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n8n is the kind of tool you start using lightly and then quietly route half your operations through. At which point "it's running on someone's cloud seat, metered per execution, with my API keys living on their servers" starts to feel less great. Self-hosting fixes all three — flat cost, no execution cap, and your keys stay on a box you own. With Docker it's a fifteen-minute job. How much server it actually needs Honest numbers first, so you don't over- or under-buy: ~2 GB RAM is the sweet spot — n8n plus its Postgres database plus normal workflows sit comfortably here. 1 GB works if your workflows are light, but you'll notice it on bigger runs. 4 GB if you do heavy parallel executions or push large payloads through. n8n isn't CPU-hungry at rest; it spikes during runs. A 2-core box is fine for most setups. (More on matching specs to workload in the sizing guide .) The Docker setup On a fresh Ubuntu/Debian box, install Docker: curl -fsSL https://get.docker.com | sudo sh Make a folder and a docker-compose.yml — n8n with a persistent volume and Postgres: services : n8n : image : docker.n8n.io/n8nio/n8n restart : always ports : - " 127.0.0.1:5678:5678" environment : - N8N_HOST=n8n.yourdomain.com - N8N_PROTOCOL=https - WEBHOOK_URL=https://n8n.yourdomain.com/ - DB_TYPE=postgresdb - DB_POSTGRESDB_HOST=db - DB_POSTGRESDB_PASSWORD=change-me volumes : - ./n8n-data:/home/node/.n8n depends_on : [ db ] db : image : postgres:16 restart : always environment : - POSTGRES_PASSWORD=change-me - POSTGRES_DB=n8n volumes : - ./db-data:/var/lib/postgresql/data sudo docker compose up -d Two things worth pointing out: the volumes ( n8n-data , db-data ) are what keep your workflows alive across restarts and upgrades — don't skip them. And n8n is bound to 127.0.0.1 , not 0.0.0.0 — it's not exposed to the internet directly. That's deliberate; the next step handles access safely. Access: HTTPS or a tunnel Public URL (needed for OAuth nodes and webhooks): point a subdomain at the server and run

Meta is bringing back the Facebook Creator Studio page manager, now "reimagined" as a standalone AI companion app. The new app aims to make it easier for creators to connect with their audiences and show them "exactly how to grow on Facebook," according to Meta's announcement. Meta's AI Creator Assistant is a central focus of […]

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

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

Slack has outlined how its AI serving infrastructure evolved through four distinct phases, moving from a self-managed Amazon SageMaker deployment to a multi-cloud architecture spanning AWS Bedrock and Google Cloud Vertex AI. By Matt Foster

A monorepo can look like one repository, but security teams should treat it as many applications living under one roof. One repo may contain 10 frontend packages, 5 backend services, 3 shared utility libraries, 2 mobile apps, and one root lockfile that does not tell the full story by itself. Monorepo dependency security means scanning the root dependency graph, every workspace package, shared libraries, lockfiles, and generated SBOMs. If you scan only one file, you may miss the vulnerable package that ships in production. Why Monorepos Create Unique Vulnerability Challenges Monorepos centralize multiple packages, apps, services, and libraries inside one repository. This improves code sharing, dependency alignment, refactoring, CI caching, and cross-team collaboration. It also creates a security problem: one repository can contain many different dependency trees, owners, deployment targets, and risk profiles. A typical JavaScript or TypeScript monorepo may include apps/web , apps/admin , apps/api , packages/ui , packages/auth , packages/logger , and packages/config . Each package may have its own package.json . Some packages are deployed to production. Some are internal libraries. Some are build-only tools. Some are used by every app. A vulnerability in one package can affect one app, many apps, or the whole repo depending on how dependency relationships are structured. The biggest issue is shared code. If packages/auth depends on a vulnerable version of jsonwebtoken , every application that imports packages/auth may be affected. If packages/ui uses a vulnerable utility such as lodash , every frontend app that consumes that UI package may inherit the same risk. If a build tool dependency is compromised, the risk may appear during CI/CD rather than runtime. Real CVEs show why this matters. CVE-2021-23337 affected lodash through command injection in template handling. CVE-2022-31129 affected moment through inefficient parsing that could cause denial of service. CVE-202

Someone on your leadership team asked a reasonable question: should we adopt passkeys? You searched for answers and found implementation tutorials - WebAuthn server libraries, credential storage schemas, ceremony diagrams. They assume you've already decided. None of that helps you answer the question you were actually asked. This article is a decision guide. The question isn't how to implement passkey login. It's whether you should, when the timing makes sense, and for which users first. Implementation details matter eventually - but they don't belong at the front of the decision. You've seen Apple's demos and Google's Chrome nudges. Your security team may have sent a memo about phishing-resistant authentication. You know the term. What you don't have is a clear way to evaluate whether passkeys fit your product, your users, and your team's capacity to ship and support them. By the end of this article, you'll have scored your app against a readiness checklist, mapped show-stoppers that can block adoption, and drafted a one-page recommendation for leadership. Plain Terms: Passkeys, Passwords, and MFA Before scoring your app, you and stakeholders need to mean the same thing when you say "passkey", "password", and "MFA". Vendor decks use these loosely. A PM might say "passkeys replace passwords" while security means "phishing-resistant credentials". Both can be true. Passwords are shared secrets the user types; your server checks a hash. They leak via breaches and phishing sites. Users forget them, reuse them, and call support. MFA adds a second factor - app push, SMS, hardware key, or biometric. It cuts credential-stuffing and many phishing attacks, but adds friction, lost-device tickets, and cross-platform complexity. Passkeys are cryptographic key pairs on the user's device. The private key never leaves the device or synced passkey manager. Sign-in means unlocking with biometrics or a PIN; your server stores only the public key and verifies a signature. On web, the b

If you've wired Swiss Ephemeris into a Node astrology app, you know the ritual. You npm install sweph , and now every machine needs Python plus a C/C++ toolchain, because the package compiles Swiss's C code via node-gyp at install time (make/gcc on Linux, Xcode on macOS, Visual C++ Build Tools on Windows). It works on your laptop. Then it explodes: Apple Silicon: node-gyp can't find full Xcode behind Command Line Tools. Slim Docker / CI images: no Python, no build-essential , so the install dies. Serverless: the .node binary you built locally won't load on Amazon Linux (wrong arch or glibc). Then there's the data. Neither sweph nor swisseph bundles the .se1 ephemeris files; you download them yourself and point the library at a path. The modern set is 2 MB, the full GitHub set is 100 MB. And since 2.10.1 , sweph is AGPL-3.0 (LGPL only under a professional license), a real obligation to weigh for a closed-source SaaS backend. The pure-Rust alternative XALEN Ephemeris is an analytical engine written entirely in Rust and licensed Apache-2.0. Three things make it interesting for JS/TS devs: No node-gyp. The Node addon is napi-rs, which ships prebuilt per-platform binaries via npm. No Python, no C compiler, no compile step. A real WASM build via wasm-bindgen, so you compute charts client-side in the browser: no server round-trip, no backend copyleft. Zero data files. The core math (VSOP87A, ELP2000-82, IAU precession/nutation, an 8,870-star catalog) is analytical and compiled into the binary. No .se1 to host. import init , * as xalen from " xalen-ephemeris " ; // WASM build, runs in the browser await init (); // load the .wasm module const chart = xalen . computeChart ({ datetime : " 1990-04-12T08:30:00Z " , lat : 28.6 , lon : 77.2 }); console . log ( chart ); // planet longitudes, house cusps, etc. Swiss via Node XALEN (pure Rust) Build deps node-gyp + Python + C compiler none: prebuilt binary / .wasm Runtime data .se1 files (2 to 100 MB) none, compiled in Browser / WASM

How to Decompose a Legacy Java Monolith Without Disrupting Business Operations The Java monolithic applications have been supporting businesses for years. In these applications, the entire business logic, presentation layer, and data access layer are bundled into a single unit. These architectures are functional but hard to scale, maintain, and improve due to changing business needs. An expert Java app development company helps growing organizations in addressing this issue through Java modernization services. Instead of developing a whole software application from scratch, firms can transform their software in stages with the right boundaries. The biggest challenge here is to determine where to make those cuts in a bundle. Poorly chosen service boundaries create operational complexity issues and long-term maintenance problems. Understanding how to identify seams in the monolith application helps in achieving modernization successfully. Let's take a look at what contributes to the success of monolith decomposing and how organizations can approach it wisely. Why Organizations Are Modernizing Legacy Java Monoliths The legacy Java monolith applications were built during a time when monolithic architecture was common. They were optimized for easy deployment and centralized management. But today, businesses require flexibility. This is due to challenges such as Slow release cycles Increasing maintenance costs Limited scalability Complex dependency management Difficult onboarding new developers Growing technical debt These issues have increased the demand for software architecture modernization in business sectors. Modern architecture gives the following advantages to the teams: Deploy features independently Scale services individually Improve system resilience Accelerate development cycles Support cloud-native environments The objective of architecture modernization is to create a technical foundation that supports future business growth. Understanding business goals of

Why developer tools deserve a design language of their own - and how I built one for my own corner of the web Somewhere along the line, we collectively agreed that "functional" had to mean "boring." Open almost any developer tool, internal dashboard, or technical log and you'll find the same thing: a sterile corporate wiki. Grey on white. The same SaaS design system everyone copied from the same three component libraries. Rounded cards, a sans-serif font, a faint drop shadow. It works. It's also completely forgettable. But here's the thing nobody says out loud: when you're building for engineers - or building your own space on the web - you are under no obligation to follow the standard playbook. The intersection of system design and visual identity is one of the most under-explored areas in frontend architecture. We obsess over latency, bundle size, and runtime dependencies, then slap a default theme on top and call it done. The backend gets all the craft. The interface gets a template. I wanted to do the opposite. Building VOID_PROTOCOL When I put together my own developer log - https://blog.naveenr.in - I deliberately stepped away from the standard minimalist tech blog. Instead, I built out a full design system I call the VOID_PROTOCOL × Manga Editorial Design System: dark-only, type-driven, built on Astro 6, Tailwind 4 (CSS-first @theme tokens), and React 19 islands. The name isn't decoration. VOID_PROTOCOL started on my https://naveenr.in portfolio, which runs in two modes. There's a minimal version, and there's an immersive one - and in immersive mode the background is a real-time 3D simulation of a sentinel entity. It's not a looping video; it actually responds to your movement, clicks, and scroll. When you leave it alone long enough, it sleeps. And when it sleeps, it dreams - it dreams my initials. (Yes, really. It started as a joke and I kept it.) That entity is the soul of the whole identity: black, empty, void-like space and a cool blue palette, a deep-sp

Building a real-time crypto trading bot sounds like a weekend project — until exchange APIs return cryptic errors, WebSocket connections drop mid-trade, and rate limits turn your strategy into a debugging nightmare. After building my own bot from scratch, I learned that reliability is what separates a hobby script from a system that actually survives in production. This guide walks through the entire process: modular bot architecture, a real-time trading loop, plug-in strategies, backtesting, paper trading, and deployment to a $5 VPS — with production reliability patterns baked in from day one. Full source code included — the free AlgoTrak Backtest Lab on GitHub has 5 classic strategies, a complete backtesting engine, and Jupyter notebooks to get started immediately. Architecture Overview Before writing code, here's the modular structure we'll build: crypto_bot/ ├── strategies/ │ ├── rsi_strategy.py │ ├── macd_strategy.py │ └── ... # Plug in your own ├── core/ │ ├── trader.py # Data fetching + order execution │ └── logger.py # File + DB logging ├── config/ │ └── settings.json ├── cli.py # Entry point ├── bot.py # Main loop └── logs/ Each strategy is a standalone Python class. The trader handles exchange communication. The CLI lets you switch between strategies, symbols, and modes (paper vs live) without touching code. Real-Time Trading Loop Here's the core loop that runs every candle interval: while True : df = fetch_ohlcv ( symbol , interval ) signal = strategy . evaluate ( df ) if signal == " BUY " : trader . buy ( symbol , quantity ) elif signal == " SELL " : trader . sell ( symbol , quantity ) sleep ( next_candle_time ()) Three key points: fetch_ohlcv() pulls the latest OHLCV candle data from the exchange Your strategy evaluates the last N candles and returns a signal Orders execute only on valid signals — no guesswork Modular Strategy Example (RSI) Strategies follow a simple class interface. Here's a complete RSI strategy: import pandas as pd import pandas_ta a

MCP + RAG: Why I Stopped Building Complex RAG Systems After MCP Changed Everything Honestly, I've spent the last four years building increasingly complex RAG systems. Chunking strategies, embedding models, vector databases, rerankers, hybrid search... you name it, I've probably wasted a weekend trying it. I had this 1,800-hour knowledge base project called Papers — six years of notes, articles, bookmarks, everything. I built RAG version after RAG version, each time thinking "this time it'll be perfect." Spoiler: It never was. Then I added MCP (Model Context Protocol) support. And I realized something that completely changed how I think about knowledge retrieval: MCP makes traditional complex RAG obsolete for most use cases. Let me explain what I learned the hard way. The RAG Trap I Was Stuck In If you've built a RAG system, you know the drill: Chunking : Should you use fixed-size, semantic, recursive, or something fancy like LLM-powered chunking? Embeddings : OpenAI text-embedding-3-large vs Cohere vs nomic-ai vs your fine-tuned model? Vector Database : Pinecone vs Weaviate vs PGVector vs Qdrant vs Chroma? Retrieval : Top-k how many? Hybrid search with keywords? Reranking? Prompt Compression : How do you fit all the retrieved chunks into the context window? I went through every iteration. At one point, my RAG system was over 2,000 lines of code. I had configurable chunkers, multiple embedding providers, caching layers, hybrid search... it was impressive. It also didn't work that well. Here's what bothered me the most: I kept throwing more complexity at the problem, but the fundamental issue never went away. I was trying to make my knowledge base smart, but AI already got smart. Why was I reimplementing all this understanding logic when the AI can already do it better than me? How MCP Changed the Game When I added MCP support to Papers, I started with the simplest possible approach: Expose two tools: search_notes and get_note_content Search is just basic text matchin

We have all been there: a meeting starts, and suddenly your phone rings. I built Muffle to automate silent profiles, but the biggest hurdle wasn't the UI—it was making sure the app didn't destroy the user's battery while monitoring GPS coordinates. The Trap of Continuous Location Updates Early prototypes used LocationManager with frequent updates. This is the fastest way to get your app uninstalled. Keeping the GPS radio active in the background forces the device to wake the CPU constantly, leading to significant battery drain. To solve this, I moved away from active polling and shifted to the GeofencingClient API. Leveraging GeofencingClient for Passive Monitoring Instead of calculating distance from a point every few seconds, I transitioned to system-level geofencing. By defining circular regions around locations like the office or a mosque, the OS handles the monitoring at the hardware abstraction layer. kotlin val geofencingRequest = GeofencingRequest.Builder() .setInitialTrigger(GeofencingRequest.INITIAL_TRIGGER_ENTER) .addGeofences(geofenceList) .build() This approach allows the OS to do the heavy lifting. The app stays in a dormant state until the location provider signals a transition. The kernel only wakes the app when the device enters or exits the radius. The Trade-off: Precision vs. Power Using GeofencingClient means accepting a slightly slower trigger time compared to raw GPS polling. Sometimes, there is a delay of a few seconds as the device wakes from a deep sleep state. For a utility like Muffle, this is a fair trade-off. Users prefer their phone to silence five seconds after entering a building rather than finding their battery dead by noon. To mitigate the delay, I combined geofencing with a secondary intent service that performs a final check once the geofence trigger hits, ensuring that we aren't just reacting to a momentary GPS jitter. Final Thoughts By offloading the monitoring to the platform's native geofencing API, I was able to keep Muffle