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You published a DMARC record months ago. Every checker says it's valid. No reports have ever arrived, and at some point you stopped thinking about it and quietly concluded nobody's forging your domain. You don't know that. You just can't see. Two causes account for almost all of it, and both leave the DMARC record itself looking perfect. 1. Your reporting address never agreed to receive them This is in the spec and almost nobody knows about it. If your rua= address is on a different organisational domain from the domain publishing the record, RFC 7489 section 7.1 requires the destination to explicitly consent. The reason is obvious once you see it: otherwise anyone could point rua= at your mailbox and use the world's mail infrastructure as a free DDoS. Consent is a DNS record, published by the receiving domain: <your-domain>._report._dmarc.<their-domain>. TXT "v=DMARC1" So if example.com sends reports to dmarc@reports.vendor.com , then vendor.com has to publish: example.com._report._dmarc.reports.vendor.com. TXT "v=DMARC1" Without it, conforming reporters send nothing at all. That includes Google, which is most of your report volume. You can watch this working in production. PayPal sends aggregate reports to a third party, and the authorisation record exists: paypal.com._report._dmarc.rua.agari.com. TXT "v=DMARC1;" The practical consequence: not a Gmail address You cannot put a Gmail address in rua= . Google is not going to publish yourdomain.com._report._dmarc.gmail.com on your behalf, and you can't create records in their zone. Same for Outlook, Yahoo, or any address at a domain you don't control. This is a common first move — publish DMARC, point reports at your personal inbox, wait. Nothing arrives, and nothing is wrong with the record. If you use a commercial DMARC service they normally publish these records during onboarding, often via a wildcard. If reports never start after you sign up, check this before anything else. When it doesn't apply: reporting to you

There's a failure mode in SPF that produces no error anywhere you'd think to look. Your record is syntactically valid. dig returns it. Your DNS provider is happy. Your own test mail arrives fine. And yet some of your mail is failing authentication at the receiver, and nothing in your infrastructure will tell you. The cause is usually that you've gone past ten DNS lookups. Ten is a hard ceiling, not a guideline RFC 7208 section 4.6.4 requires evaluators to cap DNS-querying terms at ten. Go past it and the result is PermError — a permanent failure. The important part: receivers don't degrade gracefully. They don't evaluate the first ten and shrug at the rest. They treat the entire record as unusable. You go from "SPF configured" to "SPF absent" in one step, and the only place that's visible is in DMARC aggregate reports, which most people aren't reading yet. Six terms cost a lookup. Four are free. Costs a lookup Free include: ip4: a ip6: mx all ptr exp= exists: redirect= The count isn't yours to control This is what catches people out. The limit applies to the whole resolution tree, not just your record. You publish four includes. One of those vendors publishes six includes of their own. You're at eleven, and your record lists four things. Nothing changed on your side. A vendor updated their record and broke yours. There is no notification for this, from anyone, ever. Counting it by hand You can walk the tree with dig if you want to see it directly: dig +short TXT example.com | grep spf1 Then for every include: you find, recurse: dig +short TXT _spf.vendor.com | grep spf1 Keep going until you bottom out, counting every include: , a , mx , ptr , exists: and redirect= along the way. It's tedious but instructive — you'll usually find one vendor accounting for half your budget. Three traps that make tools lie to you Doing this by hand or with a naive script, there are three places to go wrong. I've seen all three produce confidently incorrect output. 1. redirect= uses = ,

You loaded an image, got a tensor shaped (batch, height, width, channels) , and your convolution wants (batch, channels, height, width) . Stack Overflow says permute . Someone else says transpose . And reshape(2, 3, 28, 28) gives you the right shape too — so why is everyone making this complicated? Because two of those three are the same tool, and the third one silently destroys your data. The short answer transpose(dim0, dim1) swaps exactly two dimensions. permute(...) reorders all of them in one call, and you must list every dimension. transpose is a special case of permute . Both return a view — no data is copied, only the strides change — which also means both leave you with a non-contiguous tensor. reshape is not in this family at all. It reinterprets the flat memory under a new shape without moving anything, so it can produce the shape you asked for while completely scrambling what the numbers mean. import torch t = torch . arange ( 24 ). reshape ( 2 , 3 , 4 ) print ( t . transpose ( 0 , 1 ). shape ) # torch.Size([3, 2, 4]) — swapped dims 0 and 1 print ( t . permute ( 2 , 0 , 1 ). shape ) # torch.Size([4, 2, 3]) — full reorder transpose — swap two axes transpose(dim0, dim1) takes two dimension indices and swaps them. Everything else stays put. t = torch . arange ( 24 ). reshape ( 2 , 3 , 4 ) print ( t . shape ) # torch.Size([2, 3, 4]) print ( t . transpose ( 0 , 1 ). shape ) # torch.Size([3, 2, 4]) print ( t . transpose ( 1 , 2 ). shape ) # torch.Size([2, 4, 3]) The order of the two arguments doesn't matter — t.transpose(0, 1) and t.transpose(1, 0) are the same thing. A swap is a swap. On a 2-D tensor this is the matrix transpose you already know, and .T is the shorthand: m = torch . arange ( 6 ). reshape ( 2 , 3 ) print ( m . T . shape ) # torch.Size([3, 2]) print ( m . transpose ( 0 , 1 ). shape ) # torch.Size([3, 2]) — identical One caution on .T : on tensors with more than two dimensions, .T reverses every dimension, and modern PyTorch has deprecated that

Short answer To build an MCP server: install an official MCP SDK, declare your tools with typed inputs, optionally expose resources and prompts, run the server over stdio or HTTP, then connect an MCP client like Claude and test it. A minimal Python server is about ten lines; the work is in choosing what to expose and validating every input. This is the build . For what MCP is, its three primitives, and how it differs from an API, start with what is the Model Context Protocol — this page assumes that and goes straight to code. Prerequisites You need very little to get a server running locally: A language with an official SDK. Python and TypeScript are the most mature; the same protocol is also implemented for other languages. This guide uses the Python SDK (the secondary path most people search for), with notes on where the TypeScript SDK is equivalent. Python 3.10 or newer and uv (recommended) or pip to manage the environment. An MCP client to test against — Claude Desktop, or the MCP Inspector that ships with the SDK. You do not need cloud credentials to build or run the server itself. Conceptually a server exposes three things — tools (model-callable functions), resources (readable data), and prompts (reusable templates) . The steps below add them in that order. Exact SDK signatures evolve, so treat the snippets as the current shape and check the live docs before shipping. Which spec revision this builds against. The code here targets MCP revision 2025-11-25 — the revision the spec's versioning page still names as the current protocol version. Revision 2026-07-28 is published and reworks the wire format substantially. A server built against 2025-11-25 stays conformant today; what the new revision changes for a server author is set out below, so you can build now and plan the move. Step 1: scaffold the server Create a project, install the SDK, and write the smallest server that runs. With uv : uv init weather cd weather uv venv source .venv/bin/activate # Install t

Learn how to build professional PDF reports in Odoo 19 using QWeb . This guide covers everything from creating your first report to advanced techniques like reusable templates, custom paper formats, barcodes, multilingual reports, RTL support, and performance optimization. Introduction Every Odoo application relies on reports. Whether you're printing: Sales Quotations Customer Invoices Purchase Orders Delivery Slips Manufacturing Orders Inventory Labels Payroll Documents Custom Certificates you're using QWeb , Odoo's XML-based templating engine. Although creating a basic report is straightforward, building maintainable, scalable, and professional reports requires understanding how the entire reporting pipeline works. In this tutorial, we'll build a complete report from scratch while exploring best practices used by experienced Odoo developers. By the end of this guide, you'll be able to: Create custom PDF reports Understand how Odoo generates PDFs Design reusable templates Display relational data Build dynamic tables Format currencies and dates correctly Add company logos, images, barcodes, and QR codes Create custom paper formats Pass additional data from Python Optimize report performance Debug common QWeb issues How Odoo Generates a PDF Before writing any XML, it's important to understand the report generation process. User clicks "Print" │ ▼ ir.actions.report │ ▼ Report Model (_get_report_values) │ ▼ QWeb Template │ ▼ Rendered HTML │ ▼ wkhtmltopdf │ ▼ PDF Download Each step has a specific responsibility: Component Responsibility ir.actions.report Registers the report Report Model Prepares business data QWeb Generates HTML wkhtmltopdf Converts HTML into PDF Understanding this workflow makes debugging significantly easier. Project Structure A clean module structure helps keep reports maintainable. my_module/ │ ├── models/ │ └── sale_order.py │ ├── report/ │ ├── sale_order_report.xml │ ├── report_action.xml │ └── paperformat.xml │ ├── security/ │ ├── views/ │ └── _

A spreadsheet containing laptop serial numbers is not proof that every endpoint is managed. It proves only that someone created a spreadsheet. For an audit, customer security review, onboarding check, or incident investigation, the evidence needs to connect four facts: The organisation expects the device to exist. The device is assigned to an accountable owner or lifecycle state. A management or monitoring control is actively reporting from it. The reported evidence is recent enough to support the decision being made. A device can appear in an asset register while being absent from the management platform. It can also appear in the management platform while belonging to a former employee or reporting data that is months old. Control objective: Maintain a current, reconciled inventory of expected endpoints, managed endpoints, owners, security state, and unresolved exceptions. 1. Define what "managed" means before counting devices Teams often use the word managed without an operational definition. That creates false confidence. An endpoint should not count as managed merely because an agent was installed once. For a company-owned laptop, a practical definition normally requires all of the following. Criterion Minimum evidence Identity Hostname, serial number, hardware identifier, operating system, and management record can be tied to one device Ownership Named user, department, custodian, stock status, repair status, or retirement state Control Expected MDM, RMM, EDR, or other endpoint control is enrolled and associated with the correct organisation Freshness Last check-in and evidence timestamps fall within a documented threshold Posture Update, encryption, firewall, antimalware, restart, and other required states are known Accountability Deviations have a reason, owner, approval, target date, and review history A device that fails one criterion should not disappear from the report. It should remain visible as an exception. 2. Reconcile three sources of truth No sing

Despite predictions that AI agents could make traditional apps obsolete, developers are shipping new software faster than ever. From smarter bookmarking tools and neighborhood marketplaces to digital pen pals and nature journals, here are the latest App Store finds worth adding to your Home Screen.

🕉️ Devlog — गुरु-दीक्षा: Teaching Karma Without Breaking Immersion "गुरु बिना ज्ञान नहीं।" Without a Guru, there is no knowledge. The Problem Moksha is a game rooted in Sanatan Shastra — Vedic Karma mechanics, Sanskrit concepts, rebirth cycles. It's intentionally deep. And that depth was quietly becoming its biggest barrier. New players would start the game and immediately face naama-jaap, vairaagya, prarabdha, chetana-jagriti — all at once, with no guidance. Within the first 30 seconds, most had no idea what they were doing or why. The game needed a tutorial. But it needed one that didn't betray what Moksha is. Why a Normal Tutorial Wouldn't Work The obvious solution — pause the game, show a tooltip, unpause — felt completely wrong for Moksha. Spiritually, a hard pause breaks the flow of consciousness. Mechanically, isPaused = true is deeply wired into audio ducking, gamepad state, and ambient layers. Hijacking it for tutorial logic would have introduced subtle bugs across every system. An earlier attempt at a tutorial (Issue #30) tried to live inside engine.js itself. That was worse — the engine is already the heaviest file in the codebase, and embedding tutorial step state there violated the entire modular architecture we'd been building toward. So I scrapped both approaches and started over. The Solution: गुरु-दीक्षा (Guru's Initiation) The new system is built around one philosophical reframe: a Guru doesn't stop the world to teach. They walk alongside you. This became the technical foundation too. A New Module — src/tutorial.js TutorialManager is a self-contained ES6 class. It doesn't import from engine.js or touch any game state directly. Instead, main.js passes it an engine state snapshot every frame via checkCompletion(state) . The tutorial reads — never writes. engine.js ──(no connection)──> tutorial.js main.js ──(snapshot feed)──> tutorial.js Zero coupling. Zero risk to existing systems. Slow Motion, Not Hard Pause When a tutorial card is visible, the game

I maintain a non-custodial escrow protocol that runs on two blockchains: Base, an Ethereum L2, and TON, the chain behind Telegram. Same product, same core logic on paper: lock funds, deliver work, release on approval, handle disputes. I expected the second implementation to be a port. It wasn't. The two chains disagree so fundamentally about how a contract should be structured that writing the same feature twice forced me to rethink what "the same" even means. This post is about those differences, the design decisions each model pushed me toward, and what I'd tell anyone about to make the same jump. Light on code, heavy on the reasoning. The interesting part was never the syntax. The two mental models The single most important difference is not the language. It's the execution model underneath. On Base , a contract is an object with shared state. You write Solidity, and it behaves like a class instance sitting in memory that everyone calls into. A user calls a function, the function reads and writes contract storage synchronously, and either the whole thing succeeds or it reverts atomically. All my escrows live in one contract, in one big mapping, and every call reaches straight into that shared state. On TON , a contract is an actor that receives messages. You write Tact, and it behaves like an isolated process with a mailbox. You don't call a function; you send a message, and the contract handles it in its own turn. There is no synchronous cross-contract call in the EVM sense. Interaction between contracts is asynchronous message passing, and you design around that or you fight it the whole way. The shape of the entry point tells the whole story. On Base, an external caller invokes a named function: function approveWork(uint256 _contractId) external validContract(_contractId) nonReentrant whenNotPaused { // reads and writes shared contract storage, synchronously } On TON, nothing "calls" the contract. A message arrives, and a handler consumes it in the actor's own

📅 เขียนเมื่อ: กรกฎาคม 2026 ⚠️ ตรวจสอบข้อมูลจาก Go Specification, APL documentation, และบันทึกของผู้พัฒนา ถ้าคุณเขียน Go มาระยะหนึ่ง คุณคงเคยเห็น iota — เจ้า identifier ประหลาดที่ไม่มีใครรู้ว่ามันคืออะไรตอนเจอครั้งแรก const ( Monday = iota + 1 // 1 Tuesday // 2 Wednesday // 3 ) มันไม่ใช่ keyword, ไม่ใช่ type, ไม่ใช่ function — มันคืออะไรกันแน่? และที่สำคัญ — ทำไมต้องชื่อ iota ? คำตอบพาเราย้อนกลับไปถึงปี 1962 — ถึงนักคณิตศาสตร์ชาวแคนาดาคนหนึ่ง และภาษาโปรแกรมมิ่งที่เปลี่ยนโลก iota คืออะไรใน Go ใน Go spec — iota คือ predeclared identifier ที่ใช้ เฉพาะใน const declaration เท่านั้น มันทำสิ่งเดียว: นับเลขให้อัตโนมัติ const ( _ = iota // 0 (skip) KB = 1 << ( 10 * iota ) // 1 << 10 = 1024 MB // 1 << 20 = 1,048,576 GB // 1 << 30 ) ค่า iota เริ่มที่ 0 และเพิ่มทีละ 1 ทุกครั้งที่เจอบรรทัดใหม่ใน const block — แม้ว่าบรรทัดนั้นจะไม่ได้ใช้ iota ก็ตาม สิ่งที่ทำให้ iota ทรงพลังคือมันเป็น expression (ไม่ใช่แค่ตัวเลข) — คุณเอาไปคูณ บวก ลบ shift ได้หมด const ( Read = 1 << iota // 1 << 0 = 1 Write // 1 << 1 = 2 Execute // 1 << 2 = 4 ) นี่คือวิธีมาตรฐานในการสร้าง enum, bitmask, และ constant series ใน Go — ทั้งหมดด้วย keyword เดียว iota — อักษรกรีกตัวเล็กที่สุด ก่อนจะเป็นชื่อใน Go — ιώτα (iota) คืออักษรตัวที่ 9 ของกรีกโบราณ: ι มันคืออักษรที่ เล็กที่สุด ในภาษากรีก — แค่เส้นตรงหนึ่งเส้น ไม่มีหาง ไม่มีขีด ในพระคัมภีร์ไบเบิล มีวลี famous: "not one iota" — แปลว่า "ไม่แม้แต่นิดเดียว" — เพราะ iota คือสิ่งที่เล็กที่สุด และมันคือชื่อที่สมบูรณ์แบบสำหรับสิ่งที่ "เพิ่มทีละหนึ่ง" จุดเริ่มต้น — APL และ Kenneth Iverson นักคณิตศาสตร์ผู้สร้างภาษา ปี 1962 — Kenneth E. Iverson ตีพิมพ์หนังสือ "A Programming Language" (ที่มาของชื่อ APL) Iverson เป็นนักคณิตศาสตร์ชาวแคนาดา (ต่อมาได้ Turing Award ปี 1979) — เขาไม่ได้แค่ออกแบบภาษาใหม่ แต่ปฏิวัติวิธีคิดเรื่อง programming แทนที่จะเขียน for i = 1 to 10 — Iverson คิดว่า programming ควรเหมือนคณิตศาสตร์: สั้น, สัญลักษณ์, และทรงพลัง เกิดเป็น ⍳ (iota) ใน APL, Iverson สร้าง operator ⍳ — เรียกว่า iota — ที่ทำสิ่งเดียว: สร้างลำดับเลข ⍳ 5 → 1 2 3 4 5 ⍳ 10 → 1 2 3 4 5 6 7 8 9 1

📅 เขียนเมื่อ: กรกฎาคม 2026 ⚠️ อ้างอิงจากเอกสารต้นฉบับของ Bell Labs และบันทึกของผู้พัฒนา ถ้าผมถามว่า "ระบบปฏิบัติการที่เจ๋งที่สุดในโลกคืออะไร" — คุณคงตอบ Linux, macOS, หรือ Windows แต่ถ้าถาม Ken Thompson และ Dennis Ritchie — สองคนที่สร้าง Unix ขึ้นมา — พวกเขาจะตอบว่า Plan 9 Plan 9 คือระบบปฏิบัติการที่ Bell Labs สร้างขึ้นในช่วงปลายยุค 80 ถึงต้นยุค 90 โดยทีมเดียวกับที่สร้าง Unix แต่มันไม่ใช่แค่ "Unix เวอร์ชันใหม่" — มันคือการเริ่มต้นใหม่ทั้งหมด และถึงแม้วันนี้แทบไม่มีใครใช้ Plan 9 — แนวคิดของมันแทรกซึมอยู่ในทุกระบบปฏิบัติการที่คุณใช้อยู่ จุดเริ่มต้น — "Unix เริ่มแก่แล้ว" ปัญหาที่ Unix สะสมมา Unix เกิดในปี 1969 — ตอนนั้นคอมพิวเตอร์คือเครื่องเดียวที่มี terminal ต่อพ่วง พอยุค 80 มาถึง โลกเปลี่ยน — network กลายเป็นเรื่องปกติ, graphics เริ่มสำคัญ, distributed computing เริ่มเกิด แต่ Unix ไม่ได้ถูกออกแบบมาเพื่อสิ่งเหล่านี้ มันถูก patch, extend, retrofit — จนกลายเป็นระบบที่ซับซ้อนเกินกว่าที่ผู้สร้างจะภูมิใจ Ken Thompson เคยพูดประมาณว่า: "Unix เริ่มต้นด้วยความเรียบง่าย แต่มันค่อย ๆ สะสมความซับซ้อนเข้าไปเรื่อย ๆ — ถึงเวลาที่ต้องเริ่มใหม่" ทำไมต้องชื่อ Plan 9 ชื่อ "Plan 9" มาจากหนัง B-movie สุดคลาสสิกเรื่อง "Plan 9 from Outer Space" (1959) ของ Ed Wood — ที่ได้ชื่อว่าเป็น "หนังที่แย่ที่สุดตลอดกาล" แต่ชื่อนี้ไม่ได้หมายความว่า OS นี้แย่ — มันคือมุกวงในของทีม Bell Labs ที่ชอบตั้งชื่อแปลก ๆ (Unix เองก็เป็นมุก — มันล้อ Multics) หัวใจของ Plan 9 — "ทุกอย่างคือไฟล์" จริง ๆ จาก Unix สู่ Plan 9 — ทำให้สุดทาง Unix มีแนวคิด famous: "everything is a file" filesystem เป็นไฟล์ → /home/user/document.txt devices เป็นไฟล์ → /dev/sda , /dev/tty processes เป็นไฟล์ → /proc/1234 แต่มันมีข้อยกเว้น — network sockets, graphics, window system — สิ่งเหล่านี้ไม่ใช่ไฟล์ใน Unix Plan 9 เอาแนวคิดนี้ไป สุดทาง — ใน Plan 9, ทุกอย่างคือไฟล์ ไม่มีข้อยกเว้น 9P — โปรโตคอลเดียวที่เชื่อมทุกอย่าง หัวใจของ Plan 9 คือ 9P — โปรโตคอลที่ทำให้ทุกอย่างสื่อสารกันผ่าน filesystem หน้าต่าง GUI → mount เป็นไฟล์ network connection → mount เป็นไฟล์ เครื่องอื่นใน network → mount เป็นไฟล์ ลองนึกภาพ: คุณ ls ดูไฟล์ในเครื่องคนอื่นได้เหมือน

Introduction Your agent guesses. It matches names as text and hopes the right line was in...

When you first install WordPress, it is easy to think that every popular plugin will improve your website. After all, the WordPress plugin directory contains tens of thousands of plugins, each promising better SEO, stronger security, faster performance, or beautiful design. That is exactly where many beginners make their first mistake. A new website does not need 30, 40, or 50 plugins. Every plugin you install adds more code that must be maintained, updated, and secured. While the number of plugins alone does not determine performance, unnecessary or poorly coded plugins increase the chances of conflicts, slowdowns, and security issues. Security experts also continue to report that plugins account for the overwhelming majority of WordPress vulnerabilities. The better approach is simple. Install only the plugins that solve an essential problem. Choose one high quality plugin for each task, avoid duplicates, and ignore everything else until you actually need it. Here are the only five to six plugins that most brand new WordPress websites need. The Minimalist Plugin Rule Before installing anything, remember this simple rule: One plugin, one job. If one plugin already handles SEO, you do not need another SEO plugin. If one caching plugin is active, never install a second one. If your hosting company already performs automatic backups, you may not need a backup plugin running every day. Keeping your plugin list small makes your website easier to manage, faster to update, and less likely to develop compatibility problems. An SEO Plugin Recommended: Rank Math SEO or Yoast SEO Every website needs an SEO plugin. Without one, you miss important features such as: XML sitemaps Meta titles and descriptions Search engine indexing controls Schema markup Social sharing previews For beginners, Rank Math's free version includes a generous feature set, while Yoast SEO remains one of the most established and beginner friendly alternatives. Either option works well. The important rule i

If you've ever delayed updating your WordPress plugins or themes because you were afraid something might break, you're not alone. Many beginners avoid updates for weeks or even months because they've heard horror stories about websites crashing after a single click. Others do the exact opposite. They click "Update All" without preparing, then panic when their homepage displays an error or their layout suddenly changes. The good news is that updating WordPress doesn't have to be risky. With a simple maintenance routine, you can keep your website secure, stable, and running smoothly without the fear of losing your content. In this guide, you'll learn how to create reliable backups, update safely, test your website after every change, and recover quickly if something goes wrong. Why You Should Never Ignore WordPress Updates Updates exist for a reason. Plugin developers, theme creators, and the WordPress core team regularly release updates to: Fix security vulnerabilities Patch software bugs Improve compatibility with newer versions of WordPress and PHP Add useful features Improve website performance Running outdated plugins or themes leaves your website exposed to known security issues. In many cases, attackers specifically target websites that haven't been updated. At the same time, installing every available update without preparation isn't the answer either. A single incompatible plugin or poorly coded update can create conflicts that affect your website. The goal isn't to update everything as quickly as possible. The goal is to update carefully and confidently. A Safe WordPress Update Routine Follow this routine every time you update your website. Step 1: Create an Automatic Off-Site Backup Before changing anything, make sure you have a complete backup stored somewhere other than your web hosting account. If your hosting server experiences problems, a backup stored on the same server may not help. Instead, configure automatic backups to services such as: Google Dri

Introduction React is fast, but that doesn't mean every React application is. One of the most common performance problems—especially in growing applications—is unnecessary re-rendering . A small project with a few components may feel instant, but as your application grows, unnecessary renders can cause sluggish interfaces, input lag, excessive CPU usage, and poor user experience. The good news is that unnecessary re-renders are usually preventable once you understand why React re-renders components . In this article, we'll explore how React rendering works, learn how to identify performance bottlenecks, and apply practical optimization techniques such as React.memo , useMemo , useCallback , better state management, and component architecture. Whether you're building dashboards, e-commerce stores, SaaS products, or portfolio websites, these techniques will help you write more efficient React applications. Table of Contents Understanding React Rendering What Causes Unnecessary Re-renders? Identifying Performance Problems Optimizing with React.memo Optimizing Expensive Calculations with useMemo Preventing Function Recreation with useCallback State Colocation Splitting Components Optimizing Context Rendering Large Lists Using the React Profiler Best Practices Common Mistakes Performance Tips Security Considerations Accessibility Considerations SEO Considerations Real Project Example Conclusion Discussion Background Before optimizing anything, it's important to understand what React actually does. A render simply means React executes your component function to determine what the UI should look like. That does not always mean the browser updates the DOM . React compares the new Virtual DOM with the previous one and only updates the parts that actually changed. However, if many components re-render unnecessarily, React still has to: Execute component functions Recreate objects Recreate arrays Recreate event handlers Compare Virtual DOM trees All of that work adds up. Step

Building My First AI Registration Chatbot Using Python Introduction As part of my internship, I developed an AI Registration Chatbot using Python. The main goal of this project was to create a chatbot that interacts with users, collects their registration details, validates the information, and confirms successful registration. This project helped me understand the basics of chatbot development and improve my Python programming skills. Project Objective The objective of this project was to automate the registration process through a simple conversational chatbot. Instead of filling out a traditional form, users can provide their details by interacting with the chatbot. Features Greets the user with a friendly message. Collects user information such as name, email, and phone number. Validates user input. Handles invalid entries by asking the user to enter the information again. Displays a registration confirmation message after successful completion. Technologies Used Python Git GitHub What I Learned During this project, I learned: Python programming fundamentals Functions and conditional statements User input validation Basic chatbot logic Version control using Git and GitHub Challenges One of the main challenges was validating user inputs correctly and ensuring the chatbot handled different types of responses without errors. Testing multiple scenarios helped improve the chatbot's reliability. Conclusion Building this AI Registration Chatbot was a valuable learning experience. It strengthened my programming skills and gave me practical experience in creating a simple AI-based application. This project has motivated me to continue learning and build more advanced chatbot and AI projects in the future. GitHub Repository https://github.com/kamdipragati565-creator/AI_registration_chatbot

When an AI agent runs a multi-step pipeline, every step creates temporary files. Article drafts, image uploads, JSON payloads, log files. Over fifty runs, these files accumulate. Some get cleaned up, some don't. And the ones that don't cause the next run to fail in confusing ways. I hit this exact problem with my publishing pipeline. A failed cleanup from run #12 left a stale devto_article.json in the working directory. Run #13 picked it up, parsed it, and published a draft with last week's title. The logs showed "JSON loaded successfully" — which was technically true. The file was valid JSON. It just belonged to the wrong run. The fix was a Guard class that sits between the pipeline and the filesystem. Every file the pipeline creates must be registered before the pipeline starts. Any file that appears without registration halts the pipeline immediately. Run identity gets embedded into every file, so even if a cleanup fails, the next run can tell the file doesn't belong. The Problem With Temp Files Temp files are invisible by design. You create them, use them, delete them. But when deletion fails — file lock, process crash, permission error — the file becomes a ghost. It exists on disk but nobody remembers it's there. The next run scans the directory, finds the ghost, and treats it as intentional. This is especially dangerous for JSON files because they're always valid. A stale manifest.json looks identical to a fresh one. The only difference is the content, and the loader doesn't check content provenance. Here's a concrete example from my pipeline: # The naive approach — just check if the file exists def load_manifest ( path ): if not path . exists (): return None return json . loads ( path . read_text ()) This code returns valid data from any run, any day, any context. It answers "can I read this file?" but not "should I read this file?" That distinction is the entire bug. The Guard Pattern The Guard class solves this by requiring every temp file to be registered

Angular is well served for icons. Material Symbols alone runs to thousands of glyphs, and the community wrappers pull in dozens of other sets on top. Geography is where the shelf runs out. You get globes and map pins, sometimes a set of flags, rarely the outline of Japan and almost never the six states of the GCC. GeoIcons ships country and area icons as Angular standalone components, 422 of them at the time of writing. Adding one takes three steps: install the package, import the component by its ISO code, and render its selector. npm i @geoicons/angular import { Component } from ' @angular/core ' ; import { Us } from ' @geoicons/angular/countries ' ; @ Component ({ selector : ' app-root ' , imports : [ Us ], template : `<geoicon-us aria-label="United States" />` , }) export class App {} See it live on geoicons.io → That is the whole path. Below: inputs, styling, accessibility, and picking an icon when you only know the country at runtime. Key takeaways Install @geoicons/angular , import each country by its ISO 3166 alpha-2 code in PascalCase, and add it to the component's imports array. The class is Us ; the selector you write in the template is <geoicon-us /> . Styling props are explicit inputs, because Angular has no rest spread. Everything else goes on the host element. Icons render as decorative unless you name them, so reach for aria-label only where no adjacent text says the country. Step 1: Install the Angular package Add the package to your project: npm i @geoicons/angular It needs @angular/core and @angular/common 15.1 or newer, plus rxjs 7 or newer. That 15.1 floor comes from hostDirectives , which the icons use to share their styling inputs and which landed in that release alongside standalone components . The package sets "sideEffects": false , so the CLI can drop what you never import. List three icons in a component and you ship three. Why that matters for icon libraries . Step 2: Import by ISO code Every country ships as a named export under its ISO

Coding agents usually do not drift because they are incapable. They drift because the task leaves too much room for interpretation. A request like “clean up authentication” sounds clear to a human who already knows the codebase. To an agent, it can mean anything from renaming one helper to replacing the entire authentication stack. The fix is not a longer prompt. It is a brief with four explicit parts : Outcome Context Guardrails Definition of Done Below is the exact structure I use. 1. State the outcome as an observable change Describe what should be different for the user or system when the work is complete. Weak: Fix the login bug. Better: When a user submits an expired magic link, show the existing “Link expired” message and offer a button that requests a new link without leaving the page. The better version gives the agent a destination. It does not prescribe the implementation, but it makes success testable. 2. Give only the context that changes the decision Context is useful when it removes ambiguity. It becomes noise when it is a tour of the whole repository. Useful context often includes: The relevant entry point or route The existing component or service that should be reused A similar implementation elsewhere in the codebase The command used to run the relevant tests A known constraint, such as backwards compatibility Example: The page is implemented in app/auth/verify/page.tsx . Reuse requestMagicLink() from lib/auth/client.ts . The existing error-message styles live in components/auth/AuthNotice.tsx . That is enough to start investigating without pretending we already know the final patch. 3. Add guardrails that define the change boundary Guardrails prevent a small task from becoming an accidental rewrite. A useful set might be: Do not change the public API. Do not add dependencies. Keep the current visual design. Do not edit generated files. Limit changes to the authentication flow and its tests. If a database migration appears necessary, stop and expl

If you've tried building a Chrome extension recently, you've probably hit Manifest V3 and spent an hour just figuring out why your background page stopped working. MV3 replaced background pages with service workers, changed how content scripts communicate, and made permissions stricter. The official docs are... not great. So here's the structure that actually works. The folder structure chrome-extension/ ├── manifest.json ├── popup/ │ ├── popup.html │ ├── popup.css │ └── popup.js ├── options/ │ ├── options.html │ └── options.js ├── content/ │ └── content.js ├── background/ │ └── service-worker.js ├── utils/ │ └── storage.js └── icons/ The manifest.json (MV3) The biggest MV3 gotcha: background scripts are now service workers. { "manifest_version": 3, "name": "Your Extension", "version": "1.0.0", "permissions": ["storage", "activeTab", "scripting"], "action": { "default_popup": "popup/popup.html" }, "background": { "service_worker": "background/service-worker.js" }, "content_scripts": [ { "matches": [""], "js": ["content/content.js"] } ] } Communicating between popup and content script This trips up almost everyone. The popup can't directly access the page DOM — it has to message the content script. // popup.js const [tab] = await chrome.tabs.query({ active: true, currentWindow: true }); await chrome.tabs.sendMessage(tab.id, { type: 'RUN_ACTION' }); // content.js chrome.runtime.onMessage.addListener((message, sender, sendResponse) => { if (message.type === 'RUN_ACTION') { // do something on the page sendResponse({ success: true }); } return true; // keeps the channel open for async response }); The return true at the end is critical — without it, async responses silently fail. Storage that syncs across devices Use chrome.storage.sync instead of localStorage. Here's a utility wrapper that makes it clean to use anywhere: const Storage = { async get(key) { return new Promise((resolve) => { chrome.storage.sync.get([key], (result) => resolve(result[key])); }); }, async set