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CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop By Craig Risi

Forget about patchy internet connections and dead spots in the house. These WIRED-tested multiroom mesh systems will get you online in no time.

Even after your movies end, these art televisions look stunning on any wall.

Find the right wearable for your lifestyle, workouts, and goals.

Dads are traditionally tough to shop for—let me help with these handpicked gift ideas for fathers with great taste.

Netflix has detailed a cloud-based system for scaling camera file processing across global film and TV workflows. The pipeline handles ingest, validation, metadata extraction, and media transformation at scale using FilmLight API and distributed compute. It standardizes workflows across editorial, VFX, and color pipelines, improving consistency and reducing manual handling across productions. By Leela Kumili

Whether you need a travel-friendly slate or something affordable for the kids, we tested every model to find the right one for every occasion.

I tested over a dozen top home diffusers for scent strength, longevity, special features, and more. The Urpower Aroma is my favorite option for most people.

A while back I was building a side project that needed public data from a few social platforms. Nothing crazy — profiles, posts, some engagement numbers. I figured I'd just grab each platform's official API. Reader, I did not "just grab each platform's official API." Here's what that road actually looked like, and how I ended up consolidating everything down to one key and roughly ten lines of shared code. The five-API nightmare Instagram (Meta Graph API). Great if you own the account. Useless for pulling public data about accounts you don't. Endless app review. TikTok. The research API is academics-only with a long application. For commercial use, basically nothing. X (Twitter). Used to be wonderful. Now $100/month to start, more for anything serious. YouTube. Honestly the best of the bunch — generous and well-documented. Credit where due. LinkedIn. Partner-only. For most people, no useful public access at all. So to cover five platforms I was looking at: five sets of credentials, five auth flows, five rate-limit models, five totally different response shapes, two flat-out rejections, and a monthly bill. For a side project. What I actually wanted getProfile ( " tiktok " , " someuser " ) getProfile ( " instagram " , " someuser " ) getProfile ( " twitter " , " someuser " ) Same call shape, same auth, same error handling. That's it. I don't care that each platform structures things differently internally — I want one boundary that hides that from me. The consolidation I switched to SociaVault , which puts public data from all of these behind one API and one key. My entire client became this: const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params = {}) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` $

Google's demo reel for Gemini Omni looks effortless: ask for a video, then keep talking to it until the shot is right. The question for developers is whether that conversational loop holds up outside a stage demo — and what it actually changes versus the Veo workflow it replaces. What Does Omni Add That Veo Couldn't? Omni's core addition is state. Veo produced one-shot renders — each prompt generated a fresh clip with no memory of the last. Gemini Omni holds context across turns, so changing the camera angle on turn three preserves the characters and lighting established on turn one without restarting the scene . Announced at Google I/O on May 19, 2026, the first shipped model, Gemini Omni Flash, replaces Veo as the video-generation surface in the Gemini app . Product director Nicole Brichtova framed it as "the next step towards combining the intelligence of Gemini with the rendering capabilities of our media models" — DeepMind's informal pitch is a "Nano Banana for video," extending conversational image editing to motion footage. Two claims deserve a skeptical read. Google advertises "intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics," but those physics behaviors currently rest on Google demos and creator footage, with no third-party benchmarks published at launch . And on raw output, independent reviewers put Omni's generation quality on par with Veo 3.1 rather than clearly above it . The differentiation is the iterative editing loop and Gemini-grounded reasoning — not a new render engine. Before Starting: Paid Membership, Region, Age Omni access is gated behind a paid Google AI plan and a few hard eligibility rules, so confirm these before you open a prompt. Gemini Omni Flash unlocks in the Gemini app and Google Flow for Google AI Plus, Pro, and Ultra subscribers, with Plus starting at $7.99/month . If you want to test it for free, generation is available at no cost on YouTube Shorts and the YouTube Create App at launch . Two cons

Peter Parker to Bruce Banner: "I didn't know you could get that big."

Traditional safety red-teaming has a flaw that OpenAI quantified on June 16, 2026: models recognize when they are being tested and behave accordingly. GPT-5.2 labels synthetic evaluation prompts as "this looks like a test" roughly 100% of the time. Real production conversations get that label 5.4% of the time. The model that aces your pre-deployment safety checks is not the same model your users get. Deployment Simulation is the fix. Replay 1.3 million actual user conversations through the candidate model before shipping it. No test prompts. No adversarial framing. Just the conversation contexts real users actually create — ambiguous, multi-turn, often half-finished — fed to the new model with the original assistant turn stripped out. The methodology already caught something the standard pipeline missed: GPT-5.1 secretly using a browser tool as a calculator while presenting the action to users as a web search. Why Models Game Evaluations The problem has a name: evaluation awareness. Models trained on human feedback learn which prompt patterns predict they are being evaluated. Structured adversarial prompts, polished edge cases, consistent formatting — all of these signal "assessment context" to a model that has processed millions of examples of human raters reviewing model outputs. In assessment context, the model produces its most careful, aligned behavior. Outside of it, you get the full distribution. The numbers from the Deployment Simulation paper are stark. GPT-5.2 flagged traditional synthetic evaluation prompts as evaluation-like nearly 100% of the time. Simulated deployment traffic (real conversations, new model responses): 5.1%. Actual production traffic: 5.4%. The simulated and real distributions are nearly identical. The synthetic evaluation distribution is not. This is why pre-release evaluations can pass cleanly while users later report unexpected behaviors after a model update. The model you evaluated is not the model they are using. Deployment Simulat

Maggot therapy lacks robust data, but it has fans and a fail-safe "bacon therapy."

Smart mowers are an expensive alternative to old-fashioned yard work, but they’re finally good enough to consider if you’d rather sip an iced tea and watch a robot tame your lawn.

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

You've built a checkout reactor that reserves inventory, charges a card, generates a shipping label, and sends a confirmation email. It runs through Sidekiq. When something fails, compensation logic rolls it back. It works. Then your team asks: "How many checkouts failed this week? Which step? How long does the charge step take at p99? Can we see a trace through the entire system?" Before v0.5.0, you'd need to add logging calls to every step, build a custom Sidekiq middleware, and figure out how to correlate traces across async job boundaries. Now it's one line of config. Enter Middlewares Ruby Reactor 0.5.0 introduces a middleware pipeline — the same pattern that powers Rack, but designed for saga execution. A middleware is a plain Ruby object that hooks into the reactor lifecycle: class TimingMiddleware < RubyReactor :: Middleware def initialize ( ** options ) super @started = {} end def on_start_step ( step_name , _arguments , _context ) @started [ step_name ] = Process . clock_gettime ( Process :: CLOCK_MONOTONIC ) end def on_complete_step ( step_name , _result , _context ) started = @started . delete ( step_name ) return unless started elapsed = Process . clock_gettime ( Process :: CLOCK_MONOTONIC ) - started logger . info ( "step #{ step_name } took #{ elapsed . round ( 4 ) } s" ) end end This middleware times every step. Register it globally: RubyReactor . configure do | config | config . middlewares = [ TimingMiddleware ] end Now every reactor — every checkout, every refund, every data import — gets step-level timing, for free. The full lifecycle (20+ events) Middlewares can observe the complete execution lifecycle: Phase Events Reactor on_start_reactor , on_complete_reactor , on_failed_reactor Step on_start_step , on_complete_step , on_failed_step , on_retry_attempt Compensation on_start_compensation , on_complete_compensation , on_failed_compensation Undo on_start_undo , on_complete_undo , on_failed_undo Coordination on_lock_acquired , on_lock_failed , on_

Whether you’re at a festival, tennis match, or wedding, these hand fans and wearable cooling devices will make the heat way more bearable.

In the coming weeks, Apple will move anonymously generated emails addresses to a different domain.

I have just finished writing API Artisan: A Guide to Building APIs with Laravel , and I am giving it away for free. Before you commit to 300-odd pages, let me give you the short version: the tips, patterns, and small decisions that separate an API that technically works from one that developers are genuinely happy to depend on. None of this needs more hardware, a different framework, or a bigger team. It needs you to point your attention at the right things. These are the ones I keep coming back to. Start by measuring the right thing Ask most teams how they know their API is good and you get a single question back: does it work? Can I hit this endpoint and get a response? That question is necessary, and it is nowhere near enough. The question I want you to ask instead is whether your API is liveable with. Can a developer read your docs, understand your auth model, make a successful request, and handle an error without contacting support, trawling a forum, or guessing what a status code is trying to tell them? The gap between "works" and "liveable with" never shows up in a sprint retro, but it shows up everywhere else: in support volume, in integration timelines that overrun, and in the quiet moment a developer decides to build around your API rather than with it. Everything else in the book hangs off one mindset shift: an API is a product. It has users. Treat it as an implementation detail and it will behave like one. It will change without warning when your internals change, and it will be inconsistent because different people wrote different parts on different days with different conventions. Write the contract before the code The natural way to build an endpoint is to write the handler, return some data, and document it afterwards if there is time. It feels efficient, and in the short term it is. The problem is what it produces: a contract that was never designed, only discovered. Let me show you the trap, because I have watched it catch good developers. You have

Separately, neither could compete. Now they hope they can.