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🇮🇩 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

Before a young Athenian took his full place in the city, he spent two years in the ephebeia. Training happened in the gymnasion, organized by tribe, the same tribes that would later send him to represent them in the Boule itself. Nobody handed him full standing first and hoped the judgment would follow. This should have been post seven. It’s showing up as sixteen because the gap only became visible once the room was real enough to test against. The Agora described what a Spec Session does. It never asked whether everyone walking into that room shares an accurate picture of what the agent can actually do. Most rooms don’t. Someone watched a demo and thinks the agent can do anything. Someone else got burned by a bad output three weeks ago and doesn’t trust it with anything real. Nobody’s intuition has been tested against the same tasks, and the spec that comes out of that room ends up too ambitious or too conservative depending on whose untested belief happened to speak first. That gap has a name in Athens. The gymnasion existed because nobody was handed a place in the city first and expected to develop judgment on the job. The ephebeia ran two years, training built around a specific fact. Physical readiness and civic judgment weren’t taught in separate places. They happened in the same space, under the same supervisors, organized by the same tribal groupings that would later structure how the city actually governed itself. The same word, gymnasion, ended up naming both the training ground for eighteen-year-olds and the buildings where Plato and Aristotle did their most serious thinking. Academy and Lyceum were gymnasia first. Nobody separated the trial from the reflection. The trial was how the reflection got earned. That’s the part worth taking seriously. Testing a tool and understanding a tool were never two different activities. The testing is how the understanding gets built. A team that reads documentation about what an agent can do has a description. A team tha

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

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

Balmuda has engineered a remarkably pleasant breeze with its new NatureWind Studio, but is it worth hundreds more than its competition?

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

A subscription audit should survive contact with a real app. So I started with mine. TurnTalk is a live iOS travel translator with in-app purchases. I operate the app and its RevenueCat implementation. That makes it a useful public example, but not a customer case study. I will not claim a conversion lift I have not measured. Here is the first finding I would put at the top of its audit. Evidence: the store page introduces several different jobs The subtitle makes a focused promise: Understand Any Guide, Live But the first screenshot sequence spreads attention across: AI travel translation Voice translation Photo translation Instant translation Each feature may be useful. The issue is not feature quality. The issue is that a visitor has to decide which product TurnTalk is before deciding whether to download it. A traveler who wants to understand a live tour guide is evaluating a specific job. A visitor comparing general translator apps is evaluating a much broader category. Those users arrive with different intent. Why I would rank this before a paywall redesign The paywall cannot repair ambiguous acquisition intent. If the store page attracts people for four different jobs, aggregate trial and purchase rates become difficult to interpret. A low conversion rate could mean: The paywall is weak The first session does not prove the promised value The visitor downloaded for photo translation but reached a live-translation flow The listing attracted broad curiosity instead of durable travel intent Changing the paywall first would alter one screen while leaving those explanations mixed together. That is not a clean experiment. P0 action: make the first three screenshots tell one story I would test a narrower opening sequence: Situation: You joined a tour, but cannot understand the guide Mechanism: Put in your existing earphones and start live translation Outcome: Hear the guide in your language without staring at the screen Photo translation and secondary conversation mod

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

“Context bombing” tricks malicious AI agents into shutting down before they can do harm.

DoltHub has recently released Dolt 2.0, a major update to the open source version-controlled SQL database. The latest major version adds automatic storage optimization, including garbage collection and compression, along with improved support for large and vector data types. By Renato Losio

Cheater, I'd grumble between huffs as yet another e-bike rider casually skittered past me on a steep ascent. It's this purist attitude that, for years, has left me blind to one simple fact: electric mountain bikes are fun! My attitude adjustment came a few weeks ago, the very first time I rode an Amflow PX […]

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

A few months ago, I noticed something interesting about Bluesky. The people who were growing weren't necessarily posting the most brilliant content. They were simply consistent. They showed up every day, joined conversations, experimented with ideas, and stayed visible. I wanted to do the same. The problem was that I also had code to write, bugs to fix, blog posts to publish, and projects to maintain. Opening Bluesky every couple of hours just to post something or reply to notifications quickly became another distraction. I knew I needed automation. Not because I wanted to spam the platform, but because I wanted consistency without sacrificing my development time. The obvious solution would have been renting a VPS or deploying another cloud service. But honestly, I didn't want another monthly bill. I started asking myself a different question: Could I build a Bluesky AI bot that runs entirely on free services? That question eventually led me to GitHub Actions. Why GitHub Actions? Most automation tutorials immediately recommend a VPS, Docker container, or cloud function. Those work well. But for a personal automation project, they felt like overkill. GitHub Actions already gives developers something incredibly useful: Scheduled workflows Secure secret storage Python support Free minutes for public repositories Instead of paying for infrastructure, I could let GitHub execute my script several times a day. No servers. No maintenance. No SSH. No uptime monitoring. Just commit the code and let GitHub handle the rest. The Architecture The entire workflow is surprisingly small. GitHub Actions (Cron Schedule) │ ▼ Python Script │ Generates Prompt │ ▼ Gemini API │ Returns AI Post │ ▼ Bluesky API │ ▼ Publish Content Every scheduled run follows the same sequence. GitHub wakes up the workflow. The Python script builds a prompt. Gemini generates a post. The script authenticates using a Bluesky App Password. The post gets published automatically. After that, GitHub shuts everythin

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.

The Problem with GPT-4 Pricing When I started building my AI agent hosting service, I initially planned to use OpenAI GPT-4. Then I did the math: GPT-4: ~$30 per million tokens (input) + $60 per million (output) DeepSeek Flash: ~$0.14 per million tokens That is a 200x cost difference . But Is DeepSeek Good Enough? Short answer: for most use cases, yes. I ran both models side-by-side for customer support, content generation, and code assistance. DeepSeek Flash handled 90% of tasks just as well as GPT-4. The remaining 10% (complex reasoning, nuanced writing) barely mattered for my use case. The Cache Hit Rate Secret Here is what most people miss: DeepSeek caches repeated context. With a 90% cache hit rate, the effective cost drops to ~$0.014 per million tokens. That means 100 million tokens costs about $1.40. Let that sink in. Real Numbers from My Business 24.8 billion tokens processed Total cost: ~$20 Average: $0.008 per million tokens At this rate, I can offer 100M tokens/month for $23.99 and still have 89% margin. When to Use GPT-4 Instead Be honest with yourself: Complex multi-step reasoning? GPT-4 Creative writing with specific voice? GPT-4 Everything else? DeepSeek Flash is fine The Bottom Line Do not pay 200x more for marginal quality improvement. Use DeepSeek Flash for production workloads. Save GPT-4 for the rare cases that truly need it. I run AgentChip — managed AI agent hosting powered by DeepSeek. $23.99/month with 100M tokens included.