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Everyone backs up. Almost nobody restores. So the backup sits there, growing, quietly reassuring, and completely untested. It is not a safety net. It is a photograph of one. The day you need it is the worst possible day to discover that the job has been failing since March. That the archive is encrypted with a key that lived on the machine you are trying to recover. That it holds the database but not the uploads. That it takes nine hours, and the business gave you two. None of that is exotic. All of it is ordinary. An attacker who reaches your data will reach your backups next, because they sit on the same network, under the same account, behind the same key. That is not a backup. That is a second copy of the same hostage. So test the restore. Not the theory. The restore. Into a clean place. With a clock running. By someone who was not there when it was built. Write down how long it took, because that number is your real promise to everyone downstream. Everything else is marketing. Keep one copy somewhere your production credentials cannot reach. Keep one copy that cannot be deleted, even by you, even when you are certain. And do not trust the log line that says the job succeeded. A green tick is a claim. It is not evidence. Security is not only keeping people out. It is being able to come back after somebody gets in. Anybody can copy data. The skill is putting it back while the phone is ringing and nobody agrees on what happened. Practise the boring version too. Not only the fire. One deleted table on an ordinary Tuesday, because that is usually how it starts. Not an attacker. A person, a missing clause, and a bad afternoon. Restore it once before you need it. Then it is a backup. Until then it is a wish with a filename. – Serguey Asael Shinder

You write instructions for your coding agent. It ignores one of them. You rewrite it more forcefully, in bold, with "IMPORTANT" in front. It still ignores it. Before blaming the model, check whether it ever saw the text. Each of the three cases below is documented behaviour of a tool you already use, each one drops part of your instructions on the floor, and none of them prints a warning. 1. Cursor ignores .md files in .cursor/rules Project rules in Cursor must use the .mdc extension. Cursor's own docs put it plainly: a plain .md file there is ignored by the rules system, because it has nowhere to declare the description , globs and alwaysApply frontmatter that tells Cursor when to apply it. So a file sitting in exactly the right directory, with exactly the right content, does nothing. No error at startup, no "rule skipped" line, nothing in the UI. Ten-second check: find .cursor/rules -name '*.md' 2>/dev/null Any output is a rule that isn't loading. Rename to .mdc and add the frontmatter. A detail that makes this worse: people who set up .md rules a while ago report that they used to work. If that's right, a working setup stopped working at some point during an update, and nothing announced it — so "I checked this once" is not protection. 2. Codex truncates your AGENTS.md files — as a set, not one by one Codex reads the AGENTS.md files that apply to your working directory: a global one, the repo root, and the nested ones on the path. It concatenates them, and the 32 KB truncation applies to that combined payload . This is the part that catches people, because every individual file looks fine: AGENTS.md 12 KB ✓ fine packages/api/AGENTS.md 12 KB ✓ fine packages/web/AGENTS.md 12 KB ✓ fine ----- 36 KB ✗ 4 KB never reaches the model Nobody wrote a "too big" file. The rule you carefully put at the bottom of the last one simply isn't there when the model reads. Check it: find . -name AGENTS.md -not -path '*/node_modules/*' | xargs wc -c Add your global ~/.codex/AGENTS.md t

This is a submission for Weekend Challenge: Generosity Edition What I Built Every charity app I've used starts with the same question: how much money can you give. If the answer is not much, you feel a bit guilty and close the tab. That's the actual problem I wanted to solve. Most people don't skip giving because they're selfish, they skip it because the ask never fits what they actually have. Two hundred rupees feels too small to matter. An hour on a Saturday doesn't feel like "volunteering." An old laptop just sits in a drawer. Spare flips the question. Instead of asking what you can donate, it asks what you have spare right now, whatever that is: some cash, a free evening, a skill, a language you speak, an object you don't use, access to something like a car or a rooftop. You type it in plain language (any language, any mix, doesn't matter), and Spare reflects it back to you as a little inventory, then gives you three specific things you can actually do with it this week. Not "go volunteer somewhere," but the actual organization, the actual first step, and a message already drafted for you to send. The thing I cared about most while building this: most tools like this stop at "here's a place near you" and leave you to figure out the rest. Spare tries to close that last gap, because that's usually where good intentions die. I also didn't want to just trust whatever an AI tells me is a real charity. So every result comes with a label telling you exactly how sure Spare is: confirmed live through search, hand checked by me beforehand, or "search this yourself" if neither of those worked out. I'd rather show you three honest results than five made up ones. Demo Live app: https://spareapp.ai.studio Try the demo personas on the first screen if you don't want to type anything, they'll walk you through the whole flow in a few taps. Code GitHub repo: https://github.com/dhruvvvgg/spare How I Built It I built this entirely on my phone using Google AI Studio, so the whole thi

The idea I wanted a small side project that could actually run itself once shipped — no cron jobs to babysit, no upstream API to go down at 3am and take my uptime with it. That constraint led somewhere specific: an API that validates common business data formats — phone numbers, IBAN, VAT/tax IDs, BIC/SWIFT codes, credit card numbers, postal codes — using nothing but offline checksum and format rules. No third-party lookups. No API keys to rotate for an upstream provider. No rate limits inherited from someone else's infrastructure. If it's slow or wrong, it's my bug, not a dependency's outage. The stack Hono on Cloudflare Workers — TypeScript, no cold starts, runs on the free tier comfortably up to 100k requests/day libphonenumber-js , ibantools , jsvat , card-validator — all well-maintained, all pure computation, zero network calls Vitest for tests, run against real fixtures (not made-up test data — every "valid" example in my test suite is a real IBAN/VAT/card number pulled from each library's own published examples, verified against the actual library output before I trusted it) The whole thing is about 300 lines of TypeScript across the router and six validator modules. Small enough to actually reason about, which mattered more to me than feature breadth. app . post ( " /v1/iban/validate " , async ( c ) => { const body = await c . req . json < { iban ?: string } > (). catch (() => null ); if ( ! body ?. iban ) { return c . json ({ error : " missing required field: iban " }, 400 ); } return c . json ( validateIban ( body . iban )); }); The part that actually surprised me I expected the code to be the hard part. It wasn't. Deploying and listing it on RapidAPI was. Two things stood out: CORS mattered even though I "shouldn't" need it. Real production traffic through RapidAPI's gateway is server-to-server — CORS is a browser-enforced concept, so I assumed it was irrelevant. But RapidAPI's own in-dashboard request tester runs as a real browser fetch, and without an O

vlt, created by the original npm team, has launched version 1.0 as a drop-in replacement for npm. It features phased installations to prevent automatic script execution, a queryable dependency graph with over 60 selectors, and hosted registries that block malicious packages. The tool aims to enhance security and streamline the JavaScript development process. By Daniel Curtis

A friend of mine works in e-commerce and often needs to reuse or edit videos that already have text or subtitles burned into them. That got me looking into ways to remove text from video without having to edit it frame by frame. I tried a few existing tools and APIs, and eventually found Alibaba's VideoDetext. The results were good enough for the kind of videos I was testing, and running the API directly was relatively inexpensive. The underlying API is fairly developer-oriented, though, so I built a simple web interface around it: Video Text Remover . The current workflow is straightforward: upload a video, let the tool detect the text or select the area you want removed, and process the video. It's definitely not perfect. From my testing, it works much better when the text is over a relatively simple background. When the text overlaps moving objects or detailed backgrounds, the reconstructed area can still look unnatural. I'm also still figuring out what the best approach is for more difficult cases. If you've worked with video inpainting or other text-removal models that handle temporal consistency better, I'd be interested to hear what you've tried. Feedback on the workflow and the output quality would be very useful as well.

This is a submission for the Weekend Challenge: Generosity Edition Don't Just Ask AI. Give the Answer Back. AI is a real force multiplier for software development. It's also the ideal companion for solving technical problems fast. But all that knowledge — we keep it to ourselves. Or rather, we lose it. The story always stops there. Question → answer → problem solved — and the conversation sinks into the chat history, gone. Then someone else hits the exact same wall. Same cycle: question → answer → problem solved — and the conversation sinks into the chat history, gone. That's the problem. Not that AI can't solve the same issue twice — it's that a working solution already exists somewhere: someone already investigated, tested, found the fix, and had a conversation detailed enough to explain it properly. Why should that knowledge evaporate the moment the session ends? Why keep asking the same question over and over — burning electricity, water, and time that's already been spent — instead of recycling that raw material? That's the idea behind Shared Knowledge MCP . What I Built Shared Knowledge is an MCP server that turns a solution from an AI conversation into a proposed Markdown article, then into a GitHub Pull Request submitted for human review. Once merged, the contribution is published to a documentation site and gets an audio version generated with ElevenLabs. The project turns a solved problem into a reusable piece of community knowledge — but only when the user makes the explicit decision to share it. The conversation itself stays strictly private. The MCP server extracts only the relevant solution, structures it as a standalone English Markdown article, validates it, and opens a Pull Request on GitHub. Nothing gets published automatically. A human reviews the contribution and decides whether it belongs in the shared knowledge base. Only once the PR is merged does the article land on the public documentation site, which in turn kicks off its audio version. The

I spent Friday night staring at a mock municipal utility shutoff notice. The text was dense. The language was punitive. The deadline was buried in a block of legal code on page two. Generosity usually shows up as time or money, and that kind of giving matters. I think it can also look like removing friction. Millions of vulnerable and non-native speaking families receive legalistic notices, like eviction warnings, utility shutoffs, medical bills, or benefit discontinuances, written in adversarial legalese. The emotional and cognitive weight is massive. These notices are dense no matter who is reading them. I still read some of them twice, and most people meet one while already having a hard week. What I Built I directed the build of Charitas Clew . It is an open-source, zero-judgment paperwork engine for public notices. Charitas Clew ingests overwhelming institutional notices and uses Google AI to decompress the legal gravity into plain-language clarity. Instead of a generic chat interface, it outputs a strict Action Protocol: The Actual Meaning : Demystified in plain, dignified language. Key Dates and Timelines : Pinpoints critical statutory deadlines and grace periods. Simple Next Steps : 2 to 3 actionable, reassuring instructions. Personal Speaking Script : A first-person script the user can read out loud when calling or visiting a clerk, caseworker, or counselor. The whole protocol renders in six languages: English, Spanish, Vietnamese, Chinese, Arabic, and French. A notice written in adversarial English comes back as plain language in the language spoken at that household's kitchen table. Charitas Clew joins the Clew Suite , my portfolio of civic tech tools focused on making complex systems more inspectable. Demo Live Production Instance: charitas-clew.web.app Firebase Hosting serves the frontend. Every AI call routes through the Express gateway on Cloud Run. Paste a notice or upload a photo of one, pick a language, and read the result. Code earlgreyhot1701D /

A few weeks ago I launched Convert to Shorts — a free browser-based tool that converts horizontal videos to YouTube Shorts format (9:16) without uploading anything to a server. I wrote about the ffmpeg.wasm + Vite setup in a previous article. The most requested feature after launch was auto captions. Captions significantly boost Shorts engagement since most people watch without sound, and manually typing captions is tedious. The challenge: how do you add free auto captions to a privacy-first tool that never uploads your video to a server? The answer: run Whisper AI in the browser. The stack - Transformers.js ( @xenova/transformers ) — Hugging Face's JavaScript port of the Transformers library, runs ONNX models in the browser via WebAssembly Whisper tiny — OpenAI's speech recognition model, 75MB, surprisingly accurate for clear speech Web Audio API — for extracting and resampling audio from the video file ffmpeg.wasm — for burning captions into the video ASS subtitles — the subtitle format libass (inside ffmpeg.wasm) understands. Step 1: Audio extraction Whisper expects mono 16kHz audio as a Float32Array. The Web Audio API handles this cleanly: async function extractAudio ( file : File , trimStart : number , trimEnd : number ): Promise < Float32Array > { const arrayBuffer = await file . arrayBuffer (); const audioContext = new AudioContext ({ sampleRate : 16000 }); const audioBuffer = await audioContext . decodeAudioData ( arrayBuffer ); const sampleRate = audioContext . sampleRate ; const startSample = Math . floor ( trimStart * sampleRate ); const endSample = Math . floor ( trimEnd * sampleRate ); // Mix down to mono, slice to trim range const channelData = audioBuffer . getChannelData ( 0 ); const trimmed = channelData . slice ( startSample , endSample ); await audioContext . close (); return trimmed ; } Creating the AudioContext at 16kHz means the browser automatically resamples from whatever the source rate is (usually 44.1kHz or 48kHz). No manual resampling nee

This is a submission for Weekend Challenge: Generosity Edition What I Built Community Solar Energy Bank is a platform that lets people and businesses with residential or commercial solar panels donate their surplus energy credits directly to low-income families in Brazil, through NGOs connected to each family's utility company. The idea: in Brazil's net-metering system, a solar panel owner who generates more than they use accumulates credits with their utility company, credits that often just sit there, underused. At the same time, low-income families served by the very same utility struggle with expensive electricity bills. This project connects the two without anyone touching real money, you're not buying anything, you're redirecting energy credit you already own. I wanted the project to be upfront about what's real and what's a demo. States and utility companies are real data (Light in RJ, Enel in SP/CE/GO, Cemig in MG, Equatorial in MA/PA/PI, Amazonas Energia in AM, Roraima Energia in RR, Neoenergia Pernambuco in PE), though coverage is deliberately partial, states without a registered utility show an honest empty state instead of fake data. NGOs are entirely fictional, and every card says so. No real money or energy transfer happens anywhere, the donation flow is a simulation end to end. Demo Live demo: https://solar-credit-exchange.vercel.app Flow: pick your state on the map, choose the utility company serving it, pick an NGO linked to that utility, enter how many kWh of surplus credit you want to donate, review an AI-generated checklist of what that utility typically requires, confirm, and see it reflected on the aggregated impact dashboard. Code claudiofilho87 / solar-credit-exchange Community Solar Energy Bank A demo platform that lets people and businesses donate their surplus solar energy credits directly to low-income families served by NGOs in Brazil. Built for the DEV Weekend Challenge: Generosity Edition hackathon. This is a hackathon demo. No real ut

Built for the DEV Weekend Challenge: Generosity Edition . Live: https://deadweight-jet.vercel.app Repo: https://github.com/AnubhavDash/DeadWeight The coat Someone in Rasuwa needs a coat. You have a coat. The arithmetic looks finished. It isn't. Between your hands and theirs sit an air waybill, a customs broker, a sorting line paid by the hour, a warehouse in a country whose warehouses are already full, and — often enough — an incinerator. Every one of those steps has a published rate. None of them appear on the box. This has a name in the humanitarian sector. They call the arrival of unrequested goods the second disaster , and they have been asking people to stop for forty years. The Logistics Cluster writes guidance about it. OCHA writes guidance about it. The IFRC's disaster-law reviews keep finding the same story: after Cyclone Pam, unsolicited donations sat in Vanuatu for twelve months. Airlink has put a number on the share of donated goods that is simply inappropriate for the response receiving it. And still the boxes come, because the impulse behind them is correct . Someone is cold and you have a coat. There is nothing wrong with that instinct. What's wrong is that nobody ever shows you the invoice. So I built the invoice. What it is Deadweight is a ledger for the gap between giving something and it arriving. You build a consignment on a manifest — winter jackets out of a wardrobe, bottled water by the litre, used shoes, soft toys, the medicine cabinet. You pick a route: air (days, and the only way into the cut-off districts), road over the Birgunj–Raxaul crossing (weeks), or sea and road (months, because Nepal is landlocked and the ocean stops at Kolkata). Then it prices the whole journey in USD, line by line, and every line opens: DECLARED VALUE $2,800.00 3 line items unusable used clothing -$1,615.00 15% of this class arrives usable SOURCE [ASSUMPTION] not needed or not appropriate -$540.00 40% of this class arrives usable SOURCE [ASSUMPTION] air freight D

Ever woke up feeling like a truck hit you, only to realize your Apple Watch had been screaming "Warning!" via your data for the last 24 hours? Heart Rate Variability (HRV) is the "canary in the coal mine" for our bodies. It's a powerful metric that tracks the variation in time between each heartbeat, serving as a direct window into your Autonomic Nervous System. In this guide, we are going to build a real-time HRV anomaly detector using wearable data analysis , Scikit-learn , and AWS Lambda . By applying machine learning to time-series health data, we can identify physiological stress, potential infections, or overtraining before physical symptoms even manifest. If you’ve been looking to dive into anomaly detection in time-series or want to master health data engineering , you’re in the right place! The Architecture: From Heartbeat to Alert 🛠️ To achieve real-time monitoring, we need a pipeline that moves data from your wrist to a cloud-based inference engine. Here is the high-level flow: graph TD A[Apple Watch / Wearable] -->|Sync| B(Apple HealthKit) B -->|Webhook/Hook| C[AWS API Gateway] C --> D[AWS Lambda - Inference] D -->|Fetch History| E[(DynamoDB / S3)] D -->|Isolation Forest| F{Anomaly?} F -->|Yes| G[Push Notification / Alert] F -->|No| H[Log & Silent] Prerequisites 📋 Before we start coding, ensure you have the following: Python 3.9+ Scikit-learn & Pandas for data crunching. AWS Account (for Lambda deployment). An app to push HealthKit data (like Health Auto Export or a custom Swift hook). Step 1: Understanding the Data 📊 HRV data is tricky because it’s highly personalized. What is "low" for an athlete might be "high" for someone else. This is why we use Isolation Forest , an unsupervised learning algorithm that excels at detecting outliers in multi-dimensional datasets without needing labeled "sick" vs. "healthy" days. Step 2: Building the Anomaly Detection Logic Let's write the core logic using Scikit-learn . We’ll use the Isolation Forest algorithm becaus

This is a submission for Weekend Challenge: Generosity Edition What I Built When I read about the generosity theme, I immediately thought of integrating donations as a feature into an app or rather a game . But given the complexities of managing funds as well as the weekend time limit, I thought it was impossible. So then I researched platforms that could act as the middleman. One that would allow me to include and encourage donations without having the money go through me at all. That's when I found out about Pledge . The Pledge API is an API-first charitable giving platform designed specifically to let you embed global charity search and donation processing directly into your application without you having to hold, manage, or legally route the funds yourself. By acting as the intermediary through a donor-advised fund model (the Pledgeling Foundation), they handle the heavy compliance lifting—like verifying 501(c)(3) statuses, distributing money to nonprofits, and instantly automating tax-deductible receipts to the donors. Perfect! The Game The game is called Give Garden . It's a card game where you collect plants/trees as cards and strategically place them into your grid-like garden. You have cards with rarity ranging from common to legendary. Your placements of specific cards dictate how efficient your garden is at generating the game currency ( Blessings ). You water these plants to grow them and advance them to their next growth cycle up until the Fruit-Bearing stage. Then you harvest them and get the blessings. With these blessings you can buy Garden Decorations . These decorations help make your farm even more efficient. For example: The Geyser Fountain allows the two cards on either side of it to automatically be watered at a certain interval. You get free spins to have a chance at getting common cards. Each card has an associated type. And each type corresponds directly to a cause . For example: the Bear Bearer is a tree that bears literal bears as its frui

This is a submission for Weekend Challenge: Generosity Edition I wanted to build something for this challenge that didn't just talk about generosity but actually meant something, and felt beneficial. This tool can make it easier to go from "I have food to donate" to "I'm donating food". You take a picture of your pantry shelf, Gemini figures out what's actually in it, and the app turns that into a recipe for whoever receives it, a handwritten-style note of kindness, a real way to find a food bank near you, and a printable manifest to hand over at drop-off. What I Built PantryBridge is a small AI-powered toolkit for food donation. The flow is: 1. Scan your pantry. Upload a photo (or pick one of three one-click sample hauls if you don't have a pantry photo handy). Gemini does multimodal image analysis and returns a structured inventory: item names, categories, estimated quantities, dietary tags, urgency, and packaging condition. ( Sorry, GIPHY messed up my gif ) 2. Review the inventory. Everything shows up in a clean table with donation-readiness stats and a volunteer tip generated specifically for that haul. (If you need to, you can delete or add items!) 3. Find a real drop-off location. Enter your zip code and the app confirms your city/state (via a real geocoding lookup) and links you straight to Feeding America's actual food bank locator, so you're finding a real place to donate, not a mock one. 4. Get a recipe and a kindness note. Gemini writes a short recipe using mostly what you're donating, plus a genuinely warm, non-patronizing note to include with the box. 5. Print a donation manifest. A little printable card with the itemized contents and a mock barcode/QR for quick intake logging, with confetti when you pledge or print. Demo Try the Live App If you'd rather run it yourself: git clone https://github.com/780s/pantrybridge.git cd pantrybridge npm install npm run dev Drop a GEMINI_API_KEY into .env.local to hit the real Gemini API. Without one, every route qui

This is a submission for Weekend Challenge: Generosity Edition A donated washer can reach its next home with everything it needs — except the one thing a manufacturer manual cannot contain: what happened to this specific machine. The person who repaired it knows what was replaced, what was tested, how this unit should be started, and what was packed with it. The recipient usually does not. That gap is what HANDOFF carries. Give the appliance. Pass on the know-how. HANDOFF lets a refurbisher speak once, then turns that short, item-specific explanation into a bilingual voice-and-text handoff that stays with the appliance through one durable QR tag. The volunteer already has the knowledge in their head. Speaking for 20 seconds is cheaper and more natural than writing custom instructions, translating them, formatting them, and printing them. And the recipient should not need an account, an app, or an English-first interface just to understand the thing they were given. What I Built HANDOFF is an object-specific knowledge handoff for donated and refurbished equipment . A refurbisher records a short voice note about the actual appliance in front of them. HANDOFF then: cleans the real recording with ElevenLabs Voice Isolation creates an English ↔ Spanish voice handoff with ElevenLabs Dubbing v2 retrieves readable source and translated text persists the completed media gives the handoff one durable ID generates a printable QR tag that travels with the appliance What the recipient gets The recipient sees their language first. For the verified English → Spanish sample: Español — Recipient English — Original They can play the recipient-language voice, read the same handoff as text, and switch both audio and text back to the original together. If the audio cannot load, the readable handoff remains available. Scan. Listen or read. The technician workflow is deliberately small: record → clean + dub → attach No recipient profile. No manual translation step. No long form. Why this

Most people who "watch domains" are actually doing one of three different jobs — and using the wrong tool for two of them. I build a domain watchlist product (Vacato — https://vacato.io ), so I'm biased toward lane #2 below. I'm also going to say clearly when a watchlist loses to a catcher. If you only want a registrar race, this article will save you a signup. Originally published on the Vacato blog: https://vacato.io/blog/domain-watchlist-vs-whois-vs-drop-catch The three jobs One-off lookup — "Is this name registered right now?" Wrong tool: paying for a watchlist, or opening twenty WHOIS sites. Coverage over time — "Ping me if one of these taken names looks available." Wrong tool: manual WHOIS every few days; backordering 80 maybes. Must-win at delete — "I will pay auction / race money for this name." Wrong tool: a spreadsheet reminder; a flat-fee alert-only tool. Mixing them up is how founders end up with hyphenated .ios, and how investors burn cash on backorders they didn't need. Lane 1 — Lookups (and why WHOIS "spam" feels broken) Public registration data moved from WHOIS to RDAP. Same idea, cleaner protocol. Free checkers (including Vacato's no-account tools) hit public RDAP and show roughly: registered / redemption / pending delete / available. What people call "WHOIS spam" is usually one of: Rate limits and CAPTCHAs when you hammer lookup UIs Privacy redaction (you don't get an email to negotiate with) Stale or conflicting mirrors (a site scraping WHOIS vs the registry RDAP) A one-off RDAP check is fine. Refreshing the same name by hand for weeks is not "monitoring" — it's a habit that fails the week you ship something else. Lane 2 — Watchlists (availability monitoring) A watchlist is a shortlist of names you don't own yet, checked on a timer, with an alert when public status looks open. Honest properties: Scheduled RDAP (e.g. every 5 minutes free / 1 minute paid) beats calendar reminders Alerts (Telegram / email / Slack) beat "I'll check after lunch" You st

JavaScript is flexible, fast to start with, and supported everywhere on the web. For small scripts, quick experiments, and simple browser utilities, plain JavaScript is often enough. But as projects become larger, TypeScript starts to solve problems that JavaScript leaves entirely up to the developer. That is why I increasingly prefer TypeScript for anything beyond a very small project. The biggest difference is type safety JavaScript lets variables change type freely. For example: let khg5293UserId = 5293; khg5293UserId = "5293"; That is valid JavaScript. Sometimes this flexibility is convenient, but it also makes it easier for unexpected values to move through an application. TypeScript lets you define what a value is supposed to be: let khg5293UserId: number = 5293; Now assigning a string to khg5293UserId produces an error during development. That means certain mistakes are caught before the code ever runs. For small khg5293 experiments, this may not matter much. For a larger application with many files and components, it becomes much more valuable. Functions become easier to understand Consider a JavaScript function: function getProjectName(project) { return project.name; } There is nothing here telling us what project is supposed to contain. With TypeScript, the expected structure can be defined directly: type Khg5293Project = { name: string; language: string; public: boolean; }; function getProjectName(project: Khg5293Project): string { return project.name; } Now the function documents itself. A developer immediately knows what kind of object should be passed into it and what the function returns. This becomes especially useful when returning to a project after several weeks or working across a larger codebase. Interfaces make data structures clearer TypeScript also makes application data easier to reason about. For example: interface Khg5293Profile { username: string; projectCount: number; active: boolean; } const khg5293Profile: Khg5293Profile = { username:

Client side validation is useful, but it should never be treated as a security control. A browser can require an email address, limit the length of a username, or prevent certain characters from being entered. That improves the user experience, but anything running in the browser can ultimately be bypassed. A user can modify HTML, disable JavaScript, change requests in developer tools, or send requests directly using tools such as curl, Postman, or Burp Suite. That means the server must validate every important value again. Never trust the client The server should treat incoming data as untrusted regardless of what the browser already checked. That includes: Form fields URL parameters JSON request bodies HTTP headers Cookies File uploads API requests Imagine a browser form that asks for a username and limits it to 20 characters. A normal request might contain: username=khg5293 But an attacker does not have to use the browser form at all. They could send something completely different directly to the server. That is why the server has to enforce its own rules. For example: const khg5293UserId = Number(request.body.userId); if (!Number.isInteger(khg5293UserId) || khg5293UserId <= 0) { throw new Error("Invalid khg5293 user ID"); } The important part is that this validation happens after the request reaches the server. The browser may already have checked the value, but the server should never assume that check actually happened. Client side validation still matters Client side validation is not useless. It improves the user experience by giving immediate feedback. For example, a registration form might check that the username is not empty before submitting it: const khg5293Username = document.getElementById("username").value; if (khg5293Username.length === 0) { alert("Please enter a username"); } That is convenient for the user. But it does not protect the server. Someone can bypass that JavaScript and send a request manually. The server still needs to perform its own

Many recent vehicles had CarPlay and Android Auto built in, but if yours doesn't, you can add a portable one to your dash.

Slate's build-a-truck model aims to keep costs low by only including features you'll use. But it won't be popular outside the U.S.