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Every interface you create is a constraint on future behavior. Every abstraction emphasizes certain patterns and discourages others. You are not just building tools. You are shaping how people think about problems. I have been paying attention to how API design encodes values, not just technical decisions, but philosophical ones. What Your API Communicates Consider these design choices: Mutability vs Immutability. Do you encourage stateful modification or pure functions? This is not just about performance. It is a philosophy about side effects and reasoning. If your default is mutable state, you are telling users that local mutation is fine, that they can reason locally. If your default is immutability, you are telling them to think about data flow. Explicit vs Implicit. Do you make users specify parameters or infer from context? This trades convenience for transparency. I lean toward explicitness. Magic is convenient until you need to debug it. Fail Fast vs Fail Safe. Do you throw exceptions or return error codes? This encodes beliefs about who should handle errors and when. Fail-fast says "don't let bad state propagate." Fail-safe says "keep running if you can." Both are defensible, but they lead to very different code. My Design Values When I build libraries, I try to encode: Explicitness over magic. I would rather make users type more than hide behavior behind conventions they have to discover. Composition over inheritance. Small pieces that combine flexibly beat deep class hierarchies. Clarity over cleverness. Code should be obvious, not impressive. Safety by default. The easy path should be the safe path. Why This Matters Your API is a value statement. It says what you think is important, what you think is dangerous, and how you think about the problem domain. This is why I spend so long on interface design. The APIs we create shape future thought. They outlast the code that implements them, because the patterns they teach persist in the minds of the people wh

As a web developer, I've noticed that many beginners spend months watching tutorials but struggle when it's time to build something from scratch. That's one reason I started building WebCoDeveloper — a place where I can share practical web development knowledge, real coding examples, and solutions to problems I've faced while working on projects. My goal isn't to create another tutorial website. It's to build a resource that helps developers move from "I watched a video about it" to "I actually built it." I'm curious: What's the biggest challenge you faced while learning web development? Understanding JavaScript? React/Next.js concepts? Building projects? Finding quality learning resources? Getting your first developer job? I'd love to hear your experiences and learn what resources have helped you the most.

A scraper can pass every check you wrote and still be wrong about the one thing you actually care about: how much it collected. No exception. No 500. No broken row. Exit code 0, logs green, every field valid. And the set on disk is a quarter of what the site actually has. I have run scrapers in production enough times to stop trusting a green run on its own, and this is the failure that taught me to count. TL;DR A paginated source can serve fewer rows than it claims and never throw — page caps, hidden offset limits, infinite scroll that "ends" early. Your status check (200), schema check (valid row), and byte check (you got data) all pass. None of them counts records. The tell: declared total vs unique ids collected. Or, when there's no declared total, the page that quietly repeats an earlier page. Below is a 40-line probe you can run right now. On a source that caps at 1,500 of a declared 4,000, it returned VERDICT: INCOMPLETE (missing 2500 rows) . This is a completeness check, not a correctness check. Different layer, different bug. What actually goes wrong You write the loop everyone writes. Walk ?page=1 , ?page=2 , keep going until a page comes back empty. Stop. Save. Done. The source has other plans. It says it has 4,000 records — the count is right there in the envelope, or in a "Showing 4,000 results" line in the HTML. But it only ever hands out real data for the first 30 pages. Page 31 doesn't error. It doesn't return empty either. It returns page 1 again. Still HTTP 200. Still 50 valid rows. Your loop has no reason to stop, so it grinds on until its own page budget runs out, collects a pile of rows, and exits clean. You now have 5,000 rows in hand and feel great about it. Looks like plenty. The catch: only 1,500 are unique. The page cap fed you the same first page over and over, and those duplicates hid the shortfall behind a big-looking row count. That is the exact shape of "50 rows passed every check while 4,000 existed" — the scraper saw a lot of rows an

Most web scraping projects are not unique snowflakes. Track competitor prices. Enrich a list of leads. Audit a site for SEO. Pull training data for a model. It is the same handful of recipes, over and over. A web scraping template is one of those recipes, pre-wired: a ready-to-use JSON config that chains the right tools in the right order, so you copy it, point it at your targets, and run. CrawlForge ships 24 of them in the templates gallery . This guide is about using them well — not just copy-paste, but read, adapt, and cost them out before you scale. TL;DR: A CrawlForge template is a copy-paste JSON config that chains multiple MCP tools into one workflow (price monitoring, lead enrichment, SEO audits, market research, AI training data). There are 24 across 9 categories, each costing 3–19 credits per run. Run them from Claude/Cursor, the crawlforge CLI, or the REST API. Free tier = 1,000 credits, no credit card. Table of Contents What Is a Web Scraping Template? Templates Gallery vs the scrape_template Tool How to Use a Template the Right Way 8 Templates Worth Copying First The Other 16 Templates Customizing or Building Your Own FAQ What Is a Web Scraping Template? A template is a saved configuration that orchestrates two or three CrawlForge tools into one workflow with a business outcome attached. Instead of wiring search_web then scrape_structured then analyze_content yourself — and guessing every parameter — you copy a config that already does it. Each template in the gallery carries: A category — E-commerce, Research, Data Collection, Monitoring, AI & LLM, Sales, SEO, Content, or Advanced Scraping (nine in total). A difficulty — beginner, intermediate, or advanced. The tool chain it runs and a fixed credit cost per run (3–19 credits). A copy-paste JSON config with sensible default parameters. You run that config from any MCP client (Claude, Cursor, Windsurf), the crawlforge CLI, or the REST API. Same config, same shape of result. Templates Gallery vs the scrap

In the previous article, we explored how to build our first CRUD API using FastAPI. While our API worked correctly, there was one major problem. We were storing data inside Python lists, which exist only in memory. If you've ever wondered how applications like Instagram, LinkedIn, or ChatGPT remember information even after a server restart, the answer is simple: databases. In this article, we'll solve the problem of in-memory storage by connecting our FastAPI application to SQLite using SQLAlchemy. If you haven't read the previous post, check it out: FastAPI for AI Engineers - Part 2: Building Your First CRUD API Ananya S Ananya S Ananya S Follow Jun 1 FastAPI for AI Engineers - Part 2: Building Your First CRUD API # ai # backend # fastapi # python 7 reactions Comments Add Comment 4 min read By the end of this article, you'll understand: Why in-memory storage is a problem What SQLite is What SQLAlchemy is How ORM works How to create database tables using Python classes How to perform CRUD operations using a real database The Problem with In-Memory Storage Previously, our application stored students inside a Python list. students = [ { " id " : 1 , " name " : " Ananya " , " department " : " CSE " , " cgpa " : 8.9 } ] This worked for learning CRUD operations. However, consider what happens when the server restarts: FastAPI Server Stops ↓ Python Memory Cleared ↓ All Student Data Lost This is unacceptable in real-world applications. We need a place where data can survive application restarts. This is where databases come in. What is SQLite? SQLite is a lightweight relational database. Unlike MySQL or PostgreSQL, SQLite doesn't require a separate database server. Instead, everything is stored inside a single file. students.db Advantages of SQLite: No installation required Lightweight Easy to learn Perfect for local development Great for small projects For this article, we'll use SQLite. What is SQLAlchemy? Before SQLAlchemy, developers often wrote raw SQL queries. Exampl

Ideogram 4.0 launched this week and within 48 hours it was available on seven platforms. That is unusual. Most model launches trickle onto one or two platforms over weeks. Ideogram went wide immediately, which suggests the open weights strategy is working as intended. Here is what you will pay depending on where you use it. fal.ai The cheapest API access. Turbo mode at three cents per megapixel. That is roughly three cents per 1K image. Balanced at six cents. Quality at ten cents. Pay-per-use, no minimums. If you are generating through an API, this is your starting point. Krea Included in all paid plans. Basic is $5.25 per month billed annually with 5,000 compute units. Pro is $21 per month with 20,000 CUs. The CU cost for Ideogram 4.0 specifically is not published yet, but Krea includes 150 plus models in their CU pool, so you are not paying extra for access. If you already use Krea for other models, Ideogram 4.0 is effectively free to try. ComfyUI Free if you have the GPU. The model is open weights at 9.3 billion parameters. Native ComfyUI support means you can download the weights and run it locally. No per-generation cost. No API calls. Just your electricity bill and GPU time. For volume generation or iteration, this is the cheapest path by far. Leonardo Announced as a day zero launch partner but the pricing page still lists Ideogram 3.0. Plans range from $12 to $60 per month with token allowances from 8,500 to 60,000. Third party models on Leonardo always consume tokens, no relaxed generation. Until they publish the 4.0 token cost, you are guessing. Assume it will be similar to their other premium models. Replicate The Ideogram 3.0 listing is live but 4.0 is not there yet. Replicate prices by hardware time rather than per-image, which can be cheaper or more expensive depending on your batch size and the GPU allocated. Worth checking when it lands. FLORA Available in FLORA. Pricing unclear. FLORA is primarily a creative platform, not an API provider, so you are

Search results look simple from the outside. You type a keyword into Google, Bing, or another search engine, and you get a page of links, snippets, ads, maps, news, images, videos, and sometimes AI-generated answers. But if you have ever tried to collect search results at scale, you know it gets messy quickly. A result page is not just a list of links. It changes by country, language, device, location, query intent, and search engine. The same keyword can show different rankings in New York, London, Singapore, or Berlin. A page may include organic results, paid ads, local packs, shopping results, People Also Ask, news results, images, videos, or other SERP features. For humans, that is just a search page. For SEO teams, AI teams, data teams, and developers, it is a data source. That is where a SERP API becomes useful. What is a SERP API? SERP stands for Search Engine Results Page . A SERP API is an API that lets you collect search engine results in a structured format, usually JSON and sometimes HTML. Instead of manually searching a keyword or building a scraper to parse search result pages, you send a request to a SERP API with parameters such as: keyword search engine country language location device type output format The API then returns structured search data. A simplified response might look like this: { "query" : "best project management software" , "organic_results" : [ { "position" : 1 , "title" : "Best Project Management Software Tools" , "link" : "https://example.com" , "snippet" : "Compare features, pricing, and reviews..." } ] } This is much easier to work with than raw HTML. You can store it in a database, send it to a dashboard, compare rankings over time, feed it into an AI workflow, or generate automated reports. Why not just scrape search results yourself? You can build your own scraper. For a small test, that may be enough. You can send a request, parse the HTML, extract titles and links, and save the data. The problem starts when the workflow bec

The dangerous part of web extraction is not the error. The dangerous part is a clean JSON response that looks correct and is not. If an AI agent uses that output, the mistake does not stay inside a scraper. It moves into a report, a lead list, a price alert, a CRM update, or an automated decision. So before you trust any web extraction API in an agent workflow, test whether it fails honestly. Here is the checklist I use. 1. Test a real page, not a demo page Demo pages are usually polite little museum exhibits. Use pages that behave like the actual internet: a JavaScript-heavy product page a search results page a page with missing fields a login-gated page a blocked or rate-limited page a thin page that has layout but little useful content If the tool only looks good on clean static pages, you have not learned much. 2. Check whether the API separates fetch success from extraction success A page can return HTTP 200 and still be useless. Common examples: the final page is a login screen the visible content is a bot challenge the useful data loads after hydration and never appeared in the fetch the page contains a generic country selector instead of the product the source has navigation text but no actual item data The extractor should not treat these as success just because the request completed. A better response says something like: { "status" : "failed" , "failure_type" : "login_required" , "extracted" : null , "next_step" : "Use a public URL, authorised access, or sample content." } That is boring. Boring is good here. Boring means the agent can stop safely. 3. Ask for fields that are not on the page This is the easiest lie detector. Ask the extractor for something impossible: Extract the product name, price, stock status, warranty length, CEO name, office address, and refund policy. If the page only contains product name and price, the API should say the other fields are missing or low confidence. It should not politely invent them because the schema asked nicely.

“Why wouldn’t you want to be in both Pepsi and Coke?” says one venture capitalist. “It’s the same here.”

We’re a tiny team of 2–9 engineers who believe business messaging should be simple, reliable, and accessible to everyone. Today we’re officially opening up BulkSMSOnline to the Dev.to community, and we’d love your feedback. What’s BulkSMSOnline? A global bulk SMS platform that lets you send campaigns, alerts, OTPs, and notifications via: A clean web portal A REST API An HTTP API It’s designed for developers who want reliable global delivery without fighting arcane telecom protocols or opaque pricing. Why We Built It We noticed a pattern: most SMS platforms either overcomplicate things with bloated SDKs or hide behind enterprise gatekeepers that don’t listen. We wanted something different a lean, transparent API backed by real people who actually care about your deliverability. So we built BulkSMSOnline around three principles: Reliability : Messages must arrive, every time. Radical simplicity : A clean API you can integrate in minutes. Transparency : Honest pricing, clear limits, no surprises. Quick Start: Send an SMS in Under 5 Minutes Here’s how simple it is with our REST API. For full docs, check out our developer portal . curl -X POST https://api.bulksmsonline.com/v1/sms \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "to": "+1234567890", "message": "Hello from BulkSMSOnline!", "sender": "MyApp" }' You’ll get a JSON response with a message ID and status. That’s it. No multi-page setup or carrier negotiations. What else can you do? Send bulk messages with a single API call Track delivery in real time via webhooks Pull reports programmatically Use our HTTP API for legacy systems Who’s Behind This? We’re a small, agile team (2–9 people). That means: No bureaucracy: Fixes and features ship fast. Direct access: When you email support, you reach the engineers who built the platform. Your feedback shapes our roadmap: Many of our recent features came from developer conversations. Tech stack we love: Python, Node.js, PostgreSQL, an

Fallback rules are where an AI API gateway becomes operationally valuable. The goal is not to blindly retry every failed LLM call. The goal is to choose the right backup model, provider, or budget path based on the workflow, customer tier, latency target, and risk of a lower-quality answer. A practical fallback policy should define: which failures are retryable; which workflows may downgrade models; which customers or API keys are allowed to use premium fallback routes; how budget caps change routing behavior; what metadata gets logged so the team can debug cost and quality later. 1. Classify traffic before routing Do not write one global fallback rule for every request. Start by classifying traffic: Critical user-facing : support chat, checkout assistance, customer-facing agent answers. Non-critical user-facing : summaries, title generation, enrichment, recommendations. Internal automation : triage, labeling, data cleanup, back-office agents. Batch jobs : long-running summarization, extraction, report generation. Experiments : tests, staging, evaluation, prompt tuning. Each class should have a different fallback budget and quality floor. 2. Decide what counts as a retryable failure Good retry candidates: upstream timeout; 429 rate limit; temporary 5xx provider error; network interruption; overloaded model endpoint; streaming connection drop before useful output. Poor retry candidates: invalid API key; malformed request payload; unsupported tool-call schema; content policy rejection; user quota exhausted; deterministic validation failure. Retrying non-retryable failures usually burns tokens and hides product bugs. 3. Example fallback policy matrix Traffic class Primary route First fallback Second fallback Hard stop Critical user-facing frontier model same-class model on second provider cheaper model with explicit uncertainty after 2 provider failures Non-critical user-facing balanced model cheaper model cached/default response after budget cap Internal automation lo

Most AI memory features are a black box. The assistant remembers things about your users, but you...

Cloudflare Turnstile in Playwright: Why Your Tests Stall and How to Solve It in 8 Lines If you're running Playwright or Selenium against any site behind Cloudflare, you've already met Turnstile. It's the new "managed challenge" widget Cloudflare started shipping in 2023, and it now appears in front of login flows, contact forms, signup pages, and increasingly the entire site root. Here's the part most teams miss: Turnstile doesn't always show a checkbox. A lot of the time it just sits invisible, runs its scoring loop, and either issues a token silently or stalls forever. Your test doesn't crash. It just times out at the next page.click("button[type=submit]") . The CI log says "element not interactable." Nobody knows why. I work on CaptchaAI. I'm going to show you exactly what's happening, then drop in 8 lines that fix it. The real scenario You have a Playwright suite that runs every PR. One day a test starts failing on the signup flow. You re-run it. It fails again. Locally on your laptop it passes. On CI it doesn't. What's actually happening: Cloudflare flagged your CI runner's IP block (GitHub Actions, GitLab runners, Hetzner, OVH, DO — all of them are on Cloudflare's "elevated risk" list). Turnstile decides to switch from invisible mode to "managed challenge" mode. Now there's a widget in the DOM that needs a real token before the form submit will accept. Your test never interacted with the widget because last week it didn't exist. Why retries don't help The instinct is to add a retry: 2 and move on. Don't. Cloudflare's scoring is per-IP-per-fingerprint, and each retry from the same runner makes the next challenge harder, not easier. After ~3 attempts you'll get full block pages instead of the widget. The right move is to solve the widget once, inject the token, and submit normally — exactly what a human user does, just faster. How Turnstile actually issues a token The widget renders an iframe pointing at challenges.cloudflare.com . Inside the iframe it runs a fi

If Alphabet's record-breaking $85 billion stock sale signals investor appetite for AI-related offerings, we can see that investors are ready to chow.

Books API Structure Create folders app/ app/controllers/ app/core/ app/models/ app/models/DAOs/ app/models/DTOs/ app/models/entities/ app/utils/ config/ public/ api/composer.json Configure Composer and the PSR-4 autoload so that classes with the namespace App\ are searched inside the app/ folder. Key content: { "name": "user/api", "autoload": { "psr-4": { "App\": "app/" } } } After creating it, run this command inside the api folder: composer dump-autoload api/config/config.php Defines the base URL of the project. The router removes it from REQUEST_URI to keep only routes such as /books/get. <?php define('BASE_URL', '/proyect/api/public'); api/config/dbconf.json MySQL DB connection: { "host": "localhost", "user": "root", "password": "", "db_name": "books_db" } api/public/.htaccess Makes Apache send all routes to index.php. RewriteEngine On RewriteCond %{REQUEST_FILENAME} !-f RewriteCond %{REQUEST_FILENAME} !-d RewriteRule ^ index.php [QSA,L] api/public/index.php This is the entry point. It loads Composer, loads the configuration and calls the router. <?php use App\Core\Router; require_once DIR . '/../vendor/autoload.php'; require_once DIR . '/../config/config.php'; (new Router())->dispatch($_SERVER['REQUEST_URI']); api/app/core/Router.php <?php namespace App\Core; class Router { protected array $routes = [ '/' => 'HomeController@index', '/books' => 'BookController@index', '/books/get' => 'BookController@getAll', '/books/getById' => 'BookController@getById', '/books/create' => 'BookController@create', '/books/update' => 'BookController@update', '/books/delete' => 'BookController@delete', ]; public function add($route, $params): void { $this->routes[$route] = $params; } public function dispatch($uri): void { $uri = parse_url(str_replace(BASE_URL, '', $uri), PHP_URL_PATH); if (!isset($this->routes[$uri])) { $this->sendNotFound(); return; } [$controller, $method] = explode('@', $this->routes[$uri]); $controller = 'App\\Controllers\\' . $controller; if (!class_exists($co

We run Quackr, a virtual phone number platform that lets developers and individuals receive SMS verifications without exposing a real number. We just published our first inventory transparency report and the data was surprising enough that we thought the dev community would find it useful. The Numbers Right now, 97.6% of our entire virtual phone number inventory is actively rented. 66,214 out of 67,815 numbers assigned and in use across 15+ countries. Over 1,000 numbers available at any given moment but they move fast. That utilisation rate tells you something about how the market has shifted. Virtual numbers are no longer a niche throwaway tool. Developers, businesses, and privacy-conscious users are holding them long term. What Developers Actually Use Virtual Numbers For The obvious use case is SMS verification during testing. Spin up a number, verify an account in staging, move on. But that is not what drives the bulk of demand on our platform. The real volume comes from: Multi-account management — developers and businesses running multiple instances of platforms that require unique phone verification per account. Privacy layers in production apps — applications that need to verify users without collecting their real numbers. A virtual number sits between the user and the platform. Automated verification pipelines — this is where our API and MCP Server come in. If you need to provision numbers programmatically and retrieve OTPs without manual intervention, this is the use case we built for. Geographic flexibility — needing a UK number from Australia, a US number from Ukraine, or any combination that your real SIM cannot provide. The OTP Blocking Problem Something worth knowing if you are building anything that involves SMS verification: platform-level VoIP blocking has become significantly more aggressive over the past two years. WhatsApp, Telegram, Google, and TikTok all run detection on incoming verification requests. A VoIP number gets flagged and the OTP simp

Most API teams treat the contract as an afterthought: write code, generate a spec, then watch the two drift apart. Git-native API design reverses that flow. You treat the API contract as source code, version it in Git, and review every change the same way you review application logic. Try Apidog today This guide focuses on implementation discipline, not a single tool. You’ll design contracts in branches, review them in pull requests, and turn a committed spec into mocks, tests, and docs. The goal is simple: your Git history should also be your API history. If you already know what Spec-First tooling looks like and want the product walkthrough, read the companion piece on the git-native API workflow . This article stays focused on practice. What “git-native” means for API work Git-native means your API definition lives in your repository as a plain text file. Not in a proprietary cloud database. Not behind a vendor login. A .yaml or .json file sits next to your code and is tracked by the same version control system your team already uses. In many cloud-locked API design tools, the contract lives in the vendor’s backend. You edit through a web UI, and your repository only contains an export. That export can become stale, and your Git history no longer explains how the API evolved. The git-native model inverts that relationship: The file in main is the contract. Any GUI is a view onto that file. Branches, commits, pull requests, blame, and rollback all apply to your API surface. Mocks, docs, tests, and generated clients derive from the committed spec. A git-native setup has three core properties: The spec is a text file in the repo. Changes flow through normal Git operations: branch, commit, PR, merge. Downstream artifacts derive from the committed file, not from a separate database. Why design and develop APIs in Git You already trust Git with your code. Your API contract deserves the same treatment. 1. History When someone asks, “When did we add the cursor pagination

If you've ever built a REST API with Express, you've probably felt it. Middleware registration, type definitions, body parser setup, connecting Joi or Zod... the structure is simple, but the boilerplate is excessive. When I first saw Hono, I was skeptical. "Another Express clone," I thought. That changed when I actually ran it. Bottom line: Hono v4 is more than just lightweight and fast. TypeScript type inference flows naturally all the way to route handlers. Zod validation connects via a single official package. On Bun, response times are noticeably faster than Express. Everything in this post is based on what I ran in a sandbox in June 2026. Why Hono — Compared to Express and Fastify Understanding where Hono fits means answering three questions. Bundle size : Hono v4 core is about 12KB. Express is 58KB, Fastify is 77KB. The gap might not sound dramatic, but in edge environments like Cloudflare Workers or Deno Deploy, bundle size directly affects cold start time. Edge functions sometimes initialize a new runtime per request — smaller means faster first response. Runtime compatibility : Express is Node.js-only. Fastify targets Node.js by default. Hono was designed from the start to "run anywhere." The same code deploys to Bun, Deno, Cloudflare Workers, Node.js, and AWS Lambda Edge. TypeScript support : Express requires @types/express as a separate install, and properties added to req via middleware don't get type inference. Hono is written in TypeScript from the ground up, and the Hono<{ Bindings: Env; Variables: Variables }> generic gives you type-safe access to environment variables and middleware state. I'm not saying Hono is the right choice for every situation. If your team is deeply invested in Express, or you need a mature plugin ecosystem, there's no compelling reason to switch. But if edge deployment is the goal, or you want type safety from day one, Hono is the most convincing TypeScript API framework right now. Installation and First Server — Response in

pluckmd exists so an agent can pull blog posts into markdown, index them into a wiki, and generate interactive HTML to learn from. This post is about the first step, the part with no per-site code, because the design is the interesting bit. If you want the practical side, how I actually use it day to day, I wrote that up separately: https://dev.to/taisei_ide/how-i-use-pluckmd-to-read-blogs-with-an-ai-agent-1jpe It downloads articles from a blog without any per-site code. No handler for Medium, no handler for Substack, nothing keyed on a domain. Here's how that works. The core idea: treat extraction as data, not code. AdapterSpec Instead of branching on which site you're on, pluckmd resolves an AdapterSpec . It's a plain object that says which selector finds article links, what the URL pattern looks like, and how pagination behaves. interface AdapterSpec { listing : ListingExtractionSpec ; // how to find article links article : ArticleExtractionSpec ; // how to pull the body pagination : PaginationSpec ; // none | scroll | button-click | next-url | auto evidence : string ; } Because it's data, the same shape can come from a heuristic, an LLM, an agent, or a person typing it by hand. They all produce the same thing, and they all go through the same checks. Resolving it, cheapest path first cache -> heuristics (local, free) -> LLM (only if needed) Cache first, rechecked against today's DOM so a stale entry can't sneak through. Then local heuristics. The LLM only gets called when the heuristics aren't sure. Every result that works gets written back, so the second run on a site is basically instant. How the heuristics find an article list This part has no idea what site it's looking at. It takes every link, normalizes the path, and collapses the parts that vary into wildcards. / blog / my - first - post -> / blog /* / blog / another - article -> / blog /* / about -> / about Group by that shape. Any group with the same pattern repeated three or more times is a candidate f

Qwen3.7-Plus has appeared on Qwen's official research release page, with a release date of June 1, 2026. Chinese media covered the launch on June 2. The important part is not that Qwen 3.7 Plus can understand images. The bigger signal is that Qwen is pushing it as a multimodal agent model: vision, language, coding, tool use, and productivity workflows inside one task loop. For developers, the real question is simple: can it keep the same goal across software screens, web pages, screenshots, code, terminal output, and tool calls long enough to finish useful work? If your team is evaluating new agent models, keep the model shortlist in one place and compare quality, latency, cost, and failure modes by task: Compare AI models on WisGate . What Is Qwen3.7-Plus? Qwen3.7-Plus is a multimodal agent model from Qwen. Qwen describes it as an agent foundation that unifies vision and language. It builds on the Qwen3.7 text backbone, adds stronger vision-language capabilities, and keeps the agent-oriented strengths developers care about: coding, tool use, and productivity workflows. That makes it different from a basic image-question-answering model. The more useful use cases look like this: Read a UI screenshot and decide the next action. Combine web pages, docs, charts, screenshots, and text context. Turn a design or product screen into maintainable code. Use tools to verify results instead of only returning static answers. Move between GUI, CLI, browser, and code environments during one task. That is why Qwen3.7-Plus should be evaluated as an agent model first, not just as another chat model with vision support. Why This Release Matters More teams are moving models into longer workflows: read the request, inspect the code, run tests, check logs, fix the issue, verify again, and write the summary. The hard part is that real work is rarely text-only. Frontend bugs come with screenshots. Dashboards come with tables and charts. Debugging comes with terminal output, browser state,