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Dai metadati in una lingua a 20 localizzazioni senza impazzire tra click e schermate: un flusso pratico per indie e piccoli team. Localizzare un’app non significa solo tradurre le stringhe dell’interfaccia. Una buona parte dell’acquisizione organica passa dai metadati su App Store Connect : titolo, sottotitolo, descrizione e keyword. Il problema è che, quando provi a farlo “a mano” dal pannello web, diventa subito un lavoro di pura resistenza: apri la scheda, cambi lingua, compili i campi, salvi, ripeti. Ora moltiplica per 10–20 lingue. Per molti indie (e in generale per chi ha poco tempo e zero voglia di click ripetitivi) il punto di svolta è usare ASC CLI per rendere questa attività automatizzabile, ripetibile e verificabile . Perché la localizzazione dei metadati è un caso d’uso perfetto per una CLI Dal punto di vista del flusso di lavoro, i metadati App Store hanno tre caratteristiche che li rendono ideali per l’automazione: Sono campi strutturati (title, subtitle, description, keywords): non stai “inventando” contenuti ogni volta, stai trasformando contenuti. Sono ripetitivi per lingua : la sequenza di operazioni è identica, cambia solo la locale. Sono tanti : più lingue aggiungi, più l’approccio manuale scala male (tempo, errori, incoerenze). Con una CLI, invece, il lavoro si sposta dal “fare cose” al definire un processo : prendi i metadati di partenza, generi le varianti linguistiche, applichi l’update in batch. Cosa conviene localizzare (e cosa no) In genere ha senso includere in un passaggio di localizzazione “massiva”: App name / title (attenzione ai limiti e ai trademark) Subtitle (spesso è la parte più ASO-oriented) Description (qui conta più la leggibilità che la traduzione letterale) Keywords (campo delicato: va adattato, non tradotto alla cieca) Al contrario, è meglio trattare con più cautela: Claim e frasi marketing molto creative : in alcune lingue risultano innaturali se tradotte letteralmente Keyword strategy : la ricerca utenti cambia per mercat

Two men pleaded guilty in the United Kingdom this week to criminal charges stemming from an August 2024 cyberattack that crippled Transport for London, the entity responsible for the public transport network in the Greater London area. The duo were key members of a prolific cybercrime group known as Scattered Spider, and their guilty pleas came on the first day of what was expected to be a six-week trial.

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

A stat from 2026 that should stop you cold: 88% of organizations reported a confirmed or suspected AI agent security incident in the past year (92.7% in healthcare). And more than half of all agents run with no security oversight and no logging — naked. The problem isn't that the AI isn't smart enough. It's that almost nobody welded boundaries around it. And boundaries are exactly where rigor lives. The incident list: speed flooring it, boundaries naked The last couple of weeks of security signals line up scarily well: 88% of orgs reported confirmed/suspected AI agent incidents in the past year; healthcare 92.7% ; over half of agents have no security oversight or logging. Supply chain is the front door. A plugin-ecosystem supply-chain attack harvested agent credentials from 47 enterprise deployments ; attackers used them to reach customer data, financial records, and proprietary code — undetected for six months. A public skills marketplace at one point hosted 824 of 10,700 malicious "skills." Config is an attack surface. Check Point disclosed remote code execution in a popular coding agent via poisoned repository config files ; MCP (Model Context Protocol) is the connective tissue across nearly every incident this year — poisoned configs, malicious marketplace skills, unauthenticated exposed MCP servers. By early 2026, at least ten public incidents across six major AI coding tools were attributed to " agents acting with insufficient boundaries. " The industry's own summary: AI agent security in 2026 is a supply chain problem first, a prompt-injection problem second. And every one of these shares a single root cause — the agent can act, but there's no architectural boundary on what it can touch, change, or call. Why "naked" is inevitable: bolt-on boundaries always leak Why do half the agents run with no oversight? Because in the mainstream approach, boundaries are bolt-ons : an allow-list here, a gateway there, logs you read after the fact. The trouble: The tools an

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight . What if you could predict your stress levels 24 hours in advance? 🚀 In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN) . By leveraging the Oura Ring API and deep learning, we’ll transform non-stationary biometric time series into actionable insights. Whether you're into time series forecasting or building the next big health-tech app, mastering Temporal Convolutional Networks (TCN) is a game-changer for handling long-term dependencies without the vanishing gradient headaches of traditional RNNs. For those looking for more production-ready examples and advanced biometric signal processing patterns, I highly recommend checking out the deep-dives at WellAlly Blog , which served as a major inspiration for this architecture. The Architecture: Why TCN? Traditional LSTMs are great, but they process data sequentially, making them slow and prone to memory loss over long sequences. TCNs, however, use Dilated Causal Convolutions , allowing the model to look back exponentially further into the past with fewer layers. Data Flow Overview graph TD A[Oura Cloud API] -->|Raw JSON| B(Pandas Preprocessing) B -->|Cleaned HRV/Activity| C{Feature Engineering} C -->|Sliding Windows| D[TCN Model Training] D -->|Dilated Convolutions| E[Stress Trend Prediction] E -->|24h Forecast| F[Dashboard/Alerts] style D fill:#f9f,stroke:#333,stroke-width:2px Prerequisites To follow along, you'll need: Tech Stack : Python, TensorFlow/Keras, Pandas, Scikit-learn. Data : An Oura Cloud Personal Access Token (or use the mock data generator provided). Difficulty : Advanced (Buckle up! 🏎️). Step 1: Fetching Biometric Data First, we need to pull our "Readiness" and "Sleep" data. Oura provides high-resolution HRV samples (usually 5-minute intervals during sleep).

This week's AI tooling news splits cleanly between infrastructure you can ship today and capability bets that require more careful evaluation. Anthropic dropped two significant releases—Fable 5 and Managed Agents updates—while the Workflow SDK landed a cancellation primitive that eliminates entire categories of homegrown plumbing. Underneath all of it, a sharp incident review from Anthropic is the most practically useful thing published this week if you're running multi-turn agents in production. Workflow SDK adds AbortController cancellation support The Workflow SDK now threads AbortSignal through workflow steps, using the same web-standard API you already use with fetch . Pass an AbortSignal into your workflow, inspect it inside steps, and you get cooperative cancellation that survives durable suspension and replay. This matters because cancellation in long-running workflows has historically required custom infrastructure—timeout flags passed through context, manual cleanup hooks, bespoke race logic. That's not interesting code to write or maintain. With AbortController support, you get timeout steps, request racing, and parallel work cancellation with patterns your team already knows. Two important caveats: this requires workflow@beta , and cancellation is cooperative. The runtime won't forcibly terminate a step—your step code needs to inspect the signal and respond. If you have steps with opaque third-party calls that don't accept signals, you're still writing wrapper logic. Verdict: Ship. If you're on Workflow SDK 5 and running long-horizon workflows with timeout or race requirements, upgrade and wire this in now. The pattern is standard, the boilerplate reduction is real, and there's no meaningful downside if your steps are already structured around explicit control flow. Anthropic adds dreaming, outcomes to Managed Agents Two distinct additions here. Outcomes let you define explicit success criteria enforced by a separate grader agent—replacing manual prompt

This is Lowpass by Janko Roettgers, a newsletter on the ever-evolving intersection of tech and entertainment, syndicated just for The Verge subscribers once a week. When Fox announced its acquisition of Roku earlier this week, executives of both companies were quick to promise that not much would change in the near future. Sure, getting its […]

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

Introduction Enterprise orchestration is evolving. As systems grow more distributed and...

Stack Overflow has announced Stack Overflow for Agents, a beta API-first knowledge exchange aimed at AI coding agents rather than human developers. The service is presented as a way to close what the company calls the Ephemeral Intelligence Gap, where agents repeatedly rediscover the same fixes and patterns in isolation instead of sharing them through a common memory. By Matt Saunders

Obsidian prompts beat open-ended reflection every time: median review time across 6 weeks was 14 minutes, fastest was 9, slowest was 22 (and that week genuinely deserved 22). I ran the GTD-adjacent version faithfully for six weeks — 90 minutes, full capture sweep, energy audit, the works. Then less faithfully for two months. Then I stopped entirely and didn't notice for three weeks. That last part is the failure mode nobody writes about. The format wasn't wrong; it was sized for a version of my week that rarely existed. The fix wasn't a better framework. It was shorter, closed questions. My Obsidian template has seven prompts, none of them open-ended: what shipped, what didn't, what I avoided and why, one thing to drop, one thing to protect. One-to-three sentence answer ceiling per prompt, hard stop. Open questions like "how was your week?" generate rumination. Closed questions generate decisions. That distinction is doing almost all the work. The Notion version I ran before this taught me something useful about tool selection too. I built rollups — tasks closed this week, open tasks by project, inbox count, stalled for 7+ days — and they worked exactly as designed. What Notion couldn't do was get out of its own way during actual reflection. Every time I tried to think through what went wrong, I'd end up reorganizing a database instead. Forty minutes later, new linked database, zero review completed. The same flexibility that makes Notion a good data layer makes it a bad "close the loop and move on" environment. Obsidian's plain-file simplicity is the right call for the thinking layer — and completely wrong for the data layer. Neither tool alone is the honest answer. There's also a cautionary note from my automation setup: a Zapier zap that pushed completed tasks into Notion for weekly rollup ran cleanly for two months, then silently broke when my task manager updated their API response format. Modified tasks started logging as completed. My rollup became noise befo

PyTrees look deceptively simple. You flatten a nested Python object into leaves, keep a structure descriptor, and later rebuild or map over the same shape. That abstraction is powerful enough to carry optimizer states, model parameters, batched inputs, gradients, and sharding annotations. It is also just ambiguous enough that three major frameworks implement three subtly different languages under the same idea. This note compares JAX jax.tree_util , PyTorch torch.utils._pytree , and TensorFlow tf.nest . I tested the behavior in two environments: an older stack with JAX 0.4.35, PyTorch 2.2.2, TensorFlow 2.20.0, and a newer stack with JAX 0.10.0, PyTorch 2.12.0, TensorFlow 2.21.0. Most flatten/unflatten semantics were stable across these versions. The main version-sensitive result is PyTorch: _pytree.tree_map in 2.2.2 accepts only one pytree, while 2.12.0 supports multiple pytrees and behaves much closer to JAX prefix-style mapping. The short version: JAX treats pytrees as a transformation language, PyTorch is converging toward that model in torch.func , and TensorFlow exposes a broader nested-structure utility through tf.nest . Those differences show up exactly where backend-agnostic libraries usually hurt: None , dictionary order, custom containers, tree_map , autodiff, and vectorization. The Shape Of The APIs The three APIs have the same surface story but not the same contract. from jax import tree_util as jtu from torch.utils import _pytree as tpu import tensorflow as tf leaves , treedef = jtu . tree_flatten ( tree ) tree = jtu . tree_unflatten ( treedef , leaves ) tree = jtu . tree_map ( f , * trees ) leaves , spec = tpu . tree_flatten ( tree ) tree = tpu . tree_unflatten ( leaves , spec ) tree = tpu . tree_map ( f , tree ) # PyTorch 2.2.2 tree = tpu . tree_map ( f , * trees ) # PyTorch 2.12.0 leaves = tf . nest . flatten ( tree ) tree = tf . nest . pack_sequence_as ( structure , leaves ) tree = tf . nest . map_structure ( f , * structures ) Flattening means "whi

This is Lowpass by Janko Roettgers, a newsletter on the ever-evolving intersection of tech and entertainment, syndicated just for The Verge subscribers once a week. Severance. Pachinko. Silo. Ted Lasso. Over the past couple of years, a number of Apple TV shows have become hits with audiences and critics alike. And yet, compared to the […]

Webflow's code export has two problems. It is only available on paid Workspace plans, and even when you pay, it does not include your CMS content: collection lists export as empty states, collection pages export with nothing in them. If your site has a blog, the export gives you a site without a blog. Forms and search are disabled in exported code too, per Webflow's own docs. Meanwhile, the published site is sitting on a CDN, fully rendered. Every CMS page is real HTML. wget --mirror will happily fetch all of it. What wget gives you, though, is not deployable. I migrated a production Webflow site this way and hit the same five breakages everyone hits, so I turned the fixes into a Claude Code skill that runs the whole workflow. This post is the five breakages, because they are useful whether or not you use the skill, and they apply to Framer, Squarespace, and friends with different domain names. Setup: the mirror itself The one wget incantation that matters, because Webflow serves assets from a separate CDN domain and you have to tell wget to follow it: wget --mirror --convert-links --adjust-extension \ --page-requisites --span-hosts \ --domains = yourdomain.com,cdn.prod.website-files.com \ --no-parent https://yourdomain.com/ This downloads every page plus the CSS, JS, images, and fonts they reference, and rewrites URLs to relative paths. It looks complete. It is about 90% complete, and the missing 10% is invisible until the page renders blank. Breakage 1: the page renders blank, console says "integrity" The symptom: your mirrored page shows raw unstyled text or nothing at all, and the console says Failed to find a valid digest in the 'integrity' attribute . The cause is subtle. Webflow ships its <link> and <script> tags with SHA-384 SRI hashes. wget's --convert-links rewrites URLs inside the downloaded CSS files, which changes their bytes, which means the SRI hash no longer matches, which means the browser silently refuses to apply the stylesheet. The file is right

Every low-code vendor says "we support customization." But supports is a weasel word — recoloring a button is customization, and rewriting a scheduling engine is also customization. What actually decides whether a platform locks you in is how far up its extensibility goes. Here's a ruler. The three levels of customization Level What you can do Most no-code A real dev framework L1 — Config Fields, forms, workflows, permissions, themes ✅ ✅ L2 — Extension Custom components, custom actions, external API calls, business rules ⚠️ limited ✅ L3 — Framework Modify/extend the core, custom engines, deep rewrites, source under control ❌ wall ✅ (when open/controllable) Where it stops is where your ceiling is. Plenty of no-code platforms are delightful at L1, then hit "can't do that" at L2/L3 — and you retreat to writing your own thing next to it. Now low-code is the burden. Why you get locked in Black-box SaaS — no source, so any extension point the vendor didn't expose is simply out of reach. Two sources of truth — your extension code and the platform's config live in different systems, so a platform upgrade breaks/voids your work. Crippled self-hosting — the on-prem edition quietly drops extension capabilities. Closed ecosystem — only their component marketplace; your stack can't get in. How model-driven + open source raises the ceiling One unified extension system — your extensions (custom fields/components/actions) and the platform itself are built on the same metadata. Extension isn't a bolt-on, it's a first-class citizen — upgrades don't wipe your customizations. Source under your control — open + self-hostable is what makes L3 framework-level extension actually possible: an extension point you can't reach, you can add. AI at the metadata layer — AI-generated extensions land in the same model, so they stay maintainable and evolvable. That's the road Oinone takes: 100% metadata-driven, front + back end open source, self-hostable — customization reaches L3. How to stress-tes

Data Visualizer Live Demo 🌐 Try it live: https://datavisualizer.urlmediainspector.dev/ What It Is Data Visualizer is a visual workspace where developers can explore, transform, execute, and understand data using interconnected nodes on an infinite canvas. Instead of jumping between API tools, JSON viewers, spreadsheets, code editors, schema inspectors, and visualization platforms, everything happens inside a single interactive environment. Each node represents a specific capability and can be connected together to create powerful workflows for data exploration, processing, automation, and analysis. Key Features Infinite Visual Workspace Work on an unlimited canvas where data, code, APIs, documents, and visualizations can be organized as connected workflows instead of isolated files and tabs. API Exploration Connect to APIs, inspect responses, analyze payloads, and build reusable visual pipelines for data processing. JSON & YAML Visualization Navigate deeply nested structures through interactive visual representations that make complex data easier to understand. JavaScript & TypeScript Execution Run JavaScript and TypeScript directly inside workflow nodes to transform, filter, and manipulate data in real time. Browser-Based Python Runtime Execute real Python entirely in the browser without requiring local installations or external servers. CSV & Dataset Analysis Import and explore tabular data visually, making it easier to inspect records, understand relationships, and process large datasets. Schema Exploration Visualize schemas and nested structures to quickly understand how data is organized and connected. PDF, Image & Video Support Work with documents and media assets directly inside the workspace without constantly switching applications. Visual Data Pipelines Create workflows by connecting nodes together, allowing data to flow naturally between APIs, transformations, code execution, schemas, and visualizations. Interactive Data Transformation Modify and reshape

Apple's new dictation system could compete with Wispr Flow and others.

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

Plug an oscilloscope probe into almost any real circuit and the trace will be fuzzy. Riding on top of the signal you actually want is a haze of higher-frequency noise — switching hash, radio pickup, digital crosstalk. The signal and the noise occupy different parts of the frequency spectrum, and that separation is an opportunity. If you can build something that passes the low frequencies and quietly turns down the high ones, the fuzz disappears and the signal stays. That something is a low-pass filter, and in its simplest form it is just a resistor and a capacitor. This article explains where the cut-off frequency comes from, works a concrete RC example, and clears up the misunderstandings that most often trip up a first filter design. Why this calculation matters Low-pass filters are everywhere a clean signal is needed. They sit in front of analog-to-digital converters as anti-aliasing filters, smooth the ripple out of power supplies, condition sensor outputs, and recover audio from a noisy line. Even an averaging operation in software is a low-pass filter wearing different clothes. The calculation matters because the cut-off frequency is a design decision with real consequences in both directions. Set it too low and you blur the signal you were trying to protect — its fast edges and genuine high-frequency content vanish along with the noise. Set it too high and the noise sails straight through. The cut-off is a deliberate line drawn through the frequency spectrum, and a passive RC filter places it with just two component values. The core formula A first-order RC low-pass filter is a resistor in series with the signal and a capacitor from the output node to ground. At low frequencies the capacitor is effectively an open circuit, so the output simply follows the input. At high frequencies the capacitor's impedance becomes small, shorting the high-frequency content to ground. The crossover between those two regimes is the cut-off frequency: f_c = 1 / ( 2 * pi * R * C

LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs. By Sergio De Simone