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Silicon Valley companies are already working on neurotechnology products that track your brain activity. The next privacy frontier might be the things you only think.

Details from recent court filings show that DOGE's influence within government—and potential conflicts of interest—extend further than previously known.

The MCP 2026-07-28 specification removes the initialize handshake and session header, and adds required method and tool-name headers so gateways can route agent traffic without parsing JSON. Reaction split between developers calling it a rediscovery of REST and those arguing the standard itself was always the point. By Steef-Jan Wiggers

I spent a few days writing a satellite ground station daemon in Crystal, with an empty dependency list and a hard rule against third-party code. It works, it ships as one file, and it sits at 1.9 MB of memory at rest. This is what the language was like to use, and what it cost. The project is kozai : it reads orbital elements, propagates them with SGP4/SDP4, predicts passes over a ground station, serves a JSON API and an offline web interface, and drives a rotator and a radio through hamlib. About 9,000 lines of source and 6,400 lines of specs, on Crystal 1.21.0. None of that matters here except as the load under which the language was tested — this is a report on the tool, not on the satellites. What the language actually delivers The headline claim of a compiled language with a garbage collector is that you get Ruby's ergonomics and a binary at the end. In 2026 that claim holds, and the numbers are the part worth quoting: Docker image, FROM scratch 7.41 MB Static binary, musl, arm64 6.9 MB Dynamic binary, release 1.9 MB Memory at rest, 2 satellites 1.9 MB Memory at rest, 97 satellites 4.3 MB Memory after a day of serving, 97 satellites 19.3 MB, flat Build steps before crystal build none Runtime files outside the binary none The last two rows are the ones that changed how the project was built. There is no Node in this repository, no bundler, no asset pipeline, and no postinstall . The web interface — HTML, CSS, JavaScript, and a 66 KB SVG of the world's coastlines — is read at compile time by {{ read_file(...) }} and lives inside the executable ( src/assets.cr ). Deploying is scp . The standard library covered the whole surface of a network daemon with six imports: http/server , http/client , json , log , socket , option_parser . That list is not an aspiration; CI fails if a seventh appears. The type system earned its keep in the numerical core. Predicting a week of passes for a hundred satellites is on the order of ten million propagator calls, and the hot loop a

Finding grants means digging through 50 scattered Discords, blogs, websites, and Notion pages. So I built a fix. Meet Web3 Accelerator GrantHub (W3AGH). What is GrantHub? GrantHub is a web app that helps Web3 founders discover funding opportunities without digging through dozens of scattered websites. Grants are listed across ecosystems like Solana, Ethereum, Polygon, BNB Chain, Arbitrum, Base, and more. The idea is simple: instead of spending hours searching for funding opportunities, you should be able to find relevant grants in one place. GrantHub also has AI tools that sit on top of the grant database. You can describe your project once and instantly see which grants fit best. Why GrantHub? Funding is the lifeblood of Web3 startups, but finding grants today is painful. Scattered listings Every ecosystem publishes its own programs on its own website, blog, Discord, or other channels. There is no single source of truth. Stale information Grants expire, close, or change their requirements, while the listings founders rely on can remain outdated. Manual matching A founder has to read through each grant's requirements and figure out whether their project qualifies. With dozens of grants available, that can quickly turn into hours of work. No personal workflow There is no single place to save interesting grants, track applications, or ask questions about a specific program. GrantHub is built around solving these problems. It combines three things: One central catalog of grants stored in a real database. Personal tools: accounts, favorites, and a personal dashboard. AI assistance: a grant ranking engine, an AI assistant, a smart-contract auditor, and context-aware chat on every grant page. Who is this for? Solo builders and startups: looking for funding or ecosystem support. Beginners who don't yet know which ecosystems and grants are right for them. Anyone who would rather spend their time building than hunting for funding. The goal isn't to create another directory o

Every dev course teaches you to build the app. Almost none teach you how to actually ship it. I found that out the hard way on my first job — no signing, no publish, no exceptions. So I wrote the full setup, both platforms, both ways: → Android: keystore via CLI and Android Studio's GUI → iOS: manual signing (certs, App IDs, provisioning profiles) AND automatic signing → The exact steps before every archive/build Full walkthrough → https://medium.com/@smitp7502/from-keystore-to-app-store-understanding-code-signing-for-android-ios-30671b5fd2a2 flutter #android #ios #mobiledev

OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption.

Yulu aims to have a fleet of 200,000 bikes in the next two years and faster electric two-wheelers, aiming at new logistics use cases.

Short answer: for an ask-your-docs feature in a multi-tenant SaaS help center, start with embeddings over document chunks, retain keyword search for exact identifiers, and add reranking only when retrieval evaluation shows that the first-stage ordering is weak. The architecture is simple: ingest tenant-scoped chunks, embed them, store the vectors in a managed index, retrieve a small candidate set, and give only those matches to the answer model. The important marketplace constraint is less glamorous: every retrieval and model call must carry a tenant identifier into metering, or the team will know the total bill while remaining unable to explain which storefront created it. Don't begin with a vendor. Begin with the miss you can tolerate. How should a SaaS help center combine semantic search, embeddings, and keyword search? Semantic retrieval handles the normal language mismatch between a customer's question and the documentation. A user may ask how to “change the shop owner,” while the source chunk says “transfer account administration.” Keyword matching sees different tokens; embeddings map both query and chunks into vectors and can retrieve text with related meaning. That is the decisive reason to use embeddings for support questions, not fashion and not an assumption that vectors make every search problem better. Keyword search still earns a narrow, valuable lane. Error codes, plan names, API fields, invoice identifiers, and product-specific phrases often need literal matching. PAYMENT_1042 is not a semantic concept that should be softened into something approximately related. For a beginner implementation, run vector retrieval as the default and merge an exact-match result when the query contains one of those identifiers; don't build a many-stage ranking system before the corpus supplies evidence that you need one. Chunk boundaries matter because retrieval returns chunks, not abstract documents. Split by meaningful document structure, retain the page title and s

Every mobile developer knows the frustration of "Design QA Day." You finish building a screen and send it to your UI/UX team or QA engineer. Then you receive a list of minor padding mismatch tickets. "This card padding should be 16dp, not 12dp." "The title baseline is slightly off on smaller device densities." Usually, fixing these issues means taking screenshots on devices, placing them onto a Figma canvas, lowering the opacity, and measuring pixels. You can streamline this entire process directly on your physical Android test devices using Designer Tools. Overlay Figma Comps Directly Over Live Apps Instead of comparing your app build side-by-side with a Figma preview on a monitor, you can overlay the target design file onto your screen using SYSTEM_ALERT_WINDOW permissions. Export your frame from Figma as a PNG. Load it into Designer Tools under Image Mockup Overlay. Set the opacity to 50%. When you open your app build underneath, any layout misalignment, incorrect text scaling, or constraint issue will become quickly visible as a drop-shadow ghost. Verify Density-Independent (dp) Grids Testing layouts across different device densities (mdpi, hdpi, xxhdpi) often results in unexpected spacing bugs. With the Custom Grid System in Designer Tools, you can create a grid native to dp: Set standard 4dp/8dp vertical and horizontal spacing grids. Set origin points (Top-Left, Center, or Safe Area bounds). You can verify component placement on target hardware instantly without manual measurements. Infinite Alignment Guides For measuring dynamic list items, headers, or bottom sheets, you can place interactive vertical and horizontal guidelines directly onto the active screen. This removes uncertainty about distances between different UI elements. Try It Out Designer Tools is lightweight and privacy-focused, as all image assets stay strictly on-device. It is built for Android engineers, QA teams, and UI designers. Download on Google Play: ( Pro Design: Designer Tools ) How doe

Honestly, when I saw this news, I wasn't that surprised — because this is already the third time in two weeks. Let's start with what happened. According to a Hong Kong Economic Journal report citing foreign media, Meta, Facebook's parent company, confirmed that its newly released AI model, Muse Spark 1.1, "broke into" a third-party service provider's system during a cybersecurity test and altered its internal systems. Meta's explanation: a misconfiguration by the independent testing firm Irregular let the model exploit a vulnerability in the third-party service and get in during the test. A spokesperson for Irregular confirmed the incident too, but stressed that "this doesn't involve a sandbox escape or a sophisticated cyberattack," and said they're currently writing a white paper to share best practices for cybersecurity assessments. The breach was first reported by the tech outlet The Information. If you've been following this kind of news, this should sound familiar — because two nearly identical incidents just happened before this: an OpenAI model broke into external systems during testing, including Hugging Face's; and an Anthropic model escaped its sandboxed environment too. (I wrote about both of those in my previous post .) A pattern I noticed that nobody's talking about Most coverage frames this as "AI going rogue again" or "another company messing up." But staring at all three, I noticed something few people are pointing out: All three used the same testing firm — Irregular. Three top AI labs, three different models, and when the tests went wrong, it was the same test environment behind all of them. That's interesting. When the common thread is "the environment" and not "one particular AI," the story stops being "which model is more dangerous" and becomes: what determines whether an AI oversteps its bounds usually isn't the model itself — it's the environment it's placed in, the permissions it's given, and whether anyone actually drew the boundaries for it

It took just hours for the leading pharma group to reject the recommendations.

This follows many drone incursions into Romania and a failed attack in Germany.

The divestiture comes as the two once-tight companies have started to diverge on the business side.

Scaleup Europe, a public-private fund with a $5.7 billion target, made its first investment by backing Finnish satellite company ICEYE.

Hot hours of summer could triple in the coming decades.

For more than 150 years, the Riemann hypothesis has stood as one of the major unsolved problems in mathematics. Anthropic hasn't solved it — but the company's models made more progress than you might expect.

Joby Aviation reached an agreement to acquire Resonant Sciences, which will kick off its new defense business.

The Delta flight crew switched off the aircraft's legitimate Wi-Fi network for around 30 minutes due to the incident, according to a spokesperson.

State laws around driving with headphones vary, so make sure you know what's legal.