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on may 28 anthropic announced a $65 billion series h round at a post-money valuation of about $965 billion, which makes it, on paper, the most valuable ai startup in the world. the round was led by altimeter capital, dragoneer, greenoaks and sequoia, on top of earlier hyperscaler commitments that included around $15 billion with $5 billion of it from amazon. the headline everyone ran with is that anthropic passed openai. that part is true, but the comparison is messier than the headline, and the more interesting story is what is generating the number. i build small dev tools and write comparison content, and a lot of what i ship runs on top of anthropic's models. so when the company that makes the tools i depend on nearly touches a trillion dollars, i do not read it as a sports score. i read it as a question about whether the thing i am betting on is durable, and what i should do differently because of it. here is the honest version of both. the number, with the caveats intact the $965 billion figure is consistent across cnbc, axios, morningstar, al jazeera and euronews, so i trust it. what i would not do is state the gap over openai as a precise fact, because the sources do not agree on openai's number. axios pegged openai's most recent valuation at $730 billion. other outlets put it closer to $850 billion off a record round earlier in the year. either way anthropic is ahead right now, but "ahead by $115 billion" and "ahead by $235 billion" are different sentences, and anyone quoting one as gospel is rounding away the uncertainty. the safe claim is the one i will make: as of late may 2026, anthropic is the most valuably-priced private ai company, and it got there fast. the reporting has it roughly tripling from a $380 billion mark in february. the part that matters more to me is the revenue. anthropic crossed a $47 billion run-rate earlier in may. that is the line that turns a valuation from a vibe into something with a floor under it. you can argue about whether $

Table of Contents Introduction Double-check everything Configure your Environment Variables Install/Update your Vendors Clear your Symfony Cache Install symfony/apache-pack Update composer.json public directory Build the assets Update Kernel.php Upload the project to cPanel Final Thoughts Introduction I'm new to Symfony and recently, I deployed a Symfony app to a shared hosting environment running cPanel with no SSH access. I could not figure out the best way to do it, let alone find useful resources online as most of them are outdated and felt inefficient. I faced a lot of errors like: failing to load the app with a 500 Internal Server Error, some static assets not loading, etc. I figured it out in the end. This motivated me to write this post to reduce the headache for other Symfony newbies like me. Alright, let's get into it. Most deployment guides online assume you can run Composer, clear caches, execute migrations, and run Symfony commands directly on the server. In shared hosting environments, that is often not possible so, my examples will assume you don't have it installed on the server. 1. Double-check everything The first step to building for production is double-checking everything if it's in intact. Yes, this is very important. In my case, I was faced with some static image assets failing to load because of wrong reference which was ignored on dev mode. I had something like: asset('/images/<filename> ) which was working in dev mode but failed to load the image in prod. the paths had to be like: asset('images/'). This was after checking how I defined other assets. So avoid things like this before hand. 2. Configure your Environment Variables For this, we will use the dotenv:dump command which is not registered by default, so you must register first in your services: # config/services.yaml services : Symfony\Component\Dotenv\Command\DotenvDumpCommand : ~ Then, run the command below. After running this command, Symfony will create and load the .env.local.ph

In May, we experienced nine incidents that resulted in degraded performance across GitHub services. The post GitHub availability report: May 2026 appeared first on The GitHub Blog .

Shion arrives when Season 3 starts on June 16.

Something is slow. Maybe a page takes forever to load, maybe a migration is hanging, maybe your Supabase dashboard just spins. You suspect a query is stuck somewhere in your database, but you can't see what's happening — Postgres doesn't exactly surface this on its own. Turns out it does. You just need to ask. Seeing what's running Postgres keeps track of every active connection and what it's doing in a system view called pg_stat_activity . You can query it like any table: SELECT pid , state , query , age ( clock_timestamp (), query_start ) AS duration FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC ; That gives you every non-idle process — its process ID, current state, the SQL it's running, and how long it's been at it. If something has been running for minutes when it should take milliseconds, you've found your problem. A few things worth knowing about the columns: pid — the process ID, which you'll need if you want to kill it state — usually active (running right now), idle in transaction (sitting inside an open transaction doing nothing), or idle (waiting for work) query — the actual SQL text query_start — when the current query began If you want to include the user and database to narrow things down: SELECT pid , usename , datname , state , query , age ( clock_timestamp (), query_start ) AS duration FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC ; The dangerous one — idle in transaction An active query that's been running for a while is usually just slow. An idle in transaction connection is a different kind of problem — it means someone (or some code) opened a transaction and never committed or rolled it back. The connection is doing nothing, but it's still holding locks, which can block other queries from running. These are the ones that tend to cause cascading slowdowns. If you see one that's been sitting there for longer than expected, it's almost certainly a bug in application code — a missing COMMIT , an unhandled e

A complete solution: expressive range types in your domain layer, full PostgreSQL translation in your data layer - no compromises at either end The Two-Column Trap Almost every developer has written it at least once. An object with two date properties: public class MemberSubscription { public int Id { get ; set ; } public int MemberId { get ; set ; } public DateTime StartDate { get ; set ; } public DateTime EndDate { get ; set ; } } Imagine you need to answer a seemingly simple question in a booking system: "Is this subscription still active, and does it conflict with the proposed new one?" With two bare fields, that code ends up looking something like this: // With two bare DateTime fields — the check you always end up writing public static bool IsActive ( MemberSubscription sub , DateTime at ) => sub . StartDate <= at && ( sub . EndDate == default || sub . EndDate > at ); public static bool ConflictsWith ( MemberSubscription a , MemberSubscription b ) { // Partial overlap: a starts inside b if ( a . StartDate >= b . StartDate && a . StartDate < b . EndDate ) return true ; // Partial overlap: b starts inside a if ( b . StartDate >= a . StartDate && b . StartDate < a . StartDate ) return true ; // b is fully contained by a if ( a . StartDate <= b . StartDate && a . EndDate >= b . EndDate ) return true ; // What about open-ended subscriptions? What about same-day boundaries? // What about inclusive vs exclusive end dates? ... return false ; } It looks perfectly reasonable. But start asking questions — as Steve Smith (Ardalis) does in his essay on making the implicit explicit — and you notice how much invisible knowledge this design requires. Should EndDate ever precede StartDate ? The type system doesn't say. Can a subscription have a null end date meaning it never expires? Nothing in the model communicates that. Is a subscription that ends today still active at 11:59 PM? Ask three developers and get three answers. The EndDate == default sentinel for open-ended subsc

Building an AI-Powered Content Scanner for Windows: Performance, Multithreading and GPU Acceleration in .NET Building software always looks straightforward from the outside. You load a machine learning model, point it at some images, and display the results. At least that's what I thought when I started building DetectNix Vision , a Windows desktop application that performs local AI-powered image analysis without uploading user data to the cloud. In reality, the project became a deep dive into performance optimization, memory management, multithreading, GPU acceleration, and user experience. This article covers the engineering challenges I encountered and the architectural decisions I made while building the software from the perspective of a senior developer. The Original Goal The initial goal was simple: Scan images stored on a Windows PC Detect potentially explicit or sensitive content Keep all processing local Support both CPU and GPU execution Process large image collections efficiently Remain responsive while scanning Privacy was a major requirement. I didn't want users uploading personal files to third-party services. Everything needed to run locally on the user's machine. That decision immediately influenced every technical choice that followed. Challenge #1: Model Loading Performance One of the first mistakes I made was loading the AI model too frequently. A modern computer vision model can be hundreds of megabytes in size. Loading it repeatedly creates significant startup overhead and quickly destroys performance. My initial implementation worked perfectly during testing because I was only processing a handful of images. Once I started testing larger image collections, the bottleneck became obvious. The Solution I moved to a singleton-style architecture where the model is loaded once during application startup and remains resident in memory. private readonly InferenceSession _session ; public VisionEngine () { _session = CreateSession (); } This reduced in

Thibault Sottiaux helped make AI coding one of OpenAI’s fastest-growing businesses. Now he’s overseeing a sweeping overhaul of ChatGPT.

submitted by /u/r_retrohacking_mod2 [link] [留言]

Activists raised a 40-foot-tall inflatable Elon Musk in Manhattan to draw attention to the risk he allegedly poses to investors.

Wits its official share pricing announcement, SpaceX's IPO has begun.

The tech giant warned of a security flaw that a cybercrime gang said it's exploiting as part of a mass-hacking campaign. Google said it notified more than 100 organizations that had potentially vulnerable servers.

Even with decades of documentation, SQL Code Injection remains a top threat. Train your developers and TPMs! submitted by /u/casaaugusta [link] [留言]

Bluesky's latest feature is group chats, arriving amid a shift in focus on building features for smaller communities.

A WIRED investigation found dozens of “nudified” deepfake images and videos on Grok's website, including nonconsensual depictions of celebrities and at least one prominent US politician.

From a wet winter in the Southwest to fewer Atlantic hurricanes, this is what to expect as a potential super El Niño takes shape.

The old app "still needs to be retired," AcuRite tells us.

Service binding is a feature which allows apps to get an isolated schema/database on a shared Postgres or MySQL. This post explain how it works. submitted by /u/avkijay [link] [留言]

Author here. We kept writing migrations that were fine as a final schema but unsafe during the rollout itself - old pods still reading a column while new pods have already dropped it. Django solved this ages ago with django-migration-linter, which I leaned on for years on Grafana OnCall. Drizzle has nothing like it, so we wrote one for our CI. It diffs new migrations against the base branch and fails on drops, renames, and required columns added in one step. It’s buried in our monorepo right now. There’s an issue linked in the post if you’d want it published to npm. submitted by /u/joey-archestra [link] [留言]

I Built a Programming Language Called Zen Building a programming language had been something I wanted to do for a long time. What I didn't realize when I started was how much work exists beyond parsing a few tokens and generating some code. A language is not just a parser or a compiler backend. It is tooling, developer experience, documentation, installation, error handling, runtime support, and countless design decisions. After multiple attempts and many lessons learned, I'm excited to share Zen. Why a Third Attempt? Zen is not the first language project I started. My first attempts taught me a lot, but they never reached a stage where I felt comfortable sharing them publicly. The architecture was incomplete, important components were missing, and the overall developer experience wasn't where I wanted it to be. Instead of abandoning the idea, I kept iterating. Each attempt helped me better understand: Compiler architecture Language design LLVM Runtime integration Tooling and usability Error handling Project structure Zen is the result of those lessons. What Is Zen? Zen is a programming language with its own compiler pipeline and LLVM-based backend. The goal was not just to generate code, but to create a complete language ecosystem that developers can actually install and use. Zen currently includes: Lexer Parser AST generation LLVM IR generation Native executable generation through LLVM Runtime integration Standard library integration Command-line tooling Installation system Documentation website Compiler Pipeline The compilation process follows a traditional compiler architecture: Source Code ↓ Lexer ↓ Parser ↓ AST ↓ LLVM IR Generation ↓ LLVM Optimization ↓ Object Files ↓ Native Executable LLVM handles optimization and machine code generation, allowing Zen to produce native binaries. Command Line Interface Zen provides several commands for development and inspection: zen run zen build zen ir zen ast zen tokens zen clean This allows users to inspect different stage