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While everyone in AI is chasing "superintelligence," Alexandre LeBrun, CEO of Yann LeCun’s world model startup, AMI Labs, dismisses the word.

A lot of platform teams face a problem: they build a lot of really cool stuff, and then their developers don't use it. Be visible to management, talk to stakeholders and listen to their problems, make your value measurable with metrics like DORA, create narratives, and show the hidden pain to make it personal: these are lessons that Lucas Hornung and Christian Matthaei presented. By Ben Linders

Parallel Routes let you render multiple pages simultaneously within the same layout. Instead of navigating away from a page to show another one, you can display both at the same time — a dashboard with independently navigable sections, a main content area alongside a sidebar that changes with its own navigation, or a feed with an openable detail panel that doesn't replace the feed. This is one of the more powerful App Router features and one of the more confusing to set up initially. Here's the complete pattern, including the design approach used for complex multi-panel interfaces like the generation tool at Pixova . The Core Concept — Named Slots Parallel routes use named slots: special folders prefixed with @ that define independent rendering areas within a layout. app/ ├── layout.tsx ← Receives slot props ├── page.tsx ← Default slot content ├── @sidebar/ │ ├── page.tsx ← Sidebar default │ └── settings/ │ └── page.tsx ← Sidebar settings view └── @modal/ ├── page.tsx ← Modal default (null) └── photo/[id]/ └── page.tsx ← Photo modal The layout receives each slot as a prop: // app/layout.tsx export default function Layout ({ children , sidebar , modal , }: { children : React . ReactNode ; sidebar : React . ReactNode ; modal : React . ReactNode ; }) { return ( < div className = "flex h-screen" > < aside className = "w-64 border-r" > { sidebar } </ aside > < main className = "flex-1" > { children } </ main > { modal } </ div > ); } Now children , sidebar , and modal can each navigate independently. Independent Navigation — The Key Behavior Each parallel route slot navigates independently. When a user navigates from / to /settings , the children slot updates. The sidebar slot stays exactly where it was — its navigation state is independent. This is the key difference from nested layouts. Nested layouts rerender from the changed segment outward. Parallel routes don't affect each other. User is at / (children shows home, sidebar shows default nav) User navigates to /setti

Researchers identified traces of erythrulose, a monosaccharide, thousands of light-years away.

This week's tooling news clusters around two themes that don't usually arrive together: token-efficient multimodal reasoning and infrastructure-level agent safety. The Inkling model launch dominates the conversation, but the more quietly significant story is Microsoft and Vercel independently shipping primitives that make running untrusted agent code and managing agent credentials meaningfully less dangerous. Here's what's worth your attention. Inkling mixture-of-experts model enables token-efficient reasoning Inkling is a decoder-only MoE with 1T total parameters and 40B active per token, native multimodal I/O (text, image, audio), and a reasoning_effort API parameter that lets you tune compute depth per request. It's live on Together Serverless today with no capacity queue. The practical upside is architectural simplification. If you're currently chaining a vision model, a transcription service, and a text LLM into a single reasoning pipeline, that's three API clients, three failure surfaces, and three billing relationships. Inkling collapses that into one endpoint. The reasoning_effort knob is the other interesting piece—per-request control over inference depth means you can spend tokens proportionally to task complexity rather than paying full reasoning cost on every call. The caveat: exact reasoning_effort parameter values aren't fully documented yet. Don't hardcode assumptions about accepted values into production before checking the official docs. Verdict: Evaluate. Worth spinning up against your current multimodal workload to benchmark latency and cost. Hold production migration until parameter documentation stabilizes. Inkling open model handles image, text, and audio natively This is the self-hosted side of the same model. The 1T-parameter MoE ships with day-0 support in transformers 5.14.0+ and SGLang, plus llama.cpp quantizations for teams that want to run trimmed variants. The catch is hardware: full NVFP4 precision requires 600GB VRAM; BF16 needs 2TB.

Sanity's hotspot and crop system works well when all the pieces line up — but if your rendered image is ignoring the focal point you set in Studio, one of four things is almost certainly wrong. None of them are subtle bugs; they're all configuration mistakes that are easy to miss and easy to fix. The four causes (and their fixes) 1. fit is still set to clip instead of crop This is the most common cause. The @sanity/image-url builder defaults to fit('clip') , which scales the image to fit inside the requested dimensions without cropping anything. Hotspot data is only applied when the builder is told to crop — that is, when it cuts the image down to the requested dimensions, centering the cut on the focal point. Fix: always chain .fit('crop') when you pass .width() and .height() . // src/lib/sanity-image.ts import imageUrlBuilder from ' @sanity/image-url ' import { client } from ' ./sanity-client ' const builder = imageUrlBuilder ( client ) export function urlFor ( source : SanityImageSource ) { return builder . image ( source ) } // Usage — hotspot will only apply if fit is 'crop' const url = urlFor ( image ) . width ( 800 ) . height ( 600 ) . fit ( ' crop ' ) // <-- required for hotspot to do anything . auto ( ' format ' ) . url () Without .fit('crop') , Sanity's CDN receives no crop instruction and the hotspot coordinates are silently ignored. 2. Missing options: { hotspot: true } on the schema field If the image field in your Sanity schema is not configured with hotspot support, Studio never renders the focal point UI, and the hotspot and crop keys are never written to the document in the first place. The URL builder can't use data that isn't there. Fix: add options: { hotspot: true } to every image field where editors need focal control. // schemas/post.ts export default { name : ' post ' , type : ' document ' , fields : [ { name : ' coverImage ' , type : ' image ' , options : { hotspot : true , // <-- enables the focal point UI in Studio }, }, ], } After adding

GitHub announced on July 14, 2026 that security reviews are available in the GitHub Copilot app. Primary source: GitHub Changelog, July 14, 2026 . The meaningful research question is not whether people click Accept. It is whether they can build an evidence-backed decision when guidance is useful, incomplete, or wrong. understand change -> inspect evidence -> challenge findings -> verify uncertainty -> accept, reject, or escalate This is a proposed research protocol, not a completed study. It does not invent product fields or report findings. Build scenario cards scenario_id : " SR-03" repository_type : " synthetic" seeded_conditions : - " one relevant issue" - " one plausible but irrelevant concern" - " one important omission" participant_goal : " ready, blocked, or escalate" success_evidence : - " decision cites inspected code" - " unsupported claim is challenged" - " unresolved uncertainty is recorded" stop_conditions : - " real credentials appear" - " a live repository could be modified" - " participant mistakes study output for production approval" Vary the seeded mix so participants cannot learn that every scenario contains exactly one true and one false finding. Establish ground truth independently before sessions. Recruit people who hold different review responsibilities: routine reviewers, maintainers, security specialists, less-experienced reviewers, and people using keyboard navigation or assistive technology. Do not collapse every group into one average. Require a decision record Decision: ready | blocked | escalate Evidence inspected: - file and relevant lines - test or documentation Guidance accepted: - claim and evidence Guidance rejected: - claim and reason Unresolved: - question and next owner Spoken confidence is not the outcome. This artifact exposes whether acceptance connects to evidence. Measure relevant issues identified, unsupported claims challenged, evidence references, correct escalation, time, and confidence before and after inspection. No

If you have ever built an autonomous mobile robot, you have likely run into the dreaded "Shuffled USB Port" problem. You boot up your robot, fire up your ROS 2 launch files, and... crash. Your LiDAR driver is trying to parse data from your IMU, and your IMU node is screaming about invalid serial frames. Because Linux assigns virtual serial paths like /dev/ttyUSB0 and /dev/ttyUSB1 based purely on which device initialized milliseconds faster, relying on default OS paths is a recipe for system instability. When you are scaling up to dozens of Jetson Orin nodes —each equipped with an RPLIDAR C1 and a Yahboom 10-axis IMU —manually hardcoding paths or writing rigid scripts on every individual machine isn't viable. Here is how production-grade robotics fleets handle plug-and-play USB binding dynamically using configuration-driven udev rules. The Core Concept: Vendor ID vs. Physical Port vs. Serials Linux's udev (device manager) allows us to dynamically create stable symbolic links (symlinks) like /dev/rplidar and /dev/imu when hardware is plugged in. How we identify those devices determines our fleet's flexibility: USB Serials: Unique to each individual chip. Highly secure, but requires registering every single replacement sensor in your codebase. Physical USB Ports ( KERNELS ): Tied to a physical slot on the carrier board. Great if you have identical sensors, but forces technicians to plug cables into highly specific, undocumented ports. Vendor ID (VID) & Product ID (PID): Identifies the USB-to-serial converter chip on the sensor board. Because the RPLIDAR C1 uses a Silicon Labs CP210x chip ( 10c4:ea60 ) and the Yahboom IMU uses a QinHeng CH340 chip ( 1a86:7523 ), they use completely distinct silicon. This means we can map them dynamically and reliably using just their VID/PID —allowing field technicians to plug them into any USB port on the Jetson without breaking the system. Step 1: The Configuration-Driven File ( devices.conf ) Hardcoding vendor rules inside shell scri

The Elon Musk-owned xAI is suing a South Carolina man who allegedly used the company's Grok AI chatbot to generate child sexual abuse material (CSAM). In a lawsuit reported earlier by Reuters, xAI claims Terry Wayne Harwood "knowingly and intentionally used Grok to circumvent safeguards, alter nonconsensual images, and generate and distribute CSAM," breaching the […]

I pin model IDs on purpose. Floating aliases have burned me before — a silent swap under a -latest tag once changed a tool-calling detail in production and cost me a Saturday with git bisect and a coffee I didn't enjoy. So everything I run points at a dated snapshot. gpt-5-codex was one of them. Last week I finally read OpenAI's deprecations page top to bottom instead of skimming it, and there it was: gpt-5-codex , retiring 2026-07-23 , sitting quietly next to ten of its neighbors. As I write this it's July 16. That's seven days . If you pinned any of the snapshots below, consider this your heads-up. Here's the full retirement list, how to find out in about ten minutes whether you're exposed, the replacement map with the things I'd actually regression-test, and the one structural change that turned my last forced migration from scary into boring. The shutdown list (retiring 2026-07-23) Straight from OpenAI's API deprecations page . I checked each row against the source while writing this — do the same before you act on it, because dates and replacements do get revised. Retiring model OpenAI's suggested replacement gpt-5-codex gpt-5.5 gpt-5.1-codex gpt-5.5 gpt-5.1-codex-max gpt-5.5 gpt-5.1-codex-mini gpt-5.4-mini gpt-5.2-codex gpt-5.5 gpt-5-chat-latest gpt-5.5 gpt-5.1-chat-latest gpt-5.5 gpt-4o-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-search-preview-2025-03-11 gpt-5.4-mini gpt-4o-mini-tts-2025-03-20 gpt-4o-mini-tts-2025-12-15 computer-use-preview-2025-03-11 computer-use-preview (or gpt-5.4-mini ) Note the shape of it: the *-codex line collapses into gpt-5.5 — except gpt-5.1-codex-mini , which drops to gpt-5.4-mini — the chat-latest aliases fold into gpt-5.5 too, the two *-search-preview snapshots move to gpt-5.4-mini , and the two preview families ( tts , computer-use ) just roll to a newer dated snapshot / the undated alias. Once you see the buckets, the migration is less intimidating than the eleven-row table looks. Find out if you're exposed (about ten m

Yes, it was, in fact, a Unix system.

The safety board confirmed Tesla's account of the crash, which the company shared days after it happened last month.

OpenAI, which is in the middle of a legal battle with Apple over hardware trade theft allegations, just released a light-up keyboard designed to be paired with its agentic coding app.

After I published my last article about building a Chrome extension that speaks browser notifications aloud, a commenter asked a question I didn't have a good answer to. He pointed out that a lot of web apps — Slack, Gmail, most modern tools — fire their notifications from a service worker via registration.showNotification() , not from the page's JavaScript context. My MAIN world override of window.Notification would never reach those. He was right. And I told him I'd look into it. I spent a week researching whether there was any way to close that gap. There isn't. But the reason why is more interesting than a simple "no." Two ways a website can show you a notification When a website sends you a browser notification, it can do it in one of two ways. The first is the constructor path. The page's own JavaScript calls new Notification("You have a message") directly. This is common for in-tab alerts, real-time updates when you're actively on the site, or any notification triggered by something you just did. The second is the push path. The browser receives a push event from the website's server, wakes up the website's service worker in the background, and the service worker calls self.registration.showNotification() from inside its own scope. This is what happens when Slack notifies you of a new message while the tab is closed or backgrounded. The page never runs. No page JavaScript ever fires. My extension catches the first path. The MAIN world content script overrides window.Notification before any page code runs. But the service worker never touches the page's window. It has no window . It runs in a completely isolated thread, completely separate from the page, and calls showNotification on itself. The override is never reached. Why can't the extension reach the service worker? This is the part that took me a week to fully accept. Chrome extensions can inject content scripts into web pages. They can run code in the MAIN world or the ISOLATED world of a page. They can

A new teaser for Google's upcoming Pixel 11 lineup reveals that the phones will feature some kind of glowing orb on the camera bar, as reported by 9to5Google. Google's store page for the Pixel 11 has a short video that shows the glowing, color-shifting orb to the one side of the camera bar. The store […]

Apple has published the policies governing its upcoming Maps advertising business, revealing a strategy that differs from Google’s. The new rules prohibit home services businesses like plumbers, electricians, locksmiths, and roofers from advertising on Apple Maps, along with several other sensitive categories, suggesting Apple is taking a more curated approach to these ads.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

The stock has steadily fallen from the euphoric post-IPO high, showing that markets may be sobering up to the promises CEO Elon Musk made before and after SpaceX went public.

Google's biggest solar and battery project stands in sharp contrast with xAI's nearby unpermitted power plant.

There's a school of voice-agent development that treats turn detection as something you buy, not something you build. Pick a streaming STT provider, let its end-of-turn logic decide when the user is done, and move on. For a lot of teams that's the right move — and if you're weighing the options, our breakdown of turn detection vs forced endpoints is the place to start. But some teams have already solved turn detection. They've tuned their own voice-activity detection over thousands of calls, they know their audio, and they trust their endpointing more than any default. For those teams, a streaming model's built-in turn logic isn't a feature — it's something to work around. What they want is narrower and faster: hand over a finished chunk of speech, get accurate text back, get out of the way. That's the case for bringing your own turn detection and pairing it with fast ASR over HTTP. Turn detection is an architectural decision, not a default Here's the framing that matters. In a streaming setup, the STT model is a participant in the conversation — it's watching the audio and deciding, continuously, whether the user has finished. That's genuinely useful when you want the provider to own that judgment. But it means the model is inserting its own decision between "user stopped talking" and "you get the transcript." If you already know the turn is over — because your VAD just fired — you don't want the model deliberating. You want it transcribing. Every millisecond the STT layer spends re-deciding a question you've already answered is latency you're adding for no benefit. So the decision isn't "which provider has the best turn detection." For these teams it's "who owns the turn boundary?" If the answer is you, then the ideal STT layer is one that does exactly one thing: turn a finished clip into accurate text, fast. Built-in vs. bring-your-own Built-in (streaming). The model reads tonality, pacing, and rhythm to detect end-of-turn — with Universal-3.5 Pro Realtime, aroun