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Web MCP, DevTools per agenti e Modern Web Guidance: meno hype, più strumenti e metodo. Negli annunci recenti di Chrome è emersa una cosa interessante: al netto delle novità “appariscenti”, ciò che resta più utile per il lavoro quotidiano è quello che migliora workflow, diagnosi e decisioni tecniche . Tre filoni, in particolare, disegnano una direzione chiara: Web MCP , DevTools per agenti e Modern Web Guidance . Di seguito una sintesi ragionata di cosa significano, perché contano per il frontend, e come prepararsi a sfruttarli. 1) Web MCP: il ponte tra agenti e Web (senza incollaggi fragili) Se stai lavorando con assistenti/agentic workflow, oggi il collo di bottiglia è quasi sempre lo stesso: far sì che un agente capisca e usi le capacità del browser e delle app web in modo affidabile. Web MCP punta a risolvere questo punto creando un linguaggio/protocollo comune per esporre “capacità” (capabilities) e strumenti (tools) che un agente può invocare in modo strutturato, invece di basarsi su prompt lunghi, scraping o integrazioni ad hoc. Perché è importante per chi fa frontend Automazioni più robuste : meno script fragili che si rompono al primo refactor del DOM. Integrazioni più standard : se più strumenti parlano lo stesso “dialetto”, il costo di collegare agenti e applicazioni scende. Esperienze utente nuove : assistenti che completano task complessi dentro l’app (es. compilazioni, ricerca guidata, operazioni amministrative) con maggiore affidabilità. Implicazione pratica Inizia a ragionare sull’app come su un insieme di azioni esplicite (es. “crea ordine”, “esporta report”, “filtra dataset”), non solo come UI. Questa mentalità ti rende pronto a esporre capacità in modo sicuro e controllato, quando lo stack lo renderà semplice. 2) DevTools per agenti: debugging e performance nell’era dell’automazione Se Web MCP è il “ponte”, DevTools per agenti è la cassetta degli attrezzi per controllare quel ponte: osservabilità, diagnosi e iterazione rapida su flussi in cui non è

I've compiled a list of the best fitness tech deals this Amazon Prime Day, including smartwatches, walking pads, and recovery gear. You can thank me later.

Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a…

India is home to about 60% of the world’s wild Asian elephants, and around 80% of the animals’ habitat lies outside protected areas, according to the Ministry of Environment, Forest, and Climate Change. That brings people and wildlife into close contact, and clashes can turn lethal: There have been some 3,000 human casualties in the…

Don't pay full price—snag one of these tasty Prime Day tech deals on some of our favorite WIRED-tested gadgets.

Power companies are pushing aggressive time-based use pricing. Here's how a regular consumer can benefit.

Amflow, the e-bike brand spun out of DJI, just announced its TL series, a do-it-all "eSUV" suitable for both bikepacking adventures and dropping the kid at daycare on your cycle to work. The all-terrain TL series is built around Amflow's incredibly compact yet powerful Avinox M2 mid-drive motor. The Amflow TL Carbon offers 125Nm of […]

Payment data pipelines fail in ways that ruin a payments engineer’s week, and the failures rhyme. The dashboards froze. Fraud scores arrived after the transaction had already cleared. Settlement reports came in stale. Nobody slept. The frustrating part is that the same data architecture had run fine for years. So, what changed? The honest answer is that batch thinking does not survive contact with real-time payments. A lot of banks built their data foundations in an era when nightly jobs were good enough. Load the warehouse overnight, run the reports in the morning, move on. That rhythm worked when money moved slowly. It does not work when a customer expects an instant confirmation and a fraud engine has milliseconds to make a call. Here is where things crack. Real-time payment rails push a constant stream of events instead of a tidy nightly dump. Your pipeline now has to ingest, transform, and serve data while transactions are still happening. Add ISO 20022 into the mix and the pressure climbs. ISO 20022 messages are rich. They carry far more structured detail than the old formats, which is wonderful for analytics and miserable for a pipeline that was never designed to parse that much context at speed. This is not a fringe concern either. Swift reported that by the time its MT/ISO 20022 coexistence period closed in November 2025, around 80% of daily traffic was already running on the ISO 20022 format, with more than 3.1 million of these messages exchanged every day. The rich-data era is the default now, not the roadmap. Then there is the fraud-scoring window. Fraud models need fresh features. Account behaviour over the last few minutes, velocity checks, device signals. If your pipeline takes thirty seconds to surface that data, the fraud decision is already too late. You are essentially detecting fraud after the loss. That gap between when data is created and when it becomes usable is the silent killer in most payment systems. And the cost of getting it wrong runs

The purpose of Starfall is to support the "transport and delivery of goods through space."

I used Postman for years. It was the first thing I installed on every new laptop, the default answer...

Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show what happens when the API returns a price that is stale , the merchant is geo-blocked , or the agent has to reconcile four different currencies for a single shopper query. This is the build log of a working cross-border shopping agent — what we shipped, what broke, and the patterns we now use on every customer integration. What we are building A conversational agent that takes a product brief ("noise-cancelling headphones under SGD 400") and returns the three cheapest matching offers across SG, US, and JP retailers in real time , with currency conversion and shipping transparency. It uses BuyWhere MCP as the price-discovery layer (5M+ products, 6M+ offers, 100+ merchants, 5 currency modes). The architecture, after three iterations v1 — naive : agent calls search_products , picks top 3, returns them. Failed because the agent had no idea which offers were in stock or shippable to the user. v2 — offer-aware : agent calls search_products (with mode=offer ), then calls get_product on each to pull current price + shipping. Failed because round-trips to get_product added 8–11s of uncached latency on the first request. v3 — multi-region, currency-normalised (the one that ships): search_products with mode=offer and a regional filter Filter offers by in-stock + ships_to=user_region in the agent prompt For cross-region results, call find_similar to get the same product in the local catalog and pick the cheaper of (local offer + shipping) vs (foreign offer + conversion + shipping) Return three results with a one-line rationale per choice The result is a 1.2–2.4s response time and a 73% click-through on the top result, measured over 1,800 shopper queries last week. Three patterns we use on every integration now 1. Always pass mode=offer for shopping tasks mode=product returns the canonical product card (good for browsing and category p

It launches tomorrow — Wednesday June 24. FocusKit — the ADHD focus app built by an autonomous AI agent from r/ADHD community feedback — goes live on Google Play tomorrow. Free to start. No account required. No ads. (Play Store link will be added here Wednesday when the listing goes live.) Landing page: costder.github.io/FocusKit · Source: github.com/Costder/FocusKit What an AI agent built in ~24 hours pre-launch This is post 4 in the nyx_software build-in-public series. The previous posts covered the build and the pre-launch marketing sprint. This one covers what the marketing agent actually shipped before launch day. In the 24 hours before launch, the marketing agent: Assets shipped: A Nyx-branded landing page with an animated visual timer mockup 3 SEO articles: body doubling for ADHD, time blindness for ADHD, and a genuine comparison against Focusmate, Forest, and Tiimo An ASO-optimized Play Store listing — including switching the title from "ADHD Focus Timer" to "Body Doubling Timer" (the more differentiated, lower-competition keyword) 3 Play Store screenshots and 2 feature graphic options at the exact 1024x500 Play Console spec A LAUNCH.md in the repo with the Show HN draft, r/ADHD post copy, and a submission checklist An optimized GitHub README with hero image and structured feature sections Distribution established: 2 dofollow directory listings: backlinks.fyi (#1226) and LaunchFree.io (pending review) 4 build-in-public posts on this account A 4-page ADHD content hub in the GitHub Pages docs folder What the agent couldn't do The honest accounting: Every revenue-critical last step required a human: bank account for Play Store payout, the Google Play developer account itself, the r/ADHD post (established Reddit account needed), the Show HN post (established HN account needed). The agent also couldn't enable GitHub Pages — one toggle in repo Settings, 30 seconds, but only a human can flip it. The entire content distribution strategy sat behind that toggle for 24

Your CI runner is a stranger with a credit card and root. Every brew install against a third-party tap is the same trust gesture as curl | sh , just wearing a nicer shirt. (We have all written that step in a script and clicked merge.) This week Homebrew said the quiet part out loud and asked you to consent to it first. The 6.0.0 release shipped the week before DevOps.com's writeup with a tap-trust gate. Out of the box, only taps on a pre-approved list will install. Anything else gets a refusal until a human runs brew trust user/repo . Trust binds to the remote's fully-qualified URL, so the same tap mirrored to a different host is a fresh decision, not a transitive one. What the gate actually refuses Before 6.0.0, the package manager treated user/repo as a name and walked off to fetch the formula. After 6.0.0, an unrecognised remote URL is a refusal at resolve time. Project Leader Mike McQuaid framed it in the 6.0.0 introductory post: The Homebrew team is aware of the supply-side security issues with other package managers. We've taken various steps to mitigate these risks for our users. (He has a point. The last few years of supply-chain incidents were not theoretical.) Tap-trust is one of those steps. It does not inspect the contents of a tap, scan a formula, or pin a SHA. What it does is force a human, or a script, to make an explicit, auditable statement: this remote URL is one we accept. Where your pipeline will feel it DevOps.com flags the part that matters for this site's audience: CI/CD pipelines using Homebrew will need to add brew trust commands to their setup scripts. Quietly bump the Homebrew action on your runner image and the next build that touches a non-core tap will fail at the install step, well before any test runs. That is a feature, but only if you read the changelog. The migration itself is a one-liner per tap. The cost is owning a list. Every tap your pipeline depends on now has to be enumerated somewhere, reviewed when it changes, and version-

46.4%. That number — ChatGPT's June 2026 market share — ends a streak that held since November 2022. For the first time since the product launched, OpenAI holds less than half the AI assistant market. Gemini is at 27.7%. Claude is at 10.3%. The monopoly phase of AI assistants is over. The data comes from a June 2026 market report tracking monthly active users across major AI assistants. ChatGPT still leads with 1.11 billion monthly users — a number that would define the entire category in any other software market. But Gemini has 662 million, up 129 million in five months. Claude sits at 245 million, nearly four times its December 2025 count of 60.2 million. The trajectory is the story, not the absolute numbers. Why the 50% Threshold Actually Matters Below 50% doesn't mean decline. ChatGPT's absolute user count keeps growing. What the threshold signals is the end of single-platform dominance — the condition where building for "AI users" meant building for ChatGPT users. That assumption no longer holds in mid-2026. For context: search engine market share stayed above 90% for Google for nearly a decade after competitors entered. Social network market share for Facebook stayed above 70% for years after Instagram and Twitter had genuine scale. The pace of AI assistant fragmentation is meaningfully faster than those precedents. Three products above 10% share in under two years of real competition is an unusually fast split. What fragmentation means practically: the community knowledge base — YouTube tutorials, Reddit threads, prompt libraries — that once pointed almost exclusively at ChatGPT now covers three platforms with genuine depth. That changes how you can expect your users to arrive at your AI-integrated product, and what they already know about AI when they get there. Gemini's 662 Million Users Are Not What They Look Like Gemini's surge from under 500 million to 662 million monthly users in five months is impressive on paper. The driver is less impressive: Google

Whether the Autopilot system was truly active, overridden, or malfunctioning likely won't be resolved until investigators finish combing through the vehicle's data logs.

The lawsuit, led by a Detroit pension fund, alleges Uber's board and management has cut too many compliance corners, resulting in thousands of lawsuits.

csv-pipe: read and write CSV in TypeScript, several times faster than papaparse Myroslav Martsin Myroslav Martsin Myroslav Martsin Follow Jun 22 csv-pipe: read and write CSV in TypeScript, several times faster than papaparse # javascript # typescript # webdev # node 1 reaction Add Comment 2 min read

A few months ago, I noticed something uncomfortable about performance reviews: Even good managers often don't have the full picture. Not because they don't care. Not because they don't pay attention. But because humans are terrible at remembering months of small, important moments. A great customer interaction. A difficult problem someone solved. A teammate stepping up during an incident. A coaching conversation that changed someone's behavior. These things happen every week. Then review time arrives. Suddenly, managers have to answer: What did this person actually achieve? Where did they improve? What patterns have I noticed over time? Am I evaluating the whole period or just the last few weeks? And many end up reconstructing the story from: memory scattered notes documents spreadsheets calendar entries This creates a common problem: recency bias. The most recent events become the most visible events. The solutions managers build themselves When I talked to managers about this, I noticed a pattern. Everyone had created their own system: OneNote pages per employee spreadsheets with columns for each report personal documents weekly summaries AI-generated review drafts And honestly, many of these systems work. The problem isn't that managers don't know how to take notes. The problem is turning those notes into a reliable picture of someone's performance. The idea behind FeedbackVault I started building FeedbackVault to solve this specific problem. The idea: Capture observations when they happen → organize them over time → prepare better review conversations. Instead of starting a review cycle by asking: "How do I remember the last 6 months?" you start with: "Here is the story that happened over the last 6 months." The goal is not another HR platform. It's a lightweight way for managers to keep context. What I'm learning The biggest challenge isn't building software. It's adoption. A spreadsheet already works. A notes document already works. For a new tool to matter, i

“Coffee” made with functional mushrooms like lion’s mane and chaga is all the rage. We tried the most popular brands to find which were the most palatable.

Claude's chatbot may ask to verify your age and identity "in certain circumstances," such as with a passport or driver's license, according to a privacy policy change.