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Most embeddable widgets are surveillance with rounded corners. You paste one script tag, it opens a socket back to someone else's server, drops analytics, fingerprints the page, and turns your article into their funnel. I wanted the opposite. I had built a small screen-time calculator for an iPhone side project. You enter daily phone hours, how much of that time you'd actually want back, and your age. It returns the number not just as hours per year, but as waking years of the life you have left . The surprising part was not the maths. The surprising part was that the calculator itself was the first marketing asset I had built that people might reasonably link to. So the next step was obvious: make it embeddable. Constraints I gave myself four rules: No tracking script No backend callback No cookie or storage requirement Useful standalone, but with a real reason to click through That ruled out the normal widget pattern immediately. I did not want a script that asks the host page for DOM access. I did not want the embed to send typed values back to me. And I did not want to bolt analytics onto a tool whose whole public claim is "nothing leaves your device". So the widget became a single static iframe page. The embed snippet This is the whole thing: <iframe src= "https://shantj.github.io/sproutguard/embed.html" width= "100%" height= "620" style= "border:0;max-width:600px" loading= "lazy" title= "Screen time calculator" ></iframe> <p style= "font-size:13px;opacity:.7;margin:6px 0 0" > <a href= "https://shantj.github.io/sproutguard/screen-time-calculator.html?ct=embed-credit" > Screen Time Calculator </a> — free, no signup, runs in your browser. </p> No JavaScript include. No SDK. No npm package. Just an iframe and a credit link. The iframe points at a page that contains the calculator UI and the arithmetic. Because it is a static page, the host site never has to trust my script with its DOM. The actual calculator logic The core number is intentionally boring: const LIF

Originally published at vinpatel.com What is actually inside the Department of Energy's new Genesis Open Models Initiative? Right now, the honest answer is: a name, a URL, and a lab crest. The claim, as DOE has made it by putting the initiative live at genesisopenmodels.anl.gov, is that the federal government is now in the open-model business — training or releasing AI systems the way Meta releases Llama or Mistral releases its weights, except under a federal national lab's letterhead instead of a Silicon Valley one. The .anl.gov domain ties it directly to Argonne National Laboratory, a DOE lab that runs some of the country's largest public research compute. Here is what is measurable today: a hostname registered under a DOE national laboratory's domain, and a title. That is the entirety of what's publicly documented about this launch right now. No model card. No parameter count. No benchmark table. No license terms. No dataset provenance. If you went looking for the thing the name promises — an open model you can download, fine-tune, or audit — you'd come away with a URL and nothing to point a training run or a procurement memo at yet. That gap is not evidence of a bluff. It's what federal AI launches look like structurally. National labs run compute clusters under export-control review, multi-agency sign-off, and clearance processes that have nothing to do with how fast a model can actually train. A private lab ships a checkpoint the day it clears internal review. A DOE initiative clears communications, legal, and interagency review before it clears a single line of a model card — and the announcement is often the artifact that exists first, because it's the cheapest one to produce. The site can go live in an afternoon. The weights cannot. What would actually close this gap is specific and checkable: a published model card with parameter counts and training data provenance, an open license attached to real downloadable weights, and benchmark numbers outside resear

The bug that isn't a bug On Tuesday you attach a dead-letter queue to orders-queue . On Wednesday a batch of messages disappears and you ask Claude Code what happened. It answers immediately: orders-queue has no DLQ configured, so failed messages are dropped after the maximum receive count. That answer is wrong, and it is also not a hallucination. The assistant read a real snapshot of your AWS account. The snapshot was taken Monday. This is the failure mode that shows up once you give an AI assistant deterministic infrastructure context instead of letting it guess. Guessing produces answers that feel uncertain, and you treat them accordingly. A stale snapshot produces answers that feel authoritative, with real table names, real queue names, real ARNs. Nothing in the response signals that the underlying facts expired. Infrawise extracts your DynamoDB tables, Lambda configs, queue settings, database schemas, and code-to-table access patterns into a graph, then serves that graph to AI editors over MCP. Everything below is about the part nobody asks for in a feature list: what happens to that graph when it gets old. Why the context has to be cached at all The obvious fix is to never cache. Answer every question from a live account read. That does not survive contact with an actual session. A full infrawise analyze walks every enabled service, paginating through DynamoDB DescribeTable , Lambda configurations and their event source mappings, SQS queue attributes, SNS subscriptions and filter policies, Secrets Manager rotation state, S3 versioning and public-access configuration, ElastiCache clusters, CloudWatch log groups, plus schema introspection against Postgres, MySQL, or MongoDB, plus a local IaC parse, plus an AST scan of the repository. Every extractor is dispatched through a single Promise.all , so wall-clock time is bounded by the slowest one rather than their sum, but it is still seconds, not milliseconds. An assistant calls get_infra_overview at the start of a

GitHub热门项目 | An open-source AI coding agent that lives in your terminal. | Stars: 26,842 | 387 stars this week | 语言: TypeScript

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Half your code is now written by an AI that hallucinates the other half's package names. That's not a joke. Across 576,000 samples, 19.7% of the packages AI coding models suggested didn't exist — and attackers have started pre-registering those exact hallucinated names, waiting for someone to npm install a package that was never real to begin with. Meanwhile, public registries catalogued 454,600 new malicious packages in 2025 alone, pushing the cumulative total past 1.2 million. A modern web app now pulls in 1,200+ dependencies once you count the full transitive tree — for software that, ten years ago, would have shipped with a fraction of that. That's precisely what Zero Dependency 2026 is about. Organized by Hackathon Raptors , Zero Dependency is a 72-hour online global hackathon where participants build genuinely useful software — CLIs, parsers, servers, databases, security tools — using nothing but their programming language's standard library. No frameworks. No packages. No supply chain to inherit. Just your language and your engineering skill. Why Zero Dependency? Modern AI coding assistants can generate code fast, and they reach for a dependency by reflex — sometimes one that doesn't exist. But real engineering isn't judged by how many packages you imported. It's judged by questions like: Do you actually understand what the library you'd normally import is doing underneath? Can you build the thing yourself, correctly, from primitives? Does your code handle the edge cases a package would've handled for you? Is the implementation idiomatic, or a fight against the standard library? Could a judge verify your dependency manifest is empty in five seconds? Zero Dependency is designed around these questions. Instead of rewarding whoever pulls in the most packages the fastest, the hackathon rewards teams who can prove they didn't need to. About the Hackathon 🌍 Global Online Hackathon ⏳ 72 Hours 💰 $1,800 Prize Pool 👥 Solo or Teams of up to 4 🎓 Open to students, profess

I Turned an Android Phone Into a No-Root Cybersecurity Learning Workspace Most people don't look at an Android phone and think: "This could be a practical Linux, Python, networking, and cybersecurity learning environment." Usually, the assumption is that serious technical learning requires a laptop, a virtual machine, or dedicated hardware. I wanted to see how far I could push the opposite idea. What if the Android phone you already own could become a practical learning workspace without root access? That experiment eventually became DedSec . DedSec is a free and open-source project built around Android and Termux. Its goal is not simply to install a large collection of tools. The goal is to create an environment where someone can actually learn how the pieces fit together. Repository: https://github.com/dedsec1121fk/DedSec Official website: https://ded-sec.space/ Why Android? Android devices are incredibly capable machines. Even an older phone can provide: a Linux-like command-line environment through Termux Python Git package management networking utilities file manipulation scripting automation local development workflows And you can do a surprising amount without root access. The limitation isn't always the hardware. A bigger limitation is often knowing what to do with it. You can install dozens of packages, copy commands from tutorials, and still not understand what is actually happening underneath. That was one of the problems I wanted DedSec to address. More Than a Collection of Scripts There are plenty of repositories containing security scripts. That wasn't enough for what I wanted to build. Installing a tool doesn't automatically teach you: what problem the tool solves when you should use it what its output means what layer of the system is failing how networking concepts connect together why a command works why another command fails So DedSec gradually became an ecosystem rather than just a scripts directory. The project connects several things together:

Magento 2's full page cache is one of its strongest performance features — when it works. But every week, we see stores where a simple product save triggers a 30-second Varnish flush and subsequent cache stampede. The culprit is almost never Varnish itself. It's cache tags. This post covers how Magento 2 cache tags work, why broad tags destroy performance, and exactly how to audit and fix them. How Cache Tags Work in Magento 2 Every cached page, block, and data fragment in Magento is tagged with identifiers. When a product changes, Magento invalidates all cache entries tagged with that product's ID. The tag system is hierarchical: cat_p_123 — specific product cat_p — all products cat_c_5 — specific category cat_c — all categories cms_b_about_us — a CMS block cms_p — all CMS pages These tags are stored alongside cached content and used during invalidation. When you call $cache->clean(["cat_p_123"]) , every cache entry tagged with cat_p_123 is removed. This is elegant until someone tags a global block with cat_p , and saving any product flushes half your store. The Invalidation Storm Problem Here's what happens during a storm: Admin saves a simple product update (price change) Magento generates the invalidation list: cat_p_456 , cat_c (because the product is in categories), cat_p (from a badly written block) cat_p is too broad — it matches the product list page, layered navigation, homepage widgets, and every product detail page Varnish receives 50,000 BAN requests Store goes from sub-100ms response times to 2-5 seconds for the next 10 minutes while the cache rebuilds We've seen this on a store with 80,000 SKUs. A single product save dropped cache hit rate from 94% to 12%. Diagnosing Bad Cache Tags Check Your Current Tags Add this to any block template to inspect what tags are being applied: $block -> getCacheKeyInfo (); // Or for the full page: $block -> getIdentities (); For a full audit, intercept cache writes in development: // In di.xml: < type name = "Magento\Fr

GitHub consolidated the npm and Actions changes it shipped from March to July 2026 against supply chain attacks, several of which alter defaults rather than add options. Hacker News discussion focused less on the individual controls than on whether waiting periods are the right instrument, or a substitute for author-side package signing. By Steef-Jan Wiggers

I'm a self-taught developer. No CS degree, no funding, no team. Just me, a laptop, and a problem I kept watching people struggle with. The Problem Every freelancer and small agency I know deals with the same mess: client details scattered across WhatsApp chats, email threads, Google Drive folders, and random Notion pages. Nothing lives in one place. When a client asks "wait, didn't we already send you the logo files?" you're digging through three different apps trying to remember. I didn't just hear about this problem — I lived it. So four months ago, I started building Kray. What Kray Actually Does Kray gives freelancers and agencies one organized workspace per client — projects, links, and notes, all in a single place instead of scattered across five different tools. The part I'm most proud of: when you share a project with a client, they can open the link and see everything instantly — no sign-up, no account creation, no friction. Just a clean, simple view of what they need to see. The Stack Since I was building this entirely solo with zero budget, I leaned on tools that let me move fast without infrastructure headaches: React 19 + Vite + TypeScript (strict mode — no shortcuts) Tailwind v4 for styling Supabase for auth, database, and storage Deployed on Vercel No backend servers to manage. No DevOps to worry about. Just me shipping features. What I Learned Building Solo You will hit bugs that eat entire days. I spent hours debugging a sitemap indexing issue that turned out to be one missing header. That's the job — most of building isn't writing new features, it's fixing the thing that should've worked but didn't. Deploy discipline matters more than you think. I once tested a feature locally, assumed it was live, and spent 20 minutes confused about why production wasn't behaving — because I'd forgotten to push. Lesson learned: always verify what's actually deployed before debugging further. Marketing is its own skill, and it's humbling. I've spent the last severa

Everyone's arguing about AI search visibility. Almost nobody is measuring the boring part: whether AI crawlers can fetch your pages at all . So I built a small open-source tool — geo-crawl-audit — that probes any site with the user-agents of every AI crawler that matters (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, ChatGPT-User, and friends), measures how each is treated versus a normal browser, and checks the thing most people never think about: how many words exist in the raw HTML before any JavaScript runs . Because here's the detail the industry keeps missing — GPTBot, ClaudeBot, and PerplexityBot don't execute JavaScript. For most sites, Googlebot (feeding Gemini) and Applebot are the only AI-adjacent crawlers that render it. A site can rank #1 in Google and be a blank page to nearly everything else. I pointed it at 18 major sites on August 7. Five findings worth your time. 1. Access patterns line up with the business relationships The Guardian — which has a content deal with OpenAI — serves my simulated GPTBot, OAI-SearchBot, and ChatGPT-User a clean 200 . The same request wearing ClaudeBot, PerplexityBot, or CCBot : 403 , and those names are in its robots.txt disallow list too. Policy and enforcement agree. The New York Times — in litigation with OpenAI — 403s nearly everyone: GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Common Crawl, meta-externalagent. Two user-agents got through: bingbot and Amazonbot. I want to be careful about the claim here: a status code tells you who is blocked , not why . Any individual 403 has mundane explanations. But when the allow/deny matrix maps this cleanly onto public deals and public lawsuits, the firewall config has become a business document — and it's readable by anyone with a terminal. 2. robots.txt is a polite sign. Some doors are unlocked anyway. Reddit's robots.txt blocks every AI bot in my list — fourteen tokens, no exceptions. Enforcement tells a different story: my GPTBot UA got a 403 and ClaudeBot and CCB

Originally published by InvisibleHill Research . This cross-post preserves the original research cut-off and source list. Research cut-off: July 30, 2026. Miner signaling is a live metric and may have changed after publication. BIP 110 begins with a problem that many Bitcoin users can recognize. A miner can collect a one-time fee for including an image, token payload, or other arbitrary data, while thousands of node operators absorb the cost of downloading, validating, and sometimes storing it. The proposal's authors see that mismatch as a subsidy for data storage and a threat to Bitcoin's use as money. Their answer is a temporary soft fork. For about one year, BIP 110 would make several currently valid transaction structures invalid under consensus rules. It would cap OP_RETURN outputs at 83 bytes, limit many data pushes and witness items to 256 bytes, restrict large output scripts and Taproot control blocks, and disable several Taproot upgrade paths and script features that can carry data. Adam Back agrees with the premise more than his opposition sometimes suggests. He has said that Bitcoin is about money and that spam has no place in its timechain. He also designed Hashcash to make spam costly. His objection is to the remedy. In Back's view, an annoyance that fits inside Bitcoin's existing block limit does not justify a contentious consensus change, especially one that can be bypassed, can interfere with legitimate scripts, and has not earned technical or economic agreement. That distinction is the center of the debate. BIP 110 asks whether Bitcoin should discourage an unwanted use through local policy and fees, or declare some forms of that use invalid for everyone. Back's case is stronger on this question. The proposal identifies a real externality, but it offers an asymmetric bargain: incomplete filtering in exchange for a new consensus precedent, a more complicated upgrade path, and a material risk of a minority chain. A policy dispute became a consensus pro