标签：#web

I made a website for my company and it was deployed on hostinger using wordpress a little over a month ago. About a week ago something went wrong and it started having so many problems. When I google the company name, and click on company's website it redirects me to some shopping website If I open the URL manually, it just opens a blank webpage, the source is also empty All the files are their in hostinger and all pages and details are visible in wordpress Somehow Google crawled over 800 URLs while my website only has about 25 pages and now when i google "site:companyname.com" all those weird URLs with Japanese name come up I tried fixing the site with hostinger AI, I myself looked a files and database for any mallicious activity, but I can up with nothing. Things work fine in localhost, and I tried creating a staging site with everything same at a subdomain that works fine too. If any one can help it would be great, I don't wanna loose this internship. I have not revealed the company name for my safety. submitted by /u/maybeamit [link] [留言]

I'm interested in building a new website for my business and am debating on whether or not I should hire a professional or design one by myself using AI. I've seen a lot of pretty nice sites built with AI tools like Claude, but I'm skeptical as to whether or not they are built appropriately. If anyone has opinions about the pros/cons of using an AI tool vs hiring someone I would appreciate hearing them. Thanks in advance! submitted by /u/HawgBandit [link] [留言]

When I decided to ship this blog in four languages, I had a clear mental ranking. English would win on volume. Spanish would be runner-up because of the sheer speaker count. Japanese would stay steady because it's my native language. Portuguese, I figured, was the long tail. I added it mostly out of completism. Twenty-two days later, the GA4 snapshot disagrees with every part of that ranking. PT: 748 pageviews , 709 sessions EN: 195 pageviews , 176 sessions JA: 27 pageviews , 29 sessions ES: 7 pageviews , 7 sessions That is Portuguese pulling roughly 3.8× English, 28× Japanese, and 107× Spanish on the same blog, same publishing cadence, same author. One Portuguese article on its own (a post about a 24-hour security agent: 375 PV) got more pageviews than my entire English blog combined. I wrote that article hoping Spanish would surprise me. Instead Portuguese surprised me, and Spanish quietly continued to not exist. The setup, so you can discount my numbers properly This is not a clean comparative experiment. It's a single blog, kenimoto.dev , running four language directories ( /en/ , /ja/ , /pt/ , /es/ ). Articles get translated through a cross-language LLM pipeline, then hand-edited for register and locale (BR Portuguese vs PT Portuguese, LatAm-neutral Spanish vs Spain Spanish). The window: 2026-04-30 to 2026-05-21, 22 daily snapshots. EN has 26 articles. JA has 25. PT has 17. ES has 10. So PT has fewer articles than EN and still beats it almost 4 to 1. If you stop reading here, take this one thing: language asymmetry can swallow article-count asymmetry whole . Adding articles in a saturated language is slower than adding articles in an underserved one. Why Portuguese pulled ahead I don't think the answer is "Portuguese readers like me more." I think three asymmetries are stacking on top of each other. 1. TabNews is a community door English doesn't have TabNews is a Brazilian developer community where you can post a technical article and have it actually read by h

AI agents are getting smarter fast. They can reason through tasks, manage workflows, call tools, and automate decisions across applications. But as these systems become more capable, one challenge becomes impossible to ignore: reliability. How do you know your AI agent is making the right decisions consistently? How do you test workflows that involve memory, reasoning, and multiple execution steps? And how do you debug failures when outputs become unpredictable? That’s exactly what this free live webinar, “ Testing AI Agents in Python: Building Reliable Evals with LangGraph & LangSmith ,” is focused on. The session includes a “ Live demo of the AI agent evaluation pipeline ,” where you’ll see how developers are building structured evaluation workflows using LangGraph and LangSmith to test, trace, and improve AI agent performance in real-world scenarios. Here is the link to register .. Who Should Join This Session? This webinar is designed for developers and technical teams working with AI systems, especially: Python developers building AI agents or LLM workflows AI engineers exploring evaluation and observability Architects designing production-ready AI systems Product teams experimenting with AI automation Founders building intelligent applications faster Whether you’re actively deploying AI agents or still evaluating the ecosystem, this session will give you a clearer understanding of how reliable AI systems are actually built. What You’ll Learn During the Webinar This isn’t a high-level AI trends session. The focus is practical implementation, testing workflows, and evaluation strategies developers can actually use. In this webinar, you’ll learn: Why evaluation matters for modern AI agents How LangGraph helps manage complex agent workflows How LangSmith can trace and monitor agent execution Ways to create repeatable and scalable evaluation pipelines Practical approaches for debugging and improving AI agent behavior See the AI Evaluation Pipeline Live One of the b

Take Aways Performance and Scalability : Pinecone is a managed machine-learning database that provides exceptional levels of performance and scaling capability due to its cloud-based design. Because of its distributed architecture and ability to do near-neighbor searches, Pinecone handles such tasks as similarity searching and anomaly detection on very large datasets efficiently. Easy to Integrate : One of the standout benefits of Pinecone is how easily it integrates through a high-level API and SDKs across several programming languages. This gives developers a real productivity boost by making vector storage, indexing and querying for machine learning applications far less complicated to implement. Strategic Factors : Pinecone brings advanced features and managed services that genuinely enhance machine learning workflows, though it does come with considerations like recurring costs and vendor lock-in. Organizations should think carefully about these factors alongside the benefits of streamlined database management and optimized performance before committing to adoption. The importance of storing and accessing information properly to build the best possible machine learning model really cannot be overstated. Pinecone addresses this directly by offering a Vector Database built specifically for ML queries, creating a strong opportunity to tap into the power of cloud databases. Designed from the ground up as a cloud-native application, Pinecone makes it straightforward to index and search complex, high-dimensional vector data — which in turn makes building state-of-the-art machine learning applications much more approachable and helps software development companies deliver more value to their clients through custom software development. What is Pinecone? Pinecone is a fully managed Vector Database that lets you store, index, and query complex vector data quickly and efficiently. Because of its vector-native design, the primary use cases for Pinecone fall within similar

How combining Non-Transferable and Permanent Delegate extensions changed my understanding of digital ownership on Solana. Imagine earning a high rank in an online game. You can use it. You can benefit from it. Other players can see it. But you cannot transfer that rank to someone else. If you break the game's rules, the game company can revoke it. And if the game gets acquired by another company, authority over that rank can be handed over to a new administrator. That was the closest mental model I found for understanding one of the most interesting things I built this week on Solana: a credential token using Token-2022 extensions. What are Token Extensions? One thing I've learned during this phase of the challenge is that Token-2022 is not just about creating tokens. It allows you to define behaviors directly at the token level through extensions. Instead of relying on application logic to enforce rules, the token program itself can enforce them. For this experiment, I combined two extensions: Non-Transferable — prevents ownership from being transferred to another wallet. Permanent Delegate — gives a designated authority the ability to manage the token even after it has been issued. Individually, those are useful. Together, they create something that behaves less like a currency and more like a credential. The Transfer That Was Supposed to Fail After creating the token, I tried transferring it. spl-token transfer Gn5PZzwDENvpQESaFwgVqzCUFba5sSka59iLtjFTYNvz 1 $THIRD_PARTY \ --owner ~/recipient-wallet.json \ --fee-payer ~/.config/solana/id.json \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --fund-recipient --allow-unfunded-recipient The result: Recipient: ErYcpQYcpdoaaiufrB3MqQE2QaVEhoACZGcssppszPpY Recipient Token Account: 8CvyoKt11UbkWYRVXey6UKK2gqQcvVvbh9Lv3crzo8C3 Funding ATA: 8Cvyokt11UbkWYRVXey6UKK2qqQcvVvbh9LV3crz0803 Status: ❌ Transaction failed during simulation Error: Transfer is disabled for this mint (Token-2022 restriction) What I liked a

Due to a growing influx of questions on this topic, it has been decided to commit a monthly thread dedicated to this topic to reduce the number of repeat posts on this topic. These types of posts will no longer be allowed in the main thread. Many of these questions are also addressed in the sub FAQ or may have been asked in previous monthly career threads . Subs dedicated to these types of questions include r/cscareerquestions for general and opened ended career questions and r/learnprogramming for early learning questions. A general recommendation of topics to learn to become industry ready include: HTML/CSS/JS Bootcamp Version control Automation Front End Frameworks (React/Vue/Etc) APIs and CRUD Testing (Unit and Integration) Common Design Patterns You will also need a portfolio of work with 4-5 personal projects you built, and a resume/CV to apply for work. Plan for 6-12 months of self study and project production for your portfolio before applying for work. submitted by /u/AutoModerator [link] [留言]

Hi everyone, We're building an automation platform using Playwright where all browser automation runs on the backend. For portals that require manual intervention (OTP, CAPTCHA, MFA, document uploads, etc.), we're exploring a way to let users temporarily view and interact with the running backend browser from our React application, after which automation would resume automatically. Our goals are: Keep all automation logic on the backend Support human intervention only when necessary Scale to bulk processing workflows Deploy reliably in production We're currently evaluating approaches such as CDP screencasting, VNC/noVNC, and WebRTC-based browser streaming. Has anyone built something similar in production? What architecture did you choose, and what were the biggest challenges around scalability, latency, security, session management, and CAPTCHA/OTP workflows? Also, is there a better alternative than live browser streaming for this use case? Any advice, experiences, or open-source projects would be greatly appreciated. submitted by /u/Loud_Ice4487 [link] [留言]

The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows how, to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent. By Amanul Chowdhury, Vinay Gummadavelli

Hello Dev Community! 👋 It is officially Day 8 of my journey to master the MERN stack! After spending the first week structuring with HTML and styling with CSS, today I finally started learning the core language of web logic: JavaScript . Moving from static designs to actual programming logic feels like unlocking a whole new level of web development. 🧠 Key Learnings From Day 8 Today was all about setting up the foundation in JavaScript and understanding how code runs in the browser. Here is what I covered: 1. The Browser Console & Execution I learned that every browser has a built-in environment to run JavaScript. Writing my very first console.log("Hello World"); and seeing it print in the developer tools console was the perfect start. 2. Variables: Storing Data Safely I learned how to store information using variables and the crucial differences between modern variable declarations: let : For values that can change later in the program (mutable). const : For values that must remain constant and cannot be reassigned (immutable). Note: I also read about var , but learned why modern JavaScript avoids it due to scoping issues. 3. Data Types Fundamentals Data needs a type so the computer knows how to handle it. Today I practiced with: Strings: Plain text enclosed in quotes (e.g., "MERN Stack" ). Numbers: Integers and decimals without quotes (e.g., 2026 ). Booleans: Simple true or false states (e.g., isLearning = true ). 🛠️ What I Actually Code / Experimented With Since I am just starting with core logic, I didn't write code directly into my HTML webpage layout today. Instead, I created a script.js file, linked it to my project, and built a basic script in the console that: Stores a user's name and learning status in variables. Dynamically calculates values (like years left until a milestone). Outputs formatted statements into the browser console. It is simple, but understanding how data moves in the background is incredibly exciting. 🎯 My Goal for Tomorrow (Day 9) Tomorr

Not every old business web app needs a full Internet Explorer environment. That sounds obvious, but it is easy to miss when a legacy intranet, ERP, OA, or ASP.NET WebForms page fails in Chrome or Microsoft Edge. The first instinct is often to put the whole system into IE mode. Sometimes that is absolutely correct. Other times, the page mostly works in Chromium and only breaks on older JavaScript or DOM assumptions. The useful first step is to separate those two cases. Case 1: the page needs a real IE engine Use Microsoft Edge IE mode, a Windows virtual machine, remote desktop, or another managed legacy-browser path if the page depends on: ActiveX controls COM integration VBScript Trident or MSHTML rendering behavior Browser Helper Objects Java applets strict IE7 or IE8 document modes A Chrome extension or JavaScript compatibility layer should not be presented as a replacement for those requirements. If the workflow depends on the IE engine, the browser engine is part of the application runtime. Case 2: the page mostly works, but old browser assumptions fail There is another common category. The page loads in Chrome or Edge, authentication works, and the main UI appears, but a small set of old behaviors fails. Examples include: empty frameset entry pages loading pages that do not finish redirecting attachEvent window.event event.srcElement showModalDialog -style picker flows document.frames older WebForms date fields that call a calendar function on focus For maintained source code, the best answer is still to fix the application. Replace old event APIs, remove synchronous dialog assumptions, and modernize generated WebForms scripts where possible. But in many real organizations, the legacy page is owned by a vendor, frozen department system, or migration backlog. In that situation, a scoped compatibility layer can be worth testing before moving the whole workflow into IE mode. A low-risk triage sequence I use this sequence: Pick one legacy hostname. Pick one failing

Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

Integrations kill product velocity. Every SaaS team knows this. You ship a killer feature, customers love it, then they ask: "Can it sync with Salesforce? What about HubSpot? Zendesk?" Suddenly your roadmap is hostage to building connector after connector. Each one takes 2-3 weeks. Your engineers hate it. Your customers wait. Competitors who solve this faster win deals. The standard response has been iPaaS platforms. They help, but they don't fundamentally change the game. You still need engineers to map fields, handle edge cases, and maintain brittle connections. The real breakthrough isn't just automation , it's making integrations LLM-native from the ground up . What Actually Makes an Integration Layer "AI-Native"? Let's cut through the marketing speak. Every B2B tool now claims to be "AI-powered." Most just added a ChatGPT wrapper to their UI. Real AI-native architecture means three things: 1. LLM-Ready Connectivity via MCP Servers Model Context Protocol (MCP) is Anthropic's standard for connecting LLMs to external data sources. If your integration layer doesn't support MCP servers natively, your AI features will always be bolted on, not built in. MCP servers expose your SaaS data to language models in a structured way. Instead of engineers writing custom API wrappers for every LLM interaction, you get a standardized interface. Claude, GPT-4, and future models can query your integration layer directly. Example: A customer support tool with native MCP integration lets an AI agent pull ticket history from Zendesk, check Stripe subscription status, and update Salesforce records in one conversation flow. No custom code. No brittle middleware. 2. AI-Mapped Data Migration Data migration is where most SaaS deals die. Customer says "we'll switch from ServiceNow to your ITSM if you migrate our 50,000 tickets." Your team estimates 6 weeks. Deal stalls. Traditional migration means: Manual field mapping spreadsheets Custom scripts for data transformation Downtime windows Hi

I've been thinking about a specific version of AI-assisted debugging. Not asking which tool is smarter, but asking about the behavior change that would come from removing the context-setup step entirely. Right now I self-select which debugging problems to involve AI on. The threshold is roughly: is this complex enough that the setup cost is worth paying? Copy the relevant code, copy the error, add context about the project structure, ask my question. It takes a few minutes to do well. If the AI could see my IDE directly, no copying, no pasting, just "look at this and tell me what's wrong," I think my threshold would drop significantly. I'd ask about more things. Smaller things. Things I currently just push through myself because they don't seem worth the setup overhead. Whether the answers would be better because the AI sees the actual screen is a separate question. But the behavioral change from removing the friction might matter more than any quality improvement. If AI debugging had zero setup cost, would you use it differently? Or do you think the current copy-paste step is actually useful because it forces you to think through the problem before you ask? submitted by /u/Professional-Peach-3 [link] [留言]

I use Claude a lot for complex problem-solving and Copilot inline in VS Code for quick completions. The difference in actual usage frequency is something I've started noticing. Copilot I use constantly, basically without thinking about it. Claude I use maybe five times a day, and half the time I talk myself out of opening it because I already know what's coming: copy the relevant code, paste it, add context about the project structure and what I was trying to do, explain the error I'm getting, then finally ask the actual question. The friction is small. But it's consistent enough that I default to struggling through something myself, or Googling, instead of opening Claude. The specific case that gets me most is when the problem is visible on screen. I can see the bug. Claude could probably help me solve it faster. But the work of getting what's visible into Claude's context is just enough friction to make me not bother. Is the gap between ""AI embedded in the tool you're working in"" and ""AI in a separate chat window"" meaningful to other developers, or is this a habit thing I should just push through?" submitted by /u/Queasy_Hotel5158 [link] [留言]

Hey r/webdev 👋 I recently launched UtilCraft , a collection of free browser-based tools for devs and creators. The whole idea is that everything runs locally, nothing gets uploaded anywhere. Right now it has 16 tools across 6 categories: 🛠️ Developer tools (UUID generator, JSON formatter, Regex tester, Cron generator…) 🖼️ Image tools (compressor, converter, resizer) 📄 PDF tools (merge, split) 🔒 Security (password generator, hash generator) 📝 Text tools 🔄 Converters (color, unit) I'd love to know two things from people who actually write code for a living: 1. What tool do you find yourself Googling way too often that you wish was just… there? 2. Any of the existing tools that feel half-baked or could be way more useful? No account needed, no sign-up, just open and use. Roast it, suggest stuff, I'm all ears. 🙏 submitted by /u/Yolmack [link] [留言]

Hi everyone, I need some career advice. I'm from a non-IT background and have been working in a small company for the last 2.5 years, mainly doing HTML and WordPress work. I don't have much exposure to modern development, and with AI changing the industry so fast, I'm worried about my future career growth. I'm thinking of joining an offline Full Stack Development course in Chennai because online learning hasn't worked well for me. I'm confused between **Java Full Stack** and **Python Full Stack**. For those who have experience in this field: * Which stack would you recommend? * Which institute is better: Besant Technologies, Greens Technology, or FITA Academy? * Are there any better alternatives in Chennai? I want to learn real-world projects and build skills that can help me get better opportunities. I'm 2.5 years into my career and don't want to make the wrong decision at this stage. Any guidance, personal experiences really help full for me. Any advice would be greatly appreciated. Thanks! submitted by /u/Best-Quantity-4749 [link] [留言]

15 years ago Akamai was the CDN network every tv station was using to distribute their content on, but from what I can remember they were also crazy expensive. Nowadays I still noticed that most major companies use Akamai but with strong competition from AWS. Did Akamai became more price friendly or did AWS become to have to same amount of local nodes? submitted by /u/Sure-Guest1588 [link] [留言]

The biggest headache im having is learning how to separate elements on the page correctly. I can center things and all, but dont know when to create a new section or just keep using the same since most things i can center or adjust using some propertie. Is there anywhere I can learn about something like this? i was trying to learn about the structure of websites trough the inspect in the browser(just to know what what are some best practices to adopt) and it just feels confusing. Im new to webdev, and was doing it to learn how to separate the divs on the page, then i asked claude to generate some random site just to read through the code, and simply everything is divs, like everything, no h1, title, nothing. From what i understand divs are the favorites because they dont have any set properties, and there's the thing about being able to arrive at the same result in different ways. But is it really the optimal way? Thank you in advance. submitted by /u/SkullDriv3rr [link] [留言]

The row looked perfect. rating: 7 . Valid JSON, right type, no nulls, no missing keys. My schema check waved it through. The page had returned HTTP 200. The selectors hadn't moved. Everything green. A rating of 7 on a 5-star site is impossible. The model invented it, formatted it correctly, and handed it to me with total confidence. That's the failure I want to talk about. Not the scraper that breaks loudly. The one that hands you a clean-looking row that is quietly, plausibly false — and sails past every check you have, because your checks are all looking at the shape of the data, and the lie is in the value . TL;DR HTTP 200, intact selectors, and valid JSON tell you the form is fine. They say nothing about whether the value is true. When an LLM extracts from messy free-text, structured-output mode guarantees you get valid JSON. It does not guarantee the content is real. The model fills uncertain fields rather than leaving them empty — because the schema demands a complete row. A ~60-line value-level sanity gate (ranges, dates, cross-field, reference, language) catches the obvious lies before they hit your database. Real code and real output below. The honest catch: this gate catches rule violations , not plausible lies inside the allowed range . A rating: 4 where the truth is 2 slides right through. I'll be specific about where the gate stops. Two different ways a scraper lies to you I wrote about source drift last week — the case where the page changes underneath you and a 30-line schema check catches the structure shifting. That's an input problem. The source mutated; your agreement with the page broke; you detect it by watching the shape. This is the other end of the pipe. The source is fine. The page is intact, the selectors are correct, the structure is exactly what you expected. The thing that lied to you is the model , on the extraction step, when you asked it to pull structured fields out of a paragraph of human prose. Those two failures feel similar and t