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xAI accused of training Grok on real and AI-generated child pornography.

Michele Spagnuolo was arrested for alleged insider trading on Polymarket, but the Switzerland-based engineer says he was simply gambling—activity that is beyond the reach of US commodities law.

Every hackathon has the same 90-second moment of dread: someone hands you a codebase you've never seen, and you have to make sense of it before the clock runs out. File trees don't help. grep doesn't help. You waste the first 30–60 minutes reading the wrong files, missing a hidden dependency, and stepping straight into a production trap nobody warned you about. In other words: before writing code, you spend half your time trying to figure out where the hell the code is. The idea wasn't "AI that writes your code." It was "AI that tells you where to look before you write it." For InnovaHack Chapter-1, my team built Waypoint — a dev onboarding platform. Point it at any GitHub repo or a local folder, describe a task like "Add a new global configuration flag to app.set()", and instead of you reading the whole codebase to figure out where that even goes, it hands you a Mission Brief : exactly which files you'll touch, the traps waiting in them, what to learn first, and the order to do it in. Waypoint made the Top 50 — one of the 50 chosen to advance to Round 2. Here's how it actually works under the hood, what it took to build, what's next for it — and since I don't believe in only posting the highlight reel, what happened after we placed that made us walk away from the next round. The problem: the cold-start tax Every time a developer joins a new codebase, or picks up an unfamiliar task in one they already know, there's a tax paid in wrong files read, missed dependencies, and traps hit blind. For me, that moment came when I wanted to contribute to Forem — the open-source project that actually powers DEV. I didn't know a line of Ruby on Rails, and between learning the language, understanding the framework, and preparing for interviews, I didn't have the time to read an entire unfamiliar codebase just to figure out where one feature belonged. I never ended up making that contribution. But the problem stuck with me. It's also a pattern for our team more broadly: we delibera

We build GPTree with several coding agents working the same repository at once: Claude Code, Codex, and Cursor, each in its own git worktree. The failure that finally made us build tooling for it was small and completely silent. One session was told to replace PaymentService with a Stripe-specific implementation. Another was told to add PayPal support to PaymentService . Different worktrees. Different files. Zero textual conflict. Git merged both branches cleanly, and the second change now depended on an extension point the first had deleted. Nothing in the toolchain had an opinion about it at any moment. Git compares diffs. It cannot compare plans. Worktrees isolate files, not plans Worktrees became the standard answer to parallel agents for a good reason: two sessions editing one checkout will overwrite each other's files and poison each other's context. Isolated checkouts fix that completely. But three failure modes survive file isolation, because they were never about files: Destructive versus additive. One agent removes or replaces a thing another agent is building on. The example above. Merges clean, breaks the design. Duplicate work. Two agents solve the same problem from different angles because nothing assigned ownership. You pay twice and then pay again to reconcile. Contract drift. One agent changes an API, a schema, or a config contract while another codes against the old shape. Compiles, runs, disagrees at runtime. A shared task list helps with the second one, if every agent reads it, every time. Nothing in that setup catches the first or third, because the collision is between intentions, and intentions live in prompts, not in any file a tool can watch. Declare the work before doing it Foremerge is the internal tool we built for this, open-sourced this week. It is a coordination protocol that sits above Git: agents declare what they are about to do, before they do it, in a form precise enough to check. A declaration is an intent with one or more semant

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

Recently, I built a mobile app with Expo; everything worked well with the development build on a simulator and a real device. Yet when I published the app to test the production build on a real device, it crashed without any explanation. With the crash, I had to get the crash report from Apple and download it to read it and try to understand the issue, yet even with that, I did not find any details that could help me, so I did what any normal guy during this age can do, I gave my code base to claude code and the crash report to anaylze them and tell me the issue. Guess what happened here? It hallucinated! Reading Claude's output made me feel I wasn't going in the right direction; for that reason, I had to go the old way: Stack Overflow and Reddit. Going to read the issues there helped me with three main extra things that AI does not provide: Knowing what other people tried: When I go to Stack Overflow or Reddit , I read the question, the thread, and other people's comments, even if it's not the correct one; this helps me get context, grasp the idea, and even learn some historical data about the issue. That might be the one I am facing. Sense of community: When I read other people's struggles and experiences, it gives me the feeling that I am not alone- not just me and a machine trying to prompt it to work- and it makes me feel that I belong to something bigger. It helps me keep up, not get frustrated, and feel that it's me who cannot solve issues with AI. Slow learning: Our brain does not remember the information when you read it once and forget it; we learn when we put effort and push the limits of our brain. With AI, this is getting easier by sending the question directly and getting the answer, so we forget even the issue if we face it again (spoiler alert: I had this exact issue a few months ago and forgot about it). That's why slow reading and similar methods help keep our brains alive and help us improve. With this, I am not saying to fully remove AI and not t

I'm part of an awesome community where senior devs, product managers, founders, and experienced folks from different domains discuss how they use AI and automation tools to boost productivity — without sacrificing real learning. The group is a goldmine . Tool recommendations. Automation workflows. Latest trends. Migration war stories. I've learned more from this group than from most tutorials. Last week, I needed to find something specific about running local LLMs. I searched with keywords. I scrolled. I found a whole lot of messages — but none of them answered my question directly. I still had to manually read through dozens of messages , open the links they shared, and try to piece together the context myself. It took me over an hour, and I wasn't even sure I'd found everything. Someone might say: "Just Google it." Or "Ask an LLM." But that defeats the purpose. The value here isn't just information — it's the context . The "this library is a game changer" comment only makes sense if you know what the person was working on before, and who else agreed or disagreed. That context is lost in scrollback. And I'm tired of trying to keep it all in my head . I've seen people send important links to themselves on WhatsApp. But that becomes a messy pile with no structure. No connections. No way to see how one message relates to another. So I'm curious: How do you deal with this? Have you lost important knowledge in group chats? Do you have a system for recovering it? Or are you also just scrolling endlessly? I have an idea I'm working on. But I'd love to hear your approaches first. Drop your thoughts in the comments — I'll document what I learn .

The startup just raised a $26 million Series A to grow its customer base and help reduce red tape for utility and construction work.

As Tesla gears up for the official launch of its steering wheel and pedal-less Cybercabs, Waymo is issuing a stark warning about Elon Musk's approach autonomous driving. Srikanth Thirumalai, Waymo's VP of Onboard Software, doesn't specifically call out Musk or Tesla in a blog post, published Wednesday, entitled "10 AI Lessons from Driving 200+ Million […]

Test drivers for Waymo and Zoox sustained more than two dozen injuries in 2024 and 2025 from hard braking or other sudden movements made by the autonomous vehicles, according to a TechCrunch review of data submitted to OSHA.

Today on Decoder, I’m talking to Verge senior AI reporter Hayden Field about some pure Decoder bait: the seemingly-endless org chart changes at OpenAI, and how all of them seem to consolidate power under cofounder Greg Brockman, the company’s president. While Sam Altman is the CEO and still OpenAI’s most public face, Brockman has amassed […]

GitHub热门项目 | A smarter cd command. Supports all major shells. | Stars: 38,927 | 70 stars today | 语言: Rust

GitHub热门项目 | Apache OpenDAL: One Layer, All Storage. | Stars: 5,341 | 4 stars today | 语言: Rust

Sätteri is a high-performance Markdown and MDX processor developed by the Astro team. Built in Rust, it enhances build speeds by up to 61% for Astro 7.0. Sätteri supports flexible JavaScript plugins and integrates various Markdown features natively. It maintains compatibility with the unified ecosystem while offering faster parsing and reduced dependencies. By Daniel Curtis

By using a sustainable testing strategy, you can skip unnecessary tests, ensure failing fast and early, and only run tests affected by code changes. Tracking energy use per test and using static code analysis can help spot inefficiencies and guide optimization efforts. By Ben Linders

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

1. Crypto & Security Tools Crucial for authentication setup, payload verification, and security testing. JWT Parser / Decoder: Decodes JSON Web Tokens ( Header , Payload , and Signature ) without transmitting secret keys over the internet. Token & Password Generator: Generates cryptographically secure random passwords and API tokens with customizable character sets, lengths, and complexity rules. Hash Text Generator: Computes cryptographic hashes (MD5, SHA-1, SHA-256, SHA-512) for strings to verify integrity or check signature matching. Bcrypt Hash / Verifier: Hashes plain-text passwords or checks plain text against an existing hash using the bcrypt algorithm. UUID / ULID Generator: Creates universally unique identifiers (v4 UUIDs) or time-sortable lexicographically sortable unique identifiers (ULIDs). BIP39 Mnemonic Generator: Generates seed phrases and cryptographic keys used in wallet initialization and HD key generation. RSA Key Pair Generator: Generates public and private RSA key pairs directly in the browser for local testing of asymmetric encryption systems. Basic Auth Generator: Quickly constructs Authorization: Basic <base64> HTTP header credentials from a username and password. 2. Formatters & Prettifiers (Development) Saves hours when dealing with messy logs, API responses, or raw system configurations. JSON Prettify & Minify: Formats unformatted API JSON strings with customizable indentation or compresses them into a single line to reduce payload sizes. JSON Diff: Highlights additions, deletions, and structural changes between two JSON payloads. SQL Prettify: Formats raw SQL queries into clean, readable multi-line statements with capitalized keywords. YAML / XML Formatter: Cleans up indentation, validates structure, and formats raw XML and YAML files. Docker Run to Docker Compose: Translates single CLI flags ( docker run -d -p 80:80 ... ) into a structured docker-compose.yml file. Cron Expression Generator & Parser: Provides human-readable schedules from

Last month my WhatsApp stack moved 89,479 messages. I got no invoice for any of them. That is not a brag, it is the setup for an honest accounting. Because "self-hosting is cheaper" is the least interesting sentence in infrastructure, and it is usually said by someone who has never been paged at 7am by a bot that went quiet at 2am. I want to put a real number on both sides of that trade: the money Twilio would have charged, and the money self-hosting quietly takes back. All the numbers below were pulled or fetched on August 27, 2026 . The rate cards move quarterly, so check yours. The traffic, measured rather than estimated Five WhatsApp inboxes, bridged from WAHA into a self-hosted Chatwoot. Thirty days: messages Total 89,479 Inbound (from users) 45,563 Outbound (from us) 43,916 Most benchmarks stop here, multiply by a per-message rate, and publish. That answer is wrong, because Meta does not charge per message. It charges per template sent outside an open customer service window. Multiplying my full 89,479 by a template rate overstates the Meta line by about 3x. Multiplying just the outbound half still overstates it by about 1.5x. Since November 1, 2024 non-template messages are free. Since July 1, 2025 utility templates answering a user inside an open 24-hour window are also free. So the only line that costs money is the outbound message that goes out when nobody has written to you in the last day. Which means the number you actually need is not "how many messages," it is "how many outbound messages had no inbound message from that contact in the preceding 24 hours." The query that produces the real bill Here it is against Chatwoot's schema. It uses a window function rather than a correlated NOT EXISTS , because on a messages table of any size the correlated version will happily eat your connection pool. WITH src AS ( SELECT m . conversation_id , m . created_at , m . message_type FROM messages m WHERE m . inbox_id IN ( 27 , 23 , 46 , 50 , 48 ) -- your WhatsApp in

Java Service Steward is a new Windows service host for Java applications. It reads the wrapper.conf format used by the Java Service Wrapper, follows the same command line and log format, and is licensed Apache-2.0 OR MIT. I wrote it because the Community Edition of the Java Service Wrapper has no 64-bit Windows build, and I did not want to buy a license or rewrite the service integration of applications that already had working configuration files. Repository: https://github.com/jayyanez/java-service-steward What it is The distribution is two files, wrapper.exe and wrapper.jar . The executable is written in Rust and does the Windows part: it registers the service, launches the JVM, keeps a control channel to it over a loopback socket, restarts it when it exits unexpectedly or stops answering pings, writes and rotates wrapper.log , and handles Service Control Manager requests (stop, pause, resume, custom control codes). The JAR is compiled for Java 8 and contains the launcher classes and a small API. There is no native DLL and no JNI. It only runs on 64-bit Windows. There is no Unix version. What is compatible Configuration. wrapper.conf with #include , #encoding , set.VAR=value , %VAR% expansion and numbered properties such as wrapper.java.additional.<n> . Relative paths resolve from the executable's directory, as before. Command line. -c runs in a console, -i and -r install and remove the service, -t and -p start and stop it, -q queries it, -d requests a thread dump. Property overrides on the command line and -- pass-through of application arguments work the same way. Service registration. An installed service's ImagePath calls wrapper.exe -s <conf> , so an existing registration keeps working. Log format. Records use the same LPTM layout, the same column widths and the same SIZE , WRAPPER and JVM roll modes, so scripts that parse wrapper.log do not need changes. Launchers. A configuration that names the original SimpleApp , StartStopApp or JarApp launcher in wrappe