标签：#X

Elon Musk's xAI files first lawsuit against Grok user accused of making child sex images.

The social-engineering technique has primarily been a tool of financially motivated criminals.

Creator says he will "very loudly ignore" those arguing for a ban on AI tools.

Roblox's new "Build" feature lets users generate basic games using a single text prompt.

If the large, rocky planet is losing helium, then we can infer what is left behind.

X will use Grok AI to better detect stolen content, redirect payouts to original creators, and crack down on engagement bait.

You may have heard that OpenAI released its first piece of hardware this week. You may not have heard about the ChatGPT basketball.

Exposing your data over MCP is the easy part. Designing a tool an agent uses well is the hard part. An agent can only use a tool it can read, so the work is shaping the tool for how the model calls it, not just for the human looking at the result. These are the techniques behind @bonnard/mcp-charts and the visualize tool. Discovery-first, so the agent stops guessing An agent that guesses your schema writes wrong queries. So the first tool the agent meets is a discovery tool. It calls visualize_read_me to load the chart options, the tool schema, and worked examples before it ever calls visualize , and an explore_schema tool to learn your tables and columns before it writes SQL. The agent reads, then acts. A small set of purpose-built tools The temptation is one tool per metric, or a single tool that takes arbitrary SQL and hopes. Both fail: too many tools blow the agent's attention budget; one firehose tool gives it no guardrails. Bonnard ships a small set, discover, query, visualize, each with a narrow, obvious job. The agent picks the right one because there are few of them and each does one thing. // a small, purpose-built set, not one tool per metric server . registerTool ( " explore_schema " , { /* list tables + columns */ }, listSchema ); addCharts ( server , { runSql }); // registers visualize_read_me + visualize Compact, honest responses A tool that returns 10,000 raw rows poisons the context window and the agent's next decision. Bonnard's responses are sized for a model to read: Row caps with a completeness flag. Results are capped and tagged partial or complete , so the agent knows whether it is looking at everything. Partial-result warnings. When results are capped, the response says so and tells the agent not to sum or average the visible rows, use a measure instead. Summaries over dumps. The chart comes back with a compact text summary the model can reason over, not just an image it cannot read. Errors that guide the next action A bare "error: invalid co

The startup, founded in 2021, lets enterprise customers use smartphones to scan and spot vehicle damage.

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.