app. That one was worse. The app has its own custom way of starting different flavors, and both the web application and Flutter app are white-labelled products. That means there are a lot of variations to cover. Different branding, configurations, screens and sometimes slightly different user journeys. Before I could even test the login flow, I needed a pile of setup code just to launch the correct version of the app. The Flutter test eventually went beyond 500 lines, includ

AI photo editors look simple from the outside: upload an image, describe a change, and download the result. The hard part is everything around the model call. If you are building or evaluating a browser-based image editor, the workflow needs to protect the original file, reject bad inputs early, make retries safe, and help the user compare the result with the source. This article walks through a small implementation pattern that does that without turning the UI into a complex desktop editor. 1. Validate the image before upload Do not rely on the file extension. Check the MIME type, file size, and whether the browser can actually decode the image. const ACCEPTED_TYPES = new Set ([ " image/jpeg " , " image/png " , " image/webp " , ]); async function validateImage ( file ) { if ( ! ACCEPTED_TYPES . has ( file . type )) { throw new Error ( " Use a JPG, PNG, or WebP image. " ); } const maxBytes = 10 * 1024 * 1024 ; if ( file . size > maxBytes ) { throw new Error ( " The image must be smaller than 10 MB. " ); } const bitmap = await createImageBitmap ( file ); const dimensions = { width : bitmap . width , height : bitmap . height }; bitmap . close (); if ( dimensions . width < 64 || dimensions . height < 64 ) { throw new Error ( " The image is too small for a useful edit. " ); } return dimensions ; } This catches renamed files, broken images, and tiny inputs before they consume bandwidth or model credits. 2. Treat the prompt as a single edit contract Open-ended chat is useful, but it can make image editing unpredictable. A clearer UI asks for one concrete change at a time: remove the person on the right; replace the background with a plain white wall; repair the crease across the top-left corner; extend the image to a 16:9 frame. The request object should preserve that intent without mixing it with UI state: function buildEditRequest ( file , prompt , options = {}) { const normalizedPrompt = prompt . trim (). replace ( / \s +/g , " " ); if ( normalizedPrompt . length < 5 )

A lawsuit accuses Homeland Security of violating protesters’ free-speech rights—but the agency is using it to try to get access to the plaintiffs’ encrypted communications.

The serial entrepreneur joins the e-commerce company as CPO to lead its AI agents.

SpaceX won't build large cell towers but plans small base stations across US.

The legendary Google executive is joined by other outgoing Google execs in a joint mission to use AI to push forward the process of scientific discovery.

Google's AI brain drain continues.

The owner had already asked for the alert emails to stop. A fix shipped. Then another email landed. Then another. "ong it just ssent me abother email," he said, voice-dictated, unedited. Fifteen minutes later: "go another one." The system was reporting an outage that did not exist. The Transport That Only Ever Failed A 14-PR merge train had just moved every cron producer's alerting off shared email and onto Buzz, a Nostr-relay team chat. One producer per PR, each with its own liveness contract and a bead receipt. It shipped cleanly. But the library backing those producers carried a default that had only one job: fail. AF_BUZZ_CMD = " ${ AF_BUZZ_CMD :- af_default_buzz_post } " af_default_buzz_post returned 1 with "no Buzz transport injected". Every caller that sourced the library (which is every cron producer) exhausted its Buzz retries and fell through to the email floor. The system reported a false Buzz outage while the relay was healthy. It did this 2 to 5 times per hour. Evidence arrived in the logs: 581 dedup markers, a steady stream of "[INTENT ALERT FLOOR: Buzz unreachable]" emails, and sweep.log showing buzz=ok only for the handful of callers invoked through the CLI entrypoint rather than by sourcing the library. That asymmetry was the bug. The CLI had a one-line fixup swapping in the real transport, annotated in a comment as "the library path is unchanged". The library path did not, and the cron producers all take the library path. The fix promoted the real transport to the default for both seams. af_buzz_transport already discovers the installed buzz-notify.sh and already fails closed when it is genuinely missing. The dead CLI fixup was deleted. Fail-closed behavior survives, but now it is conditional on genuine absence rather than on every caller remembering to opt in. Why not migrate callers one at a time? Because the per-caller route leaves the next new producer to rediscover this the same way. Flipping the default fixes the class, not the instance. The

TypeScript Strict Null Checks in 2026: Real-World Patterns for Handling undefined Without the Noise This article was written with the assistance of AI, under human supervision and review. Most TypeScript null safety problems stem from teams treating strictNullChecks as a boolean toggle instead of a design constraint. The compiler flag eliminates an entire class of production bugs, but codebases that flip it on without adjusting their patterns end up drowning in type assertions and optional chaining operators. The result is worse than the original false confidence wrapped in noise. The fundamental issue is that JavaScript conflates absence and failure. A missing property, an API error, and an uninitialized variable all return undefined or null , but they represent completely different failure modes. When teams enable strictNullChecks without encoding these distinctions into their types, the compiler forces them to handle every potential undefined the same way. That leads to defensive checks that obscure intent and catch nothing of value. The correct approach treats null safety as a type design problem. Discriminated unions encode why a value is missing. Branded types prove non-nullability at the boundary. Type guards narrow only when the business logic demands it. The patterns are simple, but they require understanding what the compiler is actually checking and what guarantees your code actually needs. This post covers the essential patterns teams need to write null-safe TypeScript in 2026 without the noise. Apply these in production and the difference will be immediate. Key Takeaways strictNullChecks eliminates runtime null errors only if your types encode why values are missing, not just that they might be missing. Discriminated unions outperform null returns for API responses because they force exhaustive handling of failure cases at compile time. Non-null assertions ( ! ) are acceptable at proven boundaries where external systems guarantee non-null values, but ne

Bridging Design and Code to Empower Local Businesses As a full-stack developer specializing in JavaScript and React, one of the most exciting ventures I'm currently on is building ready-made websites and Next.js templates through Softchic. This isn't just about coding; it's about deeply understanding the needs of businesses, particularly within the vibrant and rapidly evolving Nigerian market, and translating those into high-performance, beautiful web solutions. Why Next.js? Performance, SEO, and Developer Experience My choice of Next.js as the primary framework for these templates was deliberate: Performance: Server-side rendering (SSR) and static site generation (SSG) capabilities are crucial. In areas where internet speeds might vary, a fast-loading website isn't just a nice-to-have; it's essential for user retention and conversion. SEO: For businesses looking to establish a strong online presence, robust SEO capabilities out-of-the-box mean our templates provide a solid foundation for discoverability. Developer Experience: Building with Next.js allows for efficient development, leveraging the power of React while simplifying routing, data fetching, and API routes. This means faster iteration and higher quality templates. The Nigerian Market: Unique Challenges, Immense Opportunity Crafting templates specifically for the Nigerian market presents a fascinating set of considerations: Design Aesthetics: Understanding local preferences in terms of color palettes, layouts, and user flows is critical. It's not just about what looks good globally, but what resonates locally. This is where my dual role as creative director for promotional materials comes into play – applying that eye for design directly to the templates. Mobile-First Mentality: A significant portion of internet users in Nigeria access the web via mobile devices. Every template is meticulously designed with a mobile-first approach to ensure optimal responsiveness and user experience on smaller screens. Aff

I tried the obvious nerd experiment on a fresh Windows machine: let an AI agent handle setup. It looked clever for about two minutes. Then I watched OpenClaw get stuck on installer checkboxes, pause on modal windows, and generally do the digital equivalent of forgetting why it walked into the room. While it was still fighting one installer, I switched tactics: Ninite for the common app bundle WinGet for package installs I wanted to keep and rerun PowerShell for the boring system-level stuff GPT-5 or Claude for planning, not clicking That combo finished 18 app installs before the agent recovered. And after reading through this r/openclaw thread , I think the real lesson is bigger than Windows setup: GUI-driving agents are the wrong abstraction for deterministic work. If the task is "figure out what this machine needs," use a model. If the task is "install these 18 things and stop being interesting," use scripts. The mistake: asking an agent to be a mouse I’m not anti-agent. I’m anti-fragile-automation. OpenClaw, GPT-5, and Claude are useful when the problem is ambiguous: "Set this machine up for Python, Docker, VS Code, Node, and a local Ollama stack" "Compare package managers and suggest the cleanest install path" "Draft a setup script and explain what might fail" They are much less useful when the problem is fully deterministic: Click Next Decline the bundled toolbar Choose default install path Wait Repeat 17 times That second category is where WinGet, Ninite, and PowerShell win by being boring. Boring is good. This is the same pattern you see in real automations in n8n, Make, Zapier, or custom agent workflows: let GPT-5 or Claude interpret messy input let deterministic steps execute the plan keep the model out of the loop unless judgment is required That architecture is faster, easier to debug, and usually cheaper. What actually worked on a fresh Windows setup Here’s the split I’d use again. Job Best tool Install common desktop apps fast Ninite Create a repeatable