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India's largest food delivery platform processes over 1.5 million orders every single day. One missed...

If you have never written a line of game code and you are trying to choose between Unity, Godot, and Unreal, the internet will give you fifty contradictory answers in your first hour of searching. This article is the answer we give to people who ask us in person. We are a Unity-specialist studio. I have spent 12 years building games, including a tenure as Mobile Team on RuneScape Mobile at Jagex. Over that time I have mentored a steady stream of new developers entering the industry. We picked Unity for our commercial work, deliberately, and we will say upfront where that lens does and does not serve you. The advice below is what I would tell a friend's teenager who asked which engine to learn first, not the version where I am trying to win you as a client. Three things drive whether a beginner finishes their first game or quietly abandons it: the engine's first-week friction, the quality of the free learning material, and whether the language and tools punish you or reward you when you make a mistake. Comparison articles obsess over feature lists. Beginners obsess over whether they can get something on screen by Saturday. We are going to talk about Saturday. The 30-Second Answer If you skim nothing else, take this: Pick Godot if you want the gentlest first week. The editor is small, the language reads like Python, and you can ship a 2D game to the web in a single afternoon. Best chance of you actually finishing a project. Pick Unity if you want a future career in the games industry. Largest tutorial library, biggest job market, most transferable skills. C# is harder than GDScript but every hour you spend on it pays back in the long run. Pick Unreal if you have always wanted to make games specifically because of the visuals. Blueprints let you avoid C++ at the start, the rendering looks beautiful from day one, and the long learning curve has the highest payoff if you commit. For the rest of the article, we will explain why each of those is true, where the engines gen

I Read Your AI Agent Logs So You Don't Have To: A $149 Service That Beats Another Dashboard What if the cheapest fix for your broken AI agent is a stranger reading 40 hours of traces for $149? I spent the last month doing exactly that — reading roughly 40 hours of production logs from teams running LangGraph, CrewAI, and AutoGen agents for paying customers. Not building observability dashboards. Not comparing LangSmith vs Langfuse. Reading the actual traces and writing up what was wrong, what to fix, and in what order. Three observations from those 40 hours: The dashboard was never the problem. Every team already had LangSmith or Helicone or a homegrown equivalent logging every LLM call. None of them were reading the logs. The "fix" was almost always one of seven patterns. I kept seeing the same shapes — stuck retry loops, idempotency gaps, tool-call argument drift, etc. — dressed up in different framework jargon. The teams that asked for "another tool" were the ones least likely to use it. They had 14 tools. The teams that paid for an hour of my time were the ones who said "I don't have time to look at this myself." That second group is who I'm now building a $149 service for. Here's why I think it works, what the deliverable looks like, and where the limits are. Why a $149 fixed-fee reading and not an hourly rate I tested three pricing models against the same deliverable: a written diagnostic of an agent's last 7 days of traces, prioritized fixes with code-level examples, and a 30-minute async follow-up. Model Conversion Avg revenue / inquiry Notes $200/hr (estimated 3hr) 2/40 inquiries $15 (lost 38 to sticker shock) Freelance default, fails on cold traffic $1,500 flat project 0/40 inquiries $0 Above the "I'll just keep it broken" threshold for most small teams $149 fixed diagnostic 11/40 inquiries $41 Below "another contractor" threshold, above "free advice" The $149 number is the inversion point — low enough that a stressed eng lead can expense it without a meet

Anyone who works with Ruby on Rails knows that, despite the framework being incredible for productivity, there are some classic workflow deficiencies that haunt almost every project. You are focused on writing code, but suddenly you need to open an external tool like Postman to test a route. Then, you run a complex script to generate a static database diagram. And at the end of the day, you still need to manually update the API documentation, which will inevitably become outdated in the next sprint. This constant context switching and manual maintenance generates enormous friction. To cover these deficiencies, I developed three gems that bring these tools inside your application. They are so practical that they quickly become indispensable in any Rails project. Meet each one of them: 1. rails-api-docs : The End of Outdated Documentation The deficiency: API documentation always starts with good intentions, but as the system evolves—new routes, parameters, and response fields—it quickly stops representing reality. Keeping this updated manually is repetitive and frustrating work. The solution: The rails-api-docs gem solves this by leveraging what Rails already knows. It inspects your routes, controllers (via AST analysis using Prism), and the ActiveRecord schema to automatically generate the first draft of your documentation. Everything is saved in a single YAML file ( config/rails-api-docs.yml ), which serves as the single source of truth. Why it is indispensable: Append-only strategy: When adding new routes and running the generator, the gem only appends what's new. Your descriptions, custom examples, and tags are never modified or deleted, making the documentation a living document. Zero development friction: You edit the YAML in one window and view the updated documentation in the browser at localhost:3000/rails/api-docs instantly, with no build step required. For production, it exports a single static HTML file without any external dependencies. 2. rails-http-lab

This popular ad blocker app for iPhones, iPads, and Macs can now block ads from loading inside apps, including web browsers, thanks to a new feature in the latest Apple software.

With just two weeks to go, StrictlyVC Los Angeles is quickly approaching. On Thursday, June 18, at The Aerospace Corporation Campus in El Segundo, investors, founders, and tech leaders will gather for an evening of conversation exploring some of the most consequential shifts taking place across venture capital, defense technology, artificial intelligence, and advanced industry. Secure your spot here. […]

You just merged an AI-assisted feature branch, the code review looks clean, and the app works in your local smoke test. Now comes the real question: do you add another traditional browser test, let an AI tool generate the coverage, or spend the time improving the observability around the existing suite? That decision is where a lot of teams get stuck. AI-assisted development changes more than coding speed. It changes the shape of bugs, the pace of UI churn, the expectations for review, and the amount of test maintenance you can tolerate. If you treat AI testing as a magic replacement for your current process, you will probably add noise. If you ignore it entirely, you miss a chance to reduce repetitive work and catch gaps earlier. The real choice is not AI vs non-AI The useful decision is usually this, should AI help create and maintain tests, should it assist human review, or should it stay out of the critical path and only support investigation? That splits into three practical modes: 1. AI assists development, but humans own test strategy This is the safest default. AI can help draft test cases, suggest assertions, summarize failing traces, or propose missing edge cases, but the team still decides what belongs in the suite. If your product has regulated flows, complex permissions, or revenue-critical paths, that ownership matters more than any automation shortcut. 2. AI generates or heals tests inside a human-defined framework This is useful when the team already knows what it wants to cover, but not every selector, fixture, or assertion has to be hand-written. AI can reduce repetitive maintenance, especially for UI-heavy apps that change often. The hidden cost is that you still need a way to judge whether the generated test reflects product intent or just mirrors the current page state. 3. AI becomes part of the evaluation and triage loop Here the value is not test creation, it is speed of diagnosis. AI can summarize logs, cluster failures, or explain a flaky pa

FCC did not violate carriers' right to jury trial, court says in 8-1 ruling.

Flight map from cabin Wi-Fi Discussion | Link

Microsoft published two Defender vulnerabilities on May 19, 2026 that are being actively exploited in the wild, and CISA has already pushed both into the Known Exploited Vulnerabilities catalog. If you run Windows endpoints, this is a same-week update item, not a "schedule it for the next maintenance window" item. The patches exist, the abuse is happening, and the BOD 22-01 deadline for federal civilian agencies is June 3, 2026. what follows: what happened, who needs to act, and what to do today before someone else makes the decision for you. What's being exploited CVE-2026-41091 is an Elevation of Privilege bug in Microsoft Defender's scanning logic, rated Important. The root cause is improper link resolution before file access. An authenticated local attacker plants symbolic links or NTFS junctions that point at attacker-controlled paths, then triggers Defender to follow them. Defender operates with SYSTEM privileges during scan operations, so the file actions Defender performs on those crafted targets execute as SYSTEM. Net result: a non-admin local user gets full SYSTEM on the host. The attacker needs an authenticated session already. That sounds like a high bar until you remember that initial-access malware lands at user-level, then chains a local privilege escalation to get persistence and lateral-movement capability. CVE-2026-41091 is the second-stage tool intrusion sets are looking for. The Hacker News and BleepingComputer both confirm the in-the-wild abuse is happening. CVE-2026-45498 is a Denial of Service in the Microsoft Defender Antimalware Platform itself. Attackers can trigger a platform-level crash that takes Defender's protection capabilities offline. The exploitation pattern here is the obvious one: kill the EDR/AV before deploying the actual payload, get a clean window for follow-on actions, restore Defender or leave it broken depending on how careful the operator is. CISA's KEV listing tells you this is being chained operationally, not a theoreti

The Framing Problem When industry analysts discuss "AI CAD," they are frequently conflating two fundamentally different computational paradigms: generative mesh synthesis and parametric feature modeling. This conflation has produced a decade of inflated expectations, underwhelming demos, and a persistent belief that real AI CAD is still "coming." It is not coming. For a specific and technically meaningful definition of AI CAD, it has arrived. Mesh Generation vs. Parametric Modeling: Why the Distinction Is Everything Contemporary generative 3D tools including neural radiance field reconstructions, diffusion-based mesh generators, and implicit surface networks produce geometry as an unstructured point cloud or polygon mesh. These representations are geometrically expressive but engineering-inert. They carry no feature history, no constraint graph, no dimensional intent. A mesh cannot be toleranced. A mesh cannot propagate a design change. A mesh cannot be submitted to a manufacturer without full reconstruction from scratch. Parametric CAD, by contrast, encodes design intent as a structured sequence of operations — extrusions, revolves, fillets, boolean operations each governed by explicit dimensional constraints and parent-child dependency relationships. The parametric model is not merely a shape; it is a design process, replayable, modifiable, and transferable across manufacturing contexts. The meaningful technical question for AI CAD in 2026 is therefore not " can AI generate a 3D shape? " that has been demonstrable since 2019. The question is: can AI generate a valid parametric feature tree from natural language input, with embedded manufacturing constraints, that survives downstream engineering use? What This Requires Architecturally Answering that question in the affirmative requires a system that can: Parse engineering intent from unstructured natural language distinguishing, for instance, between a cosmetic fillet and a stress-relief fillet, or between a cleara

On August 19, Startup Battlefield is returning to Sydney in partnership with Stripe, one of the world's most iconic technology companies. We're taking over Stripe Tour Sydney for a night that the Australian startup ecosystem won't forget.

Paying users will have tools for reaching either wider or more-specific audiences.

Estonian government benchmark shows how dozens of models combat Russia's "strategic narratives."

By targeting large numbers of users, attackers increased their chances of success.

The native macOS file manager Finder should be Discussion | Link

Musk can't be trusted to protect X user privacy, public commenters warn FTC.

Poke, the startup that lets people use AI agents through simple text messages, has become the first AI agent approved for Apple’s Messages for Business platform.

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Fusion startup Helion is racing to complete a power plant for Microsoft by 2028. A fresh infusion of cash should help with that.