This is an updated rewrite of my 2021 article on protected routes . A lot has changed in the React ecosystem since then. React Router moved from v5 to v7, class components have faded out, and the patterns we use for authentication state have matured. This version reflects how protected routes are built in modern React applications. Almost every web application requires some form of authentication to prevent unauthorized users from accessing parts of the application meant for signed-in users only. In this tutorial, I'll show how to set up an authentication flow and protect routes from unauthorized access using modern React patterns: function components, hooks, React Router v6+, and the Context API. First things first Install the dependency: npm i react-router-dom That's it. React Router v6 and above ships as a single package, so you no longer need to install react-router and react-router-dom separately. It is worthy of note that we will not be using Redux for authentication state in this version. For something as simple as "is the user logged in?", React's built-in Context API is the standard approach today. Redux still has its place, but it is overkill here. The Auth Context Instead of writing to localStorage directly from components and reading it in random places, we centralize authentication state in a context. This gives us a single source of truth and a clean useAuth() hook we can call anywhere in the app. Create ./src/auth/AuthContext.jsx : import { createContext , useContext , useState } from " react " ; const AuthContext = createContext ( null ); export function AuthProvider ({ children }) { const [ user , setUser ] = useState (() => { // Rehydrate on page refresh const saved = localStorage . getItem ( " user " ); return saved ? JSON . parse ( saved ) : null ; }); const login = async ( username , password ) => { // In a real app, this is an API call to your backend. // We simulate it here with hardcoded credentials. if ( username . toLowerCase () === " admin

Target: pnpm/pnpm Issue: pnpm/pnpm#12240 PR: pnpm/pnpm#12301 Public branch: https://github.com/scarab-systems/pnpm/tree/fix/deps-status-no-manifest Latest pushed commit: cb68ac1af0dcffbe4fb607a10b0df2046d2490ba This field test targeted a pnpm command-routing failure where pnpm self-upgrade could fail outside a project directory with: ERR_PNPM_NO_PKG_MANIFEST The issue looked simple at the surface: a global/self command should not require a project manifest just because the current working directory is not inside a package. But the repair boundary was more specific than “ignore missing manifest.” The problem was in the dependency-status verification path. When dependency status was unavailable because there was no project manifest, the command could fall through into the auto-install path. That made a self-upgrade/global-style command behave as if it needed a local project manifest. Failure shape The failing behavior was: pnpm self-upgrade run outside a project directory dependency status cannot be established from a project manifest the command path falls into install/manifest expectations result: ERR_PNPM_NO_PKG_MANIFEST That is the wrong ownership boundary. A self-upgrade command should not inherit project-manifest preconditions when there is no local project context. Boundary The boundary here is: global/self command execution versus project dependency-status verification Dependency-status verification can be useful when a command is operating inside a project. But when there is no project manifest and the command is not recursive/all-projects, “dependency status unavailable” should not automatically mean “try to auto-install project dependencies.” There are two different cases: Dependency status is unavailable because there is no project manifest. Dependency status is unexpectedly unavailable even though a root project manifest exists. Those cases should not behave the same. The repair preserves that distinction. What changed The patch updates: exec/commands/src

Have you ever stared at two different medicine boxes, squinting at the tiny font of the active ingredients, wondering: "Can I actually take these together?" Modern healthcare is complex, and drug-drug interactions (DDI) are a leading cause of avoidable ER visits. In this tutorial, we’re going to leverage GPT-4o Vision , React Native , and the FDA OpenData API to build a "Drug Conflict Scanner." We will utilize multimodal AI to transform messy pill-box photos into structured data and cross-reference them against official medical databases for safety. By the end of this guide, you'll master GPT-4o OCR structuring and automated knowledge graph verification for real-world health tech applications. 🚀 The Architecture 🏗️ The logic flow involves capturing images of multiple medicine labels, using GPT-4o's multimodal capabilities to extract chemical compounds, and then querying the FDA's database for potential interactions. graph TD A[React Native App] -->|Capture Multi-Photo| B[Node.js Backend] B -->|Image Buffer| C[GPT-4o Vision API] C -->|Structured JSON: Ingredients| B B -->|Search Interactions| D[FDA OpenData API] D -->|Drug Labels & Warnings| B B -->|Safety Report| A A -->|UI Alert| E{Safe or Warning?} Prerequisites 🛠️ To follow along, you'll need: GPT-4o API Key (via OpenAI) Node.js (for our backend relay) React Native (Expo is recommended for camera access) An account at open.fda.gov (though the public API works for limited requests) Step 1: Extracting Ingredients with GPT-4o Vision Traditional OCR struggles with curved medicine bottles and shiny packaging. GPT-4o excels here because it understands context. We don't just want text; we want the Generic Name of the drug. The Backend Logic (Node.js) // backend/scanner.js import OpenAI from " openai " ; const openai = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); async function analyzeMedicineLabels ( imageUrls ) { const response = await openai . chat . completions . create ({ model : " gpt-4o " , messages :

Migrated 4 of 7 Notion automations to an MCP server in one weekend Two workflows stayed in Notion because the database UI beat any tool call MCP scope rule: one tool does one verb, never a Swiss Army function Result: 12 manual steps collapsed into 3 Claude prompts per publish I spent a weekend pulling four automations out of Notion and rebuilding them as MCP tools. Three of them got faster and one got worse before it got better. The biggest lesson was not about code. It was about deciding which jobs should never leave Notion in the first place. Why I Moved Off Notion In The First Place My Notion setup was not broken. It was just slow in a specific way. I had seven automations stitched together with Notion buttons, formula properties, and two third-party connectors. Every blog publish meant clicking through four pages, copying a title here, pasting a tag list there, and triggering a sync that took 90 seconds to confirm. Multiply that by the 18 articles I push in a normal month and the clicking adds up. The breaking point was a Tuesday where I lost 40 minutes to a connector that silently stopped firing. No error, no log, just a row that never updated. I checked the connector dashboard and it told me everything was healthy. It was not healthy. That kind of invisible failure is the worst kind because you trust it until you do not. MCP changed the math for me. An MCP server lets Claude call my own functions directly. Instead of Claude writing text and me ferrying that text into Notion by hand, Claude can call a tool that does the writing into my systems. The model becomes the operator, not just the writer. If you want the deeper context on what MCP actually is and why it matters at scale, MCP: The 97 Million Agentic Foundation goes through the bigger picture. So I made a list. Seven automations, sorted by how much human judgment each one needed. The ones at the top were pure mechanical steps: format this, push that, fetch a status. The ones at the bottom needed me to loo

A SaaS interface that works well for US users may confuse or alienate users in China, Germany, or Japan. Cultural differences affect everything from layout preferences to color meanings, form field expectations, and payment preferences. This guide covers the cross-cultural UX principles applied at tanstackship.com , a SaaS serving users across English, German, and Chinese markets. Cultural Dimensions and UI Impact Hofstede's Dimensions Applied to SaaS Design Dimension High vs Low UI Impact Power Distance Acceptance of hierarchy Navigation depth, permission displays Individualism vs Collectivism Personal vs group focus Social proof, testimonials Uncertainty Avoidance Tolerance for ambiguity Error messages, help text density Long-term Orientation Pragmatic vs normative Pricing display, trial periods By Market UX Element US (Low PD, High IND) DE (Low PD, High UA) CN (High PD, High COL) Navigation Flat, exploratory Structured, organized Hierarchical, guided Error handling Forgiving, "try again" Specific, detailed Authority-mediated Social proof Individual testimonials Expert reviews + data Group endorsement Pricing Monthly preferred Annual preference (trust) Discount attraction Help/Support Self-service docs Detailed documentation Chat + personal support Color meanings Green=success Green=environment Red=prosperity Color and Visual Design Color Meanings Across Cultures Color Western (US/EU) China Germany Japan Red Danger, passion Prosperity, luck Financial (deficit) Life, energy Blue Trust, technology Trust, immortality Trust, stability Trust, calm Green Success, nature Health, harmony Environment Youth, energy White Purity, clean Mourning Clean, efficiency Purity Yellow Warning, caution Imperial, power Jealousy Courage Practical Application // Adapt color scheme based on locale const colorSchemes = { en : { primary : " #2563EB " , // Blue — trust success : " #16A34A " , // Green — success danger : " #DC2626 " , // Red — danger warning : " #F59E0B " , // Yellow — warnin

RubyConf holds a special place in my heart. It was the very first tech conference I attended after receiving a scholarship fresh out of Flatiron School back in 2017 (you can read about my experience here ), and then in 2021, it was the stage for my first conference talk in Denver. Now, in another first, I joined the Program Committee for RubyConf 2026 to help put the program together, and what a program it is! We have an absolutely amazing lineup this year, and I'm so excited to see it come to life! Who else is planning on attending? Let's make plans to meet up and say hi!

If you've ever spun up an EC2 instance for a side project, accessed a remote work desktop from your personal laptop, or stored files on Google Drive without thinking about the physical hard drive it lives on, you've used virtualization. As the foundational technology behind all modern cloud computing, virtualization transformed how we build, deploy, and manage IT infrastructure—cutting hardware costs significantly for enterprises and making on-demand scalability a reality for teams of all sizes. In this guide, we'll break down exactly what virtualization is, how it powers the cloud, the 6 core types of virtualization, and best practices to implement it safely and efficiently. Table of Contents What is Virtualization in Cloud Computing? Core Virtualization Concepts You Need to Know Role of Virtualization in Cloud Computing 6 Key Types of Virtualization (With Use Cases) Top Benefits of Virtualization for Teams of All Sizes Virtualization vs. Related Technologies Virtualization vs. Cloud Computing Virtualization vs. Containerization Common Virtualization Challenges and Mitigations Real-World Virtualization Use Cases Virtualization Best Practices Conclusion References What is Virtualization in Cloud Computing? Virtualization is a technology that creates virtual, software-based representations of physical hardware (servers, storage, networks, etc.) and abstracts these resources from the underlying physical machine. A software layer called a hypervisor separates operating systems and applications from physical hardware, allowing multiple isolated, self-contained systems called Virtual Machines (VMs) to run simultaneously on a single physical host. Each VM has its own virtual CPU, memory, storage, and network interface, and operates independently of other VMs on the same host. For cloud providers, this technology is the backbone of all on-demand infrastructure services, allowing them to share physical hardware across thousands of customers securely and efficiently. Core Vi

I think it's fair to say that someone who has never done non-AI web development will always be vibe-coding. For, say, an experienced (20+ years) developer, would it still be vibe coding if they craft technically sound prompts (i.e. explicitly mention things to avoid/include, and define methodologies and algorithms as well as goals), and fully test (and have AI fix) the output, but never review the actual code? What if the prompts are loose, but they are fastidious about reviewing all code generated? submitted by /u/lindymad [link] [留言]

GitHub shipped a small changelog entry this week that says more about the future of coding agents than most of the launch demos. Security validation for third-party coding agents is now generally available. Not just for GitHub's own Copilot cloud agent. For third-party agents too, including Claude and OpenAI Codex. The feature sounds boring in the best possible way. When an agent creates code, GitHub can run CodeQL, check new dependencies against the GitHub Advisory Database, and use secret scanning to detect tokens, API keys, and other sensitive material. If it finds a problem, the agent tries to fix it. That is not the flashy part of agentic coding. It is the important part. Because once agents are allowed to act inside repos, the question stops being "which model wrote this diff?" and becomes "can the repository apply the same policy to every automation actor?" authorship is the wrong abstraction We still talk about generated code as if authorship is the primary thing that matters. Was this written by Copilot? Claude? Codex? A human with tab completion? A human who pasted something from a chat window and cleaned it up? A junior engineer following a Stack Overflow answer from 2018? Those distinctions matter for procurement and product marketing. They matter less for the repository. The repository has a simpler problem: a change is trying to enter the system. It may introduce a vulnerability, add a risky dependency, leak a secret, violate an internal rule, or be perfectly fine. That is why the GitHub change is interesting. It moves the useful boundary from "our approved coding assistant" to "any coding agent operating in this repository." the agent is now an actor For years, repository automation was mostly boring and legible. CI ran tests. Dependabot opened dependency updates. Release bots bumped versions. Linters complained. Security scanners commented. Humans reviewed. The automation could be annoying, but its shape was predictable. Coding agents are different.

PostgreSQL Error 2201X: invalid row count in result offset clause PostgreSQL error code 2201X ( invalid_row_count_in_result_offset_clause ) is thrown when the value provided to an OFFSET clause is not a valid non-negative integer. This commonly surfaces in applications that implement pagination using dynamic queries or user-supplied parameters, where the offset value may be negative, NULL, or a non-integer type. Top 3 Causes 1. Negative OFFSET Value The most frequent cause is a miscalculated page offset. When computing (page - 1) * page_size , a bad page number can produce a negative result. -- Triggers error 2201X SELECT * FROM orders ORDER BY created_at DESC OFFSET - 5 LIMIT 20 ; -- ERROR: invalid row count in result offset clause -- Fix: Use GREATEST to clamp the value to 0 SELECT * FROM orders ORDER BY created_at DESC OFFSET GREATEST ( 0 , - 5 ) LIMIT 20 ; 2. NULL Passed as OFFSET When an application fails to initialize or bind the offset parameter, NULL gets passed to the query. This often happens with ORMs or query builders where optional parameters are not explicitly set. -- Triggers error 2201X SELECT * FROM products ORDER BY id OFFSET NULL LIMIT 10 ; -- ERROR: invalid row count in result offset clause -- Fix: Use COALESCE to provide a default value SELECT * FROM products ORDER BY id OFFSET COALESCE ( NULL , 0 ) LIMIT 10 ; -- Parameterized query with safe fallback SELECT * FROM products ORDER BY id OFFSET COALESCE ( $ 1 :: BIGINT , 0 ) LIMIT COALESCE ( $ 2 :: BIGINT , 10 ); 3. Non-Integer Type (Float or String) Passing a float or a string that cannot be cleanly cast to a whole number causes this error. This typically happens when Python float values or unvalidated user input strings are interpolated directly into a query. -- Triggers error 2201X SELECT * FROM employees ORDER BY last_name OFFSET 10 . 7 LIMIT 5 ; -- ERROR: invalid row count in result offset clause -- Fix: Use FLOOR and explicit cast to BIGINT SELECT * FROM employees ORDER BY last_name OFFSET F

I've been using pronounciation apps for a few weeks and decided to intentionally butcher some words just to see how strict the feedback was. not subtle mistakes either. I was fully committing to the wrong pronunciation. I've noticed that it still rated some of them as correct or nearly correct. now I'm wondering how much trust I should actually put into pronunciation scores in general. do these apps genuinely analyze pronunciation, or do they sometimes just check whether you're vaguely in the right area? submitted by /u/no-cherrtera [link] [留言]

See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.

I've been with Omnis for years (they were awesome) and they were bought out by JetHost. The transfer took down a bunch of clients. Now I'm arguing with them and they don't offer an online guarantee or a refund. I have to deal with clients whose websites were down. I'm really upset just because I have never had to worry about this. Anybody have cheap reliable registrars? My fave never showed in google, so I figured I'd axe the community if anyone has a good secret one in your pocket submitted by /u/dash-dash-hyphen [link] [留言]

Parents want one thing, and one thing only, out of AI: to add a list of soccer games or "spirit week" theme days from an email or a poorly formatted flyer onto their calendar in one shot. And I have good news for parents with iPhones - the new Siri can finally do this. After […]

Booking travel or paying for subscriptions or for running procurement through Claude or a custom GPT wrapper no confirm button is required anymore. The capability side is mostly solved. What doesn't get talked about enough is what happens when it goes sideways. A stored card sitting in the agent's context means it holds that access the whole session. One bad tool call and it's spending outside what you intended with nothing at the infrastructure level stopping it. Real time card issuance is the cleaner model. Agent requests a card for the specific transaction, purchase completes, card cancels and nothing persists. Who is running agent initiated payments in production right now and what does the architecture look like? submitted by /u/Significant-Plant-4 [link] [留言]

Instead of spending a year raising a formal venture fund, the Sabertooth VC founder used a captive network of LPs to invest in startups like Anthropic, Anduril, and SpaceX.