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An email file is not just text plus a few attachments. It can contain HTML, nested MIME parts, misleading filenames, inline resources, remote tracking pixels, malformed encodings, and enough data to exhaust a browser tab. Moving parsing into the browser removes an upload from the architecture, but it does not automatically make the viewer safe. It changes the security job: untrusted content is now being interpreted next to the user’s active web session. This is the checklist I use for a local EML and winmail.dat/TNEF reader. Treat every parsed field as untrusted The sender, subject, recipient, filename, MIME type, and message body all came from a file. Render headers and filenames as text, never by concatenating HTML. The same applies to errors. A parser exception can include a filename or fragment of malformed input. Showing that message verbatim may leak data into logs or turn it into markup. Map parser failures to stable error categories, then display a controlled explanation. Normalize into one internal model EML and TNEF have different container structures, but the UI should not contain two independent security implementations. Both parsers can produce a common message model: subject, sender, to, cc, date, plain body, sanitized HTML candidate, attachments[] { safe filename, MIME type, bytes, inline flag, content ID, content location } The normalization layer is the right place to enforce per-source limits and reject unsupported structures. The viewer and download code then work against the same constrained data regardless of input format. Sanitize HTML as hostile input Email HTML was designed for mail clients, not for direct insertion into an application DOM. A conservative policy removes: scripts and event handlers; forms and interactive controls; iframe , object , and embed elements; styles and CSS URLs; unsafe protocols; executable or unexpected embedded content. Use a maintained sanitizer with a pinned version, but do not stop at its default configuration.

Book: AI That Ships The series: AI in TypeScript — 5 books, from your first LLM call to agents in production — all five here My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub A new model lands. Someone on your team opens a pull request that changes one string in one config file, the model id. The diff is green in five minutes. Evals look fine, maybe a point better on the suite you happen to have. It ships. Three weeks later the invoice arrives and it is a different shape than the one before it. Nobody wrote a bad loop. Nobody shipped a prompt-injection. The system does exactly what it did last month. It just costs more to do it, because a one-line diff moved every request from $2 and $10 per million tokens to $10 and $50. OpenAI announced GPT-6 Astra on 3 September 2026. OpenAI calls it the most capable model it has shipped. That is the company's claim and I am not going to argue with it. But "most capable model available" and "the model your service should call by default" are two different questions, and the distance between them shows up on your infrastructure bill. What the launch numbers say The API list price at launch, per OpenAI: Standard tier: $10 per 1M input tokens, $50 per 1M output tokens Fast tier: $20 per 1M input tokens, $100 per 1M output tokens Astra takes text and image input and returns text only, with a 1M token context window. It went first to a limited set of organisations under OpenAI's Daybreak Access programme, with wider access to the paid ChatGPT tiers and the API announced as planned for the days after launch. It is also listed on AWS Bedrock and Microsoft Azure. Now the third-party read. Artificial Analysis runs its own evaluations independently of the vendors. On its Intelligence Index, Astra scores 60 , which puts it #14 of the 202 models the site tracks. Its cost per Intelligence Index task comes out at $0.96 . The median model in that set scores 36 a

zarazhangrui/follow-builders is gaining attention for a simple reason: it focuses on the people building AI systems, not just the influencers discussing them. With 84 new stars today, the project is positioned as an AI builders digest that monitors notable creators across X and YouTube podcasts, then remixes their ideas into shorter, easier-to-scan summaries. That workflow addresses a real productivity problem. AI research and engineering conversations are scattered across long videos, fast-moving social feeds, and repeated announcements. A focused digest can reduce the time spent collecting links while preserving the practical signal: architectural decisions, implementation lessons, tools, and emerging patterns. A sensible first step is to inspect the repository locally before deciding how deeply it fits your workflow: git clone https://github.com/zarazhangrui/follow-builders.git cd follow-builders # Inspect the setup instructions and available scripts ls -la find . -maxdepth 2 -type f | sort | head -80 For an AI-assisted workflow, I would pair the project with a small review loop: Collect the generated digest. Extract claims, links, and mentioned tools. Open the original source before acting on important technical advice. Save durable findings in a project notes file or knowledge base. This keeps summaries useful without treating them as authoritative research. It also makes the tool a good companion for developers using Cursor or another AI IDE: the digest supplies discovery, while the IDE helps turn validated ideas into experiments and code. Before production use, watch for two trade-offs: Summary fidelity: compressed content can lose context, caveats, or disagreements from the original conversation. Source coverage: ranking “top builders” may introduce selection bias, so important perspectives can be missed. The strongest use case is not replacing primary sources. It is building a high-signal starting queue for developers who want to follow AI progress without

Hello, I'm Shrijith Venkatramana, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. A modern LLM can contain hundreds of billions of parameters, run on extremely expensive accelerators, and still spend most of its inference time doing something that looks embarrassingly sequential: token 1 -> token 2 -> token 3 -> token 4 -> token 5 -> ... That is the awkward part of autoregressive generation. The model may process a whole prompt in parallel during the initial prefill, but once generation starts, the next token depends on the previous token. So generating 100 tokens looks conceptually like running the model 100 times. And for many serving workloads, that is exactly where the money goes. A family of techniques tries to break this bottleneck by asking a deceptively simple question: What if the model could predict several future tokens at once, then verify them in parallel? That idea leads to speculative decoding, Medusa-style multiple decoding heads, and the broader multi-token prediction approach used during training. The interesting part is that these are not merely "optimization tricks." They change the computational structure of decoding. This article develops that idea from first principles and then gets into the engineering details. 1. The problem: your GPU is doing an expensive sequential loop Consider ordinary autoregressive decoding. Given a prompt: The capital of France is the model predicts: Paris Then it feeds the new sequence back through the model: The capital of France is Paris and predicts the next token. Then again: The capital of France is Paris . and so on. Formally, the model factorizes the probability of a sequence as: P(x1, x2, ..., xT) = product over t of P(xt | x1, ..., x(t-1)) That conditional dependence is what makes language modeling so useful. It is also what makes decoding

I ship Codenames AI, a web game. Each game mode keeps its own save in localStorage. Reload the tab,...

Originally published at nlocoding.com 92%of IT leaders say legacy systems slow digital transformation (IBM, 2026) Every minute, a bank somewhere spends $1,200 just keeping 1970s code alive. Not replacing it, just making sure it doesn’t explode. A senior developer at Citi told McKinsey in January 2026: “We spend 53% of our engineering budget patching COBOL.” Legacy code isn’t a quirky artifact anymore. It’s a financial anchor chained to your cloud ambitions... Why AI Code Tools for Legacy System Modernization Matter in 2026 AI code tools have redefined how companies approach system upgrades. In 2026, 61% of modernization projects fail due to manual errors or missed dependencies (Gartner, 2026). You can’t afford human error when one typo in ancient assembler code can cost $500,000 in downtime. The rise of generative AI for code refactoring is the only thing standing between you and a multi-million dollar rewrite. AI Code Tools Are Slashing Modernization Timelines by 63% AI code tools for legacy system modernization have cut modernization project timelines by 63% on average (Accenture, 2026). Manual migration can take 18 months—AI-powered tools like IBM watsonx Code Assistant and Google Gemini Advanced do it in under 7 months. This isn’t a hypothetical. Banco do Brasil migrated 2.8 million lines of COBOL to Java in 2025 with Cognizant’s AI tool; downtime: 14 hours. Average cost per line dropped from $3.60 (human) to $1.15 (AI-assisted). 💡 Pro Tip: Start with small pilot modules (1000-5000 lines). Measure defect rates before scaling. AI-Assisted Code Understanding Reduces Failure Rates Code comprehension is the single biggest risk in legacy system modernization. 47% of failures in 2026 were due to “unknown dependencies” (Forrester, 2026). AI code tools now map data flows, detect dead code, and generate architectural diagrams from raw source. Microsoft’s Copilot for Azure can parse 1.5 million lines in two days and flag 96% of “code rot” blocks. One insurance company in

Refactoring Safely: A Step-by-Step Guide We all know that feeling: a function that's 200 lines long, a class that does too many things, or a variable named data2 . Refactoring is the cure, but doing it recklessly can break your app and your confidence. Here's how I approach refactoring safely, step by step. 1. Start with a Safety Net Before touching any code, make sure you have tests. If your project lacks tests, write a few key ones first. Focus on the behavior you're about to change. The goal is to have a safety net that tells you when you've broken something. # example test for a function we'll refactor import unittest from mymodule import calculate_total class TestCalculateTotal ( unittest . TestCase ): def test_with_discount ( self ): self . assertEqual ( calculate_total ( 100 , discount = 0.1 ), 90 ) If tests aren't feasible, at least have a manual checklist. But automated tests are worth the effort. 2. Make Small, Atomic Changes Don't try to refactor everything at once. Pick one logical change. For instance, extract a method or rename a variable. Each change should be small enough that if it breaks, you know exactly what caused it. // before function processOrder ( order ) { const total = order . items . reduce (( sum , item ) => sum + item . price , 0 ); const tax = total * 0.08 ; const final = total + tax ; return final ; } // after step 1: extract tax calculation function processOrder ( order ) { const total = order . items . reduce (( sum , item ) => sum + item . price , 0 ); const final = total + calculateTax ( total ); return final ; } function calculateTax ( amount ) { return amount * 0.08 ; } Run your tests after each tiny step. If they pass, move on. If they fail, you know the last change caused it. 3. Use Your IDE's Refactoring Tools Modern IDEs can rename variables, extract methods, and change signatures safely. They update all references automatically. This reduces human error. For example, in VS Code, right-click a function and choose "Extract to

Design engineers increasingly work across two systems: the visual language of a product and the implementation details that make it usable. Skills for Design Engineers from ibelick focuses on that overlap, packaging practical guidance for building interfaces with stronger visual quality, clearer interaction patterns, and more consistent engineering decisions. The project is attracting attention, with +46 stars today . That momentum makes sense: design-focused AI workflows are moving quickly, but many generated interfaces still need human judgment around spacing, typography, responsive behavior, accessibility, and component reuse. The useful way to approach this project is not as a drop-in framework. Treat it as a reference layer for your development workflow. Read the relevant skill instructions, adapt them to your stack, and keep the resulting guidance close to the codebase so it can be applied consistently during implementation and review. A lightweight local setup might look like this: mkdir -p .ai/skills/design-engineering curl -L https://github.com/sponsors/ibelick \ -o .ai/skills/design-engineering/reference.html For a real team workflow, I would convert the useful parts into a checked-in Markdown file: .ai/ └── skills/ └── design-engineering/ ├── interface-quality.md ├── responsive-layouts.md └── review-checklist.md This keeps the process portable across editors and AI assistants instead of tying it to one tool. It also makes design decisions reviewable in pull requests, which is more valuable than keeping them inside an undocumented prompt. Before using the approach in production, watch for: Context drift: generic design guidance can conflict with an existing design system, so define project-specific tokens and component rules first. AI overconfidence: generated UI still requires manual checks for accessibility, keyboard navigation, mobile behavior, and performance. The strongest ROI comes from using these skills as repeatable engineering standards—not as a

Most hackathons ask you to build whatever you want. Dogfood 2026 does the opposite. Everyone builds the same thing: a submission and judging platform for hackathons. The challenge is simple: Build the platform that will judge you. And there is a reason this is more interesting than it sounds. Hackathon Raptors has run 35 hackathons across 85+ countries since 2023. They have seen the same problems appear again and again: registrations, teams, submissions, judge assignments, scoring, normalization, results, certificates, and exports all becoming separate pieces of an increasingly messy workflow. Now they want to build the platform they actually wish they had. That is what Dogfood is about. About the Hackathon Dogfood 2026 is a 72-hour online hackathon organized by Hackathon Raptors . The event runs from September 25 to September 28, 2026 . At a glance 🌍 Online and global ⏳ 72 hours 💰 $2,500 prize pool 👥 Solo or teams of up to 4 💸 Free to participate 🔓 Open source 🐳 Self-hosted 🛠️ Build with the stack of your choice But this is not a normal platform-building challenge. The winning project is intended to be forked, self-hosted, and used for actual Hackathon Raptors events. So instead of building a demo that gets abandoned after the weekend, you are building something that could become real infrastructure. Why Build Another Hackathon Platform? Hackathon platforms already have most of the features organizers expect. Registration. Team formation. Project submissions. Public galleries. Judge scoring. Community voting. Organizer dashboards. CSV exports. So what is missing? The difficult part is not building another CRUD application. The difficult part is making the entire system reliable when real people start using it. Consider judging. Two judges can look at the same project and give completely different scores. One might give almost everything a 4 or 5. Another might rarely give anything above a 3. Simply averaging those scores can produce a ranking that reflects the judg

Ever wondered what's happening right now on a beach in Mexico, in Red Square, or at a harbor in Norway? I run Cam-World — a free live webcam aggregator that pulls together 7,000+ public streams from 100+ countries into one searchable place. No registration, no paywall. Here's a tour of what's inside and a few things I learned along the way. 🗺 The world map is the product The heart of the site is a dark globe where every green dot is a live camera. Click a cluster, zoom into a city, open a stream — you never leave the map. Watching it for a while teaches you something: the planet has a rhythm. Webcams go online with the morning sun, and the "online" wave rolls west around the clock. 📊 Honest uptime — you can tell a dead cam from a live one Aggregators usually show you a thumbnail and pray. We check every camera automatically and show a statistics widget: the last 24 hours and 30 days as color-coded slots (online / outage / offline / no data) plus an uptime percentage. The lesson here: webcams are ephemeral. Streams die, hotels turn off cameras, storms break them. Honest stats became our most-loved feature — users check reliability before clicking play. 🔎 Search, cities, collections Search works by name, city, country and tags. There are dedicated hubs for countries and cities, and themed collections: beaches, traffic, mountains, northern lights. 🌙 Small things that matter Dark & light themes (night couch-travel vs daytime browsing), 20 interface languages, "Near me" sorting by distance, live online/offline badges on every card. Try it 🗺 World map — pick a dot, watch live 🔎 Search — find a place you love 🏠 Home feed — a rotating mix of live cameras It's free, works on mobile, and there's always something happening somewhere. What would you check first — a beach, a mountain, or your own hometown square? 👇

Dynamic rendering in Angular sounds like a fairly narrow technical problem: “I don't know which component I need until runtime.” Angular already gives us several good tools for that. But there is a big difference between dynamically choosing a component and dynamically constructing an entire UI from a runtime specification. And that difference becomes especially important with Server-Driven UI and Generative UI. 1. ngComponentOutlet : when the problem is really just component selection For simple cases Angular already gives us: <ng-container *ngComponentOutlet= "componentType" /> This works very well when the application already knows its possible components and runtime logic only decides which one to display. componentType = condition ? UserCardComponent : AdminCardComponent ; The advantages are obvious: very little infrastructure, normal Angular lifecycle, AOT-compatible components and a relatively declarative template. But this approach starts becoming uncomfortable when the runtime input is no longer: UserCardComponent and instead becomes: { "type" : "Card" , "children" : [ { "type" : "Input" , "props" : { "label" : "Name" } } ] } Now we are no longer selecting a component. We are interpreting a UI description. 2. ViewContainerRef.createComponent() : more control, more responsibility Angular also allows components to be instantiated programmatically: const ref = viewContainerRef . createComponent ( componentType ); ref . setInput ( ' label ' , ' Name ' ); This is a powerful primitive. We control where the component is created, which component is used, how inputs are assigned and when the component is destroyed. For relatively contained dynamic behavior, this can be exactly what we need. But once a runtime specification controls many components, application code often starts evolving into something like: switch ( node . type ) { case ' input ' : ... case ' select ' : ... case ' button ' : ... case ' dialog ' : ... } Then we add input mapping. Then events. Then ne

Moving a Next.js app off Vercel and onto a plain Ubuntu VPS usually starts with a painful realization: either your serverless functions are timing out on background jobs, or your client just handed you a strict "you must host this on our infrastructure" requirement. Deploying the app itself is easy. What trips people up (and what cost me hours of debugging and locking myself out of my own server) is everything around the app. Here are the 12 things that actually break when you leave the serverless ecosystem, in the order you'll hit them. 1. Next.js needs a process manager, not just npm start Running npm start in a terminal dies the moment you disconnect. You need something that keeps the process alive, restarts it on crash, and survives a reboot. PM2 is the simplest option for a single-server Node deploy. npm install -g pm2 // ecosystem.config.js module . exports = { apps : [{ name : " my-app " , script : " node_modules/.bin/next " , args : " start " , cwd : " /var/www/my-app " , instances : 1 , exec_mode : " fork " , autorestart : true , max_memory_restart : " 512M " , env : { NODE_ENV : " production " , PORT : 3000 }, }], }; cd /var/www/my-app && pm2 start ecosystem.config.js pm2 save pm2 startup systemd -u YOUR_USER --hp /home/YOUR_USER That last line is the one people forget - without it, PM2's process list doesn't survive a server reboot. 2. Nginx needs to proxy to the port, not serve the files Next.js is not a static site (unless you've explicitly exported it as one). Nginx's job is to forward requests to the Node process, not serve files from disk: upstream nextjs_upstream { server 127.0.0.1 : 3000 ; keepalive 64 ; } server { listen 80 ; server_name example.com www.example.com ; location / { proxy_pass http://nextjs_upstream ; proxy_set_header Host $host ; proxy_set_header X-Real-IP $remote_addr ; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for ; proxy_set_header X-Forwarded-Proto $scheme ; # WebSocket support - required for HMR and any realtime f

You open a website. A blank screen appears. You wait. Then finally, the page loads. But what actually happened during those few seconds? Why does one website feel almost instant while another feels painfully slow? It isn't just about writing “better code.” Website performance is the result of many things working together: DNS + networking + servers + HTML + CSS + JavaScript + images + caching + browser rendering And most performance problems come down to two simple questions: What is the browser waiting for? What is the browser doing unnecessarily? Let's break it down. What Actually Happens When You Open a Website? Suppose you enter: https://example.com Your browser has quite a journey ahead. A simplified version looks like this: URL ↓ DNS Lookup ↓ Connect to Server ↓ HTTP Request ↓ Receive Response ↓ Parse HTML ↓ Download CSS / JS / Images ↓ Build DOM + CSSOM ↓ Layout ↓ Paint ↓ Interactive Page Every step takes time. So the goal of performance optimization isn't simply: “Make the code faster.” It's: Reduce unnecessary waiting and unnecessary work. 1. Send Less Data Imagine your homepage downloads: HTML 250 KB CSS 400 KB JavaScript 4 MB Images 8 MB Fonts 2 MB That's a lot of data just to display a page. Now imagine: HTML 80 KB CSS 100 KB JavaScript 500 KB Images 1 MB Fonts 300 KB The browser has significantly less to download and process. This is why techniques such as: Compression Code splitting Lazy loading Responsive images Removing unused dependencies can have a huge impact. A simple rule: If the user doesn't need it yet, don't make them download it yet. 2. Images Can Be Your Biggest Bottleneck You can optimize your JavaScript perfectly... …and still have a slow website because of images. Consider a: 5 MB hero image That's potentially more expensive than many of your JavaScript files combined. Instead of sending a huge original image: <img src= "hero-original.jpg" /> serve an appropriately sized and compressed image. Modern formats such as: WebP AVIF can reduce

Playwright test data is the set of database rows or API records your application needs to already contain before a browser test runs against it — a logged-in user, their orders, the products those orders reference — generated deterministically so the same run produces the same data every time. Unlike unit tests, a Playwright (or Cypress) spec drives a real browser against a real, running app, which means the backend behind it needs real rows to serve, not an intercepted network response. Getting that data right, and getting it there before the first test starts, is most of what makes a browser E2E suite fast and non-flaky instead of slow and order-dependent. Why is E2E test data hard to manage? Three patterns keep showing up, and each causes a different failure mode: Tests create their own data through the UI. A test that needs an order to exist first signs up a user, logs in, adds a product to a cart, and checks out — all before the actual assertion it cares about. That's slow multiplied across every spec that needs similar setup, and it means the thing under test (the UI) is also the thing doing the setup, so a bug in signup breaks fifty unrelated tests. A shared, mutable test database. If every spec reads and writes the same rows, test order starts to matter: a test that deletes a user breaks a later test that assumed that user still exists. This is one of the most common sources of a suite that passes locally, one file at a time, and fails intermittently in CI when specs run in parallel or in a different order. Hand-maintained fixture SQL or JSON. A fixtures.sql file or a static users.json works until the schema changes — a column gets renamed, a new required field is added — and the fixture silently stops matching what the app expects, or starts failing inserts with no clear signal about which of forty rows is the problem. The fix for all three is the same shape: generate the data the suite needs from a definition (a template), with a fixed seed, right before t

An AI coding agent working inside Claude Desktop or Cursor can read your code, write new files, and run your test suite — but it can't open a browser, log into a dashboard, and click "generate" to get a batch of realistic test data. It has no hands for a UI. AI agent test data generation only works if there's something the agent can call : a tool with a defined schema it can invoke mid-session, the same way it calls a file-write or a shell command. That's exactly what the Model Context Protocol (MCP) is for, and it's why we shipped @jsonfabrica/mcp-server on npm. What AI agent test data generation requires over MCP MCP lets an AI client — Claude Desktop, Cursor, or anything else that speaks the protocol — launch a small local server over stdio and treat its exposed functions as tools it can call during a conversation. The agent decides when to call jsonfabrica_generate_from_template the same way it decides when to call read_file . For that to work, three things have to exist: a server process the client can start, a set of tool definitions with typed inputs and outputs, and — underneath all of it — some actual operation the tool call triggers. MCP server test data generation is that last piece: the tool call has to result in real, schema-conformant data coming back, not a stub. @jsonfabrica/mcp-server , concretely We published @jsonfabrica/mcp-server v0.1.1 as a local MCP server: the AI client launches it itself over stdio, no separate process to manage, no port to open. It exposes the JsonFabrica gateway as a set of MCP tools — jsonfabrica_create_template , jsonfabrica_generate_from_template , jsonfabrica_generate_adhoc , jsonfabrica_create_batch , jsonfabrica_create_sequence , and more. Mid-session, an agent can create a template matching the shape of your User or Order model, generate a batch of realistic records against it, and drop the result straight into a fixture file or a seed script — without you leaving the editor to go configure anything by hand. Why thi

If you're generating test data today, you've probably landed on one of three approaches: click through a UI like Mockaroo, pull in a library like Faker.js and write generation code yourself, or call a hosted API like JsonFabrica. Comparing these test data generation tools side by side, the real differences aren't about which one produces "better" fake data — Faker.js, Mockaroo, and JsonFabrica are all capable of that. The differences are about where the tool lives, how it handles relationships between records, and who's responsible for running it. Three test data generation tools compared, shape by shape Mockaroo is a browser-based UI: you define columns and types through a web form, preview rows, and export a file — or hit its API directly, which is available even on the free tier (paid tiers raise the volume ceiling rather than gate API access itself). Faker.js is a JavaScript library: you import it into your own code and call functions like faker.person.fullName() or faker.internet.email() to build up objects yourself, one field at a time. JsonFabrica is an API-first hosted service: you send a schema (or use a template) to an endpoint and get structured, schema-conformant JSON back, with no UI step and no library to install in your own codebase. That distinction matters more than it sounds. A UI tool is something a person operates by hand. A library is something a developer owns and maintains inside their own project — you write the loops, the relationships, the edge cases. An API-first tool is infrastructure: something your CI pipeline, your seed script, or an AI coding agent can call directly, without a human in the loop or generation logic living in your repo. UI vs. library vs. API, in practice Mockaroo's UI is genuinely fast for a one-off task — sketch a schema, click generate, download a CSV or JSON file. What it isn't built for is wiring generation into an automated pipeline where nobody is clicking anything. Its API can cover that, but at free-tier volume

Plenty of test data tools are built as a UI first and an API second, if there's an API at all. You open a dashboard, configure some fields, click "generate," and download a file. That works fine for a one-off demo. It falls apart the moment test data generation needs to be part of your actual engineering workflow — running in CI, seeding a database on every branch, or producing ten thousand records instead of ten. That's the case for a test data generation API over a click-driven dashboard: the primary interface is a request you can make from code, and everything else — a UI, a CLI — is built on top of that same API. Automation and CI integration A UI is something a person operates. CI doesn't have a person sitting at it. If test data generation only exists behind a login screen and a click, it can't run as a step in your pipeline — someone has to generate the data ahead of time, commit it, and hope it doesn't drift from what the tests actually need. An API-first tool is just another HTTP call your pipeline makes: fetch fresh, schema-conformant data as part of the build, every run, with no manual step in between. Scriptability — no clicking required Generating test data through a UI means clicking through the same sequence of dropdowns and fields every time you need a new batch. That's tedious for one dataset and untenable for the dozens of shapes a real test suite needs — different entity types, different edge cases, different volumes. An API call is a script. Write it once, parametrize it, and reuse it for every collection you need, without a human repeating the same clicks. Wiring a test data generation API into pipelines and seed scripts Seed scripts are code that runs at a specific point in a workflow — before a test suite, on container startup, in a migration. They need a function call or an HTTP request they can invoke programmatically, not a browser tab. With a test data generation API, "seed the dev database with realistic orders" is a line in a setup scrip

Can I Afford This? 1. The Story Behind the Code ​Every developer knows the late nights, the stubborn bugs, and the quiet satisfaction of seeing a project finally come to life. For the past few weeks, my world has revolved around a single mission: building ToolVerse. ​ 2. Like many of you, I looked at the current landscape of financial tools—cluttered with intrusive trackers, forced sign-ups, and paywalls—and asked a simple question: What if we could do better? ​ 3. What if people could calculate their debt consolidation, check their ACA health insurance premiums, or map out their tax withholding scenarios instantly, securely, and completely privately right inside their browser? ​ 4. What is ToolVerse? ​ToolVerse is a collection of high-intent, lightning-fast financial decision tools designed for the US audience. It runs on a lean, efficient stack: ​ 5. Frontend & Hosting: Hosted seamlessly on GitHub Pages for blazing-fast load times and global reach. ​ ** Backend Intelligence:** Powered by Vercel server-side API execution to handle complex lookups (like ACA subsidy calculations) securely without storing user data. ​Privacy-First Architecture: No mandatory accounts, no email walls, and zero data selling. Calculations happen right where they belong—on the user's device. **​ The Reality of Solo Building** ​Building this as a solo creator hasn't been a straight line. From battling server-side routing issues to optimizing sitemaps for Google Search Console indexing, every single line of code taught me resilience. There were days when things broke, but seeing those first users land on the platform and find actual value in these tools made every sleepless night worth it. ​ 8. Let's Build Together! ​ToolVerse is growing, and its infrastructure is ready for scale. 9. I am currently looking for: ​Collaborators & Open-Source Contributors who are passionate about building useful, privacy-first web utilities. ​10. Sponsors & API Partners in the US financial and health tech space

Sometimes a simple line of JavaScript can do more work than you expect. For example, array.includes() is fine for small arrays, but using it again and again with large datasets can affect performance. Things get even more interesting when it is used inside another loop. I recently wrote about this with simple JavaScript and React examples, including when using Set or Map can be a better choice. 👉 Read the full article: https://nirmitkotadiya.dev/dsa/big-o-javascript-array-includes You don't need to optimize everything. The important part is knowing where a small change in your data structure can make your code much more efficient.

Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for...