Apple says it won't limit the capabilities of devices leased through its new Upgrade program if you miss a payment. In an emailed statement to The Verge, Apple spokesperson Brian Bumbery says, "There will be no restricted mode and/or there will be no limitations put on device functionality due to missed payments or default with […]

Record flight shows viability of Qantas Airways’ Project Sunrise routes in 2027.

How agents remember - and why deciding what to forget is the real skill An agent that starts every step with a blank mind cannot really pursue a goal. It would reintroduce itself to you on every message, forget what it just tried, and repeat the same mistake forever. Memory is what turns a stateless model into something that accumulates - that knows who you are, what it has already done, and what it learned last Tuesday. This post is about how that works and, more importantly, about the discipline of deciding what an agent should remember at all. The context window is not memory. The first thing to unlearn: a model’s context window is not its memory. The context window is working memory - RAM, not a hard drive. It is finite, it is reset on every request, and every token in it costs money and dilutes the model’s attention. Stuffing an entire conversation history and knowledge base into the prompt does not scale, and past a point it actively hurts - the model loses the important signal in a sea of stale detail. Real memory lives outside the window and is selectively loaded into it when needed. Four kinds of memory Borrowing loosely from cognitive science, agent memory is usually split into four types, and good systems use all of them: Short-term/working memory - the current conversation and the agent’s recent thoughts and observations. Lives in the context window. Long-term episodic memory - a record of what happened : past conversations, decisions, and the outcomes of previous tasks. Long-term semantic memory - facts and knowledge: who the user is, domain information, documents. This is what retrieval-augmented generation pulls from. Procedural memory - how to do things : learned skills, tool-use patterns, and reusable strategies. Short-term memory: the rolling buffer The simplest memory is just keeping recent turns in the prompt. The problem is that conversations outgrow the window, so the standard move is to keep the last few turns verbatim and summarise the older

Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free and source-available on GitHub. Star git-lrc to help more developers discover the project. Do give it a try and share your feedback Past few years, when things like GPT came out, we were mainly learning how to use them. Which basically meant using better prompts. So we were continuously refining prompts and trying to get better outputs. And that is fine. This was basically an exchange back and forth between us and the bot. We ask something. The AI responds. We refine our prompt. The AI responds again. This loop continues. Agents coming into the picture But now we are seeing the rise of AI agents. We see them everywhere, in every LinkedIn post, every YouTube video. The main idea behind agents is to hand over our work to them, and they will handle the remaining tasks with less constant back and forth compared to traditional chatbots. Here, agents are expected to work in loops. They do one thing. Then they review the result, identify additional things that need to be done, take action again, and repeat. The main input we give is usually a goal or a prompt. But this alone will not give us better control over how the agent works. Reliable agents also need things like success criteria, access to the right tools, feedback mechanisms, and rules for when to stop or ask for human input. The agent works in loops. So we need a better way to design and control these loops. Introducing Loop Engineering Agents perform tasks through loops. So instead of only focusing on prompt engineering, we also need to engineer the loop in which the agent operates. This is where Loop Engineering comes in. The term has recently gained attention as people started exploring better ways to build reliable workflows around AI agents. The idea is simple: Instead of only telling an agent what to do, we design the process that helps the agent complete the task reliably. Prompting is still an important pa

Ina and David Steiner inspired the ire of high-level eBay executives after occasionally criticizing the company in their newsletter. In 2019, a plot was concocted to intimidate the couple into halting their negative coverage.

Parking and matching puzzles look almost insultingly simple from the outside. A few cars, a cramped lot, tap or drag to move them out. But if you've actually tried to build one that feels tight — no janky collision resolution, no ambiguous "why didn't that move register" moments, no lag once the board gets crowded — you know there's a real systems design problem hiding underneath what looks like a weekend project. I recently went through the architecture of a parking/matching hybrid template built in Unity and wanted to break down the core systems the way I'd want them explained if I were reskinning or extending one myself. This isn't a marketing post — it's a walkthrough of the actual mechanics: how vehicle movement and collision resolution work, how the match/clear pipeline is structured, how level data is separated from movement logic so hundreds of levels don't require touching code, and how monetization hooks slot into natural break points without polluting gameplay scripts. If you want to see the finished product this breakdown is loosely based on, there's a working template here: Park Match Unity Game Template . Everything below applies whether you're building something similar from scratch or extending an existing base. Why Parking Puzzles Are Harder Than They Look The core loop — tap or drag a vehicle, it exits along a valid path, the lot clears one piece at a time — is trivial to describe and genuinely fiddly to implement well. Four problems show up almost immediately once you move past a static mockup: Valid-move detection : how do you know, at any given moment, which vehicles can actually move given their orientation and the current board state? Path resolution : once a vehicle starts moving, how does it navigate around other vehicles and obstacles without clipping through them or getting stuck mid-animation? Match/clear logic : when does a vehicle actually "clear" the board — on reaching an exit, on matching color/type with another vehicle, or both — an

So I had this idea that kept nagging at me. Every AI chat app works the same way. You type something, the model returns text or markdown, the UI renders it as a nice formatted paragraph. That is fine if you want an answer. It is genuinely boring if you want to actually build something. What if the AI could respond with a working game board you could click? What if saying "make it Barbie themed" actually transformed the whole interface while you watched? What if "add a starfield in the background" dropped an animated canvas behind your chat in real time? I spent a few weeks building exactly that. I call it FlowChat . Here is the live version: https://flowchat-public.varshithvh.workers.dev And yes, someone immediately asked it to play Tic Tac Toe and then asked it to switch to an Oppenheimer theme mid-game. I could not be prouder. The Idea Normal AI chat: model returns markdown, client renders it as text. Simple, predictable, boring. FlowChat: model returns raw HTML with CSS and JavaScript, client injects it directly into the DOM using a streaming protocol built on the browser's native template system. That one change makes the entire experience different. You are not reading about a game. You are playing one. You are not reading about a Barbie color palette. You are sitting inside one. The AI does not just answer questions. It rebuilds the UI from its responses . What You Can Actually Do With It I want to give you a feel for what this means in practice before getting into the technical bits, because the demos are more interesting than any architecture diagram. Games : Ask it to build Tic Tac Toe. You get a playable board, click-to-move, an AI opponent, win detection. Ask for Connect 4. Ask for Snake. The game renders in the chat as an agent bubble with a form inside it. Each move submits to the LLM which processes it and updates only the cells that changed. Themes : Say "change to a Barbie theme". The model injects CSS overrides and the whole interface turns pink. Me

1.13.1. The Basic Responsibility of Types Every Rust value has a type, and the responsibility of that type is to tell you how to interpret the bits in memory. For example, the bit pattern 0b10111101 has no meaning by itself, but: Interpreted as u8 , it becomes the number 189 Interpreted as i8 , it becomes the number -67 When you define a custom type, the compiler decides where each part of that type is placed in memory. 1.13.2. Alignment Alignment determines where a type’s bytes may be stored. Once a type’s representation is determined, you might think it can be stored anywhere in memory. In theory that is possible, but in practice computer hardware places constraints on where a given type can live. The most typical example is a pointer. A pointer points to bytes, not bits; one byte equals 8 bits. In other words, it does not point to an individual bit. So if a value of some type were placed at bit index 4 in memory, you would not be able to address it, because pointers address bytes rather than specific bits. That is why alignment is done at the byte level — that is, at 8-bit boundaries. For this reason, all values, regardless of type, must begin on a byte boundary . All types must be at least byte-aligned. In other words, the storage address must be a multiple of 8 bits. 1.13.3. Stricter Alignment Rules Some types have alignment requirements stricter than byte alignment. In CPU and memory systems, memory is often accessed in blocks larger than a single byte. For example, on a 64-bit CPU, most values are accessed in 8-byte blocks, and each operation begins at an address that is 8-byte aligned . This is also called the CPU word size. Of course, CPUs can also handle reads and writes of smaller values, as well as values that cross block boundaries. But as developers, we should try our best to ensure that hardware operates at its native alignment. For example, if the i64 value you want to read begins in the middle of an 8-byte block, then reading it requires at least tw

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TL;DR AI editors add a login check to your API routes but skip the ownership check, so any logged-in user can read another user's data by changing the ID in the URL (CWE-639, IDOR). It happens because tutorials treat "authenticated" as if it means "authorized," and the AI learned from those tutorials. The fix is one line: scope every lookup to the current user instead of trusting a raw ID from the request. I asked Cursor to build an endpoint that returns an invoice by ID. It gave me clean code. Auth middleware on the route, a database lookup, a JSON response. It ran on the first try. Then I logged in as a different test user and changed the number at the end of the URL. Invoice #1001 belonged to someone else. I got the whole thing back: amount, line items, billing address. No error, no warning. Just another user's private data on my screen. That is IDOR, an Insecure Direct Object Reference, and it is one of the most common holes I find in AI-generated APIs. The frustrating part is that the code looks secure. It even has an auth check. It just checks the wrong thing. The Vulnerable Code The endpoint below is broken because it confirms you are logged in but never confirms the invoice is yours. findById takes the ID straight from the URL and returns whatever it finds. // CWE-639: authenticated, but no ownership check app . get ( ' /api/invoices/:id ' , authenticate , async ( req , res ) => { const invoice = await Invoice . findById ( req . params . id ); res . json ( invoice ); }); The authenticate middleware does its job. It proves the request comes from a real, logged-in user. What it does not prove is that this particular user has any right to invoice :id . Change the ID, get someone else's record. Increment it in a loop and you can walk the entire table. Why This Keeps Happening AI editors confuse authentication with authorization because almost every tutorial they trained on does the same thing. Authentication is "who are you." Authorization is "are you allowed to

NVIDIA shipped a 30-billion-parameter model that can see, hear, and talk — and gave the weights away. The catch: the seeing and hearing parts didn’t run on a Mac. So I spent an afternoon writing the missing piece. Here is the whole thing in thirty-five seconds — the model reading a real cart off my own store, on the laptop, with nothing leaving it. Thirty-five seconds: the model reads a real cart off my own store, on the laptop, with nothing leaving it. The elapsed counter is the real measured latency. Every couple of weeks I go looking for whatever new open-weight model just dropped, pull it onto my laptop, and see what it can actually do. Most of the time it’s a coding model, I run it against the one I already use, and my current favorite wins again. That’s a fine result. It’s just not much of a story. This time I found something different. What Nemotron Omni actually is NVIDIA released Nemotron-3-Nano-Omni-30B-A3B — a “tri-modal” model, which is a fancy way of saying one brain with eyes and ears attached. You can hand it a picture, a sound file, or a video, and talk to it about what it saw or heard. It’s 30 billion parameters total, but only about 3 billion of them fire for any given word, which is why something this capable can run on a laptop at all. The weights are public. Someone had already done the hard, unglamorous work of shrinking it down to a 4-bit MLX version that fits on Apple Silicon — that’s yayr over at mlx-community, and this project doesn’t exist without that upload. About 19 GB on disk. Ready to go. Except for one line, buried in the model card: The text backbone loads with standard MLX nemotron_h tooling. The vision and audio towers require a multimodal runtime that implements the C-RADIO ViT-H and Parakeet Conformer forward passes (e.g. the Evorix on-device engine). Translated: the brain works on a Mac. The eyes and ears don’t. The weights for them are right there in the file — all the knowledge, sitting on your disk — but nothing I could find

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This tutorial builds a starter "Hello World" style agent using Kotlin and the native Kotlin version of the Agent Development Kit (ADK). The full sample project is available on GitHub: xbill9 / adk-hello-world-kotlin Kotlin ADK and MCP Hello World This project is a runnable Kotlin Agent Development Kit (ADK) demo. A Kotlin LlmAgent uses Gemini to decide when to call a greet tool discovered from a local Kotlin Model Context Protocol (MCP) server. The project has two Gradle modules: agent : the Kotlin ADK agent, Gemini model configuration, MCP toolset, and interactive ReplRunner ; server : the Ktor MCP server that exposes greet . Technology Stack Kotlin: 2.3.0 Kotlin ADK SDK: com.google.adk:google-adk-kotlin-core (v0.6.0) MCP Kotlin SDK: io.modelcontextprotocol:kotlin-sdk-jvm (v0.8.1) Ktor Framework: 3.0.0 (Netty, SSE, ContentNegotiation, CORS) JDK: Java 25 Build System: Gradle 9.2.1 (Kotlin DSL) Prerequisites Java 25 A Gemini Developer API key The Gradle wrapper is included. Configure Gemini Create the local environment file: cp .env.example .env Set GOOGLE_API_KEY in .env , then load it: source ./set_env.sh The file is ignored by Git. Run the Demo Start the Kotlin MCP server in one… View on GitHub What Is Kotlin? Kotlin is a modern, statically typed programming language created by JetBrains. It runs on the Java Virtual Machine (JVM), works alongside existing Java libraries, and is widely used for Android, backend, and multiplatform development. Static typing is especially useful when building agents. Agent configuration, tool schemas, and tool results can all be checked by the compiler before a prompt reaches the model. Installing Java This sample uses Java 25 . If Java is not installed, SDKMAN! is a convenient way to install and switch between JDK versions on Linux and macOS: Home | SDKMAN! the Software Development Kit Manager SDKMAN! is a tool for managing parallel versions of multiple Software Development Kits on most Unix based systems. sdkman.io After installing