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
Cover: a real qa-probe run against our own stack, cropped to the summary - internal product detail withheld. We are building LightShield, a SIEM that is in active demo right now. We built most of it pair-programming with an AI coding assistant wired in over MCP - it ran our stack, read the errors, and patched its own code. For a small team that is a superpower. Until an endpoint failed. Here is the loop we kept hitting. A route returns a 500, or a 404, or an empty [] . The assistant looks at the status code and announces the cause with total confidence. Then it rewrites a handler that was never broken - because a status code is not a cause, and it had nothing else to go on. So it guessed, and it guessed wrong, and the diff made things worse. The thing is, that empty [] had at least six possible causes: the database was empty (nothing seeded) a feature flag was off a contract mismatch between the frontend and the backend an auth token that never got attached a 428 precondition a schema drift Same symptom, six different fixes. We could bisect to the real one. The AI could not - it had no ground truth, so it manufactured one. So we built qa-probe It analyzes the app, probes the live endpoints, and classifies each failure with a root cause and a fix hint. Three decoupled, cached phases: qa-probe analyze # parse source + OpenAPI -> route graph qa-probe probe # hit live endpoints (HTTP/SSE/WS), record evidence qa-probe report # classify root cause -> HTML / Markdown / JSON / AI-context # or just: qa-probe run It has adapters for FastAPI, Express, Next.js, tRPC, GraphQL, and a generic fallback, so it discovers your routes instead of you hand-listing them. The part that actually fixed our problem: every result is falsifiable Each result carries the evidence (the real request, a bounded response sample, the timing), a root cause from ~25 categories, and a calibrated confidence - high , medium , or none . When it cannot tell, it returns none instead of bluffing. No neural net
Most developers think password hashing is about authentication. It's not. Authentication is just a side effect. Password hashing exists for a much darker reason: because databases get stolen. Every year, companies invest millions in firewalls, monitoring systems, cloud security, and access controls. Yet breach after breach continues to make headlines. The uncomfortable truth is that security teams don't assume a breach will never happen. They assume it eventually will. And when that day comes, one question determines whether the incident becomes a minor security event or a full-scale disaster: Did you hash the passwords? The Difference Between an Incident and a Catastrophe Imagine an attacker gains read access to your production database. Not a far-fetched scenario. A leaked backup. A vulnerable API. A compromised employee account. A misconfigured cloud bucket. The attacker runs a simple query: SELECT email , password FROM users ; If your system stores passwords in plain text, the breach is over. The attacker already won. No advanced techniques. No brute force. No expensive infrastructure. They now possess something more valuable than your database itself: your users' digital identities. Because users rarely reuse databases. They reuse passwords. The same password protecting an account on your platform might also unlock their Gmail, GitHub, LinkedIn, banking app, or company VPN. What started as your security problem instantly becomes everyone else's. This Is Not Theoretical History has repeatedly shown what happens when passwords are handled incorrectly. The RockYou breach exposed more than 30 million passwords stored in plain text. Attackers didn't need to crack anything. They simply read the data. Years later, those leaked passwords were still appearing in credential stuffing attacks across the internet. A single backend decision survived longer than the company itself. That's the thing about password leaks. They don't expire when the incident report is published.
Picking colors is the fun part. Wiring them into a codebase that stays maintainable is where most palettes fall apart. Here's the approach I use. 1. Name colors by role, not value Don't scatter hex codes everywhere: css .button { background: #6366f1; } .link { color: #6366f1; } Define them once as custom properties, referenced by role: :root { --color-bg: #f7f7f8; --color-surface: #ffffff; --color-text: #1f2937; --color-muted: #9ca3af; --color-accent: #6366f1; } .button { background: var(--color-accent); } .link { color: var(--color-accent); } Now re-theming the whole app is a few edits in one place. 2. Skip pure black and pure white #000 on #fff feels harsh on screens. Pull both back: --color-text: #1f2937; /* near-black */ --color-bg: #f7f7f8; /* off-white */ Most layouts instantly look more intentional. 3. Dark mode is almost free Because the colors are role-based variables, you just override the values: @media (prefers-color-scheme: dark) { :root { --color-bg: #0f1115; --color-surface: #1a1d24; --color-text: #e5e7eb; } } Every component using var(--color-bg) adapts automatically. A shortcut I got tired of hand-converting palettes into this, so I built a free tool, PaletteCSS, that copies any palette straight out as CSS variables, Tailwind or SCSS — and has a color palette generator if you need a starting point. But honestly, the three rules above matter more than any tool. What conventions do you use to keep a color system maintainable? PaletteCSS — a free tool to discover, create and share color palettes and CSS gradients. Copy any palette as hex, CSS variables, Tailwind or SCSS. No signup. https://palettecss.com**
Over the past few months, I've been building a gaming website focused on Elden Ring guides, calculators, and tools. While the project started as a simple hobby, it quickly became an interesting experiment in SEO, content strategy, and web development. Here are some lessons I learned along the way. Building the Site Was Easier Than Getting Traffic Launching a website with Next.js was straightforward. Getting visitors was much harder. Many developers underestimate how competitive search traffic can be, especially in gaming niches where large sites already dominate search results. Publishing a website is only the first step. Why I Chose Next.js The project uses: Next.js TypeScript React Tailwind CSS The biggest advantage was SEO. Server-side rendering and static generation helped ensure that search engines could easily crawl and index pages. Performance was also excellent compared to many traditional CMS solutions. Tools Attract Different Users Than Articles One interesting discovery was that calculators and interactive tools behave differently from standard content pages. For example: Guides answer questions. Tools solve problems. A player may read a guide once, but they might return to a calculator dozens of times while planning different character builds. This makes tools valuable long-term traffic assets. Internal Linking Matters More Than Expected When new content was published, internal links helped search engines discover and understand related pages. For example: Build guides linked to calculators. Calculator pages linked to stat guides. Stat guides linked to weapon builds. This created a stronger topical structure around the Elden Ring ecosystem. Search Traffic Takes Time One of the biggest lessons was patience. Many pages received: Zero impressions Zero clicks No rankings for days or even weeks. Then suddenly search impressions started increasing as Google tested pages across different queries. Traffic growth was rarely linear. Content Clusters Work Well Inst
OpenAI has rolled out an upgrade for the free model you interact with the most on ChatGPT.