标签：#ev

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

If you refresh a page quickly, sometimes the new AudioContext will be faulty, it won't pick up any audio and throws no errors. Fix is to create and immediately close a throwaway context before creating your real one: await new AudioContext().close(); const ctx = new AudioContext(); submitted by /u/GramDanD [link] [留言]

In your opinion what would you say is the quintessential CRUD app? If you were to make a sample demo app utilizing and comparing the different tech stack (eg Laravel vs Django vs Spring Boot etc), which CRUD app would be the best? Thanks submitted by /u/Temporary_Practice_2 [link] [留言]

Making a personal website and trying to showcase projects I've worked on and trying to make it filterable. But I can not for the life of me figure out how the javascript part works? W3schools has been amazing and digestible up until this point. Using this as my base but what do I replace in the java part to make it filterable???? https://preview.redd.it/a35guno7yt3h1.png?width=1990&format=png&auto=webp&s=169b5610856995b84e1cee0881dac68dbc8be32d submitted by /u/ToxicGhostJuice [link] [留言]

The beleaguered luxury phone maker is pushing the AlphaFold, which has decent specs and comes with Vertu’s new Hermes Agent on board, to wealthy would-be buyers.

The conversation about cognitive debt in AI-assisted development has been framed as a tradeoff: you can go fast, or you can understand your system, but not both. The proposed mitigations — pair programming, code reviews, requiring a human to understand each change — are braking mechanisms. They trade speed for comprehension. TRIZ (Theory of Inventive Problem Solving) says braking is a compromise, not a resolution. A resolved contradiction eliminates the conflict. You don't choose between speed and understanding. You restructure the system so they don't conflict. There are six root causes of cognitive debt in AI-augmented development. Each one is a contradiction. Each one has a TRIZ resolution that doesn't involve slowing down. Root Cause 1: The Velocity-Comprehension Gap AI generates complex logic in seconds that would take a human hours to write. The human never spends the time typing the code during creation. The theory of the program is never fully formed. The Contradiction Technical contradiction: Improving development speed (AI generates code faster) worsens depth of understanding (human doesn't internalize the logic). Physical contradiction: The development process must be simultaneously FAST (to capture AI's productivity gains) and SLOW (to allow human assimilation of the system's behavior). Resolution: Separation in Space (Principle 2 — Extraction + Principle 1 — Segmentation) The contradiction assumes that the thing being understood IS the code. Extract the understanding target from the code and put it somewhere else — a smaller, slower-moving, human-readable artifact that captures what the code must satisfy, not how it works. Segment the system's theory into independent, composable units. Each unit is one property: "this service must never accept unauthenticated requests," "this data pipeline must preserve ordering," "this retry loop must terminate within 30 seconds." Each property is 1-3 sentences in natural language or 3-10 lines in a predicate language.

I’m a big fan of PostgreSQL and recently started thinking about building a new database system as a side project. I know creating a database is a huge challenge, and I’m still learning, but I really like the idea. If a few passionate people team up together, I believe we can build something interesting. I’d love to know: What struggles do you face with current databases? What features are missing in existing systems? What problems do you wish databases solved better? Performance? Scaling? RAM usage? Replication? Developer experience? Your feedback and ideas would really help. Or shorter and more Reddit-style: Title What problems do you face with existing databases? Content I’m thinking about creating a new database project because I really enjoy working with PostgreSQL. I know it’s a huge challenge, but I like the idea and want to learn deeply by building something. If a few passionate developers team up, maybe we can create something interesting. What problems or frustrations do you currently face with databases? What would you improve if you could design your own database? I’m deeply interested in backend architecture, distributed systems, and database engineering. Previously, I built a backend framework in Go called KVolt, which gave me strong experience in scalability, API architecture, performance optimization, and system design. Recently, I started exploring a bigger challenge: I’m a big fan of PostgreSQL, but I believe there’s still room for innovation in areas like: Performance Scalability Distributed systems Replication Memory optimization Developer experience Modern architecture design I know database engineering is one of the hardest areas in software development, and that’s exactly why it interests me. I’m also looking for a few passionate developers, backend engineers, and system-level programmers who genuinely enjoy solving hard engineering problems and would like to team up on this open-source journey. I’d love to hear from the community: What struggl

I was playing around with antigravity/claude last weekend and put together a 2026 rendition of a project I did almost 15 years ago. Basically it's an app that lets you watch videos with friends. Back in the day it was a great way to share and discover music, especially stuff you normally wouldn't listen to. I'm not quite sure it would work in today's age but maybe there's something there. Either way it's pretty awesome that you can prototype something so quick by just chatting with an LLM. submitted by /u/Zenpher [link] [留言]

give some suggestion in that my company two partner and one partner has no knowledge of development and he will do the busineess related things submitted by /u/Party-Tension-2053 [link] [留言]

GitHub Finish-Up-A-Thon Challenge Submission There’s something strangely emotional about reopening an old unfinished game project. Especially one that once felt like “the next big idea” at 2 AM during a hackathon 😭 You open the folder expecting nostalgia… …and instead find: broken UI random commits duplicated code missing assets unfinished features and functions named things like test2_final_REAL.js That’s exactly what happened when I reopened WhatUsee . A multiplayer browser game I originally started building as a fun experimental idea. At first, it wasn’t meant to become anything serious. It was just a simple concept: “What if players had to race against each other to identify hidden objects inside chaotic images?” That tiny idea slowly turned into a real-time multiplayer hidden-object game. And honestly? At the beginning, building it was insanely fun. 💡 The Original Idea Behind WhatUsee Most multiplayer browser games focus on: shooting drawing trivia racing But I wanted something different. Something that created those chaotic: “WAIT I SEE IT—NO WAY 😭” moments. The idea was simple: Players join a room together. An image appears. Somewhere inside that image is: a hidden object an animal a logo a random item or something cleverly camouflaged And everyone races to identify it before the timer ends. Fast reactions. Visual focus. Pure multiplayer chaos. That became WhatUsee . At first, the project was extremely small. Just: Socket.IO basic image display simple guessing and a rough scoreboard No polish. No proper lobby. No smooth UI. But even in that early state… …the game already felt fun. And that’s what made me continue building it. 😭 Then The Project Slowly Got Abandoned Old unfinished WhatUsee multiplayer game interface with basic UI and minimal styling Like most side projects… life happened. College work. Burnout. Other responsibilities. Random unfinished ideas. And slowly, WhatUsee became: “that project I’ll definitely finish later.” The game technically worked.

When you build a PowerApps Component Framework (PCF) component that will be used across multiple geographies, need to serve labels, button captions, validation messages, and tooltips in the user's preferred language. PCF has a built-in answer based on .resx resource files, the same format used by .NET applications. The mechanism is elegant in production — but surprisingly tricky during local development. This walkthrough takes you through the full setup, step by step, and then explains a problem that arises while locally debugging your PCF. Step 1 — Create the strings folder and your first .resx file PCF expects your localized strings to live in a folder (the conventional name is strings ) inside your component directory. Each language gets its own file, named with the pattern: <ComponentName>.<LCID>.resx The <LCID> part is the numeric Locale ID , not the textual code ( en-US , it-IT ). The framework relies on this naming convention to identify which file to load for a given user. Common LCIDs: Language LCID English (en-US) 1033 Italian (it-IT) 1040 German (de-DE) 1031 French (fr-FR) 1036 Spanish (es-ES) 3082 Japanese (ja-JP) 1041 Chinese Simplified (zh-CN) 2052 Portuguese (pt-BR) 1046 For a component called EquipmentGrid , the structure looks like this: EquipmentGrid/ ├── ControlManifest.Input.xml ├── index.ts └── strings/ ├── EquipmentGrid.1033.resx ├── EquipmentGrid.1040.resx └── EquipmentGrid.1031.resx Tip: Always include 1033.resx (English). The PCF runtime falls back to the first <resx> declared in the manifest when the user's preferred language isn't available, and English is the safest default. Step 2 — Author the resource file content A .resx file is just XML. Here's a minimal Italian version ( EquipmentGrid.1040.resx ): <?xml version="1.0" encoding="utf-8"?> <root> <resheader name= "resmimetype" > <value> text/microsoft-resx </value> </resheader> <resheader name= "version" > <value> 2.0 </value> </resheader> <resheader name= "reader" > <value> System.Resou

We let AI agents loose on a payment platform. They crushed the boring stuff. Then they silently broke the stuff that matters. A survey came out last week. 54% of all code is now AI-generated. Up from 28% last year. I read that number and thought: yeah, that tracks. We're probably in that range too. But here's the thing nobody's asking — which 54%? Not all code carries equal weight. A CRUD endpoint for fetching merchant details? Low risk. The webhook handler that transitions a payment from pending to complete ? That's someone's rent. Someone's payroll. Get that wrong and money moves where it shouldn't, or worse, money doesn't move at all. I'm the CTO of a payment platform. FCA-authorised, processing real money, real merchants, real consequences. We run NestJS microservices, Docker, Traefik — the usual stack. And we've been using AI agents aggressively for over a year now. I'm not here to tell you AI is dangerous. It's not. I'm here to tell you it's dangerous when you forget what it's actually good at. The 80% Where AI Agents Are Genuinely Brilliant Let me give credit where it's due. AI agents have made our team faster in ways that would have seemed absurd two years ago. API scaffolding. Generating service boilerplate. Writing Zod validation schemas. Spinning up new endpoints. Creating test stubs. Refactoring imports. Migrating patterns across repos. We run multiple microservices. When we need a new service, an agent can scaffold the entire thing — module structure, base configuration, Docker setup, Traefik labels — in minutes. What used to be a half-day of copy-paste-and-tweak is now a conversation. When we overhauled our env management across all repos, AI agents did the grunt work. They mapped every .env file, found naming conflicts, identified common variables, and generated a unified Zod schema. What would have taken a team days of grep-and-spreadsheet work took hours. For this 80% of the codebase — the predictable, pattern-following, structurally repetitive code

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built Photremium is an all-in-one, lightning-fast web utility platform engineered for high-performance image processing. Built to eliminate the friction of clunky, ad-heavy design tools, it provides users with instantaneous, client-side and serverless tools like high-fidelity background removal, image resizing, custom QR code generation and many more. As a software engineering student, this project represents my vision of creating a modern production platform that prioritizes raw speed, high usability, and robust SEO architectural patterns. Live Platform: photremium.com GitHub Repository: itsaminaziz/photremium.com Demo The Live Application Experience the full toolset live right now at photremium.com . Key Features in Action Feature Implementation Speed / Processing Compress IMAGE Client-side Canvas / Web Workers Instantaneous local compression Resize IMAGE Client-side React & HTML5 Canvas Real-time pixel/percent adjustment Crop IMAGE Client-side UI & Visual Crop Editor Instantaneous browser-based cropping Convert to JPG Client-side File Readers (Bulk Upload) Instant batch conversion via browser Convert from JPG Client-side Canvas (PNG/GIF compiler) Multi-format local generation QR Code Generator Vector-based SVG/Canvas rendering Instant download generation QR Code Scanner Client-side WebRTC Camera / File API Real-time local camera processing Blur Face Hybrid Client-side Face Detection Instant local privacy overlay mapping Remove Background (AI) Cloud-based Serverless / Cloudflare Edge < 2 seconds (Any device image processing) Watermark IMAGE Client-side Layer Composition Instantaneous text/graphic stamping The Comeback Story The Before (A Half-Baked Local App) Photremium started as an ambitious prototype on a local machine. While the fundamental image-processing utilities worked locally, the project hit a massive wall when it came to global deployment and production readiness. It was plagued with

The Problem We Were Actually Solving During load testing at 50k concurrent hunters hitting the hunt endpoints, p99 latencies stayed under 200ms. But at 270k concurrent users in production, the hunt page suddenly took 1.8 seconds to load, triggering cascading 502s from our CDN. The error surfaced in Datadog as hunt_page_render_time_bucket{le=2.0} = 42% while le=0.5 dropped to 18%. The fingerprints were identical across three regions: high latency correlated exactly with Redis cache miss rate spiking from 12% to 48% during the hunt start window. Our cache-aside pattern with a 30-second TTL was amplifying miss storms. We discovered that the treasure hunt start time was synchronised by marketing campaigns. When the clock struck 10:00:00 UTC, 270k users hit the endpoint within 30 seconds. Each request would check the cache (miss), fetch from PostgreSQL, render the page, and write the cache entry. But PostgreSQL couldnt keep up with 9k queries per second during that window, causing query queueing and connection exhaustion. The Redis layer, designed for 150k ops/sec, was not the bottleneck. The database was. What We Tried First (And Why It Failed) Our first attempt was to increase Redis TTL from 30 seconds to 5 minutes. This reduced cache misses from 48% to 24%, and p99 latency improved to 650ms. But at 320k concurrent users, the latency still spiked to 1.4s because the underlying database queries were still hitting the same table with the same indexes. The Redis layer was masking symptoms, not solving the root cause. Next, we tried database read replicas. We spun up three read replicas and routed hunt queries to them using a weighted service mesh. This worked for a few minutes, but then we hit replication lag. The replicas fell 800ms behind primary, causing hunt pages to display stale treasure locations. Our operators started getting customer complaints about seeing the wrong treasure coordinates. We rolled back within 15 minutes. We even tried increasing PostgreSQL share

Just starting a discussion on breakpoints. Not like this hasn't been beaten to death before :D (͡(¬‿¬)) Tailwind sm: 40rem - roughtly 640px xs: is commonly set to 30rem or 320px for the smallest iphone width folding phones might be a use case for even smaller viewports.. idk, i cnat afford one so mauybe that's not how they work? supposedly they can be 280px there's smart glasses. no idea. VR. no idea. windows on desktop, people like me get really annoyed when some content of a window goes off screen or it transforms into a mobile version of the site just because i shrunk the window when i wanted to keep using the desktop site. (oh you and your tiling window manager, lol) submitted by /u/xThomas [link] [留言]

Maybe I’m just getting older, but frontend tooling changes so fast now that sometimes it feels harder keeping up with the ecosystem than actually building products. Feels like every few months there’s a new framework, rendering strategy, state management pattern or deployment workflow people suddenly expect you to know. submitted by /u/Bladerunner_7_ [link] [留言]

I’m working on a school Design & Technology major project and need help finding a subreddit to choose the best tool. The project is a browser-based low-poly 3D educational prototype about Greek mythology. It has a small Mount Olympus hub which is basically a decorated hub area with the one portal to the one playable myth scene: Theseus and the Minotaur in Daedalus’ Labyrinth which is essentially like a maze where you can explore/follow the myth through some mediums like cutscenes and some small puzzles. My constraints are: needs to run in a browser should work on normal school laptops/devices low-poly/simple 3D style minimal coding experience must be realistic for a student prototype ideally easy to share with a link More context is that essentially I have my whole design planned out with multiple eventualities based on the constraints on the tool I end up using and so I essentially just need a medium to construct my project in which is why I have left it so late to ask for advice on what tool to use to construct this prototype. I am confident with my extensive designs and a decently functioning prototype of this one playable scene and hub area along with potential future designs that I will show the design process adequately for a band 6 hopefully. submitted by /u/Bigbrainmc [link] [留言]

A while ago I tried to build a local coding assistant. I downloaded Qwen3, fired it up on my MacBook with 16GB of RAM, and within a day realized the output quality was nowhere close to Claude or GPT-5. The model could fit . It just couldn't compete . So I changed the question. If I can't make the model smarter on my hardware, can I make what I feed it smarter? Where the tokens actually go I started watching where my Claude / Cursor / Copilot sessions actually spent their tokens. The surprise: most of it wasn't reasoning. It was lookup . Every fresh chat about my company's database re-discovered the same things: What does status = 3 mean? (cancelled) How does orders join to users ? ( orders.user_id → users.id ) What's that cryptic JobStatus enum? (a dozen integer codes nobody remembers) The model figured it out, the session ended, and tomorrow it figured it out again . Same tokens, same latency, every single time. The expensive part of working with an AI wasn't the thinking — it was re-teaching it things it had already learned yesterday. There's a lot of attention right now on trimming AI output tokens (talk like a caveman, strip the pleasantries, etc.). But in my workflow the bigger leak was on the input side: paying full token cost every session to re-establish context that never changed. "Memory" isn't a feature, it's an architecture question AI clients are starting to bolt on "memory" features. But they're proprietary, opaque, and locked to one tool. Claude's memory doesn't help Cursor. Cursor's doesn't help Copilot. You can't inspect it, you can't share it with a teammate, and you can't diff it. What I actually wanted was an explicit, inspectable, shareable context layer that any AI client could read deterministically — same answer every time, same file my team could hand off. I picked the highest re-learn cost in my world to start with: SQL databases. Enter amnesic amnesic is an open-source MCP server that gives any AI client persistent semantic memory of your

I opened v0, typed "a settings page with profile editing, notification preferences, and a connected accounts section," and watched it generate a fully functional three-tab settings interface in under 40 seconds. The component used shadcn/ui primitives, Tailwind utility classes, and TypeScript types — the exact stack I would have chosen if I had written it from scratch. I copied the code, pasted it into my Next.js project, changed two import paths, and it rendered correctly on the first try. This is the v0 value proposition distilled: generate UI components that look like a senior frontend developer wrote them, then paste them into your real project without rewriting half the output. After generating 15 components across two weeks of real product work, I can confirm that v0 delivers on this promise more reliably than any general-purpose AI coding tool I have tested. But its scope is narrower than the marketing suggests, and understanding where v0 stops being useful is as important as knowing where it excels. The shadcn/ui Advantage v0 is built on top of shadcn/ui, and this is the single most important fact about how it works. shadcn/ui is not a component library in the traditional sense — it is a collection of copy-pasteable React components built on Radix UI primitives with Tailwind styling. When v0 generates a component, it uses these primitives directly, which means the output is consistent, accessible, and composable. The practical benefit is that v0-generated components integrate with your existing project without introducing a new design system. If you already use shadcn/ui — and a large and growing percentage of Next.js projects do — the generated components reuse your existing Button, Card, Dialog, and Input primitives. v0 just assumes you have them installed and generates code that expects them. If you do not have shadcn/ui in your project, v0 prompts you to run the initialization command before generating anything, which takes about 30 seconds. This archite

I tested GitHub Copilot Workspace on 12 real tasks across three repositories in May 2026 — a mix of bug fixes, feature additions, and documentation updates. My goal was to figure out whether the spec-first, browser-based workflow actually produces useful code, or whether it is a demo that falls apart when you ask it to do real work. The answer sits somewhere between impressive and frustrating, with the tool's success rate varying dramatically based on task size, repository maturity, and how well you write the initial specification. The Spec-First Workflow Forces Better Communication Copilot Workspace changes the AI coding interaction in one fundamental way that I have not seen elsewhere: you do not start with code, you start with a specification. When I opened Workspace on a Next.js project and typed "add rate limiting to the API routes using the existing rate-limit.ts utility," the system did not immediately generate code. It spent roughly 15 seconds reading my repository, then produced a three-step implementation plan: (1) import the rate-limit utility in each route, (2) wrap the route handler with the rate limiter, (3) add a test for the rate-limited behavior. I could approve the plan as-is, reject individual steps, or add revision notes before any code was written. On this particular task, the plan was correct and I approved it. Workspace then executed each step, modified five route files, and produced a draft pull request with a clear description and a summary of what changed. The entire process — from typing the specification to having a reviewable PR — took 4 minutes and 12 seconds. This planning phase is not window dressing. On a different task where I asked Workspace to "add WebSocket support to the chat feature," it read the repository and surfaced during planning that the project was deployed on Vercel's serverless functions, which do not support persistent WebSocket connections. It suggested using Vercel's Edge Functions with a third-party real-time serv