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Apple Intelligence, Copilot, Gemini. It feels like we're heading toward one AI layer underneath everything rather than 5 different subscriptions. do standalone AI tools actually survive that or do they just get absorbed and bundled into bigger more powerful systems? like does having everything in one place make AI more effective or does it just make it more generic? submitted by /u/aiprotivity_ [link] [留言]

submitted by /u/superdouradas [link] [留言]

Snap one photo, get listing-ready shots from every angle Discussion | Link

This past decade saw the emergence of the acronym FAANG — Facebook (now Meta), Amazon, Apple, Netflix and Google (now Alphabet) — as shorthand for tech stocks that outperformed the market. But the tech landscape is on the brink of a major shift with the rise of a new AI-centric powerhouse group known as MANGOS: Meta, Anthropic, Nvidia, Google, OpenAI and SpaceX. The new acronym has quickly gone viral on social media, according to TechCrunch, which also notes that "FAANG is not exactly dead." submitted by /u/LinkedInNews [link] [留言]

Not a hypothetical. This is the default state of most autonomous agents running in production right now. An attacker doesn’t send one suspicious message. They have a conversation. Turn 1 looks like curiosity. Turn 3 looks like clarification. Turn 6 is the pivot. Turn 8 is the payload, and by then the agent has been so thoroughly primed that it executes without hesitation. No single message triggered anything. The attack lived in the trajectory. Every prompt injection defense I know of evaluates messages one at a time. They have no memory of what came before. By the time turn 8 arrives, the context has already been poisoned across 7 clean-looking turns and nothing fires. This isn’t a theoretical attack. It’s called a Crescendo attack and it works against agents with real tool access right now. Built Bendex Arc to catch it. It tracks behavioral trajectory across the full session. When a conversation starts drifting adversarially, it catches the pattern before the payload lands. If you’re running agents that touch external data, read emails, browse websites, or call tools without human review — this is the attack you should be thinking about. Red team it yourself: https://web-production-6e47f.up.railway.app/demo Free tier: https://bendexgeometry.com GitHub: https://github.com/9hannahnine-jpg/arc-gate submitted by /u/Turbulent-Tap6723 [link] [留言]

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Studies and vendor-reported benchmarks suggest that AI-powered growth systems can compress...

AI coding tools are powerful. But they’re also wasteful. A tiny helper-function question does not need Claude Sonnet. A huge architecture review probably does. That gap costs money. So I built Badgr Auto. It’s a local OpenAI-compatible proxy that routes each AI coding request to the cheapest model that can handle it. Point your coding tool at: http://localhost:8787/v1 Badgr Auto can route between: local models cheaper OSS cloud models premium models So instead of paying premium prices for every request, you can use: local for small tasks OSS cloud for normal coding work premium only when it actually matters It also tracks: actual cloud spend which route was used fallback events tokens safely removed estimated savings vs premium models The goal is simple: stop wasting premium tokens on cheap tasks. First launch is small: small task → local normal task → cheaper cloud hard task → premium provider fails → fallback duplicate code → safely removed receipts → clear spend trail AI coding is only going to get more expensive if every agent step goes to the top model. Badgr Auto is my attempt to make AI coding cheaper without making it worse.

Local-first agent trust console with a safe local workspace Discussion | Link

This is a submission for the June Solstice Game Jam What I Built For this game jam, I...

I independently shipped my first open-source repo this week. The tool I built was a cli which accesses quickbooks online data. While Claude Code did speed up the build, it still took considerable effort shaping the entire user experience for the cli around the pre-existing public APIs! Major learnings during the entire process. Would also love additional feedback from open-source developers here.I'm currently looking for feedback from experienced open-source developers: Are there any improvements you'd suggest around project structure, documentation, testing, or contributor onboarding or the tool functionality? https://github.com/intuit/intuit-cli-for-quickbooks #

Something changed in software engineering, and I do not think we have fully named it yet. For years, the job was mostly about writing code directly. Then autocomplete got better. Then chat-based coding assistants arrived. Now the workflow is shifting again: we describe goals, hand off chunks of work to agents, inspect their output, tighten the tests, and decide what gets merged. That is not the same job with a faster keyboard. It is a different shape of work. I would call it agentic engineering. The engineer is becoming a director Agentic engineering does not mean the engineer disappears. If anything, it makes the engineer's judgment more visible. A coding agent can read files, make changes, run commands, open pull requests, and iterate through errors. GitHub describes Copilot agent mode as a workflow where the agent can plan, edit, run terminal commands, and keep working until a task is complete. Google describes Jules as an asynchronous coding agent that can take a task, work in a virtual machine, and produce a pull request. Anthropic's Claude Code guidance talks openly about using multiple Claude sessions in parallel, giving agents clear context, and treating them like workers that need direction. That is the shift. The engineer is no longer only the person typing every line. The engineer is also the person deciding what should be built, what constraints matter, how to verify the result, and when the agent is wrong. Prompting is too small a word for this People often describe this work as prompting, but that undersells it. A prompt can be a single instruction. Agentic engineering is more like delegation. You define the task, provide the relevant context, set the boundaries, create checks, review the work, and decide the next move. If the agent goes in the wrong direction, the failure is not always the model's fault. Sometimes the task was too vague. Sometimes the repository had no tests. Sometimes the acceptance criteria lived only in someone's head. This is why

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

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

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]