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exFAT is best for cross-platform usage.

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.

Meeting notes that get the details right. Discussion | Link

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.

DeepSeek V4 Flash costs $0.14 per million input tokens and $0.28 per million output, with cache hits at $0.0028, and the retrained 0731 build that now ships under that name has a defect you need to route around: with thinking on (the default) and a strict json_schema , integer fields came back corrupted in 8 of 13 default-thinking runs across two independent request paths. Turning thinking off fixed every run and cut the extraction to a seventh of the tokens. We measured deepseek-v4-flash-0731 on day one: the corruption, the sharper off-switch cliff the retrain introduced, the budget floor that rescues it, the 1,024-token cache pages, and what still separates the preview build and V4 Pro . TL;DR With default thinking plus strict json_schema , deepseek-v4-flash-0731 corrupted integer fields in 8 of 13 runs on two request paths; V4 Pro corrupted 2 of 4, and only the preview stayed clean. The 0731 retrain sharpened the off-switch cliff: 2-hop math fell 6/6 to 0/6. The cache serves 1,024-token pages from roughly a 1.1K-token floor, hits 0.3 seconds after priming, and entries outlive 45 minutes. enable_thinking: false fixed every structured run at a seventh of the tokens; for 2-hop math the safe thinking budget is 256. How do the three V4 builds compare on paper and on the meter? Same tokenizer, same cache, same thinking machinery; different prices, different failure modes. Everything measured below comes from identical probes run against all three (a dash means we did not probe that cell); the day-one analysis threads focus on benchmarks, so this is the operational half of the comparison: Flash 0731 Flash preview V4 Pro List price, in / out per 1M $0.14 / $0.28 $0.14 / $0.28 $0.435 / $0.87 Cache-hit input per 1M $0.0028 $0.0028 $0.003625 Thinking default on on on Strict JSON with thinking on 5/5 corrupted (our path) 4/4 clean 2/4 corrupted 2-hop math with thinking off 0/6 2/6 4/4 thinking_budget token-exact token-exact honored (4/4 at 16) Cache pages 1,024 tokens, hit a

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

Federal officials will update the outbreak sometime "this week," a spokesperson said.

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 […]

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]

Surely nobody you know will overuse this...

It’s time for yet another giveaway. We raided The Verge’s closet full of tech in New York City to stuff as much can fit into a Nomatic Messenger Bag. It’s overstuffed, to say the least, and if you’re chosen as the winner of this sweepstakes on August 18th, the bag and its contents (valued at […]

Notably, the company saw its subscriber base swell by 9% in the second quarter despite raising prices in several regions this year.

“We do not have, nor want, any of their trade secrets,” the ChatGPT maker says.

The legislation lays the groundwork for a potential overhaul of India's zero-merchant-discount-rate regime, under which businesses have not paid fees to accept UPI payments since 2020.

Condensation plus electronics are a bad combo.

GitHub热门项目 | Powerful AI Client | Stars: 41,302 | 163 stars this week | 语言: TypeScript

GitHub热门项目 | mailcow: dockerized - 🐮 + 🐋 = 💕 | Stars: 13,228 | 50 stars this week | 语言: JavaScript