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Disclosure before anything else: I'm Oded, co-founder of UniPaaS, the FCA-authorised Payment Institution (No. 929994) behind paas.build, and we compete with Paddle. The title above is the sentence builders keep arriving with - right here on dev.to , in Hacker News threads , and in our inbox. This is the map I give them, including the branches where Paddle is still the right call. Why good products get rejected Paddle is a Merchant of Record. The moment it approves you, it becomes the legal seller of everything you sell. So its underwriting answers exactly one question: "do we want to legally own this business's sales?" A brand-new solo builder with no history is the riskiest possible answer to that question, regardless of how good the product is. Builders who have been through it report the same four themes: No prior processing history. The chicken-and-egg: you need a payments track record to get approved, and you need approval to build a track record. An incomplete-looking site. Missing terms, no pricing page, no live domain, no visible product. Reviewers open your site. Identity mismatches. The applying entity, the domain owner and the bank account don't line up. Category restrictions. Paddle has tightened around some generative-AI products. Very little of this is documented, which is why the rejection email feels random. It isn't. It's a business model doing exactly what it was built to do. The decision tree Two questions decide your next move. 1. Is "global tax handled for me" your top requirement? If yes, stay in the MoR category. This is Paddle's genuine strength, and it's a real one: as Merchant of Record it remits VAT/GST across 100+ jurisdictions, and that liability sits with them, not you. No PayFac gives you that. Don't switch categories - fix the application and get back in the queue: Make the site look finished. Live domain, real pricing page, terms of service, privacy policy, a working demo. Explain the business plainly. What you sell, to whom, expecte

👋 Welcome to Chapter 9! Imagine a user typing in a search box. They type "i", "ip", "iph", "ipho", "iphon", "iphone" — 6 keystrokes in 2 seconds. Do you really want to make 6 API calls ? Of course not! You want to wait until they stop typing and then search once. That's what timing operators solve. They control when and how often values flow through your stream. ⏱️ debounceTime() — Wait for the Silence debounceTime(ms) waits until there's a pause of ms milliseconds, THEN lets the latest value through. Think of it like this: "Ignore everything until they stop for a moment." Like a person who waits for you to finish talking before responding. import { debounceTime } from ' rxjs/operators ' ; // User types fast: 'i' → 'ip' → 'iph' → 'ipho' → 'iphon' → 'iphone' // debounceTime(400) waits 400ms of silence, then sends 'iphone' only searchControl . valueChanges . pipe ( debounceTime ( 400 )) . subscribe ( term => { this . searchProducts ( term ); // Only called ONCE with 'iphone'! }); Timeline: Type 'i' → [400ms timer starts] Type 'ip' → [reset timer] Type 'iph' → [reset timer] Type 'iphone'→ [reset timer] ... 400ms silence ... EMIT: 'iphone' ✅ Real Angular Example — Smart Search Box import { Component , OnInit , OnDestroy } from ' @angular/core ' ; import { FormControl } from ' @angular/forms ' ; import { Observable , Subject } from ' rxjs ' ; import { debounceTime , distinctUntilChanged , switchMap , startWith , takeUntil } from ' rxjs/operators ' ; @ Component ({ selector : ' app-search-box ' , template : ` <div class="search-wrapper"> <input [formControl]="searchControl" placeholder="Search products..." (keyup.escape)="clearSearch()"> <span *ngIf="isLoading" class="spinner">🔄</span> <button *ngIf="searchControl.value" (click)="clearSearch()">✕</button> </div> <div class="results-count" *ngIf="(results$ | async) as results"> Found {{ results.length }} results </div> <div class="results"> <div *ngFor="let item of results$ | async" class="result-item"> <strong>{{ item.nam

New parents obsess over percentile numbers. I get it. I built a tool that plots your baby measurements against official WHO and CDC growth standards. What it does: Weight, height, and head circumference percentiles for ages 0-36 months Visual growth chart showing where your baby falls on the curve Uses WHO Child Growth Standards (0-24 months) and CDC reference data (24-36 months) 35 pages, all pre-rendered for fast loading The hard part: Parsing the WHO growth standard tables into usable JSON. Those tables are dense and not designed for programmatic use. That took more time than building the actual calculator UI. ?? Try it: babypercent.com Built with Next.js, no database, no tracking. Just a calculator that respects your privacy.

WordPress 7.0, released on May 20, 2026, includes new AI infrastructure, a redesigned admin interface, and updated design tools. Key features comprise an AI Client, Abilities API, and Command Palette, alongside increased PHP requirements. Community feedback is mixed, particularly regarding AI integration. Developers are advised to consult the official documentation for upgrade guidance. By Daniel Curtis

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

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Hey DEV community, I recently launched bgchanger.video , an AI video background changer for removing or replacing video backgrounds directly in the browser. The idea is simple: many creators, indie hackers, marketers, and product teams need cleaner videos, but traditional editing tools can feel too heavy for quick background cleanup. With bgchanger.video, you can: Upload a video Remove the original background Export with a transparent background Replace the background with a solid color Download the result in formats like MP4, WebM, MOV, MKV, or GIF Keep the original audio when needed I built it for quick workflows like: Product demo videos Social media clips Creator videos Ad creatives Cleaner profile or presentation videos Background cleanup before further editing The current version focuses on making the workflow straightforward: upload, configure, generate, download. I am still improving the product and would love feedback from other builders: Is the workflow clear enough? What export options would you expect? Would you prefer templates, custom background uploads, or batch processing? What would make this useful in your own video workflow? You can try it here: https://bgchanger.video Thanks for checking it out. Feedback is very welcome.

GLM 5.2 and the Collapse of AI Margins: Open-Source Models Are Rewriting the Rules of the Industry Introduction: A "Counterintuitive" Open-Source Release Figure 1: The core drivers of the AI margin collapse — open-source models, price competition, and surging usage In 2026, Zhipu AI quietly published the GLM 5.2 open-source model on Hugging Face. This news lingered in AI practitioners' information streams for less than half a day before being drowned out by the next wave of updates. But those who were truly sharp noticed a set of data: GLM 5.2's performance across multiple authoritative benchmarks was nearly on par with top-tier closed-source models like GPT-4o and Claude 3.5 Sonnet — yet its inference cost was only a fraction of theirs. This is no longer a story of "catching up." This is leapfrogging . Even more telling is that this news triggered a fierce debate in the overseas tech community: opinion leaders including a16z partners and former Stripe executives waded in, discussing a somewhat brutal topic — "AI margins are collapsing." This discussion quickly spread from tech circles to investment circles, because it points directly at a core question: When open-source models' capabilities approach or even partially surpass those of closed-source models, how long can the existing AI business model hold up? If 2023's open-source models were still "toys" — with cliff-like gaps from closed-source products in complex reasoning, code generation, and multi-turn dialogue — then the 2024-2025 open-source models are no longer "value-for-money alternatives," but a fundamentally new paradigm threat. The release of GLM 5.2 is merely the latest signal flare of this paradigm shift. In this article, we'll unpack three things: what GLM 5.2 got right, how open-source models have rewritten AI pricing power, and the true industry realignment behind this "margin collapse." Technical Core: The Architecture Secrets of GLM 5.2 Figure 2: Schematic of GLM-5.2's MoE (Mixture of Experts) la

GPT-5.6 is finally live, and three takes immediately showed up in my feed: "Sol replaces GPT-5.5 everywhere." "The API still isn't broadly available." "The 1.05M context window means you can stop thinking about prompt size." Two are wrong. The third is exactly how you end up with a bill that is almost twice your estimate. I spent the morning reading the new model pages, rollout docs, pricing table, migration guide, and system card. My conclusion is less exciting than "route everything to Sol," but much more useful: Terra is the GPT-5.6 tier I'd test first for most production workloads. TL;DR No, GPT-5.6 Sol should not replace every GPT-5.5 request. It has the same $5/$30 standard token price and different agent behavior. Yes, the API is live. Sol, Terra, and Luna are in OpenAI's public model catalog; ChatGPT access is still rolling out gradually. Terra is the practical default: $2.50 input and $15 output per million tokens, exactly half Sol's price. Luna is the volume tier: $1 input and $6 output, with the same 1.05M context window. The 272K boundary matters: go above it and the entire request moves to 2x input and 1.5x output pricing. The uncomfortable part: OpenAI says GPT-5.6 is more likely than GPT-5.5 to take actions beyond user intent in agentic coding. What actually shipped This isn't one model with three marketing labels. It is a three-tier family with explicit model IDs. Tier Model ID Input / 1M Output / 1M My default use Sol gpt-5.6-sol $5.00 $30.00 Hard coding and deep analysis Terra gpt-5.6-terra $2.50 $15.00 General production Luna gpt-5.6-luna $1.00 $6.00 Extraction, routing, batch work All three have: 1,050,000 tokens of context 128,000 maximum output tokens February 16, 2026 knowledge cutoff Text and image input Reasoning levels from none through max Responses API and Chat Completions support The unsuffixed gpt-5.6 alias points to Sol. I wouldn't use that alias in a cost-sensitive production service. An explicit model tier makes billing behavior easi

TL;DR: In 2026, the old "cheaper hourly rate vs. more control" framing is outdated. AI-assisted delivery is compressing team size, contracts are shifting from hourly to outcome-based, and onboarding windows have shrunk from months to days. Use staff augmentation when you have strong internal PM capacity and need specific skills for 3-6 months. Use a dedicated team when you're running a 2+ year product and need a self-contained unit with its own PM/QA. Below is a breakdown of the current landscape, including how providers like Toptal-style networks, 6senseHQ , Cleveroad , ScienceSoft , BairesDev , SolveIt , and Uptech fit into each model. Why this decision looks different in 2026 than it did in 2023 Three things changed the calculus this year: AI-assisted engineers ship more per head. Teams are increasingly built around a handful of seniors paired with AI coding assistants rather than a dozen mid-level developers billed by the hour — which makes the traditional "cost per hour" comparison less meaningful than "cost per shipped outcome." Contracts are moving from time-and-materials to outcome-based. Buyers are pushing vendors to tie payment to delivery milestones, not logged hours, partly because AI tooling makes hour-counting a weaker proxy for value. Onboarding windows collapsed. Several dedicated-team providers now quote 3-7 day ramp-up instead of the 2-4 week window that was standard a few years ago, which narrows the traditional "augmentation is faster to start" advantage. None of this changes the fundamental difference between the two models. It changes how much each one costs you in practice. The core difference, restated simply Staff augmentation : you hire individual engineers who join your team, use your tools, and report to your leads. You manage the work. Dedicated team : you hire a self-contained unit (engineers + QA + a PM/lead) that runs its own delivery process. You manage the roadmap, they manage the mechanics. The break-even point most guides converge

You've seen the alert. You've opened the PR. You've read the changelog. Then you realize: your code doesn't even call the vulnerable function. Every week. Hundreds of teams drowning in CVE notifications for packages sitting dormant in their node_modules — dependencies they pulled in years ago, bundled by a transitive library, and never actually executed. Meanwhile, the real vulnerabilities get buried. VigilOps is a free Node.js CLI that fixes this. How VigilOps Works VigilOps does three things: Scans dependencies against OSV.dev — the open vulnerability database used by GitHub, PyPI, and npm Runs static reachability analysis to filter out unreachable vulnerabilities (packages in your tree but never called by your code) Auto-opens a GitHub PR with the fix The result: you get one PR with one real vulnerability. Not a spreadsheet. Not a wall of Slack messages. A fix. Demo Here's a quick scan: npx vigilops scan examples/vigilops-demo-lodash And to see everything including suppressed (unreachable) deps: npx vigilops scan examples/vigilops-demo-express --all The --all flag shows what's in your dependency tree but not actually reachable from your code. That's what the noise looks like — and that's what VigilOps filters out. Why This Is Different Dependabot and Snyk scan your entire lockfile. They report every CVE in every package, regardless of whether your code ever touches the vulnerable surface. This creates alert fatigue that causes teams to eventually... stop reading. VigilOps inverts the model: only surface vulnerabilities in code you actually call. Dependabot: "Your project has 47 vulnerabilities" (but 40 are unreachable noise) VigilOps: "Your project has 1 reachable vulnerability. PR is ready." Quick Start npm install -g vigilops npx vigilops scan . Authenticate with GitHub: https://github.com/Vigilops/vigilops npx vigilops auth That's it. The first run will scan, analyze, and open a PR if there's a fixable reachable vulnerability. What's Included OSV.dev integrati

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GitHub热门项目 | YouTube到AcFun和bilibili自动化搬运工具，支持AI翻译、字幕生成、内容审核、智能监控 | Stars: 1,535 | 211 stars today | 语言: Python

Quick confession: this started as "let me stop babysitting my tests and just make them stay green," and it turned into something a lot bigger. I want to be upfront about where I'm actually trying to take it. I built Claude Basecamp, and as of today it's open source. The reconciliation loop (declare "tests always green," it holds that true) is the part you'll notice first, but it's not really the point. The point is I want this to become the operating system for everything you do with Claude Code, one place that knows about every repo, every session, every routine, every connector, every skill, and every mistake it's ever made, instead of all of that living scattered across terminal windows and dead transcripts. Right now it already covers a decent chunk of that: npx claude-basecamp No install, no config. It finds the projects Claude Code already knows about and opens at http://localhost:4747 . Standing checks , the reconciliation loop. "Tests always green," "dependencies current," "the README documents every CLI flag" — say it once, Basecamp keeps it true, dispatches a fix run when it drifts, and only bugs you for the decisions that actually need a human. Reflexes. It goes back through your old transcripts, finds every time you said "no, don't do that," and turns it into a standing memory that every Claude Code session on your machine checks before touching Bash, Write, or Edit. A mistake made once doesn't get to happen a third time. Session Rescue. Resumes the actual dead session, same session ID, full context, when Claude Code dies mid-task, instead of starting over from scratch. A manager for every repo you just talk to: "keep the tests green," "track this goal," "what's the state of this repo?" Plus routines, background runs, an activity feed, stats, GitHub issue and PR hooks, notifications, webhooks, and a one-click catalog for connectors and skills. That's where it is today. What I actually want it to become is the default place you open whenever you're workin

If you’ve built AI applications in production recently, you’ve probably hit the "Agent Wall." You build a ReAct agent, give it 10 granular tools (search, extract, route, format), a massive system prompt, and tell it to go to work. It feels like magic...until you look at your latency metrics and token bills. Today’s agents act as interpreters. They re-derive the exact same routines from scratch on every single request . They embed massive tool schemas and reasoning histories into every loop. It's slow, it's incredibly token-hungry, and occasionally, they hallucinate tool calls, drop constraints, or get stuck in endless reasoning loops. In a production environment, even occasional errors can be critical failures that waste time and tokens. The problem isn't the ReAct pattern itself. The problem is that we are forcing the LLM to orchestrate low-level, predictable logic that should be deterministic code. We got tired of paying the "reasoning tax" for sub-routines that don't need it. So, we built Sparsi —a framework for shifting complex logic out of your ReAct agent's prompt and into deterministic "Macro-Tools" built as DAGs (Directed Acyclic Graphs). The Macro-Tool Pattern There are two ways to use Sparsi: as an end-to-end solution for a specific task, or to create higher-level tools that plug into your existing agents. The latter is where the magic happens. Instead of giving your ReAct agent 10 tiny, flaky tools and hoping it chains them correctly, you build one highly reliable, deterministic Sparsi DAG to handle that specific sub-routine. You then expose that DAG to your agent as a single Model Context Protocol (MCP) tool. The overall agent still drives the conversation, but it delegates the heavy lifting to a reliable macro-tool. We chose the DAG architecture for three main reasons: Deterministic & Testable: The graph is made of plain code. You only run AI where natural language understanding is strictly required. Parallel by Architecture: Independent branches run co

Schola Interlingua: Free Software for Learning Interlingua IALA Schola Interlingua is a free, open-source platform for learning Interlingua IALA. A practical place to learn The platform is designed around an educational path that brings together lessons, readings, review tools, and progress tracking. Rather than treating learning as a single activity, Schola Interlingua supports a rhythm of studying, reading, reviewing, and returning to the material. Its focus is Interlingua IALA: a language with transparent vocabulary and morphology that rewards attention to structure and usage. Free software and accessible learning Schola Interlingua is developed as open-source software. Its code is available through the Schola Interlingua GitHub organization , making the project available for examination, collaboration, and continued improvement. The platform is available across devices, so learners can use the same educational materials and study tools in the settings that work for them. Explore Schola Interlingua Schola Interlingua website Source code on GitHub Schola Interlingua on the App Store Schola Interlingua on Google Play Chety, for Mandarin Chinese etymology and morphology Ian Blas is a developer based in Buenos Aires, Argentina, building educational software for Mandarin Chinese, Interlingua IALA, etymology, morphology, writing systems, and open-source language learning.

Kubernetes changed infrastructure forever with one idea: you declare desired state, and the system continuously reconciles reality to match it. I wanted that for my codebase, so I built Claude Basecamp and I'm open-sourcing it today. If you're running Claude Code across more than one repo, I'd genuinely love for you to try it, break it, and help me build it out. Try it in one command npx claude-basecamp No install, no database, no config. It discovers the projects Claude Code already knows about and opens at http://localhost:4747 . Runs on macOS, Linux, and Windows. What it does Standing checks, the reconciliation loop. Declare what must always be true, and Basecamp holds it: tests always green -> runs your suite on a cadence; failures dispatch a fix run that commits dependencies current -> npm outdated; safe updates applied, majors escalated to you issue backlog triaged -> gh-powered labeling and stale-closing anything in plain English -> "the README documents every CLI flag" checked read-only, fixed on drift Checks run against deterministic local facts (your real test suite, real npm outdated) wherever possible, zero tokens spent checking. Drift launches a bounded, budgeted, approval-gated convergence run. Repeated failure escalates to a decision card on Home instead of retrying forever. Reflexes, an immune system for your AI. Basecamp mines every transcript for the moments you pushed back (interruptions, "no, don't", permission denials) and turns each into an antibody. Once armed, every Claude Code session on your machine consults that memory before every Bash/Write/Edit action, so a mistake made twice gets blocked machine-wide before it happens a third time. Session Rescue. Notices when a Claude Code session died mid-task and lets you resume the actual dead session, same session ID, full context, as a background run that finishes the job and commits. A persistent manager for every repo. Each project gets an agent with full Claude Code tools plus control over Bas

When a CLI becomes useful, someone eventually tries to automate it. That is where a progress bar can quietly become a problem. For a person, this kind of output is helpful: Translating markdown files 12/40 30% docs/intro.md It tells me that the command is alive, how far it has moved, and which file it is working on. But when another system starts reading that same output, the progress bar stops being only a user interface. It becomes an accidental contract. That was the real problem behind one of the changes in Co-op Translator v0.20.0. The release added a Rich-powered CLI progress UI, but it also added structured translation events. At first, those may look like two separate improvements. They are really two surfaces for the same state: Rich output gives a person something readable, while structured events give integrations something stable. This article is about three things: First, why console output is tempting to parse. Second, how Co-op Translator separated the Rich UI from the event stream. Third, why that separation matters for CLI, Python API, MCP, and product integrations such as Localizeflow. The problem appears when logs become state Console output is written for people. When I run a translation command, I want a quick answer to a few practical questions: Is the command still running? Which stage is active? Which file is being processed? How much work is left? Did anything fail? A progress bar is good for that. It compresses the state of the run into something I can scan quickly. But a product integration needs a different kind of information. Imagine Localizeflow running Co-op Translator as part of a larger workflow. It does not only need to know that text was printed. It needs durable state: Which translation job started Which target language is active Which stage is running Which file completed Which file failed How many items are done Whether the run succeeded If all of that only exists inside console text, the integration has to parse human language