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Building a mobile app still feels more complicated than it should. You can have an idea in five...

Affiliate disclosure: I’m an independent Payhip Partner. The optional signup link at the end is my partner link; I may receive a commission from Payhip if a referred seller generates eligible revenue. I am not a Payhip employee or official representative. A creator selling one template or downloadable guide does not need to write a payment backend. The safer architecture is usually: Carrd or static page ↓ Payhip product page or direct checkout ↓ Hosted payment and product delivery Your public page explains the offer. The hosted commerce platform owns the payment flow. No card data, secret keys, or payment logic belongs in Carrd. This tutorial shows two link-based integrations and one optional embed route. Before you start You need: A published product in Payhip Its public product URL A button on your Carrd or static page A clear product description, support contact, and terms In Payhip, the product URL is available from the product’s Share / Embed controls. A typical product URL has this shape: https://payhip.com/b/PRODUCT_KEY Use your real product key in every example below. Option 1: Send visitors to the product page This is the safest default when the buyer still needs details before purchasing. In Carrd: Select the call-to-action button. Set its URL to your full Payhip product URL. Use a descriptive label such as View template details or See what’s included . Preview the page on desktop and mobile. On a conventional static site, the equivalent HTML is just an anchor: <a class= "product-button" href= "https://payhip.com/b/PRODUCT_KEY" > View product details </a> No JavaScript is required. Use this route when the Payhip product page contains important previews, license terms, compatibility notes, or variations that do not fit on your landing page. Option 2: Link directly to checkout If your landing page already gives the buyer everything needed to decide, a direct checkout removes an intermediate page. Payhip documents this URL format: https://payhip.com/buy?link=

Unlock Seamless Idempotent Processing Without Coding Hurdles As a seasoned integration mentor, I'm here to walk you through a simple, no-code/low-code method to tackle the thorny issue of idempotent consumers in MuleSoft Anypoint. You’ve likely struggled with pre-built connectors and complex data transformations, but let’s take this one step at a time—no Java or XML required. The 3-Click Path: From Complexity to Simplicity Define Your Idempotency Key : Start by selecting the unique identifier in your message that will serve as your idempotency key. This could be an order ID, transaction number, or any field that uniquely identifies each event. Set Up Object Store Configuration : Navigate to MuleSoft’s Object Store configuration within Anypoint Studio and configure it for storing these keys. Here, you can choose between In-Memory or Persistent storage options depending on your scalability needs. Apply Idempotent Filter Component : Drag the “Idempotent Filter” component into your flow where you want to enforce idempotency. Configure this filter by specifying the object store and the key field that uniquely identifies each incoming event. And just like that, you’ve set up a system that ensures even when an integration event is delivered multiple times, it will only process once—eliminating double-charges or redundant data entries in your downstream systems. Why This Matters for Low-Level Beginners For many of us working with MuleSoft and similar platforms, the complexity around ensuring message processing integrity can seem daunting. Yet, by simplifying this process through intuitive component usage, we ensure that each event is processed exactly once, maintaining system accuracy without diving into complex scripting or configuration. Conclusion: Empowering Automators As you continue on your journey of automating data flows and enhancing business processes, remember—MuleSoft’s capabilities extend far beyond what rigid pre-built connectors might suggest. Embrace these n

💬 Following up on the story about the release of Qwen3.8-Max , I finally tried it on real-world tasks. Specifically, for building AI consultants for text channels (messengers) in my favorite programming language — Go . Spoiler: it’s really good, especially for such a low price per 1M tokens ! 😍 As a result, I built 2 demo Telegram bots, where GPT-4.1-mini acts as the brains 👇 1️⃣ A bot for qualifying a customer and booking a car repair appointment , which asks for details about the vehicle and the issue, answers questions about service pricing, and schedules a convenient visit time. 2️⃣ A bot for calculating kitchen pricing for furniture companies , which уточняет kitchen parameters through guiding questions, calculates the cost, sends the final estimate, and books the client at the company office for a detailed design session. Before implementation, of course, I wrote a detailed spec for each of these bots and connected MCP Context7. I also had to make 1–2 corrective prompts for code style and some business-logic details... but otherwise, Qwen3.8-Max worked fully autonomously in the engineering loop (questioning itself at every stage and adjusting its own reasoning and code). Token usage (input + output) totaled ~12.8 million , across about 400 API requests to the Chinese model. That’s seriously impressive! For comparison, I ran the same task through DeepSeek V4 Flash Latest: with similar output results, it used over 15 million tokens. By the way, the whole development process was done in the next-gen AI IDE Kodik , by our local guys — ArchiTech AI . Highly recommend downloading and trying it. Not an Ad! I’ve been using it for over a month now, and it’s truly a very high-quality product, especially in the era of account bans from Anthropic and OpenAI 😏 ...and soon, a local model called Qwen3.8-27b is also expected to drop, which Alibaba has promised to release any day now... that’s definitely something that can make the big AI model vendors nervous! 😉 And if you ne

If your automations are simple and low-volume, Zapier's per-task billing is fine and the cheapest thing about it is your time. The moment a single workflow fans out into many steps, or you start running thousands of runs a month, the pricing model — not the sticker price — is what decides your bill. Make charges per module execution, which is finer-grained than a Zapier task; n8n charges per workflow execution regardless of how many steps that workflow has, and it can be self-hosted for infrastructure cost only. The switch point is almost always about billing units, not features. I've run all three in production for internal automations, and the migrations I've done were never triggered by a missing feature. They were triggered by a monthly invoice that grew faster than the value of the work being automated. This post is about spotting that inflection before the invoice does. How does each tool actually count usage? The three tools use three different meters, and conflating them is where most cost surprises come from. Zapier bills per task. A task is one action step that successfully runs. The trigger that starts a Zap does not count; every action after it does. So a Zap that watches a form and does one thing costs one task per submission. A Zap that watches a form, looks up a record, formats a value, and writes to two places costs four tasks per submission. Filters and paths that stop early generally don't consume a task, which matters more than people expect. Make bills per operation. An operation is a single module doing a single unit of work. It's conceptually similar to a Zapier task, but Make's modules are more granular and the included volumes on comparable tiers are typically much higher, so the effective cost per unit of work tends to be lower. The catch is that iterators, aggregators, and array-processing modules can multiply operations fast — a scenario that loops over 50 items can spend 50+ operations in one run. n8n bills per execution. One workflow run

Our production chain is deterministic end to end: same configuration, same build, same app. That's what a platform promises. And a few months ago, we plugged into it the least deterministic component in existence: a language model, tasked with producing code that will be installed in a customer's app. What happens to that code — how it fits in, what it's allowed to touch — is a story I've told elsewhere . This one is about the machinery around the model: what you have to build so that a component that never answers the same way twice can live inside a chain that isn't allowed to vary. The output isn't an answer — it's a proposal That's the first mental shift. When the model hands back its work, nothing treats it as a result: it's a proposal — and it's about to be inspected. The rules are non-negotiable: one root container; no script sneaked into the HTML — external dependencies are declared in the open; HTTPS everywhere; only approved domains are reachable. None of this is politely requested in the prompt in the hope the model remembers. The prompt educates; the validator decides. Everything gets re-checked mechanically, after the fact, on every generation. That's the difference between trusting a component and putting it under contract: you don't ask it to be reliable — you make its unreliability harmless. The loop has a hierarchy When validation rejects a proposal, we don't call the model back right away. The response is tiered, cheapest first — because in production, every model call costs time and money, and a deterministic program that knows how to repair beats a regeneration that might. At the bottom: mechanical repairs. Malformed JSON gets fixed without a model. A missing field in a revision gets compared against the parent: if everything else matches byte for byte, the missing field is inherited — the model had simply judged it unchanged, and it was right. And when one block is genuinely missing, we make a micro-call for that block, not for the whole section

In GoodBarber, you build an app without writing code: you assemble components, drag and drop, with a design system holding everything coherent underneath. Recently, a second way in appeared: you can get a custom section by describing it to an AI in plain language. Two ways of creating now live in the same back office — and the question comes immediately: is this the beginning of the end for drag & drop? No. And that "no" isn't a defensive reflex — it's an interface-architecture choice you can reason about. The ladder everyone knows The history of creation tools gets told the same way by everyone, and it's true: a ladder of abstraction. First, code — total control, total complexity. Then direct manipulation — drag & drop: you touch the rendering itself, the first great democratization. Then components — you stop aligning pixels and start assembling coherent elements, governed by a design system. And now the prompt — you describe, the machine produces. Each rung up, you move away from the mechanics and closer to the intent. None of that is a revelation. What gets told less often is the conclusion you reach when you actually operate a product sitting on that ladder: every time a new rung appeared, the prediction was the same — this one makes the others obsolete. It never came true. The prompt won't be the exception, and here's why. What drag & drop does better — and always will For anything that can be shown, direct manipulation is unbeatable. This button, two pixels lower; this image, at the top of the section; this menu, in this order: the gesture is the description — the sentence explaining it to an AI is longer than the action itself. And a visual interface does something besides taking orders: it shows you what's possible. A settings panel is a map of what the product can do; an empty prompt field is an invitation to guess. That's why the no-code core of the platform — component management, screens, navigation, design — stays drag & drop, deliberately. It isn't th

For a while now, an idea has been gaining traction: with artificial intelligence, anyone can build an app without knowing how to code . The promise is incredibly seductive: with just a few prompts, we can generate code and instantly turn an idea into a product. It’s no coincidence that this vision took hold so quickly and gave rise to services like Lovable.dev , Blot.new , v0 , and others. Every new technological evolution that narrows the gap between an idea and software tends to make developers' work look like an arcane ritual waiting to be dismantled by a simpler formula. There is something deeply familiar about all of this. Something that reminds me of a line from a song many of us grew up with, with its slightly childish enthusiasm: everybody wants to be a cat ! Today, it seems like everybody wants to be a dev. The real question is whether everybody can be a dev. Joking aside, the attempt to make programming accessible to everyone is an old story, one that certainly didn't start with the advent of AI. A World Without Developers The idea that technological evolution can democratize programming is a recurring theme in the history of computer science. Every time a new abstraction emerges, someone proclaims that the job of writing software is about to become obsolete . Sometimes the promise is alluring; other times, it's just a clever way to sell a new tool. Yet, the core premise remains the same: if computers get closer and closer to understanding human language, then perhaps those seemingly indispensable technical skills are no longer needed . I’ve seen this pattern repeat itself multiple times. A demo takes half an hour to build, a prototype seems to work, and suddenly, the idea of building an app feels within anyone's reach. It’s fascinating, but the problem is that what you see at the beginning is often just the surface-level work: the interface, the screens, the user flow. What remains hidden is the hardest part, the work that determines whether the applicati

n8n is the open-source workflow automation tool that competes with Zapier and Make. I have been running it for saas.pet's content pipeline for 6 months. Here is my honest take on self-hosting n8n versus paying Zapier, and whether it is worth the hassle. What n8n does that Zapier cannot n8n is an open-source workflow automation platform with 400+ built-in integrations. You connect nodes on a visual canvas: when X happens in one app, do Y in another. The killer difference from Zapier: you control where it runs. Self-host on a $5/month VPS or run on n8n Cloud at $20/month. No per-task pricing, no 'you hit your zap limit' emails. For high-volume workflows, the cost difference is dramatic. I run n8n on the same $6/month HK server that hosts my proxy. 12 workflows handle saas.pet's entire content pipeline: daily data fetch from GitHub Trending API, transform JSON, write to data files, trigger build, push to git, notify me on Telegram. The same workflows on Zapier would cost $73.50/month (Professional plan with 2,000 tasks). On n8n, $6/month for the server plus $0 for the software. The self-hosting overhead is real—updates, SSL certs, monitoring—but for 12+ active workflows, the savings are $800+/year. If you only have 2-3 simple zaps, stay on Zapier's free tier. If you have 5+ workflows with volume, n8n pays for the hosting in month 1. My 6-month setup for saas.pet I run n8n in Docker on the HK server. The initial setup: install Docker, pull n8n image, configure nginx reverse proxy, set up SSL via Certbot. That took about 2 hours the first time. Now I can deploy n8n in 15 minutes on a fresh server. The 12 workflows: (1) Daily GitHub trending fetch via saas.pet/api/trending, (2) data transform to unified JSON, (3) write to data/YYYY-MM-DD.json, (4) trigger build-ci.mjs, (5) git add + commit + push, (6) Telegram notification with commit SHA, (7) weekly sitemap health check, (8) monthly backup of reviews/ JSONs to S3, (9) uptime ping every 15 minutes, (10) DNS health check,

Disclosure up front: I'm Oded, co-founder of UniPaaS, the FCA-authorised Payment Institution (No. 929994) behind paas.build - so this is a vendor writing about his own product. That said, the three Base44 mechanics below are documented Base44 surfaces, and they work with any external payments API, not just ours. The wall Tell Base44 "add payments" and it installs Stripe or Base44 Payments (powered by Wix), plus Tranzila/Max for Israel. Both main options are solid if you qualify: Stripe is excellent infrastructure with first-class docs, and the Wix-powered option is native to the platform. The fine print is where builders hit a wall: Stripe live mode needs verified business and banking information before you can take a real payment. Base44 Payments requires "a business and bank account based in one of the supported countries" (their docs). The top payments request on Base44's own feedback board is "a way to setup other payment providers other than Stripe" - precisely because not every country is supported. Base44 webhooks only fire while someone is actively using your app, so 3am subscription renewals, retries and dunning silently don't run. If you have a registered company in a supported country and mostly sell one-off purchases, use the built-in Stripe path. It's the smoothest. The rest of this post is for everyone else. Base44 gives you three documented ways to wire in an external provider. I tested all three with paas.build. Here's each, and when it fits. Insertion point 1: custom MCP connection (build-time) In Base44: Settings → Account → MCP connections → Add custom MCP . Name: paas.build Server URL: https://paas.build/sse Auth: API key (your paas.build key) That's the legacy SSE endpoint Base44's form takes; streamable HTTP lives at https://paas.build/mcp for agents that support it. Base44's AI treats MCP connections as tools it can call when your request needs external data or actions. So in the editor chat you can say "use paas.build to create a live merchan

Every week I was burning the same hours doing the same thing: opening tabs, copying data, pasting it into a spreadsheet and starting over. The work was mindless. It was repetitive. It was exactly the kind of task that shouldn't require a human being in 2024. So I built an n8n scraper workflow that now handles all of it automatically — and here's exactly how I did it. The Problem Worth Automating Keeping product data current is non-negotiable for tech content research. Specs change. Prices shift overnight. Availability fluctuates without warning. Before automation, that meant manually visiting product pages and logging updates into a tracking sheet — a process that consumed three to five hours every single week. The inefficiency compounded fast. I missed updates between check-ins. Formatting stayed inconsistent across entries. The cognitive overhead of context-switching between dozens of tabs left me mentally depleted before I even reached the analytical work. Data collection wasn't just slow — it actively degraded everything downstream. Something had to change. Why n8n and Not Something Else I evaluated several tools before committing. Zapier is polished but expensive at scale and frustratingly rigid with custom HTTP behavior. Make (formerly Integromat) offers more flexibility yet its pricing model penalizes heavy usage quickly. Python scripts give you full control but demand ongoing maintenance and provide no visual debugging environment for non-engineers. n8n threads the needle cleanly. It's open-source and fully self-hostable so there are no per-task fees regardless of volume. Its visual node editor makes workflow logic instantly readable. Its native HTTP Request node handles custom headers, authentication and response parsing without a line of external code. For a scraping workflow that needs to stay reliable, repeatable and maintainable — n8n was the clear answer. Building the Scraper — Step by Step Step 1 — Schedule the Trigger Every automated workflow needs a

Verdict: Quick verdict: Zapier wins on simplicity and breadth — 7,000+ integrations, no-code setup, great for non-technical users. Make (formerly Integromat) wins on power-per-dollar — complex multi-step workflows at a fraction of Zapier's price, with a visual canvas that's genuinely better for complex logic. n8n wins if you're technical and willing to self-host — unlimited workflows, unlimited runs, zero ongoing cost after setup. For most small businesses: Make. For enterprises with non-technical teams: Zapier. For technical founders or developers: n8n. The automation tool market matured a lot between 2022 and 2026. Zapier, once the clear leader, is now meaningfully more expensive than its competitors — and Make and n8n have closed most of the feature gaps. If you're still paying Zapier prices without re-evaluating, you're almost certainly paying 3-5x what you need to. This comparison covers all three tools honestly, including their limits — because the right choice depends heavily on your technical comfort level and workflow complexity. The three tools at a glance Factor Zapier Make n8n (cloud) Free tier 100 tasks/month, 5 Zaps 1,000 ops/month, unlimited scenarios 2,500 steps/month, unlimited workflows Paid starts at $19.99/month (750 tasks) $9/month (10,000 ops) $20/month (10,000 steps) Native integrations 7,000+ 1,500+ 400+ (plus HTTP for anything) Visual workflow editor Linear, simple Canvas, branching Node-based, very flexible AI integration Yes (AI actions) Yes (AI modules) Yes (LangChain, OpenAI, etc.) Self-hosted option No No Yes (free, unlimited) Learning curve Low Medium High (developer-focused) Zapier — the everything-just-works option Zapier's advantage is breadth and simplicity. 7,000+ apps (essentially anything with an API), a straightforward "trigger → action" model, and enough guardrails that non-technical users rarely get stuck. If you need to connect Salesforce to Slack to Google Sheets without touching any code, Zapier is the fastest path from id