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Canonical: this is a cross-post. The original lives at https://four-leaf.ai/blog/openai-interview-process Most OpenAI interview prep hands you a list of hard coding problems and tells you to grind. That calms the nerves and misreads the loop, because at OpenAI the coding bar sits next to something the grind can't touch: a genuine point of view on where AI is going and how it could go wrong. Candidate-facing guides describe that thread running from the first recruiter call to the final behavioral round. You can solve every problem and still stall if you can't hold that conversation. We've mapped the loops at Amazon , Google , Apple , Meta , and Bloomberg by reading each process through how the company actually runs. The map now includes the other AI labs and high-growth names candidates weigh alongside it, including Anthropic , SpaceX , and Robinhood . OpenAI is the one candidates most often prepare for as if it were a standard FAANG gauntlet. It isn't. The coding is practical rather than puzzle-flavored, a whole round asks you to present and defend work you built, and the loop varies more team to team than almost any large employer. Generic big-tech prep leaves you exposed on exactly the parts specific to OpenAI. A note on sourcing. OpenAI doesn't publish its interview process. There's no stage list, no scoring rubric, no candidate-facing equivalent of Google's structured-interviewing guidance. So this map comes from reputable secondary sources that collect named and dated candidate accounts, primarily interviewing.io's OpenAI question guide and Exponent's OpenAI software engineer guide . Where those accounts agree, this guide states the pattern. Where the loop varies or the record thins out, it says so rather than inventing detail. Treat everything below as the common shape, not a guaranteed sequence. Why the loop varies so much Start with the thing that makes OpenAI different to prep for. Hiring is decentralized, and secondary guides are blunt that the loop varies

After previewing HDR10 Plus Advanced late last year, Samsung's not-Dolby Vision 2 spec will launch globally on Amazon's Prime Video this month. The first TVs announced with support are Samsung's 2026 lineup, which will be able to make use of the extra metadata to deliver more precise HDR that is optimized for what you're watching, […]

Texas Governor Greg Abbott has paused new data center development until an audit has been completed.

The acquisition, which was first announced in June, brings Vibe.co into Walmart Connect, Walmart's connected TV advertising platform.

PR #5421 adds RichTextComponent , a read-only component for formatted application text. It supports headings, inline styles, lists, links, and images without embedding a web view. What is Codename One? Codename One is an open-source framework for building native iOS, Android, desktop, and web apps from a single Java or Kotlin codebase. Learn more at codenameone.com . A SpanLabel applies one style to wrapped text. A BrowserComponent renders a complete web page. RichTextComponent covers formatted document content between those two cases and participates in ordinary Codename One layout. Rich text inside a scrollable container A common screen mixes formatted text with buttons, images, forms, and other Codename One components inside one scrollable container. A BrowserComponent is a poor fit for that layout because it owns a rectangular native surface and its own page viewport. The browser's document height does not naturally become the height of a child inside the parent Codename One layout. RichTextComponent measures wrapped runs for the width it receives and reports the corresponding height. In the default SizeMode.SHRINK , it behaves like a SpanLabel : the parent container scrolls the rich text together with the surrounding components. SizeMode.SCROLL is available when the rich text should keep an assigned height and scroll its own content. The read-only view and editor agree on paragraph attributes, inline styles, links, image runs, and wrapping because they do not maintain competing renderers. Supply the format you already have HTML is not the only input: RichTextComponent view = new RichTextComponent (); view . setMarkdown ( "# Trip summary\n\n" + "Departs **09:40**, arrives *11:15*. " + "See the [itinerary](app://itinerary).\n\n" + "- Window seat\n" + "- Carry-on only" ); form . add ( view ); setContent(...) accepts RichTextFormat.HTML , MARKDOWN , ASCIIDOC , or RTF . The model covers headings, emphasis, inline code, links, images, lists, quotes, literal blocks, p

exFAT is best for cross-platform usage.

"A Stack isn't designed to store data. It's designed to make the most recent piece of work the easiest to access." In the previous article, we discovered a new kind of design problem. Some systems don't need to find the fastest item. Some don't need to process tasks in arrival order. Instead, they need to work with whatever happened most recently . That's exactly the problem a Stack solves. In this article, we'll understand how a Stack works and why its behavior appears naturally in many software systems. Imagine a Stack of Plates Think about a stack of dinner plates. Plate 4 ────────── Plate 3 ────────── Plate 2 ────────── Plate 1 ────────── When you need a plate, which one do you take? The one on the top. You don't pull out the bottom plate. Likewise, when placing a new plate, you put it on top. This simple rule defines the behavior of a Stack. What Is a Stack? A Stack is a data structure where both insertion and removal happen from the same end. The last item added is always the first one removed. This behavior is called LIFO (Last In, First Out). Push A ↓ Push B ↓ Push C ↓ Pop ↓ C Notice something important. A Stack isn't trying to preserve arrival order like a Queue. Instead, it preserves recency . The newest item is always the easiest to access. Every Data Structure Solves a Different Design Problem By now, we've seen several data structures, each answering a different question. A HashMap asks: Where is this object? A Heap asks: Which item has the highest priority? A Queue asks: Which task has been waiting the longest? A Stack asks: What happened most recently? Choosing the right data structure begins with identifying which of these questions your system needs to answer. Push and Pop Stacks are built around two simple operations. Push Adding a new item. Before Top ↓ B ↓ A Push C After Top ↓ C ↓ B ↓ A Pop Removing the most recent item. Before Top ↓ C ↓ B ↓ A Pop After Top ↓ B ↓ A Only the top item is removed. Everything below remains untouched. Real-World Examp

A deep dive into the 3-way battle for the developer's desktop—comparing AI depth, flow state, autonomous agents, and real-world productivity. Three years ago, choosing a code editor was simple: you downloaded VS Code, installed your favorite syntax theme, added a few extensions, and got to work. Today, developer tooling has undergone a seismic shift. AI isn't just an extension sitting in a sidebar; it's driving entire workflows, editing dozens of files simultaneously, and executing complex engineering tasks. Enter the primary contenders dominating the developer landscape: VS Code (+ GitHub Copilot): The battle-tested industry titan with unmatched ecosystem depth. Cursor: The pioneer of the AI-native fork, built specifically for flow state and multi-file orchestration. Windsurf: Codeium's AI-first editor featuring autonomous flow state agents and deep context tracking. If you're trying to figure out which editor will give you or your engineering team the highest return on productivity, here is a practical, data-informed breakdown. The Architectural Divide: Plugins vs. AI-Native Forks Before comparing feature lists, it helps to understand the underlying architecture: VS Code remains an extension-first model. The core editor is unchanged, while GitHub Copilot operates alongside it as an assistant. Cursor and Windsurf are VS Code forks. Their creators modified the editor at an architectural level to give the AI direct access to your local workspace, terminal, file system, and git context. This distinction dictates how each editor feels when you're in the middle of a complex coding session. 1. Inline Autocomplete & Flow State When writing code line-by-line, friction is the enemy of productivity. Cursor Famous for its ultra-fast multi-line predictions. Cursor predicts not just the next token, but your next probable edit location across nearby lines. It keeps you in a continuous "flow state" where hitting Tab feels almost telepathic. Windsurf Features "Supercomplete" inlin

Facing a growing drone threat, the Pentagon is poised to sign a first-of-its-kind contract for “Enduring High Energy Lasers”—and make directed energy weapons an official part of the Army’s kit.

This tutorial was written by Néstor Daza . This is the third article in a series about building Claudius , my own Claude-based chatbot ( Github ). The previous article discussed the MongoDB data model to use for the app. The previous article decided the shape of the data. None of it matters until the app around it is working, and getting it there is the unglamorous half of this phase. It comes down to three things: an identity system the client cannot tamper with, proof that Claudius can reach the two services it depends on, and the deployment realities that decide whether any of it runs at all. This is the boring work that quietly decides whether a project survives contact with production. Identity: the client never gets a vote Any Google account on Earth can sign into Claudius safely because a user's role is never something the client sends. It is decided on the server every time. One piece of this lives outside the code. The Google provider needs an OAuth (Open Authorization) client that you register once in the Google Cloud Console, and the client identifier and secret from that registration are set in corresponding env variables. These setup steps live in the Auth.js and Google documentation, so I am not repeating them here. Sign-in runs on Auth.js v5 with the Google provider and the MongoDB adapter. There are three roles, admin, member, and guest, and they resolve in exactly one place on the server, with a clear precedence: export async function resolveRole ( email : string | null | undefined ): Promise < Role > { if ( ! email ) return " guest " ; const normalized = email . toLowerCase (); if ( normalized === env . ADMIN_EMAIL . toLowerCase ()) return " admin " ; const settings = await settingsCol (); const allowlist = await settings . findOne ({ _id : " allowlist " }); if ( allowlist && " emails " in allowlist ) { const allowed = allowlist . emails . some (( e ) => e . toLowerCase () === normalized ); if ( allowed ) return " member " ; } return " guest " ; }

An analysis of the last seven years of Tesla earnings calls shows just little attention Musk pays to Tesla's car business.

AI is not a genie. Treat it like a function. Most people use AI the way they use a search box: type a question, read the answer, move on. That works for one-off curiosity. It is a bad fit for the work you repeat every week, because you re-explain the context every time and never build anything you can trust. A small assistant is different. It is one narrow task, wired up once, with a fixed input and a fixed output shape. You run it, check it, improve it. After a few iterations it stops being a demo and starts pulling real weight. Here is how to build one without drowning in frameworks. Start narrow: one task, one input, one output Do not build "an assistant for my job." Build the thing that turns a messy meeting note into three bullet points. Pick a task that is: Repetitive (you do it weekly or daily) Boring (nobody will miss the manual version) Verifiable (you can look at the output and know if it is wrong) That last one matters most. If you cannot tell good output from bad in ten seconds, you cannot trust the assistant and you cannot improve it. Good starter tasks: drafting reply emails, summarizing documents, normalizing scrappy data, extracting fields from text. Example: an email draft as a function Think of your prompt as a function signature. Inputs go in, a structured draft comes out. def draft_reply ( incoming_email : str , tone : str = " friendly, brief " ) -> str : prompt = f """ You are drafting a reply on my behalf. Do not invent facts. If information is missing, leave a [PLACEHOLDER]. Tone: { tone } Incoming email: --- { incoming_email } --- Write only the reply body. """ return llm ( prompt ) # any model client you like Two lines do the real work: "Do not invent facts" and the [PLACEHOLDER] rule. Together they turn a confident hallucination into a visible gap you can fill. The goal is to make errors loud instead of silent. Example: summaries you can actually trust The failure mode of summaries is a plausible sentence that never appeared in the source.

The Asus Chromebook Plus CX34 is a dependable laptop that doesn’t cost a fortune, despite being nearly three years old. It’s cheaper than usual right now, and you have a few options in the sub-$400 range. The option with the most storage is currently on sale for $399.99 (about $100 off recent prices) at Amazon. […]

Introduction Artificial Intelligence has evolved far beyond answering questions and generating code. Modern AI systems can search databases, interact with APIs, read files, execute commands, access cloud services, and even coordinate multiple tools to complete complex tasks. This shift has given rise to AI agents - systems that don't just generate responses but can actively perform work on behalf of users. However, enabling an AI model to interact with external tools introduces a challenge. Every application, service, and API exposes its capabilities differently. Without a common standard, every AI platform would need custom integrations for every tool it wanted to support. This is where the Model Context Protocol (MCP) comes in. MCP provides a standard way for AI models to discover, understand, and use external tools, data sources, and services. Instead of building separate integrations for each AI model and every application, developers can expose capabilities through a common protocol that different AI clients can understand. In this article, we'll explore what MCP is, why it matters, how it works, and how it's changing the way developers build AI-powered applications. The Problem Before MCP Imagine you're building an AI assistant that needs to interact with: GitHub Slack Google Drive PostgreSQL Jira Notion Local files Internal company APIs Without a shared protocol, every integration becomes a custom implementation. For each tool, you need to define: Authentication API endpoints Request formats Response parsing Error handling Documentation Now imagine supporting multiple AI models. Every model may require different integration logic, increasing development effort and maintenance costs. This creates unnecessary complexity. What Is MCP? At its core, the Model Context Protocol (MCP) is a communication standard between AI models and external systems. Instead of hardcoding every integration, MCP defines a consistent way for an AI client to: Discover available tools U

Want to tap into the energy of 1,100+ startup founders, investors, and tech leaders descending on Boston for the Founder Summit 2026 on June 9? Host your own Side Event during “Founder Summit Week,” happening June 4-10!

Apple is working on a feature that will allow users in the European Union to copy content on their iPhone and paste it onto their Windows PC (or vice versa), as spotted earlier by MacRumors. The move comes in response to an interoperability request from Microsoft that asks Apple to open up its Universal Clipboard […]

The main features of HDR10+ Advanced include Enhanced Overall Brightness and Intelligent Motion Smoothing.

Babylon 5 is being released, episode by episode, for free, on YouTube. You should watch it

An internet connection is essential for your Roku to stream your favorite movies and shows. Let's get you back online.

AI systems can change without the code changing. That makes “is this correct?” a much harder question. I talked with Diana Pfeil about evals, input drift, prompt versioning, model deprecations, and the maintenance work hiding behind all this new capability. Curious how teams here are handling it in production. https://maintainable.fm/episodes/diana-pfeil-building-confidence-in-probabilistic-systems submitted by /u/robbyrussell [link] [留言]