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This is a submission for DEV's Summer Bug Smash: Clear the Lineup . Project Overview Formbricks is an open-source survey and experience-management platform. Its packages/surveys package ships the client-side survey runner, and inside it, packages/surveys/src/lib/logic.ts is the module that decides, for every question and every "skip logic" / branching rule a survey author configures, whether a given condition ( equals , doesNotEqual , contains , isEmpty , and friends) is true for a respondent's answer. This is pure, unglamorous logic — but it's load-bearing: it's what routes respondents through the correct sequence of questions. I picked up issue #8527 for the "Clear the Lineup" track, which reports that doesNotEqual conditions weren't behaving correctly for single-choice questions. Bug Fix or Performance Improvement The doesNotEqual operator is supposed to be the exact logical negation of equals . For most answer shapes it was. But for MultipleChoiceSingle answers, the survey runtime sometimes represents the selected value as a single-element array rather than a bare string (this happens via getLeftOperandValue , e.g. when a choice answer is merged with an "other" option path). To handle that shape, both equals and doesNotEqual had a special-case clause that unwraps a one-element array and compares its only entry to the right-hand value. equals 's fallback: return ( ( Array . isArray ( leftValue ) && leftValue . length === 1 && typeof rightValue === " string " && leftValue . includes ( rightValue )) || leftValue === rightValue ); doesNotEqual 's fallback (before the fix): return ( ( Array . isArray ( leftValue ) && leftValue . length === 1 && typeof rightValue === " string " && ! leftValue . includes ( rightValue )) || leftValue !== rightValue ); At a glance this looks like a faithful "negate every sub-expression" mirror of equals . It isn't. The array-includes clause was correctly inverted ( !leftValue.includes(rightValue) ), but the second disjunct, leftValue !==

This is a submission for DEV's Summer Bug Smash: Clear the Lineup . Project Overview Typst is the Rust-based markup typesetting system that's been quietly eating LaTeX's lunch for the last couple of years — you write plain-text markup, Typst compiles it to PDF (or HTML) with a real incremental compiler underneath instead of a multi-pass macro soup. Part of what makes it pleasant is that it exposes its own evaluator to itself: a document can call #eval("some typst code") and get back whatever that code produces, at runtime, inside the document being compiled. It's a small feature, but it opens up a surprisingly deep rabbit hole, which is exactly where this bug was hiding. Bug Fix or Performance Improvement The problem: typst/typst#8632 reports that Typst crashes instead of erroring when #eval is used to recursively re-import the file it's running in. The repro is one line. Save this as overflow.typ : #eval("import \"overflow.typ\"") Then: $ ./target/debug/typst compile overflow.typ overflow.pdf thread 'main' (750833) has overflowed its stack fatal runtime error: stack overflow, aborting No PDF, no diagnostic, no exit code you can act on — just a native stack overflow and a SIGABRT. Typst has had cyclic-import detection since forever; a normal top-level import "overflow.typ" inside itself gets caught cleanly with an error: cyclic import message. Something about routing the import through #eval specifically was bypassing that check. The hunt I'll be upfront about how this investigation actually went: I worked through it with Claude (Anthropic's coding agent) rather than solo. The triage pass it did before I sat down with the code had already narrowed the search to one function — eval_string in crates/typst-eval/src/lib.rs — and named the suspect line. That's a big head start, but a named suspect isn't a confirmed root cause, so the actual work was reading that function next to its sibling, the file-level eval() a few lines above it, and checking the claim against the s

The problem with "just add another tool" When you're building one image tool, performance is easy. When you're building 71 of them in the same app — resize, compress, crop, PDF merge, format converters, exam-photo presets, social media templates — the naive approach (import everything, bundle it all together) turns your app into a multi-megabyte JavaScript payload before a user has even picked a tool. This is the actual engineering problem behind ResizeHub , which now has 71+ tools across 11 categories, all running client-side with zero server uploads. Here's how the architecture holds up at that scale. Stack, and why each piece earns its place React + TypeScript — type safety matters more, not less, as tool count grows. A shared ImageProcessor interface that every tool implements catches integration bugs at compile time instead of in production. Vite — its native ES modules dev server and Rollup-based production build made code-splitting dramatically easier to reason about than older bundlers, which matters a lot once you have dozens of independent tool routes. HTML5 Canvas API — the actual compression/resize/crop engine, shared across tools rather than reimplemented per-tool. Cropper.js — for interactive cropping UI specifically (aspect-ratio locking, circular crop for signatures) rather than rebuilding drag-handle math from scratch. Pica — for high-quality image downscaling; the browser's native canvas scaling can introduce visible aliasing on large downscales, and Pica's algorithm handles this noticeably better. Cloudflare Pages — static hosting with edge caching, which matters since 100% of the actual processing work happens in the user's browser, not on any server at all. Lesson 1: Route-level code splitting isn't optional past a handful of tools With React Router and dynamic import() , each tool becomes its own chunk: const PhotoResizer = lazy (() => import ( ' ./tools/PhotoResizer ' )); const PdfCompressor = lazy (() => import ( ' ./tools/PdfCompressor ' ));

📓 Stop Memorizing English — I Built an AI App That Actually Works ⚡ The Hook You try to learn English. You memorize words. And then… you forget them. 🧠 The Real Problem Most people learn English the wrong way: memorizing random word lists switching between apps and dictionaries learning without context That’s why nothing sticks. 🚀 The Idea What if learning English felt like this: 👉 You read something interesting 👉 You click a word you don’t know 👉 You instantly understand it No interruptions. No friction. ✨ Introducing: English Notebook A modern AI-powered app where you learn English by interacting with real content . generate stories click unknown words build vocabulary automatically 🤖 Generate Stories Based on Your Level This is one of the most powerful features. You choose your level: A1 → Beginner A2 B1 B2 C1 C2 → Advanced And the app generates a custom story just for you . 🖼️ Story Generator 📖 Learn by Clicking Words No more: copy-pasting opening dictionary tabs losing focus Just click any word: definition translation (your language 🌍) example sentences 🖼️ Word Interaction 📚 Smart Vocabulary Notebook Every word you click: 👉 automatically saved 👉 turned into a flashcard You can: review anytime mark as mastered ✅ 🖼️ Vocabulary Notebook 📊 Track Your Progress (Game Changer) This is where things get serious. You don’t just learn — you measure your growth . Dashboard shows: total words learned active vs mastered words daily streak 14-day activity trend 🖼️ Dashboard What gets measured gets improved. 🕘 Nothing Gets Lost (History System) Every story you generate is saved. You can: revisit old stories continue reading anytime track your learning journey 🖼️ History 🔊 Learn with Audio listen to stories follow highlighted words improve pronunciation naturally 🌍 Multi-Language Support You can choose your translation language: Persian 🇮🇷 Arabic Spanish Hindi and more 🎯 Why This App Is Different Most apps: ❌ force memorization ❌ feel like school ❌ break your focus English Note

Why some records should be created by your system, not your users, and how to do it safely in .NET. A support ticket lands on your desk: "The Teams page is empty. I added a member, but no team shows up." You check the API. It's behaving exactly as written: { "items" : [], "totalCount" : 0 } Nothing is broken. And that's the problem. The system is faithfully returning nothing, because the row that the page reads from was never created. Somewhere in your design, you assumed a human would create it first. This article is about a small, recurring design decision that quietly causes empty dashboards, confused users, and "is this a bug?" tickets: who is responsible for creating derived records the user, or the system? and how to let the system do it without introducing duplicate rows or race conditions. The problem Let's use a fictional product: a collaboration tool called Loop . In Loop, the important entities are: An Organization (a paying customer). A Member (a person invited into an organization under a plan). A Team a grouping that members belong to, keyed by (OrganizationId, PlanCode) . The admin dashboard lists Teams . Each team card shows a member count. Here's the catch in the original design: creating a Member wrote a member row. Creating a Team was a separate, manual step an admin was expected to do first. If an admin invited members without first creating the matching team, the dashboard showed nothing even though the members clearly existed. From the user's point of view, they did everything right. From the system's point of view, a required row simply didn't exist. Why it matters The Team record isn't independent information. It is fully derivable from the first member invited under a plan. When one entity's existence is implied by another, forcing a human to create it manually is a design smell. It leads to: Empty states that look like outages. Users can't tell "no data" from "misconfigured." Support load. Every skipped step becomes a ticket. Silent data dr

🇮🇩 Originally written in Indonesian. This English version was AI-assisted and adapted for a more natural reading experience. It is not a literal translation. _open Introduction Lately, I've been having quite a few discussions with frontend developers about the frameworks they use. My question is actually pretty simple. "When you're building a web frontend, what framework do you usually use?" Almost everyone gave more or less the same answer. "It depends on the project." And honestly, I agree. There's no framework that's always the best choice for every situation. However, as the conversation went on, they started sharing their own experiences and preferences. Well... That's when I started noticing an interesting pattern. Among the developers I talked to, quite a few of them mentioned that they usually use Next.js / React for work, while Vue.js is what they often choose for personal projects. The interesting part is... I never actually asked, "What do you use at work?" or "What do you use for personal projects?" That explanation came up naturally as they explained why they preferred certain frameworks. At first, I thought it was just a coincidence. But after hearing the same pattern from several different people... I got curious. Why do so many developers who are comfortable with both frameworks end up separating how they use them? (・_・;) Disclaimer This isn't based on an official survey or research. It's simply an interesting pattern I noticed after talking with several frontend developers. Discussion Vue.js Looking at today's frontend ecosystem, Vue.js is clearly not a small framework. Its community is large. Its documentation is great. Its ecosystem is also quite mature. That said, compared to React and Next.js, its community is still smaller. Then another question comes to mind. If that's the case... Why do so many developers still choose Vue.js for personal projects? From the answers I heard, the main reason wasn't performance. And it wasn't because other framew

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Why We Built This As developers and builders at Smart Tech Devs , we love analyzing data trends. But when it comes to trading charts—whether it's crypto, stocks, or forex—reading technical signals manually requires hours of screen time. Beginners struggle with complex patterns, and experienced traders often want a quick sanity check on their thesis. We asked ourselves: Can we leverage Vision LLMs to turn static chart screenshots into professional-grade technical analysis reports in seconds? That question led us to build and launch Realtime Chart Analyzer (ChartAI Pro) , now live on the Google Play Store! ⚡ Key Architectural Features We designed ChartAI Pro to act as a seamless, secure second pair of eyes for market charting. Here is a breakdown of what the application delivers right out of the box: 🧠 1. Multi-Modal AI Chart Analysis Instant Processing: Upload or snap a screenshot from platforms like TradingView, Binance, Zerodha, Groww, Upstox, or MT4/MT5. Signal Detection: The core engine evaluates market structures to return clear Bullish or Bearish indicators alongside an AI confidence score. Plain-English Summaries: No overly dense academic jargon—you receive an intelligible breakdown of current market conditions. 🎯 2. Automated Key Price Levels & Patterns Support & Resistance: The system instantly flags primary macro price horizons. Risk Mitigation: Calculates approximate entry zones, mathematical target price targets, and clear Risk/Reward ratios. Geometric Processing: Detects complex shapes including Head & Shoulders, Double Tops/Bottoms, Triangles, Wedges, Flags, and specialized candlesticks (Doji, Engulfing lines). 📊 3. Native Live Market Infrastructure Live Scanners: Includes a built-in terminal tracking real-time crypto, forex, and stock prices (NSE & BSE). Interactive Tooling: Features built-in interactive TradingView frames directly in the layout, allowing you to scan Top Gainers and Losers without hopping between apps. 🔒 Privacy & Security First When d

I was tired of studying security tools from static cheatsheets, so I built ShellStack When I started prepping for CEH and doing more CTFs, I ran into the same wall over and over: every resource for learning offensive security tools was either a wall of text in a PDF, a GitHub gist with no context, or a cheatsheet that assumed you already knew what half the flags did. I didn't want notes. I wanted something that felt like sitting at an actual terminal — where I could browse tools, see real commands with context, and build the exact command I needed without digging through five tabs. So I built ShellStack . 🔗 Live: https://shell-stack.vercel.app/ 💻 Code: https://github.com/shlokkokk/ShellStack What it actually does ShellStack is a cybersecurity study platform built around one idea: learning security tools should feel operational, not passive. 280+ curated offensive security tools , organized into 19 categories, each with deep-dive docs — commands, common flags, when to use it, installation notes, and real examples Interactive command builders — instead of memorizing flag combos, you fill in a form and get a ready-to-copy command generated live 20 CEH-aligned learning modules , built for structured study instead of flipping through slides 1,000+ command cheat sheet with fast search and one-click copy A terminal-inspired UI that actually feels like a cyber-ops console instead of another docs site The build Stack: React 19, TypeScript, Vite, Tailwind CSS, GSAP, React Router, Radix UI primitives A few things I focused on: Search that actually ranks results. Early on, tool search was just naive string matching, which meant typing "nmap" could bury the actual Nmap entry under ten unrelated tools that happened to mention it in a description. I rebuilt it to weight exact and prefix matches higher, so the tool you're looking for shows up first, not buried on page 3. Command builders that don't feel like a form. The tricky part wasn't the UI, it was designing a data model that

Week 0 of my DevOps Micro Internship was about the foundations—the parts of the internet you use every day without thinking about them. The exercise that made it click was a simple scenario: a friend launches an online bookstore called EpicReads, hosted on a server in Finland, and asks how people anywhere in the world can open it. The answer is a short chain of technologies working together. The Chain of Technologies Packet Switching: When someone opens the site, their request does not travel as one big lump. Packet switching breaks the data into small packets that each take the best available path across the network and get reassembled at the other end. This is what keeps the internet fast and resilient even across continents. IP Addresses & TCP/IP: Every device on the way has a unique IP address, like a postal address, so the user's computer and the Finland server can actually find each other. The TCP/IP suite runs the conversation: IP handles addressing and routing, while TCP makes sure the packets arrive complete and in the right order, asking again for anything that went missing. HTTP & HTTPS: On top of that sits HTTP and HTTPS, which define how the browser and server actually exchange the web pages. HTTPS adds encryption, so a customer's details and payment stay private. DNS: The last piece is DNS. Nobody wants to type an IP address, so DNS acts as the internet's phonebook, translating epicreads.com into the server's IP. To point a domain at an IPv4 address, you use an A record . The Biggest Takeaway The biggest lesson for me was not any single term. It was seeing how these layers hand off to each other so cleanly that the whole thing feels instant to a user. Understanding that chain is the groundwork for everything else in DevOps, because once you know how a request really travels, troubleshooting stops being guesswork. P.S. This post is part of the DevOps Micro Internship with Agentic AI Cohort 3 by Pravin Mishra. You can begin your DevOps journey by joining

Wer digitale Finanzprodukte baut, trifft früh eine grundlegende Entscheidung: native App, installierbare Desktop-Software oder eine Anwendung, die komplett im Browser läuft. Capitnex hat sich für den letzten Weg entschieden. Der WebTrader ist als browserbasierte Umgebung angelegt und benötigt keine lokale Softwareinstallation. Aus Produkt- und UX-Sicht ist genau diese Weichenstellung der spannendste Ausgangspunkt, weil sie fast jede weitere Gestaltungsentscheidung beeinflusst. Auch für Leser, die nicht handeln möchten, lohnt der Blick darauf, wie ein solches Produkt aufgebaut ist. Der Browser als Laufzeitumgebung Eine Anwendung ohne Installation senkt die Einstiegshürde spürbar. Es gibt kein Setup, keine Versionskonflikte auf dem Endgerät und keine Betriebssystembindung, mit der sich Anwender beschäftigen müssen. Der Zugang erfolgt über einen gängigen Webbrowser, und die Oberfläche steht damit unabhängig vom konkreten Gerät bereit. Für Entwickler hat dieser Ansatz eine klare Konsequenz: Die gesamte Darstellung muss auf unterschiedliche Bildschirmgrößen und Eingabearten reagieren. Capitnex beschreibt die Oberfläche als responsiv und konfigurierbar, was genau diese Anforderung adressiert. Der Verzicht auf ein lokales Client-Programm ist deshalb keine Nebensache, sondern eine Produktentscheidung mit Folgen für Verteilung, Wartung und die Konsistenz über verschiedene Endgeräte hinweg. Ein weiterer Effekt betrifft die Zugänglichkeit im weiteren Sinn. Wenn eine Anwendung ohne vorherige Installation erreichbar ist, entfällt eine ganze Klasse von Hürden, die sonst zwischen Interesse und erstem Zugriff liegen. Kein Download, keine Rechteverwaltung auf dem Gerät, keine Rücksicht auf ältere Hardware-Anforderungen. Die Anwendung trifft den Nutzer dort, wo er ohnehin arbeitet, nämlich im Browser. Das ist keine formale Barrierefreiheit im engen technischen Sinn, aber es ist eine bewusst niedrige Einstiegsschwelle, die in der Produktkonzeption angelegt ist. Informationsarchitektur

Artikel ini juga tersedia dalam bahasa Inggris Pendahuluan Beberapa waktu terakhir saya sering berdiskusi dengan beberapa frontend developer mengenai framework yang mereka gunakan. Pertanyaan saya sebenarnya sederhana. "Kalau membuat frontend web, biasanya pakai framework apa?" Hampir semuanya memberikan jawaban yang kurang lebih sama. "Tergantung kebutuhan." Dan saya setuju. Tidak ada framework yang selalu menjadi pilihan terbaik untuk semua kondisi. Namun, setelah pembahasannya berlanjut, mereka mulai menceritakan pengalaman dan preferensinya masing-masing. Nah... Di sinilah saya mulai menemukan pola yang menarik. Dari beberapa developer yang saya ajak berdiskusi, cukup banyak yang mengatakan bahwa mereka lebih sering menggunakan Next.js / React untuk pekerjaan, sedangkan Vue.js lebih sering digunakan untuk personal project. Padahal saya tidak pernah bertanya, "Kalau kerja pakai apa?" atau "Kalau project pribadi pakai apa?" Penjelasan itu muncul begitu saja ketika mereka mulai menjelaskan alasan di balik framework yang mereka pilih. Awalnya saya mengira itu hanya kebetulan. Tapi setelah mendengar pola yang sama dari beberapa orang... Saya jadi penasaran. Kenapa banyak developer yang sudah menguasai keduanya justru memilih memisahkan penggunaannya? (・_・;) Disclaimer Tulisan ini bukan hasil survei atau penelitian resmi. Ini hanyalah pola yang saya temui dari beberapa frontend developer yang sempat saya ajak berdiskusi. Pembahasan Vue.js Kalau melihat ekosistem frontend saat ini, Vue.js jelas bukan framework yang kecil. Komunitasnya besar. Dokumentasinya bagus. Ekosistemnya juga sudah cukup matang. Namun memang harus diakui, jika dibandingkan dengan React dan Next.js, komunitasnya memang masih lebih kecil. Lalu muncul pertanyaan. Kalau begitu... Kenapa masih banyak yang memilih Vue.js untuk personal project? Dari beberapa jawaban yang saya dengar, ternyata alasan utamanya bukan karena performa. Bukan juga karena framework lain kurang bagus. Melainkan karena pengalama

Hot take 👀 The hardest part of React isn't hooks. It's knowing how to structure an app so it stays maintainable as it grows. Clean architecture beats clever code every time. What's been the biggest challenge in your React projects?

The moment we realized our staging environment was broken It was 3 PM on a Thursday, and our team was scrambling. A critical API change had just been merged to main, but the staging environment—our supposed safety net—was showing false positives. The integration tests passed, but the mobile app was completely broken in production. That's when we knew: our shared staging environment was failing us. The Problem: Shared Staging Is Broken by Design Like many engineering teams, we operated with a single, shared staging environment. Every developer deployed their changes to the same place, leading to: Deployment conflicts: "Who deployed that breaking change?" Cascading failures: One broken PR would block the entire team Test contamination: Data from one test would leak into another Delayed feedback: You'd only discover issues after merging your PR and deploying to staging The "works on my machine" syndrome, now at scale The worst part? Our API contracts were changing constantly, but we only discovered breaking changes during integration testing—often too late. The Solution: Ephemeral Environments per PR We made a radical change: every PR gets its own isolated, short-lived environment. Here's our architecture: Our Implementation Stack Infrastructure: Kubernetes (EKS) with namespace-per-PR Orchestration: Custom GitHub Action workflow Database: Isolated RDS instance per environment Contract Testing: Pact flow + OpenAPI validation Cleanup: AWS Lambda that runs every hour, destroying environments older than 2 hours The Game Changer: Automated Contract Testing The magic wasn't just in isolated environments—it was in what we did with them. Every time a PR deployed to its ephemeral environment, we ran: Consumer-Driven Contract Testing (Pact) Our mobile and web clients would verify their expectations against the actual deployed API. If a change broke what the client expected, the PR would fail. Provider Contract Validation We'd automatically verify that the deployed API matched ou

Over the last few months I’ve noticed a pattern emerging across AI tools. Whether it’s Claude Skills, Cursor, Codex, or custom agent frameworks, we’re increasingly giving AI agents “skills”—packages containing instructions, documentation, and sometimes scripts. The problem is… A skill is usually just a Markdown file (plus some assets). Nothing tells you: Who created it. Whether it has been modified. Whether the version your AI is executing is the same one you reviewed yesterday. Whether someone quietly injected new instructions into it. As AI agents become capable of executing increasingly powerful workflows, that becomes a real supply-chain problem. So I built Skillerr. ⸻ What is Skillerr? Skillerr is an open-source protocol and CLI that adds trust and verification to AI skills before they’re executed. Instead of treating a skill as “just another folder,” Skillerr treats it as a verifiable package. It focuses on three things. Package Integrity Every packaged skill receives a unique content-derived identifier along with cryptographic SHA-256 hashes. If any file changes after packaging—even a single character—Skillerr detects it immediately. No silent modifications. ⸻ Structured Contracts Instead of relying on long paragraphs that an AI has to interpret, a Skill contains a structured contract describing: required inputs permissions forbidden actions expected outputs whether a human has actually reviewed it This makes skills easier for both humans and AI agents to reason about. ⸻ Optional Public Provenance Authors can cryptographically sign their skills. Optionally, the package digest can also be anchored into Sigstore’s transparency log, making it independently verifiable without trusting Skillerr itself. Importantly: Only cryptographic identifiers are published. No prompts. No documentation. No knowledge base. No proprietary content. ⸻ I tried to break my own tool Before releasing it, I intentionally attacked it. First I packaged and signed a simple CSV processing s

If you work with a coding agent, count the things watching it right now. Linters. Git hooks. CI. Specs. A memory store. A rules file it's supposed to obey. Half a dozen systems, all making sure the agent builds the right thing the right way. Now count what keeps you oriented across the eleven things you have in flight. For most of us it's a markdown file we hope we remembered to update. We spent two years building harnesses for the agent and left our own work on the honor system. Map the tooling on two axes and that gap turns into a specific, hard-to-unsee hole. This post is the map. (This is part two of three. Part one, The AI orientation tax: it's missing context, not discipline , argued that the cost you're paying is a context bug rather than a character flaw. If you haven't read it, you don't need to. This one stands alone.) Two loops, not one The thing people lump together as "agent workflow" is really two loops at different altitudes: The execution loop: "is the agent building **this one task * right?"* Scope it, design it, write it, test it. One unit of work. The orientation loop: "do you and the agent share an honest picture of **what you're working on * across all the units?"* Capture, check state, prioritize, review, run over the whole board, daily and weekly. Almost every tool you've heard of lives in the first loop. That's not a criticism. It's just where the money and the visible pain were. But it means when people say "we solved agent memory" or "we solved context," they solved it for the execution loop. The orientation loop got left to you and a markdown file. You feel the difference the moment each one breaks. When the execution loop breaks, something yells: a failing test, a red build, a review comment. When the orientation loop breaks, nothing yells. The agent confidently re-suggests the thing you rejected yesterday. You rebuild a mental map you already had this morning. The only signal is a vague sense that you're moving slower than the tools prom

When building scalable systems, we often need a collection of classes to expose a fixed, read-only configuration value. Whether it is a unique API_ENDPOINT, a DATABASE_TABLE name, or a specific PERMISSIONS_MASK, handling constants across a series of classes looks simple on day one but can quickly turn into an architectural nightmare. Setting the Foundation: How to Make It In modern object-oriented programming, the standard way to declare a constant on a class is by leveraging the static readonly modifiers. This ensures the attribute belongs to the class itself, rather than an instance, and cannot be mutated at runtime. TypeScript class BillingService { static readonly SERVICE_TYPE = "BILLING"; } class InventoryService { static readonly SERVICE_TYPE = "INVENTORY"; } This works perfectly when you know exactly which class you are dealing with at compile time. You simply call BillingService.SERVICE_TYPE and move on. The Architectural Breakdown: What Will Be the Problems The clean code facade breaks the moment you attempt to handle these classes dynamically. In production environments, you rarely hardcode class names; instead, you process them as an array or a series of registry keys. Loss of Type Safety: If you pass a series of these classes into a processing function, standard type systems will treat them as generic constructor functions, wiping out access to the static property unless you resort to unsafe type casting. Polymorphism Failure: Subclasses do not inherently enforce or override static properties cleanly through standard interfaces. You cannot enforce a static readonly property on an interface, meaning a developer could easily forget to define the constant on a new service class, causing silent runtime failures. Instance vs. Class Metadata Confusion: If your architecture receives an instance of the class rather than the class definition itself, accessing the static attribute requires jumping through hoops like instance.constructor.SERVICE_TYPE, which breaks

Zero-Touch Customer Onboarding My AI agent hosting service has exactly zero manual steps between payment and deployment. Here is how: The Pipeline Customer pays via PayPal subscription Webhook fires to our server within seconds Python script validates the webhook signature Docker container spins up with Hermes Agent pre-installed API key generated via New-API Email sent to customer with credentials Customer logs in and starts using their agent Total time: ~90 seconds . No human touches anything. The Code Architecture PayPal Webhooks → Python Flask endpoint Docker API → Container creation with resource limits New-API → Token generation and quota management Gmail SMTP → Automated email delivery Caddy → Automatic HTTPS and routing Key Design Decisions Docker over VMs : Containers are faster (90s vs 5min) and cheaper. Each customer gets 0.5 CPU and 256MB RAM. New-API over custom billing : Battle-tested token management instead of rolling my own. AI over human support : The support agent is also AI. No humans in the loop at all. What Could Break PayPal webhook failures → Implement retry logic Docker daemon issues → Health checks and auto-restart Email deliverability → Fallback to backup SMTP The Result A customer can discover the site, pay, and have a working AI agent before their coffee gets cold. That is the power of full automation. Try it: AgentChip — $23.99/month, 100M API tokens included.

I'm not an engineer. I built a prompt-structuring tool anyway, using Claude Code — here's what actually went wrong 🤔 The problem I don't write code. I'm not an engineer, day to day — but I use AI chatbots constantly, and I kept running into the same annoying pattern. I'd type something lazy into ChatGPT or Claude — half a sentence, no context, whatever came to mind first — and get back a mediocre answer. Then, later, I'd realize: if I'd just written a slightly better prompt, I probably would've gotten a much better answer on the first try. Instead I'd burned a chunk of my monthly quota (sometimes on a paid plan) on something forgettable. I figured other people had to be doing the same thing. So I built something for it. 💡 What I built It's called Deep Prompt Studio . You paste in a rough, unpolished prompt — the kind you'd type without thinking too hard — and it hands back a detailed version that pulls in whichever pieces actually matter for that request: role, task, constraints, output format, and more. It's not tuned for one specific chatbot; it's meant to work well whether you're pasting the result into ChatGPT, Claude, Gemini, or something else. https://deep-prompt-studio.vercel.app 🛠 Tech stack Next.js (App Router, Turbopack) + TypeScript Tailwind CSS Anthropic Claude API for the actual prompt enhancement Stripe for payments Upstash Redis for storage Deployed on Vercel ⚙️ How it works Tone selector — Default, Professional, Casual & friendly, Concise, Direct Target-model optimization — Generic, ChatGPT, Claude, Gemini, Midjourney Snippets — save reusable context/tone/role info and toggle them on or off per enhance (free: up to 3, Pro: unlimited) Variable fill-in — template placeholders like {{product name}} you can reuse Refine mode — if your prompt is too vague, it asks a couple of follow-up questions before enhancing, instead of guessing Template gallery — writing, coding, business, learning, SEO, customer support Library — saved enhanced prompts (Pro) Free ti

footnote-mcp is a Python MCP server installable via pip, Docker, or pipx. No API keys required — it falls back to scraped Bing + DuckDuckGo search and automatic headless Chromium for JavaScript-heavy pages. The Verification Pipeline The core tool is evidence_entailment . It takes a claim and a source text, and returns whether the claim is supported, unsupported, or contradicted. The heuristic backend extracts numeric and named-entity tokens from both the claim and source, then checks for exact matches and contradictions. On its design domain — numeric and factual data claims — it achieves 100% accuracy on a labeled benchmark set. For semantic cases (negation, paraphrase), the ollama backend uses a local LLM as a judge. Three tools build on this: corroborate_claim triangulates a claim across multiple sources, locate_claim_span finds the exact supporting sentence with character offsets, and build_research_debug_report produces a compact report of queries, URLs, and verification gaps. The Fetch Ladder web_read fetches pages through a 5-tier escalation ladder: HTTP (curl_cffi) to rotating proxy to headless Chromium to Chromium through proxy to hosted scrape API (Firecrawl/ScrapingBee). A block/quality detector decides when to escalate, and per-domain rate limiting, circuit breakers, and negative cache keep it polite. Search Backends web_search supports Tavily, Brave, Google, or scraped Bing + DuckDuckGo as fallback. Pass semantic: true to reorder results by meaning using local Ollama embeddings. Structured Data and Browser Tools Beyond text, the server handles tables, CSV/XLSX/PDF/JSON, date validation, unit resolution, and time series reconciliation. For JavaScript-heavy pages, 10 browser tools let you drive a headless Chromium session. When generic parsers fail, the server can synthesize sandboxed extraction code through a controlled recipe system. Benchmark Results The heuristic backend achieves 100% accuracy on numeric and factual data claims (n=15). Overall accurac