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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

The video game industry is in turmoil. Microsoft and Sony are starting to pivot to their next consoles, but it's not looking great: Prices are soaring, Sony is killing the video game disc, and Microsoft is jettisoning studios ahead of the transition. What could entice people to pay? On the Xbox front, we genuinely can't […]

Various interesting things about sorting data: defaults, performance and determinism (or lack thereof) of paging ;) submitted by /u/BinaryIgor [link] [留言]

Most test automation tools look good during a demo. You record a login flow, add an assertion, run it in Chrome, and get a green result. Everyone is impressed. Then the real application gets involved. There are dynamic elements, delayed API responses, test accounts, verification emails, downloaded files, several deployment environments, and a checkout flow that behaves differently on Safari. A few months later, the original test suite has grown from 10 tests to 300. Some failures are product bugs. Others are test problems. A few only happen in CI. Nobody is completely sure which is which. That is when you discover whether you selected a test automation tool or merely a good demo. Creating tests is rarely the main problem When teams compare automation tools, they often begin with questions such as: How quickly can we record a test? Can AI generate the steps? Does it support plain-English instructions? Can a manual tester use it? Does it integrate with our CI pipeline? These are reasonable questions, but they mostly describe the beginning of an automation project. The harder questions appear later: Who updates the tests after a redesign? How do we investigate failures? Can another person understand a test created six months ago? What happens when the original automation engineer leaves? Can we test workflows that involve email, APIs, files, or mobile devices? How much infrastructure do we have to manage? Does the cost increase every time we run the regression suite? The first test tells you whether the tool works. The hundredth test tells you whether the approach works. Maintenance should be part of the evaluation A stable automated test is not a test that never changes. Applications are supposed to change. Buttons move. Components are replaced. Authentication flows evolve. APIs return different data. Product teams redesign entire sections of the interface. The objective is not to prevent tests from changing. It is to make those changes inexpensive and understandable.

DeepSeek vs Qwen vs Kimi vs GLM: Which One Wins My Freelance Budget? Last Tuesday I spent two hours building a client dashboard that needed AI-powered text summarization. The client is a small e-commerce shop, they get maybe 500 product descriptions a week that need condensing into bullet points. Sounds simple, right? Except when I ran the numbers on my usual OpenAI setup, the bill was going to eat into my margin harder than I'd like. That's when I went down the rabbit hole of Chinese AI models. DeepSeek, Qwen, Kimi, GLM — I've been hearing about these for months from other devs in Discord, but I never actually committed to testing them because, honestly, who has the time? Well, apparently I do, because that Tuesday I decided to run all four head-to-head against my actual workload. Here's what happened. Why I Even Bothered (The Real Math) Before we get into the benchmarks and pricing tables, let me put this in perspective. My hourly rate as a freelance dev sits at $85. Every hour I spend wrestling with a subpar API that hallucinates or charges too much is an hour I'm not billing a client. The "free" model is never free — either it costs me time or it costs me money, and usually both. I was paying roughly $0.60 per 1M output tokens on GPT-4o for the summarization work. For 500 product descriptions, each averaging maybe 150 tokens output, that's about $0.045 per batch. Sounds tiny, right? But multiply that across multiple clients, and suddenly I'm watching $40-60 a month vanish into API costs that I can't really pass along without awkward pricing conversations. So I started shopping. And what I found genuinely surprised me. The Contenders at a Glance All four model families run through Global API's unified endpoint, which means I didn't have to maintain four different SDKs, four different auth setups, four different billing dashboards. Just swap the model name in the request and ship. For a one-person operation, that's huge. Here's the landscape I was working with: Di

Connecting an AI agent to a tool is becoming easier. Letting that agent operate a real business system responsibly is still a different problem. Imagine an existing commerce system with APIs for reading orders, changing inventory, creating refunds, and disabling staff accounts. OpenAPI can describe the endpoints. A tool protocol can make them discoverable. An agent framework can select an operation and generate arguments. But those pieces do not, by themselves, answer several business questions: Which operations may be exposed to an agent-facing surface? Which invocation must carry a trusted acting subject? Which operation is high consequence? When does an invocation express approval intent? Which calls need stronger audit handling? Which execution properties should a runtime know before it invokes the API? These questions sit between tool connectivity and final business authorization. That is the layer the Agent Capability Contract, or ACC, is designed to describe. Start with a concrete operation Consider this API operation: paths : /orders/{order_id}/refund : post : operationId : createRefund parameters : - in : path name : order_id required : true schema : type : string requestBody : required : true content : application/json : schema : type : object required : [ amount ] properties : amount : type : number minimum : 0 This is enough to describe how to call the operation. It is not enough to describe how an agent-facing system should treat it. ACC adds a small, machine-readable declaration next to the operation: x-agent-capability : version : 1 enabled : true scope : refund.create risk : level : high subject : required : true approval : required : true when : - param : amount op : " >" value : 1000 audit : sensitive : true execution : readonly : false idempotent : true timeout_ms : 10000 The declaration does not grant the refund. It tells a compatible runtime how the operation should be presented and governed before the business system receives the call. The miss

I've reviewed the launch post-mortems of dozens of indie devs who got hacked after shipping their first SaaS. The common thread isn't bad code. It's a checklist no one handed them before they clicked publish. This is that checklist. Eight items. Each one has: a curl command you can run right now, why the bug matters in plain English, and a concrete fix in under ten lines of code. If you can check all eight green before you launch, you've eliminated the majority of the low-hanging-fruit attack surface on a typical Next.js + Supabase MVP. You're not done with security — but you've moved from "will definitely get popped on launch day" to "a real attacker will have to actually work for it." No sales call required. No consulting firm. Eight items in your terminal before launch. Item 1: Every API endpoint has an auth check How to check: # Test an endpoint without a cookie/token curl -s -o /dev/null -w "%{http_code}" https://your-app.com/api/your-endpoint # Should return 401. If it returns 200, the endpoint is public — intentional? Why it matters: API routes in Next.js are not automatically protected. If you forgot to add getServerSession (or your equivalent auth check) to a route handler, it's open to the internet. The route might not be linked in the UI, but it's reachable. Fix: // At the top of every API route handler const session = await getServerSession ( authOptions ) if ( ! session ) return new Response ( ' Unauthorized ' , { status : 401 }) Run this check for every file under src/app/api/ . Better yet: write a middleware that protects all /api/ routes by default and use an explicit { public: true } annotation for routes that should be unauthenticated. Item 2: Per-tenant data scoping (BOLA / IDOR) How to check: # Log in as user A, grab a resource ID from the response # Then change the cookie/token to user B's and request the same ID curl -s -H "Cookie: your-user-b-session" https://your-app.com/api/items/YOUR-USER-A-ITEM-ID # Should return 403 or 404. If it returns

I see this every week. Someone decides to learn web development. They Google "how to start web development" and within 20 minutes they're installing Node.js, npm, VS Code, and five extensions they don't understand. They haven't written a single line of code yet. But they've already spent an hour configuring their "environment." Then they get stuck. Node version conflicts. npm permission errors. VS Code extensions that break their syntax highlighting. They think they're not smart enough for programming. They are. They just started with the wrong step. The Problem Learning web development has three core technologies: HTML, CSS, and JavaScript. That's it. Everything else — Node.js, npm, webpack, Vite, React — is extra. It's not the starting point. But most tutorials assume you already have Node.js installed. They say "open your terminal" and "run npm install." Beginners follow along, copy the commands, and have no idea what any of it means. Here's what actually happens: You install Node.js (200MB+) You install VS Code (another 300MB+) You install 5-10 extensions You create a project folder You open terminal and run npm init -y You run npm install live-server You run npx live-server You finally see your HTML page in a browser That's 8 steps before you write Hello World . The Solution You don't need any of that. Not yet. Here's what you actually need to learn HTML, CSS, and JavaScript: A browser (you already have one) A text editor (Notepad works) That's it Open Notepad. Write this: <!DOCTYPE html> <html> <head> <title> My First Page </title> </head> <body> <h1> Hello, World! </h1> <p> This is my first web page. </p> </body> </html> Save it as index.html. Double-click the file. It opens in your browser. You just built your first web page. No terminal. No npm. No Node.js. No configuration. When Should You Actually Learn Node.js? Node.js becomes useful when you need: Server-side code (backend development) Package management (npm packages) Build tools (webpack, Vite) Framew

The end of the FIFA Men’s World Cup is nigh. Here’s how to watch the final games and the first ever World Cup halftime show.

OpenAI employees have donated more than $215,000 to a political effort opposing Leading the Future, a group backed by the company’s president, Greg Brockman.

Turn any 3D model into a buildable brick model Discussion | Link

SpaceX's latest residential dish - the Starlink V5 - is now available in "select areas." It's notably smaller and lighter than the V4 dish with improved power efficiency. It'll be available in more places as SpaceX ramps up production to meet global demand. The company notes that Starlink V5 is not intended for in-motion use […]

Twelve pricing tabs open. Neon, Hetzner, Supabase, Prisma, Scaleway, OVH. My database is half a gigabyte. I was comparing ten-terabyte price curves. At some point this week I typed the words "I am super lost here" about my own infrastructure. I advise companies on this exact class of decision. That sentence still came out of my hands. If you have ever spent an evening deep in provider pricing pages for a workload that fits on a USB stick from 2009, this one is for you. All numbers below come from the live pricing pages as of July 2026. Rates move, so verify before you commit. Three fears, all pointed at the wrong layers I went in worried about getting attacked, running out of space, and being locked in. All three dissolved under ten minutes of honest reading. DDoS lands on the website edge, not the database. My site already sits behind Cloudflare and Vercel, and a database is never publicly exposed. Only the app talks to it. Whichever provider I picked, that attack surface stayed identical. Here is the shape of the stack, and where each fear lives. MANAGED (what I run today) visitors ──> Cloudflare edge ──> Vercel app ──> managed Postgres [DDoS absorbed] [stateless] [never public, app-only access, provider patches, provider backups, provider on-call] SELF-HOSTED (the alternative I priced) visitors ──> Cloudflare edge ──> Vercel app ──> Hetzner CAX11 [DDoS absorbed] [stateless] [Postgres :5432 firewalled to app, SSH hardened, fail2ban + auto- patching = MINE] │ pg_dump every 6h ▼ encrypted ────> Cloudflare R2 [off-site copies] Same edge, same app, same attack surface. Everything in the right-hand box is what changes owners. Storage was a rounding error. My data is 0.5 GB. Even the cheapest self-hosted box includes 40 GB, eighty times headroom before the first extra cent. Lock-in was a phantom too. Managed Postgres is still stock Postgres. Exiting means a dump, a restore, and one connection string change in the deployment environment. Minutes of cutover, no rewrite an

submitted by /u/Remarkable_Offer_347 [link] [留言]

Body: ​Hi everyone! 👋 ​I am excited to share my latest project, LingoBridge-AI, which I have been building to solve a critical problem in rural healthcare. ​The Problem 🩺 ​In many rural areas, patients receive medical reports that are complex and filled with technical jargon. Due to this, they often struggle to understand their own health conditions, which leads to confusion and delayed medical care. ​The Solution: LingoBridge-AI 💡 ​I developed LingoBridge-AI, an AI-powered tool designed to: ​Simplify complex medical reports into easy-to-understand language. ​Translate information into local languages to ensure better accessibility for patients. ​Bridge the gap between healthcare providers and patients who have limited medical literacy. ​Tech Stack 🛠️ ​Built using Python and AI frameworks. ​Focuses on accuracy, simplicity, and user-friendly output. ​Check it out! 💻 ​You can view the source code and documentation here: 👉 [ https://github.com/cherukuriLakshmi/LingoBridge-AI ] ​I am still working on improving this, and I would love to get some feedback from this amazing community! If you have any suggestions on how to improve the AI or the user experience, please let me know in the comments below. ​Thanks for your support! ​Tags (Add these at the bottom): ai #healthtech #opensource #python #beginners

The first sign something was wrong wasn't a crash. It was a pattern. blockly-platform was the first real thing I built with Claude Code end to end — a Blockly-based platform for university programming exercises, driven entirely through Claude Code's Telegram channel. No editor open, no repo checked out on my machine, just a chat thread. I'd describe what I wanted, Claude Code would build it on a box I never looked at directly, and I'd judge the result by clicking around the deployed app. On March 22nd, the home page came up empty. GET /api/exercises/published was returning 403. I said so in the chat; a few messages later, Claude Code said it was fixed — the endpoint hadn't been added to Spring Security's permitAll() list. I moved on, tried the category filter. Also empty, also 403, also missing from the same permitAll() list — same file, same class of fix, different line. Then the exercise detail page. Same story, third time, same day. Three days later, the like button stopped working — root cause, again: POST /api/exercises/*/like had never been whitelisted either. Four times, one file, one recurring gap. None of these were hard bugs. Each one, in isolation, is a one-line fix a competent engineer makes without thinking twice. What bothered me, once I noticed the pattern, was that I hadn't noticed it as it happened. I had no diff to scroll through, no file to glance at and think "wait, didn't we just fix this exact class of thing twice already?" I had a chat log and a live app to poke at. The fourth fix looked, from where I sat, exactly like the first: a message telling me it was resolved. That was the moment I started to suspect the problem wasn't the model. It was that nobody — not the model, not me — had anything to look at. Why chat-only vibe coding breaks down Here's what makes that pattern more interesting than "the AI made a mistake": every one of those four fixes was correct. Claude Code read the error, found the missing permitAll() entry, added it, and move

Cet article a été publié à l'origine sur WP Admin Lab , le journal du web technique en français. WP-CLI est l'interface en ligne de commande officielle de WordPress, un outil indispensable pour tout développeur ou administrateur gérant des sites WordPress en production. Contrairement à l'interface graphique du tableau de bord, WP-CLI permet d'exécuter des opérations en masse, d'automatiser des tâches répétitives et d'intervenir sur des sites inaccessibles via le navigateur. En 2026, maîtriser WP-CLI est devenu une compétence fondamentale pour la gestion professionnelle de parcs WordPress, que ce soit pour des agences gérant des dizaines de sites ou pour des développeurs travaillant sur des environnements de staging et de production. Installation et configuration de WP-CLI en 2026 L'installation de WP-CLI s'effectue en téléchargeant le fichier Phar officiel depuis le dépôt GitHub du projet et en le rendant exécutable à l'échelle du système. Sur les serveurs Linux mutualisés ou dédiés, la commande curl -O permet de récupérer le binaire, que l'on déplace ensuite vers /usr/local/bin/wp avec les droits d'exécution appropriés. Les hébergeurs comme Kinsta ou WP Engine proposent WP-CLI préinstallé dans leurs environnements SSH, facilitant la prise en main immédiate. La vérification de l'installation avec wp -info fournit les détails de version, l'interpréteur PHP utilisé et le chemin vers le fichier de configuration wp-config.php. La configuration avancée de WP-CLI passe par le fichier wp-cli.yml placé à la racine du projet WordPress. Ce fichier YAML permet de définir l'URL du site, le chemin d'installation, les alias de serveurs distants pour les déploiements SSH, et des paramètres par défaut pour certaines commandes. Les alias SSH dans wp-cli.yml sont particulièrement puissants : ils permettent d'exécuter des commandes WP-CLI sur un serveur distant exactement comme en local, avec une syntaxe du type wp @production plugin list. Cette fonctionnalité simplifie considérableme

A slow AI feature rarely fails all at once. It starts with a longer prompt, then a bigger retrieval result, then one more tool call, then a retry path nobody measured. The demo still works, but users feel the delay before your dashboard explains it. That is why small AI product teams need an LLM latency budget before they start optimizing. Not a vague goal like “make it faster.” A budget says how much time each stage is allowed to spend, what happens when it exceeds that limit, and which user experience is still acceptable when the model, retrieval layer, or tool chain slows down. The payoff is practical: you stop guessing where the delay lives, stop overpaying for wasted work, and make AI workflows feel reliable even when traffic, context, and providers are messy. Why latency budgets matter now Recent AI platform news points in one direction: AI workflows are becoming longer, more tool-heavy, and more expensive to run without discipline. A current news scan showed several signals builders should notice: Production LLM cost and latency guidance is shifting from “add more compute” to “remove wasted work.” Agent environments are being designed for long-running background tasks, persistent state, and cheaper idle time. New model releases emphasize tool use, computer use, multimodal context, subagents, and larger context windows. AI gateways and enterprise platforms are adding cost controls, routing, caching, audit trails, and usage limits. Developers are asking more practical questions about why AI coding and agent workflows interrupt flow with repeated prompt-wait-evaluate loops. For AI SaaS builders, this means latency is no longer just a model selection problem. It is a workflow design problem. A simple chat completion might have one bottleneck. A real AI workflow may include: request queueing auth and tenant checks prompt assembly memory lookup vector search reranking model routing tool calls browser or API actions structured output validation fallback attempts str