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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!

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

What if the AI you chat with every day is quietly running something that looks a lot like a train of thought, and we just never had the right tool to see it? On 7th July, 2026, Anthropic published a research paper that honestly feels a little spooky. The team behind the Transformer Circuits Thread released a long, detailed study called Verbalizable Representations Form a Global Workspace in Language Models . The title is dense, but the idea inside is wild. They found a small, privileged region inside Claude and similar models. A region that behaves a lot like what cognitive scientists call the global workspace , the part of the brain associated with conscious access. The part that lets you say, I am thinking about a banana right now . In this post, I want to walk you through what they found, in plain English, with no math fear and no jargon walls. We will cover what the workspace is, how they found it, what they can do with it, and why it matters for anyone building or using AI. Grab a coffee. This one is worth your time. First, a Quick Brain Detour Before we get to the model, we need a tiny bit of background from neuroscience. For decades, scientists have noticed that the brain seems to operate on two tracks. Most of what your brain does, like parsing the sounds coming into your ears or keeping you balanced, happens automatically and quietly. You cannot really talk about it . It just runs in the background. But a smaller slice of brain activity is different. It is reportable . You can put it into words. You can hold a concept in mind, dismiss it, chain it to another concept, and use it for reasoning. Cognitive scientists call this access consciousness . One popular theory, called the Global Workspace Theory , says this happens because the brain has a shared hub. Specialized processors do their own thing in parallel. But every now and then, a representation gets posted to this central workspace, and once it is there, lots of other brain systems can read it, reason w

This post is about Hyperbole's HyWiki package for knowledge management. I've been doing a deep dive into knowledge management and PKMSs, and in my research I've found that HyWiki offers the best of all worlds. It has a truly unique behavior compared to other PKMSs that allows it to obviate the bookkeeping involved with linking your notes together, and allows your curated knowledge to propagate into the rest of your Emacs, not just your siloed notes vault. submitted by /u/misterchiply [link] [留言]

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?

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 […]

We’re excited to announce our first ever Summer Bug Smash! Every app has its share of rockstar bugs....

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 […]

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

submitted by /u/mitousa [link] [留言]

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.

Apple's program to invest in US manufacturing sees it spend $30 billion on Broadcom chips.

If you indulge in late-night binge-watching on your Roku, Fire Stick or Google TV, you're going to want to enable this feature.

Miners backed by Trump admin. sell to Japan, South Korea despite push to develop domestic supply chain.