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Crowding the gates of a major health care conference, protesters called for Palantir to be booted out of the UK’s National Health Service over privacy concerns and political grievances.

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 .

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

Your pods keep getting killed. Not crashing — killed. One moment they're running fine, the next they're gone and Kubernetes is spinning up replacements. You check the logs and there's nothing useful. The pod just… disappeared. Turns out Kubernetes killed it on purpose. And if you don't tell it how much memory your app actually needs, it'll keep doing it. Why Kubernetes evicts pods Kubernetes runs on nodes — physical or virtual machines that host your containers. Each node has a finite amount of CPU and memory. When a node runs low on resources, Kubernetes has to make a choice: which pods stay, and which ones get evicted to free up space. The decision comes down to QoS classes — Quality of Service tiers that Kubernetes assigns to every pod based on how you've configured resource requests and limits. There are three classes: BestEffort — no resource requests or limits defined. Kubernetes has no idea how much CPU or memory the pod needs. These get killed first. Burstable — requests and limits are defined, but they're different (e.g., requests: 256Mi , limits: 512Mi ). The pod is guaranteed the request amount, but can burst up to the limit. Killed second. Guaranteed — requests and limits are set to the same value. Kubernetes reserves exactly that amount of resources for the pod. Killed last. If your pods don't have resource configuration at all, they're running as BestEffort. And when the node hits memory pressure, BestEffort pods are the first to go — no questions asked. The Guaranteed class Setting your pod to the Guaranteed class is one line in your deployment config. Define requests and limits for both CPU and memory, and make them identical: resources : requests : memory : " 512Mi" cpu : " 500m" limits : memory : " 512Mi" cpu : " 500m" That's it. Kubernetes now knows this pod needs exactly 512 MiB of RAM and half a CPU core, and it reserves that capacity when scheduling the pod onto a node. If a node doesn't have 512 MiB available, the pod won't be placed there. An

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.

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

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.

Localize your app with tone-aware AI, automated workflows Discussion | Link

The platform-agnostic Logitech MX Master 3S wireless mouse is discounted to $89.99 at Amazon ($30 off), matching the best price we’ve seen so far this year. While it may look like a somewhat ordinary mouse, it has a unique second scroll wheel near the left thumb that’s surprisingly useful for horizontal scrolling in spreadsheets. You […]

Free, local AI powered Voice to Text w/ live transcriptions Discussion | Link

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.

Cruz/Wyden bill would help Americans sue federal officials over censorship.

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 TypeScript SDK for AI agents with self-improving memory Discussion | Link

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

Pokémon Go Scans Trained Military Drone Navigation Tech Meta Description: Discover how Pokémon Go Scans Trained the Navigation Tech for Military Drones — the surprising data pipeline from your phone to the battlefield. (158 characters) TL;DR: Niantic, the company behind Pokémon Go, collected millions of 3D environmental scans from players worldwide through its AR scanning features. That same spatial mapping technology and data infrastructure has now been linked to navigation systems used in military drones — raising serious questions about informed consent, dual-use technology, and the hidden value of "free" mobile apps. Key Takeaways Pokémon Go players unknowingly contributed to a massive real-world 3D mapping dataset through Niantic's AR scanning features. This spatial data and the underlying technology stack have been connected to navigation systems used in autonomous military drones. The pipeline from consumer app to defense application is a textbook example of dual-use technology — civilian tools repurposed for military ends. Users were not clearly informed their scans could be used beyond in-game features. This story has major implications for data privacy, tech ethics, and how we think about "free" apps. Regulatory frameworks around dual-use data collection remain dangerously underdeveloped. Introduction: The Game That Mapped the World When Pokémon Go launched in July 2016, it looked like a harmless — if slightly chaotic — augmented reality game. Millions of people wandered parks, city squares, and college campuses, phones raised, hunting virtual creatures overlaid on real-world environments. But beneath the Pikachus and Poké Stops, something far more consequential was happening. Niantic was building one of the most detailed, crowd-sourced 3D maps of the physical world ever assembled. And as reporting has surfaced in 2025 and 2026, the revelation that Pokémon Go scans trained the navigation tech for military drones has ignited a firestorm of debate among tech

You're about to install a VS Code extension. Maybe it's a formatter, a linter, a theme, an AI tool....