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Most people assume Redis is fast because of some complex multi-threaded. Turns out it's the opposite — it runs on a single thread and still beats most databases. The reason comes down to how it handles I/O at the OS level and why memory access patterns matter more than thread count. Here's a animated breakdown of the internals if anyone's curious. submitted by /u/lonely_geek_ [link] [留言]
IBM’s nanostack transistors could boost chip performance or energy efficiency.
As UK police embrace the AI revolution, a WIRED investigation reveals the messy inside story of one region’s experiment with predictive analytics.
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the company’s previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come. For more than half…
It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat. As the summer ramps up…
Security researchers found evidence that Russian authorities hacked the iPhone of a political opponent using a phone-unlocking device made by Cellebrite, even after the company said it would stop selling to Putin’s government.
kreuzcrawl began as a Rust core with bindings for ten languages. v0.3.0 ships fourteen, adds a tiered WAF-aware dispatch engine, cuts peak streaming memory from ~2.5 GB to ~20 MB, and enables SSRF defense across every outbound call path by default. It is the first release we consider API-stable. This post covers what changed, why each decision was made, and what the harder engineering problems looked like from the inside. At a glance Area v0.2.0 v0.3.0 Language bindings 10 14 (+Dart, Kotlin/Android, Swift, Zig) Peak streaming memory ~2.5 GB ~20 MB SSRF protection opt-in on by default Dispatch model static HTTP / bypass / browser tiered, signal-driven escalation WAF fingerprints — 35 across 8 vendors Fingerprint hot-reload — lock-free ( ArcSwap ), 500 ms debounce MCP tools partial 1:1 with CLI, safety-annotated CLI subcommands scrape, crawl + batch-scrape, batch-crawl, download, citations Robots / sitemap parsers engine-internal public modules API stability preview stable Four new language bindings v0.2.0 shipped Rust, Python, Node.js, Ruby, Go, Java, C#, PHP, Elixir, and WebAssembly. v0.3.0 adds Dart , Kotlin/Android , Swift , and Zig — bringing the total to fourteen. None of the per-language glue is written by hand. Every binding is generated from the Rust core by alef , our polyglot binding generator. The Dart and Kotlin/Android packages bind through the C FFI layer ( kreuzcrawl-ffi ) via dart:ffi and JNI respectively. Swift binds through clang. Zig uses @cImport against the same C header. The generation pipeline also hardened in this release: the Docker publish matrix now builds each architecture natively rather than via QEMU emulation, the Dart build no longer requires the Flutter SDK for pub.dev publishes, Swift artifactbundle checksums are injected automatically, and the Elixir/PHP/Ruby releases preserve their lock files through the source-publish step. === "Python" ```sh pip install kreuzcrawl ``` === "Node.js" ```sh npm install @xberg/kreuzcrawl ``` === "Rus
Last year I was doing a code review for a startup. Everything looked fine on the surface, clean code, good structure, tests passing. Then I noticed this: const query = `SELECT * FROM users WHERE email = ' ${ req . body . email } '` That's it. That's the bug. SQL injection, sitting right there in a startup that had been in production for 8 months. Nobody caught it. Not the developer, not the reviewer, not the CTO. Here's the thing, it's not that developers are careless. It's that this kind of bug is invisible until it isn't. The code works perfectly. Tests pass. Users are happy. Until someone types ' OR '1'='1 in the email field and walks straight into your database. The bugs I see most often 1. Raw SQL with user input // 🚨 This is everywhere const query = `SELECT * FROM users WHERE email = ' ${ email } '` // ✅ Use parameterized queries const query = ' SELECT * FROM users WHERE email = $1 ' db . query ( query , [ email ]) 2. Secrets in environment variables... committed to git # .env DATABASE_URL = postgres://user:actualpassword@prod-db.company.com/mydb STRIPE_SECRET = sk_live_... Then .env ends up in the repo because someone forgot to add it to .gitignore . I've seen this more times than I want to admit. GitHub's secret scanning catches some of these, but not always before someone has already cloned the repo. 3. JWT tokens that are never actually verified // 🚨 Decoding is not the same as verifying const user = jwt . decode ( token ) // ✅ Always verify const user = jwt . verify ( token , process . env . JWT_SECRET ) jwt.decode just reads the token. Anyone can forge it. jwt.verify actually checks the signature. The names are confusingly similar and the wrong one silently works in development. 4. No rate limiting on auth endpoints // 🚨 Anyone can try a million passwords app . post ( ' /login ' , async ( req , res ) => { const user = await db . findUser ( req . body . email ) // ... }) // ✅ Add rate limiting const authLimiter = rateLimit ({ windowMs : 15 * 60 * 1000 , m
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...
Most people lose hours pretending to work or study. I was one of them. I kept setting Pomodoro timers and ending up scrolling Twitter during breaks. That's not rest. That's just a different kind of distraction. So I built Mognota — a free wellness companion for screen workers. The Problem You don't have a discipline problem. You have a system problem. Your brain has a hard biological limit. Sustained attention peaks at 20-45 minutes before quality drops sharply. Long unfocused hours are not work — they are the feeling of work. The solution isn't more willpower. It's intentional recovery. What I Built Mognota pairs a Pomodoro timer with 50+ guided wellness activities: 👁️ 20-20-20 eye break reminders 🫁 Guided breathing & 7 pranayama techniques 🧘 Desk yoga, HIIT, Tai Chi, stretches 🎵 Binaural beats & ambient soundscapes 🧠 Meditation & NSDR protocols 📓 Gratitude journal, mood tracker, brain dump 🎮 Sudoku, sliding puzzle, fractal explorer The Technical Part This is what dev.to might find interesting: Pure HTML, CSS, vanilla JS — zero frameworks Everything in a single HTML file All 8 notification sounds synthesized with Web Audio API localStorage only — nothing leaves your device Works offline once loaded 109+ languages supported No npm. No build step. No dependencies. Just open and use. Try It 👉 https://mognota.com/ Completely free. No account. No ads. Forever. Would love brutal honest feedback from this community.
Let AI block distractions for you when you need to lock in Discussion | Link
Adding an AI feature looks deceptively easy. You sign up for an API key, paste in a prompt, and within an hour you've got a working demo that makes the whole team lean over your shoulder. Then you ship it, traffic arrives, and two things happen at once: your latency graph develops a long, ugly tail, and your monthly bill arrives with a number that makes finance schedule a meeting. The gap between "impressive demo" and "production feature" is almost entirely about cost and latency engineering. The model is the easy part. Here's how to cross that gap. First, understand what you're actually paying for Most LLM APIs bill by tokens — roughly ¾ of a word each — and they bill both directions: the tokens you send (input) and the tokens the model generates (output). Output tokens are usually several times more expensive than input tokens, which has a non-obvious consequence: a verbose prompt is cheaper than a verbose answer. This reframes optimization. People obsess over trimming their prompts while letting the model ramble for 800 tokens when 80 would do. If you want to cut cost, the highest-leverage move is almost always constraining the output : ask for JSON, ask for a single sentence, set a max_tokens ceiling, and tell the model explicitly to be terse. Latency follows the same logic. Generation is sequential — the model produces one token at a time — so output length is the single biggest driver of how long a request takes. A 50-token answer is fast almost regardless of model. A 2,000-token answer is slow even on the fastest infrastructure. Lever 1: Don't call the model when you don't have to The cheapest, fastest LLM call is the one you never make. Two techniques eliminate a startling share of traffic. Caching identical and near-identical requests. Many real-world prompts repeat — the same FAQ-style question, the same document summarized twice, the same classification of similar inputs. A cache keyed on the normalized prompt turns a repeat request into a sub-millisecond
Building a unified market intelligence platform for traders, analysts, researchers, and developers. After months of development, Jungletrade is now publicly available. The idea behind Jungletrade is simple: modern market analysis has become fragmented. Market data, indicators, analytical models, and trading signals are often distributed across multiple platforms, forcing users to maintain several subscriptions, workflows, and dashboards just to build a complete market view. We wanted to explore a different approach. 📊 The Problem Most market platforms focus on a specific layer of the analytical stack: Raw data Technical indicators Quantitative models Trading signals Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces. Our goal was to create a modular ecosystem where these layers can coexist within a single platform. 🧭 The Jungletrade Ecosystem Today, JungleTrade provides four product categories: 📦 Data Structured datasets for market research and discovery. 🧠 Models Analytical frameworks designed to identify patterns and relationships within market data. 📈 Indicators Tools that transform raw information into actionable insights. ⚡ Triggers Event-driven signals designed to highlight potential market opportunities. 🔍 Built for Transparency One design decision was particularly important to us: every product should explain itself. Each product includes: Product description Key features Use cases Interpretation guidelines Methodology overview The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works. 🔌 API First All products available through the platform are also accessible through API endpoints. Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform. 🏗️ Architecture JungleTrade is built using a modular, service-oriented architecture des
Dai metadati in una lingua a 20 localizzazioni senza impazzire tra click e schermate: un flusso pratico per indie e piccoli team. Localizzare un’app non significa solo tradurre le stringhe dell’interfaccia. Una buona parte dell’acquisizione organica passa dai metadati su App Store Connect : titolo, sottotitolo, descrizione e keyword. Il problema è che, quando provi a farlo “a mano” dal pannello web, diventa subito un lavoro di pura resistenza: apri la scheda, cambi lingua, compili i campi, salvi, ripeti. Ora moltiplica per 10–20 lingue. Per molti indie (e in generale per chi ha poco tempo e zero voglia di click ripetitivi) il punto di svolta è usare ASC CLI per rendere questa attività automatizzabile, ripetibile e verificabile . Perché la localizzazione dei metadati è un caso d’uso perfetto per una CLI Dal punto di vista del flusso di lavoro, i metadati App Store hanno tre caratteristiche che li rendono ideali per l’automazione: Sono campi strutturati (title, subtitle, description, keywords): non stai “inventando” contenuti ogni volta, stai trasformando contenuti. Sono ripetitivi per lingua : la sequenza di operazioni è identica, cambia solo la locale. Sono tanti : più lingue aggiungi, più l’approccio manuale scala male (tempo, errori, incoerenze). Con una CLI, invece, il lavoro si sposta dal “fare cose” al definire un processo : prendi i metadati di partenza, generi le varianti linguistiche, applichi l’update in batch. Cosa conviene localizzare (e cosa no) In genere ha senso includere in un passaggio di localizzazione “massiva”: App name / title (attenzione ai limiti e ai trademark) Subtitle (spesso è la parte più ASO-oriented) Description (qui conta più la leggibilità che la traduzione letterale) Keywords (campo delicato: va adattato, non tradotto alla cieca) Al contrario, è meglio trattare con più cautela: Claim e frasi marketing molto creative : in alcune lingue risultano innaturali se tradotte letteralmente Keyword strategy : la ricerca utenti cambia per mercat
Typing a prompt and getting a beautiful user interface in 30 seconds feels like magic. But the moment you add real users, process payments, or try to handle relational data, that magic often turns into a debugging nightmare. Many founders are hitting the "80% wall." Rapid AI code generators excel at creating stunning prototypes. They build the "dining room" perfectly, but they struggle to architect the "kitchen"—the secure, scalable backend required to run a business. Relying entirely on black-box, AI-generated code leaves non-technical founders with massive "comprehension debt." You end up owning a product that your business relies on, but that you cannot read, debug, or maintain when something inevitably breaks. Getting a prototype is easy; building software is hard. This article breaks down the top AI app builders on the market, separating rapid UI generators from the structured, full-stack visual platforms capable of handling relational databases, complex user permissions, and deterministic workflows. The "Vibe Coding" Trap vs. Real Application Architecture There is a fundamental difference between front-end UI generation and back-end reality. Visual components like buttons, layouts, and animations can be generated probabilistically. However, back-end architecture requires strict, predictable rules. When founders use text prompts to generate entire full-stack applications, they accumulate comprehension debt. If an AI writes thousands of lines of code you do not understand, your startup has a bus factor of zero. Real users frequently report spending weeks building with AI generators, only to realize they have no idea what state their application is actually in. To build an AI app without coding that actually scales, you need a relational database. Relying on flat JSON files or unstructured document stores often leads to the "overwrite trap," where simultaneous user actions silently delete each other's data. A native relational database, like PostgreSQL, enforces
It is clear that Vyshyvanka is more than just code — it is an ecosystem. The true power of an open-source workflow engine lies in its community. Today, we want to talk about how you can get involved, whether you are interested in pushing the boundaries of the core engine or building specialized solutions with custom plugins. The Core Engine vs. The Plugin Ecosystem A common question we get is: 'Should I contribute a PR to the core engine, or should I build a separate plugin?' The answer depends entirely on the scope of your contribution. When to Contribute to Core The core engine ( Vyshyvanka.Core , Vyshyvanka.Engine , Vyshyvanka.Api , Vyshyvanka.Designer ) should be reserved for changes that benefit every user of the platform. Good candidates for core contributions: Performance improvements to the execution pipeline New fundamental port types or expression functions Bug fixes in the engine, validation, or persistence layers Enhancements to the Designer UI (canvas, node editor, property editors) Improvements to the API surface (new endpoints, better error responses) Documentation improvements These changes require careful review and testing because they impact every installation. We encourage PRs here, but we also ask that you open an issue first so we can discuss the architectural impact. When to Build a Plugin Plugins ( ./plugins/ ) are the best way to extend functionality without increasing the maintenance burden of the core. Good candidates for plugins: Integration with a specific third-party SaaS tool (CRM, CI/CD, monitoring) Custom nodes specific to your industry or use case Experimental node behaviors that are not yet ready for core Proprietary integrations you want to keep separate from the open source project Plugins are independent, versionable, and can be maintained outside the core release cycle. They empower you to solve your specific problems immediately without waiting for a core release. Project Structure at a Glance Understanding where things live i
I started as a Java developer, but for some time now, I have broadened my horizons. Recently, I thought about how early languages were dedicated to a single target and platform, and now they are broadening their focus. In this post, I want to write down my thoughts in the hope that it may be useful to others, probably to my future self. Definitions You may have been wondering about the title terms. I'm pretty sure that if you read this post, you have a pretty good picture of what a programming language is. Some may disagree on some finer points or raise a hair-splitting one, but it's not a PhD thesis, only a post on my blog. I must define what I mean by target and platform in the context of this post before going further. Target A target only makes sense in the context of compiled programming languages. For example, C's target is native code , and Java's is bytecode . Platform A platform is the system that will ultimately run the target. Native code runs on the operating system; bytecode on the JVM. Early programming languages Early programming languages had a single target and platform. I mentioned C and Java, but Ruby, Python, JavaScript, etc., were all the same. Programming language Target Platform C Native code Operating system C++ Native code Operating system Java Bytecode JVM Python - Python runtime TypeScript JavaScript Browser & server-side JS JavaScript - Browser I believe it was the case for a long time. It changed at some point, though. Multi-target is the new black The first time I heard about multi-target was in Scala. Scala came from the era of single-target and targeted bytecode on the JVM platform. However, in 2015, Martin Odersky announced Scala.js, which added JavaScript to Scala's target. The original article was published on InfoWorld, but it seems to have redirection issues nowadays. Here's the introduction on a copy: Scala, developed as a functional and object-oriented language for the JVM, is now multiplatform, with developers using it in abun
Apple deals abound for Amazon Prime Day. We've rounded up the best deals on Apple Watches, iPhones, MacBooks, iPads, and accessories.
We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning.
Amazon-owned Zoox has introduced its new autonomous vehicle meant for for large-scale production.