If you ship a desktop app outside an app store, you eventually hit the same wall: how do you check a license when the user is on a plane, behind a corporate firewall, or just offline? Calling your server on every launch isn't an option. Here's how offline activation actually works, without the hand-waving. The naive version, and why it breaks The first thing everyone reaches for is "call home on launch, get back yes/no." It works in the demo and fails in the wild: No network = no app. Fail-closed locks out paying customers. Fail-open means anyone who blocks your domain runs free. Both are bad. A boolean is forgeable. If your app trusts a {"valid": true} response, a proxy or a patched DNS entry returns that for free. The fix isn't a better endpoint. It's moving the trust off the network and onto cryptography. The model that works: signed leases The durable pattern is a cryptographically signed lease (Keygen calls these license files, Keylight calls them leases — same idea): On first activation, the device talks to the server once . The server returns a small signed document: the license state, an expiry, the device binding, and any entitlements (which features/tiers are unlocked). The document is signed with the server's private key (Ed25519 is the modern choice — small, fast, boring in the good way). Your app ships the matching public key and verifies the signature locally on every launch. No network needed. Because the app only ever verifies with a public key, there's nothing secret in the binary to steal, and a forged lease fails the signature check. That's the whole trick: the server vouches once, math vouches forever after. first launch ──► server signs lease (Ed25519, private key) ──► stored on device every launch ──► app verifies signature (public key) ──► no network Device binding (so one key isn't infinite installs) A lease is bound to a device so a single license can't be pasted onto a thousand machines. The lease embeds a device fingerprint, and the SDK ch

In my journey as a backend developer, I had already spent time working with APIs, databases, authentication flows, and background processing. I understood the basic idea that not everything should occur within a request-response cycle, especially when dealing with expensive operations such as sending emails, processing files, or generating reports. Offloading work to the background felt like a solved problem to me. That confidence was exactly what led me into confusion. When I first encountered message brokers and task queues, they looked like different names for the same idea. Both involved queues, both involved workers, and both involved asynchronous processing. In my head, the distinction didn’t seem important, so I treated them interchangeably and assumed that choosing one over the other was just a matter of preference or framework availability. The real issue was that I had not yet understood the difference in intent between communication and execution. What I thought was a simple design choice actually turned into an architectural mistake that affected how I structured an entire system. How I Misunderstood the Problem At the time, I was building systems where the backend had to handle multiple heavy operations. A user could upload files, request reports, or trigger processes that should not block the main API response. Naturally, I reached for a queue-based solution because it is the standard answer for background work. However, instead of asking what role the system needed to play, I focused on what tool could make things asynchronous. That small shift in thinking created the confusion. I assumed that anything that gets delayed or processed later should automatically go into a queue, without distinguishing whether I was dealing with a job that must be executed or an event that other services should react to. This is where I started building the wrong abstraction. Where Task Queues Actually Fit A task queue exists primarily to assign work that must be complete

Every developer hates writing READMEs. It's boring, repetitive, and always gets skipped. So I built ReadmeAI — describe your project, AI writes the README instantly. What it does Fill in project name, description, tech stack, features AI generates a complete professional README.md Switch between Raw and Preview tabs to see rendered markdown One click copy Tech Stack Next.js + Tailwind CSS Groq API (openai/gpt-oss-120b) Deployed on Vercel Why I built it (Write 2-3 sentences personally — mention the challenge, that you're a student builder, makes it relatable) Live link https://readmeai-three.vercel.app/ Built this in a day as part of my 30-day AI tools challenge. Would love feedback from the dev community!

It is like pytest but for statistical tests: it ensures no regression of your metrics at a statistical level. It manages tedious things such that seeds, past benchmark results, ... Simple CLI working like pytest but with benchmarks/ directory instead of tests/: pybench # 1st time: samples seeds, saves a baseline, marks NEW pybench # later: reruns on the same seeds, marks PASS / FAIL pybench update # re-baseline after an intended change pybench show # print current baseline stats (--history for per commit) Please give me your feedback, Github: https://github.com/AnthonyBeeblebrox/pybench Docs: https://pybench.readthedocs.io/en/latest/ submitted by /u/SpecificPark2594 [link] [留言]

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built For this project, I built DevBrand AI, an AI-powered web application that helps developers create a complete personal branding kit in just a few clicks. Instead of manually writing bios, portfolio headlines, README introductions, or designing graphics, users simply provide their GitHub username, role, tech stack, experience, and preferred design theme. The application then generates everything automatically. Prompt Used I used Google AI Studio's Build apps with Gemini feature with a prompt similar to this: Build a modern React + TypeScript application called DevBrand AI that generates a complete developer branding kit. Use Gemini to generate professional bios, portfolio headlines, GitHub README introductions, project ideas, mission statements, social media introductions, CTAs, and branding recommendations. Use Imagen to generate a modern 3D developer mascot, hero illustration, and portfolio banner. Create a responsive UI using Tailwind CSS with reusable React components, loading animations, copy buttons, and download functionality. Features 🤖 AI-generated developer bio 🎯 Personal tagline 💻 Portfolio headline 📄 GitHub README introduction 💡 Project ideas 🌈 Suggested branding colors 📢 Social media introduction 🚀 Portfolio call-to-action 🎨 AI-generated developer mascot 🖼️ Hero illustration 🌐 Portfolio banner 📋 Copy buttons 📥 Download generated content 📱 Responsive modern interface Demo Screenshots Live Demo App: https://devbrand-ai-706459620449.asia-southeast1.run.app My Experience This project was my first time using the new Build apps with Gemini experience in Google AI Studio, and it was surprisingly fast to go from an idea to a working application. What impressed me most was how the AI generated a well-structured React + TypeScript project instead of just producing a single file. The generated components, services, and overall architecture made the project easy to und

TL;DR An AI agent registry is a centralized catalog of every agent in your organization — what each agent does, what tools it can access, what version is running, who owns it, and how to call it It's to agents what a container registry is to Docker images or what a service mesh is to microservices — the layer that makes distributed components governable We hit the "which agents do we have?" wall at 14 agents across 3 teams. That's when the registry stopped being a nice-to-have About four months into our agentic AI buildout, our head of security asked a question I couldn't answer: "Can you give me a list of every AI agent running in production, what systems they have access to, and what version of each is currently deployed?" I had a rough mental model. I knew about the agents my team had built. I had a vague idea of what the data engineering team had shipped. The product team had recently added two agents I'd heard about secondhand. I spent the better part of a day pulling together a spreadsheet. By the time I finished, one of the agents I'd listed had already been replaced by a newer version. Two of them had been granted access to an internal API I hadn't known about. The spreadsheet was outdated before I sent it. That was our forcing function for building a proper agent registry. This post is what I wish I'd read before that conversation happened. What an agent registry is An agent registry is a centralized catalog of AI agents — a single source of truth that tracks every agent deployed in your organization, its capabilities, its integrations, its ownership, and its current state. The analogy that landed for me: it's to agents what a container registry (Docker Hub, ECR, GCR) is to container images. When you have three containers running, you don't need a registry — you know what you have. When you have 40 containers across six teams, you need a registry to know what's running, who owns it, what version is deployed, and what depends on what. Agents are the same. At

Most model comparisons ask which model is best. This one starts with a model that never even produced a single result. We tested NVIDIA's open-weight Nemotron family, from the 30B Nano to the 120B Super, on a benchmark of real-world coding tasks: the kind of models an indie developer on a tight budget, or an enterprise cutting inference cost and keeping data in-house, would run. The main finding is that model size is not a dial you turn for a little more quality, it is a threshold. Below a certain capability floor a model cannot drive an agent loop at all, which is why the smallest variant we tried, Nano 12B, produced nothing to score. Above the floor, the question stops being which model is cheapest and becomes which one clears the bar your work actually needs: Nano 30B is an extremely cheap workhorse for narrow, well-scoped jobs, while Super 120B is the size that holds up on demanding multi-step agent work. An agent size floor is the minimum model capacity below which a model cannot reliably complete the act-observe-decide loop an agent depends on. Below it you don't get a slower or sloppier agent, you get a non-agent: a model that reads the task, takes a few steps, and never converges. For anyone choosing a model, this changes the question from "which is cheaper" to "which clears the floor for my work", and that is the question to answer first. Where the numbers come from Every scenario in the evaluation is a real-world agent task tied to a published skill, scored on two axes: instruction-following (does the agent do what it was told, in the way it was told) and task-completion (does it reach the goal). The overall score weights instruction-following at 4 and task-completion at 3, then divides by 7. Each task runs with and without the skill, so the lift from the skill is visible directly. The tasks and skills are public, in the task-evals-for-skills dataset , so you can inspect any scenario yourself. This design is deliberate. The tasks are derived from published

Honestly, deepSeek vs Qwen vs Kimi vs GLM: Which AI API Wins in 2025? I'll be honest — when I first started comparing these four Chinese AI model families, I thought it would be a quick exercise. Spoiler: it wasn't. I spent two weeks running prompts through every endpoint, tracking every dollar, and tallying tokens like a part-time accountant. The good news? I now have very strong opinions about which one deserves your money. Here's the thing: most "AI comparison" posts online are written by people who clearly haven't paid a single API bill. They throw around vague phrases like "good value" without ever showing you the math. That's not me. I'm the person who sees $0.01/M and immediately thinks "wait, that's a 99% discount compared to GPT-4o." I calculate things. I notice things. And when I noticed I could replace most of my OpenAI spending with these four providers, I lost my mind a little. So buckle up. This is going to be the most cost-obsessed AI comparison you'll read this year. I've tested DeepSeek, Qwen, Kimi, and GLM through Global API's unified endpoint, and I'm going to break down exactly what each one costs, what each one delivers, and where your dollars should actually go. The Price Reality Check Before we dive into individual models, let me set the stage. Look at these price ranges side by side: DeepSeek: $0.25–$2.50/M output Qwen: $0.01–$3.20/M output Kimi: $3.00–$3.50/M output GLM: $0.01–$1.92/M output Check this out — Qwen and GLM both start at $0.01/M for their smallest models. That's literally one cent per million tokens. If you've been paying OpenAI prices, that's a 99%+ reduction. On the other end, Kimi sits at $3.00–$3.50/M, which is the premium tier. That's not crazy compared to GPT-4o, but it's noticeably more expensive than the other three. The price spread across all four families combined is enormous. From $0.01/M to $3.50/M. That's a 350x range. Which means the model you pick matters more than any other decision in your AI stack. DeepSeek:

estou tentando resolver algo q me incomoda no Clion com C++ que é o terminal dele, estou tentando adiconar um terminal proprio com SFML 3, minha ideia é escrever o codigo e ele ser compilado e assim aparecer na janela do SFML, alguem tem alguma ideia dew como posso fazer isso? submitted by /u/wzinho_667 [link] [留言]

submitted by /u/mitousa [link] [留言]

I submitted one of my NeurIPS review ~6 hrs later than the official deadline. Will this still affect my own submission? Asking because I’m a first time reviewer. I pinged the AC a day before that I might be a few hours late, but didn’t hear back. So wondering if I might have triggered something that’ll now affect my own submission. submitted by /u/confirm-jannati [link] [留言]

OpenAI's GPT-5.6 comes in three variants, including its most powerful and its most affordable models yet.

I started treating a one-wheel like a folding bike replacement for a 3 km bus gap. The spreadsheet looked fine. Real life did not.## What broke my first estimates* Sticker range is not commute range. * Hills, cold mornings, and stop-and-go ate about 3% of the rated number on my route. I now plan at ~6% of brochure range and keep a buffer for a wrong turn. Weight shows up on stairs, not on paper. Carrying the wheel through a station twice a day mattered more than top speed ever did. Rain is a policy decision, not a gear decision. Some days I bail to transit. Pretending I will always ride made me resent the wheel.## A simple checklist I use now1. Measure your worst leg, not your best day.2. Count how many times you pick the wheel up per trip.3. Decide where you charge (home only vs. desk outlet).4. Set a weather cutoff before you are tired and annoyed.## DisclosureI work around electric unicycles professionally, so take this with that bias. I am still trying to optimize my own commute, not sell anyone a model.If you want plain spec tables while comparing wheels: https://www.kingsong.com/collections/electric-unicycle What would you add for mixed transit + one-wheel days?

title: sqlex — A Modern Drop-in Replacement for jmoiron/sqlx published: false description: sqlex is a fully API-compatible modernization of jmoiron/sqlx that fixes 20+ long-standing bugs, adds pluggable hooks, auto IN expansion, and more. Built for Go 1.21+. tags: go, database, sql, opensource If you use sqlx, this is worth 3 minutes of your time jmoiron/sqlx has been the go-to SQL extension library for Go for years. Struct mapping, named parameters, IN clause expansion — it made database/sql actually pleasant to use. I've used it in almost every Go project I've worked on. But here's the reality: its activity has been modest at best, and has slowed to a crawl in recent years. Hundreds of issues sit untouched. PRs go unanswered. Bugs reported years ago are still there, waiting to cause production incidents. This isn't a knock on sqlx — it's a great library with solid design. But an unmaintained foundational library is a liability. So we built sqlex sqlex is a drop-in replacement for jmoiron/sqlx that is 100% API-compatible . All sqlx methods ( Get , Select , Exec , NamedQuery , Preparex , etc.) work identically. Migrating takes 30 seconds — just change the import path: - import "github.com/jmoiron/sqlx" + import "github.com/go-sqlex/sqlex" 🐛 20+ bug fixes from sqlx, all fixed 🚀 New features sqlx never had Auto-Rebind — write ? everywhere, works on PostgreSQL ($1), MySQL (?), SQLite (?), SQL Server ( @p1 ). No more manual db.Rebind(). SQL parsing fixes — colons in strings, :: type casts, ? in comments are correctly handled. Silent bugs from sqlx are gone. Auto IN expansion — slices in IN (?) are detected and expanded automatically on all methods. Hook system — pluggable SQL interceptors for logging, tracing, metrics (onion model). JSONValue[T] — generic JSON column type with auto serialize/deserialize. StrictMode — lenient by default (matching sqlx Unsafe()), optionally strict for debugging. Unified interfaces — Ext / ExtContext / NamedExt / BindExt with compile-time