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I tried to build an arbitrage bot between Kalshi and Polymarket. Sports seemed to be the easiest because the matcher is relatively easy compared to the other markets (economy, bitcoin, weather, etc.) The matcher worked, we got about ~98% of the sports and e-sports market. But there's barely any sizable arbitrage between Kalshi and Polymarket, and what shows up closes in under 10 seconds. For the Argentina vs Egypt, the match with the disputed VAR call and Argentina's stoppage-time comeback from two goals down. Every price swing on that match, including the two around the VAR call, closed inside about 45 seconds. Total arbitrage opportunity net of fees across the whole match: $439, against $20.8 million moving through Polymarket's market alone and $13.8 million in Kalshi open interest. ( https://dino.markets/blog/argentina-egypt-var-price-gap ) That's not an arbitrage opportunity. That's what an efficient pair of order books looks like once you finally have the tooling to watch them at the same time. I logged this properly over a full day too: 870 cross-venue price gaps in one 24-hour window, median time open about 9 seconds, 96 percent closed inside 30. ( https://dino.markets/blog/how-long-a-mispricing-lasts ). So I shipped the Polymarket-Kalshi sports market matcher as an API instead of an arbitrage trading bot. It turned out the matcher itself was the useful part. Free REST access to the matched feed and the confirmed-arb view, 60 requests a minute, MCP server included so an agent can read it without you writing a client. Planning to open source the matching engine itself at some point. After that, either extend it to other market pairs between Kalshi and Polymarket, or look at arbitrage against traditional sportsbooks. Nothing locked in yet. Feel free to use it and tell me what you think about it. Thanks!

There are a lot of fast-growing AI startups, but some are growing even faster, they say.

The robotaxi stopped, called 911, and waited for the San Mateo Police to show up.

Hello fellow React developers! In this article we will be breaking down what React component instance is and scenarios where React component instance is at play. What is a React Component ? Before we can understand and really appreciate what a React component instance is, we first need to understand what a component itself is. Basically, components are the fundamental building blocks of any React application. They are independent, reusable pieces of code that allow you to split your application into distinct, manageable bits of logic and UI. From our knowledge of JavaScript, you can think of components in a way as what a function is. Just as we create and use functions to avoid repeating code and separate logic, components are used to divide our application into reusable visual chunks. However, they work in isolation and return HTML (via JSX) to describe what appears on the UI. Let take a look at a simple Greetings component used in a demo; Instead of writing the HTML layout for a greeting over and over again, we define it once as a component and reuse it multiple times in our application by passing different props (arguments). React Component Instances: What are they ? Now that we understand what a React component is, let's move on to React component instances. In programming, an instance is a concrete object created from a specific template (such as a JavaScript class or a Constructor function). In React, a component instance is the actual implementation of a component in a React application. It is a long-lived object that holds contextual information about a particular component. Every time a component is rendered in our application, React creates a new instance of that component. To help you visualize this, let’s take a look at a simple Counter component; // A Counter Component import React , { useState } from ' react ' ; export default function Counter () { const [ count , setCount ] = useState ( 0 ); return < button onClick = {() => setCount ( count + 1 )} > C

Most agent demos are one-shot loops. You open a terminal, give the model a task, watch it call tools, and then the process dies. That is fine for coding sessions. It is a weak shape for an assistant that is meant to live in your actual workflow. Talon is built around the other shape: a persistent agent process with frontends, memory, tools, background jobs, and swappable model backends. What it runs on Talon can expose the same agent core through: Telegram Discord Microsoft Teams terminal chat a desktop/mobile companion bridge That means the agent is not tied to one UI. The chat app is just a mouth. The core state, tools, memory, goals, and model backend live behind it. Backends are swappable The same harness can run through: Claude Agent SDK OpenAI Agents Codex Kilo OpenCode Each backend implements the same capability interface, so the rest of the system does not need to care which model runtime is active. It has real operating machinery The important parts are not flashy. They are the things that let an agent keep working after the first message: MCP plugins for tools cron jobs for scheduled actions triggers for condition-based wakeups persistent goals for multi-session work long-term memory heartbeat mode for background progress dream mode for consolidation per-chat model and effort settings This is the difference between "chat with a model" and "run an assistant". Install npm install -g talon-agent talon setup talon start Repo: https://github.com/dylanneve1/talon If this is the kind of agent infrastructure you want more of, a GitHub star helps the project get found.

We've all seen password strength meters on sign-up forms. Most of them rely on simplistic, static rules: "Must contain at least 8 characters, one number, and one special character." But from a mathematical standpoint, these rules are a poor proxy for actual password security. A password like Tr0ub4dor&3 conforms to these rules but is far easier to compromise than a randomly generated four-word passphrase like correct-horse-battery-staple . To truly measure password security, we have to look at information theory and compute its Shannon Entropy . Here is how password entropy works, the math behind it, and how you can calculate it directly in the browser with 100% client-side privacy. What is Password Entropy? In cryptography, entropy is a measure of the unpredictability or randomness of a password. It is expressed in bits . An entropy of $N$ bits means there are $2^N$ possible combinations that an attacker would have to guess in a worst-case brute-force search. < 28 bits: Very weak (easily guessed in milliseconds). 28 to 35 bits: Weak (cracked in minutes or hours). 36 to 59 bits: Reasonable protection (days to months). 60 to 127 bits: Very strong (takes years to decades to crack). 128+ bits: Extremely secure (mathematically unfeasible to crack). The Mathematical Formula To calculate the entropy ($E$) of a password, we use the following equation: $$E = L \times \log_2(R)$$ Where: $L$ is the length of the password (number of characters). $R$ is the size of the pool of unique characters from which the password is drawn. $\log_2(R)$ is the binary logarithm of the pool size, representing the amount of information carried by each character. Determining Pool Size ($R$) To find $R$, we analyze which character sets are present in the password string: Lowercase letters ( a-z ): 26 characters Uppercase letters ( A-Z ): 26 characters Numbers ( 0-9 ): 10 characters Common special characters/punctuation: 33 characters (e.g., !@#$%^&*()-_=+[]{}|;:',.<>/? etc.) If a password uses ch

MarkDown - что это? [[Markdown]] — это язык разметки, с упрощенным до человекочитаемости синтаксисом. Markdown — создан Джоном Грубером ( John Gruber ) в 2004 году. Markdown — это фактически "микро" язык для трансляции "текста markdown"в подмножество языка XHTML. Markdown - итоговая презентация текста всегда документ HTML , и только потом если нужно следующим шагом PDF, др. стандарты документации. Markdown принципиально не может задействовать весь потенциал XHTML, результатом его работы всегда является ограниченный набор элементов. Markdown — это транслятор который при анализе интегрированного в "текст" markdown HTML/XHTML кода обязан по стандарту просто его транслировать в итоговый образ документа. Markdown — это, если цитировать автора > «Markdown — это инструмент преобразования текста в HTML для веб-писателей. Markdown позволяет писать в легко читаемом и удобном для написания текстовом формате, а затем преобразовывать его в структурно корректный HTML. ║ John Gruber » Примечание Термин транслятор , не обходимо понимать как сущность алгоритм Я не нашел программу реализующая концепт MarkDown по принципам John Gruber. Ни одна программа не умеет делать из Markdown валидный по John Gruber HTML. Все проверенные мною программы Obsidian, MarkText, Typora, на выходе из 10 строк генерируют портянку HTML в несколько тысяч строк!!! Причина Obsidian, Typora и MarkText используют Markdown не для веб-писателей и блогеров, а как формат хранения баз знаний (Knowledge Management)**. John Gruber - Wikipedia

Every junior developer or solo software engineer falls into the exact same engineering trap: They conflate writing code with building a business. They spend their initial excitement phase setting up intricate user database authentication schemas, writing custom cron jobs for automated subscription reminders, or building heavy background pipelines just to resize a user’s uploaded logo image. By the time their local environment is "infrastructure perfect," weeks have passed. The momentum is gone, burnout sets in, and the repository is abandoned before ever tasting real production traffic. In 2026, computing power has completely shifted to specialized edge layers. Infrastructure has become commoditized. If you are wasting creative bandwidth trying to compete on backend pipelines instead of focusing entirely on your unique value proposition, you are systematically killing your startup. Here is the architectural matrix to decouple your operational infrastructure and shift to a lean, hyper-scalable API-first codebase. Part 1: The Production Infrastructure Decoupling Layer The golden rule of modern systems design is clear: Your application should only maintain two core pillars internally—your proprietary business logic and your core user state database. Everything else—from security to user tracking—is a solved problem that should be offloaded to third-party micro-services. Let’s look at the financial and time trade-offs of building versus outsourcing across critical technical vectors: Microservice Vector The Native Way (High Friction) The 2026 API Standard Launch Velocity Impact Merchant of Record Raw Stripe API + Custom Tax Calculators Lemon Squeezy / Paddle Saves 5 days of legal & accounting setup Feature Rollouts Custom Postgres feature-flag logic loops GrowthBook / LaunchDarkly Zero deployment overhead for major pivots Customer Feedback Manual tables + Admin CRUD boards Featurebase API Instant roadmaps directly inside frontend Media Compression AWS S3 triggers + Edge

The crust expands at mid-ocean rifts. But how?

Political operatives in Trumpworld hope that US Senate candidate Graham Platner stays in the race as long as possible.

Vermilion Cliffs Ventures announced Wednesday the close of a $25 million Fund II.

What if you had a drone that wasn't a buzzy, annoying fly people wanted to swat - but rather a cute dog that runs and jumps? What if it could do tricks on command, and film your tricks as well? What if it could get right back up after a nasty-looking crash, dozens of times […]

Google's upcoming Pixel lineup might cost more than last year's. A report from Dealabs spotted by 9to5Google suggests that Google could raise the starting price of its 41mm Pixel Watch 5 to $399, while adding LTE could bump the price to $499. That's a $50 jump from the base Pixel Watch 4, which starts at […]

Apple has signed a $30 billion+ multiyear deal with Broadcom to design and produce more than 15 billion U.S.-made custom wireless connectivity chips for Apple products.

It’s not a great time to shop for a gaming laptop. The unprecedented rise in cost for RAM (and storage to a slightly lesser extent) has made laptops with low-to-midrange specs cost much more than they should. Recent deals on gaming laptops worth your attention have been uninspiring, but one recently caught my eye: MSI’s […]

The company doesn't plan to release an offline version.

Airbnb engineers detailed Sitar-agent, a Kubernetes sidecar for dynamic configuration delivery across tens of thousands of pods, processing updates several times per minute. The system was redesigned with Java, Amazon S3 snapshot bootstrapping, and a migration from Sparkey to SQLite to improve reliability, startup performance, and configuration availability at scale. By Leela Kumili

Build a new kind of rocket engine and the world will beat a path to your door.

Itamar Friedman discusses how architects and engineering leaders can break through the AI productivity ceiling using adaptive multi-agent systems. He shares insights on moving past simple autocomplete to resilient workflows by integrating autonomous testing, intelligent code review, and robust arbitration. Learn how to govern agent communication and build a context-driven SDLC that scales. By Itamar Friedman

Blue Origin is raising $10 billion at a $130 billion pre-money valuation from Coatue Asset Management, Bezos himself, and other big-name investors, The New York Times reported.