标签：#m

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]

It's a great time to go back to theaters! The Odyssey is currently making believers in IMAX. And combined with the power of Spider-Man, it's also breaking box-office records. After Verge senior reviewer David Imel reported back on his latest 70MM IMAX adventure, it got most of the staff talking in Slack about our favorite […]

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

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.

Six months ago I started experimenting with PPO and Breakout as a way to learn about Machine Learning and Reinforcement Learning. After a few experiuments just trying to get high scores, it bothered me that everything was a "memorized" script rather than reactive play, like a human would play. Thus began my journey to try and convince PPO to actually track the ball instead of focusing on scoring points. I read a lot of articles and tried a lot of things. After 124 PPO experiments on Atari Breakout, I found that every single model, across sticky actions, cursor wrappers, entropy tuning, dynamics randomization, adversarial bumpers, and everything else, converged to a memorized action sequence, not a reactive ball-tracking policy. The argmax was always a script. The fix wasn't more environment engineering. It was three lines of reward shaping: Directly rewarding the paddle for being horizontally close to the ball during descent. A tiny bonus (0.05 per frame vs 1.0-7.0 per brick) that fires every frame the ball is descending applied during training. During evaluation, the agent plays clean Breakout with no bonus. The behavior transfers!! Every prior approach I tried to penalize scripts by making the environment harder to memorize. PPO always found a way around it: timing-robust scripts, layout-conditioned scripts, noise-tolerant scripts. The optimum was always a script; only the shape changed. Proximity reward changes what the optimum is. A center-hold script gets incidental bonus when the ball passes near center. A reactive tracker gets the maximum bonus on every descent frame. The optimization pressure is unambiguous: track the ball, get more reward. I also made a cool tool to watch the agent work! It's called the "Split-Watcher" (so clever). It shows two instances of Breakout, each being controlled by a separate instance of the same agent. The one of the left is vanilla Breakout. The one of the right is a series of custom brick configurations. With the first 123 expe

Most Laravel applications do not become difficult to maintain because somebody made one obviously terrible architectural decision. They get there through dozens of small decisions that looked completely reasonable at the time. A controller grows beyond a comfortable size, so part of its logic moves into an Eloquent model. Later, the application needs to send a notification after an order ships. Then inventory must be checked through an external API. Accounting requests an ERP integration, and marketing wants loyalty points awarded after dispatch. Each change adds only a few lines. Since the Order model is already available wherever the feature is being implemented, adding one more method feels natural: $order -> ship (); It is concise, expressive, and pleasantly object-oriented. Six months later, however, that innocent method may be updating several tables, calling two APIs, dispatching events, sending notifications, and deciding whether the entire operation is allowed. The model has quietly become the place where the application runs its business workflows. That is the real problem. It is not that the model is “fat.” It is that persistence, domain decisions, and application orchestration have been mixed together until nobody can change one without understanding all three. Business Logic in Eloquent Models Is Not Automatically Wrong The common advice to remove all business logic from Laravel models goes too far. Eloquent follows the Active Record pattern. Martin Fowler describes Active Record as an object that represents a database row, encapsulates database access, and adds domain logic related to that data. In other words, an object containing both state and behavior is not an architectural mistake by itself. A model should be allowed to answer questions about itself: class Order extends Model { protected function casts (): array { return [ 'paid_at' => 'datetime' , 'expires_at' => 'datetime' , ]; } public function isPaid (): bool { return $this -> paid_at !== nul

I maintain an MCP server that generates music. One call takes anywhere from 40 seconds to three minutes, because there is a model rendering audio on the other end. That does not fit the shape MCP tools are usually written in: call it, get an answer, move on. Everything about the transport assumes the answer is close by. It is worth writing down what actually breaks when it isn't, because "my tool is slow" turns out to be three separate problems wearing one coat. What breaks The obvious first version is a single tool that kicks off the job and awaits the result. server . registerTool ( ' generate_music ' , { /* ... */ }, async ( args ) => { const task = await lacuna . music . generations . create ( args ) const done = await waitUntilReady ( task . id ) // three minutes later return { content : [{ type : ' text ' , text : JSON . stringify ( done ) }] } }) 1. You do not own the timeout. The MCP client does. Claude Desktop, Claude Code, Cursor and the rest each pick their own tool-call deadline, and none of them ask you what yours is. A tool that usually returns in 90 seconds and occasionally takes 200 will work on your machine and fail on someone else's, which is the worst possible failure distribution to debug. 2. If you hand the polling to the model, the model quits. The obvious fix is to return a pending task immediately and expose a get_generation tool so the agent can check on it. The agent will check on it. Twice. Maybe three times. Then it decides the job is wedged and tells the user "this seems to be taking a while, would you like me to keep checking?" — which is a reasonable thing for a helpful assistant to say and a terrible thing for a job that had 40 seconds left. You have converted a slow tool into an unreliable one. 3. Polling burns the context window. Every poll puts a full task object back in the transcript. Twenty polls of a JSON blob is real budget, spent entirely on the word pending . Three tools instead of one What ended up working is splitting the

Opening Google Analytics to answer one question is a special kind of tax. You know the number is in there. You also know it's four clicks, two date pickers and a dimension dropdown away, and by the time you've found it you've forgotten what you wanted it for. So I built Metrifyr : a remote MCP server that puts my Google marketing stack behind my AI agent. Nothing to install: connect it once (Claude, Cursor, VS Code, any MCP client), then ask the question in plain language and it goes and gets the number. It's in the Cursor marketplace and the official MCP Registry, and the catalog has grown past a hundred tools, though, as you'll see, no single session loads them all. What's actually connected Metrifyr isn't a wrapper around one API. It federates the whole Google marketing surface behind a single MCP connection: Analytics 4 : run reports, realtime, metadata, compare periods. Plus the admin side: create properties, data streams, conversion events, custom dimensions and metrics. Search Console : search analytics, URL inspection, sitemaps. AdSense : accounts, earnings, payment history, revenue by keyword. Tag Manager : read and audit containers, tags, triggers, variables. Google Ads : campaign planning. Connect it once, and the agent can reach across all of them in a single train of thought. "Which landing pages lost the most organic traffic last quarter, and were any of them earning AdSense revenue?" is one question to me. It's Search Console and Analytics and AdSense to the machine, joined without me opening a single tab. Raw numbers are the boring part Pulling a GA4 report over MCP is table stakes. The part I actually care about is the layer on top, the analysis tools that answer the questions you'd otherwise pay an SEO consultant to run: Content decay scan : which pages are quietly bleeding traffic month over month. Striking-distance optimizer : the queries ranking positions 11 to 20, one nudge away from page one. Keyword cannibalization : where two of your own pag

We use AskElephant to record client calls, and we work in claude.ai. Those two things could not talk to each other, and the reason is structural rather than a missing feature. AskElephant ships an MCP server. It runs locally over stdio, which serves Claude Desktop, Cursor, VS Code and Windsurf. A browser cannot spawn a process on your laptop, so claude.ai needs an MCP server at an HTTPS address. That is a different program. Building it took about a day. Working out what it should refuse to do took considerably longer, and that part generalises to any MCP server sitting in front of a large corpus. The code is on GitHub . The arithmetic that determines the design Before writing tools, measure your payload. We measured every transcript in our account: 3,706 engagements, 2,230 of which carry one. Characters Approx tokens Average transcript-bearing call 32,485 8,100 Median 28,240 7,060 p95 75,543 18,885 Largest 175,643 43,900 The largest single call is more than a fifth of a 200,000-token context window on its own. Now consider the actual user request: "find where we discussed pricing with this client this year." That touches maybe forty calls. The naive tool returns forty transcripts, which is roughly 325,000 tokens. It does not fit. Not slow, not costly: impossible. So the design constraint is not the API. It is arithmetic, and it arrives before you write a single tool definition. Do the reading on the server The whole design collapses to one line: The Worker does the reading. Claude does the thinking. search_transcripts fetches the candidate transcripts, scans them inside the Worker, and returns only matching passages with speaker and timestamp attached. A real forty-meeting search, run through claude.ai against the live archive, returned 76,757 characters of excerpt: about 19,200 tokens rather than 325,000. The scanner is a pure function with no IO, which makes it trivial to test: export function scanTranscript ( text : string , queries : string [], opts : ScanOption

Vista previa técnica: Iter todavía no está publicado en PyPI y no existe un paquete oficial instalable. Abrir un recurso, convertir datos o cambiar de backend suele exigir aprender una interfaz diferente y repetir código de integración. Iter nace de una idea sencilla: Aprende una vez. Usa cualquier biblioteca. iter convert data.json to data.csv El usuario expresa una sola intención. Iter se encarga de abrir el recurso, detectar los formatos, seleccionar un adaptador compatible, convertir los datos y guardar el resultado. Una intención. Una instrucción. ¿Qué busca cambiar Iter? Actualmente, una tarea sencilla puede exigir: importar bibliotecas; aprender APIs diferentes; configurar formatos manualmente; escribir código de integración; seleccionar cada backend. Con Iter, el usuario indica principalmente qué quiere conseguir: iter analyze sales.csv Iter selecciona automáticamente una herramienta compatible. Si el usuario necesita controlar la biblioteca, puede indicarla: iter analyze sales.csv with pandas La automatización es el comportamiento predeterminado. El control detallado sigue siendo opcional. Everything is a Resource Iter representa archivos, datos y recursos web mediante una estructura común llamada Resource . El sistema está organizado alrededor de cinco componentes: Resource : representa el recurso. Resolver : identifica formatos, tipos y backends. Registry : registra y selecciona adaptadores. Adapter : ejecuta operaciones concretas. Engine : coordina el proceso. La meta no es afirmar que todas las bibliotecas son idénticas. La meta es unificar intenciones comunes y conservar las diferencias importantes cuando sean necesarias. Estado actual Iter 0.3.0-rc.2 está en fase de corrección de errores y validación privada. Actualmente: el código principal permanece privado; Iter todavía no está publicado en PyPI; no existe un paquete demostrativo; la sintaxis puede ajustarse antes del lanzamiento; solamente se anunciarán como disponibles las funciones implementadas