We spoke with several cybersecurity researchers, who look for unknown vulnerabilities and develop tools to exploit them, about how OpenAI’s and Anthropic’s guardrails affect their work.

I would say this is the real factory simulator game haha

Google just made multi-region Cloud Run significantly easier. Here is the full picture; what changed, what it means in practice, and how to build it right. Most teams discover they need multi-region architecture the hard way and sadly, during an outage. Whether you're running a global e-commerce platform, a real-time gaming API, or a financial services application, users expect your service to be available whenever they need it. There is a conversation that happens in almost every engineering team at some point. It usually starts with a post-mortem. A regional Google Cloud outage or a Cloud Run service that hit a cold start spike, or a single-region deployment that could not handle the latency demands of users spread across Lagos, Nairobi, and London simultaneously, caused enough pain that someone finally asked: why are we only deployed in one region? The answer is usually one of three things: it felt complex, it felt expensive, or no one had prioritised it yet. In July 2026, Google moved Cloud Run Service Health to General Availability and the timing was hard to miss. Six days earlier, a power cut at Google's Netherlands data centre had knocked three services offline. The GA release brings automatic cross-region failover to Cloud Run with what Google describes as a two-step setup: add a readiness probe, set minimum instances to at least 1. The load balancer does the rest. This article covers the full architecture, what Service Health is, how readiness probes underpin it, how to set up the Global Load Balancer correctly, and how to test that failover actually works. It also covers the production details. What changed: Service Health and readiness probes Before Service Health, multi-region Cloud Run required you to implement a /health endpoint in your application and configure a separate HTTPS health check at the load balancer level. This worked, but it had a significant gap. The load balancer's health check only knew whether the Cloud Run service endpoint was respon

Coming from a strong sysadmin and infrastructure background, I spent years managing servers, networks, and keeping systems alive. Over time, I realized a fundamental truth: the most dangerous system risks are often the ones you don't even have visible inventory for. That mindset naturally led me into the world of open source. I started building tools to solve real-world problems around API governance, edge safety, data integrity, and automation. Here is what I’ve been building in public, what each project taught me, and why these areas matter today: 1. Governing LLM & API Traffic: AI-Gateway As AI applications move to production, controlling model access, enforcing limits, and monitoring traffic becomes critical. The Project: AI-Gateway — A lightweight proxy layer designed to secure, route, and manage API requests and policies for AI services. Key Lesson: Security in the AI era isn't just about firewall ports; it's about context-aware policy management and dynamic traffic control. 2. Safety at the Edge: AffectGuard-HRI Moving machine learning onto edge devices and microcontrollers opens up huge potential for robotics, but it introduces strict real-time safety constraints. The Project: AffectGuard-HRI — An open-source framework tailored for human-robot interaction, focusing on real-time safety, intent tracking, and affective monitoring. Key Lesson: Edge AI demands extreme efficiency. You can't rely on cloud latency when dealing with physical robotic hardware—safety loops must run reliably at the hardware level. 3. Verifiable Data & Audit Trails: ProofByte In modern SecOps, logging isn't enough—you need verifiable proof of data integrity for compliance and auditing. The Project: ProofByte — A lightweight tool aimed at data validation, cryptographic verification, and maintaining tamper-evident audit trails. Key Lesson: Building trust in distributed workflows requires cryptographic validation at every step of the pipeline. 4. Modern Workflow Governance: AutoGov Processe

Introduction Having laid out some mathematical foundations in the [previous part][1], we will proceed to discuss quantum gates and circuits in depth here. As usual, we will drive home the theories with worked examples from [@Rieffel2011] and check their accuracy with qiskit and/or cirq code. Prerequisite Understanding quantum gates and circuits will be greatly aided by the knowledge of classical gates (AND, OR, NOT, XOR and the universal gates: NAND and NOR). Also, some familiarity with the Python programming language will help you understand qiskit and/or cirq code. Classical & Quantum gates A classical computer, possibly the one you're reading this with, is a sophisticated engineering piece, no doubt. However, the underlying "magic" comprises logic gates. The National Institute of Standards and Technology (NIST) gives this incredible analogical description of classical computers in relation to logic gates [@NISTQGATE2026]: Traditional computers are like microscopic cities. The roads of these cities are wires with electricity coursing through them. These roads have lots of gates, known as logic gates, which enable computers to do their job. Like physical gates that allow or block cars, logic gates allow or block electricity. Electricity that goes through the gates represents a “1” of digital data, and blocked electricity is a “0.” When you pick up a motherboard, for instance, you may not see the said gates as they are made of tiny transistors (in modern systems, MOSFETs) in specific combinations. Also, the $0$ and $1$ referred to in the analogy mean low and high voltage ranges, respectively. Their actual voltage values depend on the hardware. :::note All schematics were written in and rendered by @schemd/core . Check it out. ::: Logic Gates refresher Since we will be realizing some classical circuits using quantum gates, it's necessary to get familiar with common logic gates. NOT ($\neg$) Gate This gate takes a single classical bit and flips it. For instance, if $0

Five failure patterns from past WordPress major upgrades ended with the observation that "the same structures will recur in some form in the next major (7.0 or 8.0)." This post applies that pattern knowledge to WordPress 7.0 specifically — not as a list of things that will happen, but as a preparatory checklist for what you can verify now. This sits alongside the seven-item pre-flight checklist from W1 rather than replacing it. Think of it as the 7.0-specific supplement. Item 1 — PHP minimum requirement increase (W3 pattern 3 again) W3’s pattern 3 was "older plugins break with parse errors when the PHP minimum requirement is raised." This pattern repeats on every major upgrade cycle. WordPress 7.0 is expected to follow the same trajectory. Background: PHP 7.4 reached end-of-life from the PHP project in November 2022 (security support ended November 2023) WordPress 6.6 requires PHP 7.2 minimum WordPress 7.0 is expected to raise the minimum to PHP 8.0 or later When this happens, sites running PHP 7.x will encounter plugins and themes that "worked fine before" but break with syntax or fatal errors after the upgrade. The mechanism is the same as past cycles — the plugin hasn’t updated its code to handle PHP 8.x stricter type handling, and the errors surface only at runtime. What you can check now: # Confirm the server’s PHP version php -v # List all plugins with their current version and update status wp plugin list --fields = name,version,update,update_version --format = table # Run a JSON output check — noisy output here means PHP warnings already leaking wp plugin list --format = json The pattern to watch for: plugins with an old "Tested up to" value and no updates in the past year. These are the highest risk candidates for PHP compatibility issues when the minimum requirement moves. The post on PHP 8.2 Deprecated warnings leaking into WP-CLI JSON output covers a related symptom — even when PHP compatibility doesn’t cause a hard break, it can silently corrupt o

Redis gets reached for reflexively, "just add Redis," as if it were a single fix for slowness. It's genuinely one of the most useful tools in a backend engineer's kit, but using it well starts with understanding what it actually is: an in-memory data structure store, not just a cache. Once you see it as a fast, versatile store of real data structures, the range of problems it solves cleanly (caching, rate limiting, queues, sessions, leaderboards, locks) stops looking like a grab bag and starts looking like one idea applied many ways. This is the opening article of the Redis Masterclass, and it builds on the PostgreSQL series : Redis usually sits alongside a primary database like Postgres, not instead of it. In-memory is the whole point Redis keeps its data in RAM. That single fact explains most of its character. Reading from memory is orders of magnitude faster than reading from disk, so Redis operations typically complete in well under a millisecond, and a single instance handles a very high request rate. That speed is why it's the default choice for anything on the hot path, where a database round trip would be too slow. The tradeoff is that RAM is smaller and more expensive than disk, and volatile. Redis addresses durability with persistence options we'll cover later, but the mental model to start with is: Redis is fast because it's in memory, and you use it for data that benefits from being fast to access, not as the permanent home for everything. It's a data structure store, not a key-value blob The common misconception is that Redis is a simple key-value store, strings in and strings out. It's much more. Redis stores real data structures as values, each with its own commands: Strings for simple values, counters, and cached blobs. Hashes for objects with fields, like a user record. Lists for ordered sequences and simple queues. Sets for unique collections and membership checks. Sorted sets for ranked data like leaderboards and priority queues. Plus streams, bit

Hi, I developed Life Tile for iPhone. It tracks your time passively and visualizes it as a Mondrian-style grid. It's a one-time purchase app with a 7-day trial. I was a user of a similar app named Life Cycle. Since the app hasn't been updated in a while, I tried to make a better app with my ideas and experiences. I used a big square timeline instead of a donut chart. And I added more useful views: tree-map, map, and list. You can use categorized activities with custom colors. The data never leav

Hey everyone, my name is AbdulRahman Elzahaby ( @egyjs ), a software engineer from Egypt who’s recently fallen down the rabbit hole of LLMs. Like so many of us, you’ll find me in the thick of automation, bots, and making AI work for fast-tracking features and workflows. As I started diving into complex, multi-agent workflow automation, I experimented with… everything. I fiddled with n8n, played with OpenClaw, tinkered with Hermes Agent, andspent what felt like ages manually chaining together Python scripts with LangChain and LangGraph. Every tool showed promise, but my work became increasingly complex and every single workflow somehow felt... Unfinished. The way the industry seems to approach building these kinds of agents revealed a massive, structural gap in tool design. On one hand, we have intuitive, visual workflow tools like n8n. The drag-and-drop interface is great for a bird’s eye view of higher-level logic. But when you need more advanced concepts, like complex looping with conditional logic, custom state reduction, or a system that integrates properly with code review and versioning, these low-code boxes quickly hit limitations. On the other hand, we have powerful code-first frameworks like LangGraph. They provide incredible flexibility and raw execution power, but as soon as you start to build even a simple three-agent triage workflow, the boilerplate code starts to pile up. You have to write custom state schemas (TypedDict), initialize every node function individually, define custom logic for how to route between agents, set up the graph checkpointer, and grapple with environment and dependency management. What seemed missing was a sweet spot - a seamless bridge between the design intuition of visual workflows and the production-ready execution power of code-based frameworks. I wanted a tool that allowed me to simply design a workflow, and then run it efficiently without getting tangled in repetitive boilerplate code. Ultimately, I concluded that what we

TechCrunch Startup Battlefield is coming to Australia — and we're partnering with Stripe to find the country's most exciting early-stage startups.