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Hackers stole usernames, hashed passwords, and other data from a service that allowed players to cheat in Grand Theft Auto V.

The AI giant behind Claude submitted paperwork on Monday that would take it public, just a couple of weeks after SpaceX’s splashy IPO announcement.

Lawsuit seeks $12,000 from startup that allegedly damaged home in robot tests.

BYD is giving its self-driving tech a big vote of confidence.

After months of speculation about whether OpenAI or Anthropic would be first in their race to IPO, Anthropic on Monday reached a key milestone: filing to kick off the process with the U.S. Securities and Exchange Commission. The filing sets the stage for what's sure to be a massive IPO. As of its fundraise last […]

The company said Monday it has filed confidentially for an IPO.

The company added a warning to prospective investors that a major dilution could be in the cards after it goes public.

--- title : " Introducing rails-tenantify: Row-Level Multi-Tenancy for Rails 7+" published : true description : " A modern, safe, and robust row-level multi-tenancy gem for Ruby on Rails. Prevent data leaks, protect bulk writes, and preserve tenant context in background jobs." tags : rails, ruby, opensource, saas --- ## The Problem Every multi-tenant SaaS app eventually needs to answer the same questions: * How do we make sure School A never sees School B's data? * How do we scope every query to the right organization? * How do we keep tenant context alive in background jobs and Sidekiq retries? * How do we stop a careless `update_all` from wiping another tenant's rows? The typical answer is *"use acts_as_tenant"* or *"switch to Apartment."* But in modern Rails development, that often means: * Fighting unmaintained APIs on Rails 7+ * Losing tenant context when a background job retries * Dealing with schema-per-tenant complexity (Apartment) and heavy DevOps overhead * Rolling your own `default_scope` and crossing your fingers that nobody calls `unscoped` For most Rails apps, you just need **row-level tenancy** : one database, one `organization_id` column, and strict scoping. The pattern is simple. Getting it **safe** in production is not. --- ## What I Built **`rails-tenantify`** is a Ruby gem that adds row-level multi-tenancy directly to your Rails models and controllers. No external services, no extra databases per tenant—just your own PostgreSQL (or SQLite in dev). ruby class Project < ApplicationRecord include Tenantify::Scoped belongs_to_tenant :organization end ### Set the tenant once per request ruby class ApplicationController < ActionController::Base set_tenant_by :subdomain # acme.yourapp.com → Organization end ### Everything scopes automatically ruby Tenantify.current_tenant = current_organization Project.all # Only this org's projects Project.create!(name: "Q2 Roadmap") # organization_id is set automatically ### Switch context safely for admins or scripts

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

A while back I set three targets for my engineering team. Not velocity. Not story points. Not "things shipped." Just three numbers. Together they tell me whether the work is moving the way it should, or whether next week is shaping up to be a fire-fighting week. I check two of them most days. The third I used to watch closely...until we lost the tool that measured it. Here they are, and why they earned their spot. Why these and not just velocity The first metric most engineering managers reach for is velocity. Story points completed, tickets closed, work merged. Velocity is worth watching. It is a lagging indicator...it tells you what already happened...but it still shapes what comes next. When a sprint's work doesn't get finished, it rolls into the following one, and that rollover eats into whatever you had planned. What velocity doesn't tell you is how the work moved...whether it moved in a way that's going to come back and bite you. For that you need numbers that describe the shape and quality of the work, not just the amount of it...ideally ones that flag a problem while there's still time to act. These three do that. 1. Average PR size Target: under 300 lines changed per PR. What it tells me: how well the team is decomposing work. A team consistently shipping oversized PRs isn't producing more... they're producing PRs that no reviewer can read carefully. Big PRs get rubber-stamped. Rubber-stamped PRs are where production bugs hide. The 300-line target isn't magic. It's roughly the size below which most reviewers will actually read every line. I tell my team to aim for under 300 changes and to treat 500 as a hard ceiling, give or take a handful of genuine exceptions. Past 500 changes, I consistently see quality, review time, and thoroughness all drop sharply...the PR stops getting read and starts getting skimmed. When the team's average creeps up over a few weeks, I have an early signal that one of three things is happening: Stories are too coarse. The work does

Things are bad out there. Despite 2026 shaping up to be a great year when it comes to actual games, it couldn't really be worse for the people that make them or the industry as a whole. Hardware prices keep going up, layoffs have shown no signs of stopping, and even big-budget titles backed by […]

Over the past several years, Microsoft has largely managed to withstand populist calls to break up Big Tech while peers faced sweeping lawsuits. But a probe by the Federal Trade Commission suggests that grace period could be nearing an end. Earlier this year, Bloomberg outlined the contents of civil investigative demands (CIDs) - similar to […]

I tested the top sleep wearables for every type of sleeper, including devices from Oura, Whoop, and Eight Sleep.

An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha

When I decided to ship this blog in four languages, I had a clear mental ranking. English would win on volume. Spanish would be runner-up because of the sheer speaker count. Japanese would stay steady because it's my native language. Portuguese, I figured, was the long tail. I added it mostly out of completism. Twenty-two days later, the GA4 snapshot disagrees with every part of that ranking. PT: 748 pageviews , 709 sessions EN: 195 pageviews , 176 sessions JA: 27 pageviews , 29 sessions ES: 7 pageviews , 7 sessions That is Portuguese pulling roughly 3.8× English, 28× Japanese, and 107× Spanish on the same blog, same publishing cadence, same author. One Portuguese article on its own (a post about a 24-hour security agent: 375 PV) got more pageviews than my entire English blog combined. I wrote that article hoping Spanish would surprise me. Instead Portuguese surprised me, and Spanish quietly continued to not exist. The setup, so you can discount my numbers properly This is not a clean comparative experiment. It's a single blog, kenimoto.dev , running four language directories ( /en/ , /ja/ , /pt/ , /es/ ). Articles get translated through a cross-language LLM pipeline, then hand-edited for register and locale (BR Portuguese vs PT Portuguese, LatAm-neutral Spanish vs Spain Spanish). The window: 2026-04-30 to 2026-05-21, 22 daily snapshots. EN has 26 articles. JA has 25. PT has 17. ES has 10. So PT has fewer articles than EN and still beats it almost 4 to 1. If you stop reading here, take this one thing: language asymmetry can swallow article-count asymmetry whole . Adding articles in a saturated language is slower than adding articles in an underserved one. Why Portuguese pulled ahead I don't think the answer is "Portuguese readers like me more." I think three asymmetries are stacking on top of each other. 1. TabNews is a community door English doesn't have TabNews is a Brazilian developer community where you can post a technical article and have it actually read by h

How combining Non-Transferable and Permanent Delegate extensions changed my understanding of digital ownership on Solana. Imagine earning a high rank in an online game. You can use it. You can benefit from it. Other players can see it. But you cannot transfer that rank to someone else. If you break the game's rules, the game company can revoke it. And if the game gets acquired by another company, authority over that rank can be handed over to a new administrator. That was the closest mental model I found for understanding one of the most interesting things I built this week on Solana: a credential token using Token-2022 extensions. What are Token Extensions? One thing I've learned during this phase of the challenge is that Token-2022 is not just about creating tokens. It allows you to define behaviors directly at the token level through extensions. Instead of relying on application logic to enforce rules, the token program itself can enforce them. For this experiment, I combined two extensions: Non-Transferable — prevents ownership from being transferred to another wallet. Permanent Delegate — gives a designated authority the ability to manage the token even after it has been issued. Individually, those are useful. Together, they create something that behaves less like a currency and more like a credential. The Transfer That Was Supposed to Fail After creating the token, I tried transferring it. spl-token transfer Gn5PZzwDENvpQESaFwgVqzCUFba5sSka59iLtjFTYNvz 1 $THIRD_PARTY \ --owner ~/recipient-wallet.json \ --fee-payer ~/.config/solana/id.json \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --fund-recipient --allow-unfunded-recipient The result: Recipient: ErYcpQYcpdoaaiufrB3MqQE2QaVEhoACZGcssppszPpY Recipient Token Account: 8CvyoKt11UbkWYRVXey6UKK2gqQcvVvbh9Lv3crzo8C3 Funding ATA: 8Cvyokt11UbkWYRVXey6UKK2qqQcvVvbh9LV3crz0803 Status: ❌ Transaction failed during simulation Error: Transfer is disabled for this mint (Token-2022 restriction) What I liked a

Shopify Staff Engineer Guilherme Carreiro discusses building and scaling highly customizable platforms. Using Shopify’s Liquid theme system as a case study, he explains how to balance extreme design flexibility with low-latency performance under massive traffic. He shares insights on implementing secure domain-specific languages, native code extensions, and resilient developer tooling. By Guilherme Carreiro

Dozens of people have complained to the Federal Trade Commission about Norse Atlantic Airways’ tech-first customer service operation. Some said they lost thousands of dollars.

The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows how, to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent. By Amanul Chowdhury, Vinay Gummadavelli

The Seoul-based rocket startup is developing its own launch vehicles and engines.