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AI 资讯

I Open-Sourced MarketEye — Here's Why (and the GitHub Link)

I open-sourced MarketEye today. For anyone who missed the first post: MarketEye is a self-hosted competitor price monitor I built because I didn't want to pay $99/month for Prisync. The code is now up on GitHub under MIT license. GitHub: github.com/dachengzi065-gif/marketeye Why open source? Three reasons: 1. People actually asked for it. After my first post here, a few people DM'd me asking to see the code. They're developers too — they want to modify it, extend it, make it their own. That's fair. Selling source code to devs is like selling ice to eskimos. 2. Trust. A closed-source price tracker that "runs on your machine" — you either trust the author or you don't. Open source removes that doubt. You can read every line, check what data leaves your machine (nothing), and build it yourself if you want. 3. Longevity. Self-hosted tools have a dirty secret: if the developer disappears, you're stuck with a broken tool. Open source changes that. Even if I get hit by a bus tomorrow, you can fork the repo and keep going. What this means for the $49 version The Gumroad package still exists. It includes: The same code, pre-packaged Email support (I'll help you set it up) A clear conscience subscription (you're paying for convenience, not software) But honestly? If you can run pip install , just clone the repo. It's free. What's next I'm actively working on: Docker image (one-command deploy) More scrapers (plugins for different sites) Discord/Telegram bot alerts (requested by several people) PRs welcome. Issues welcome. Feedback welcome. 👉 github.com/dachengzi065-gif/marketeye

2026-06-16 原文 →
开发者

Introducing Zentax A New Programming Language

Hi everyone, I’m working on a new programming language called Zentax. It is still in early development, but the goal is to build a modern language focused on: Performance and low-level control Simple and clean syntax Native desktop application support A modular compiler and runtime design Zentax is not trying to replace existing languages — it is an experiment in building a unified approach for systems programming and UI development. Current Status Compiler: in development Runtime: early design stage Renderer: experimental Standard library: planning phase Looking for Contributors I’m open to collaboration from anyone interested in: Programming language design Compiler development Runtime systems Graphics / rendering engines Open-source tooling Even feedback and ideas are welcome at this stage. Links Git Hub Repo Discord Thanks for reading. Dr. Zoha Tariq Anoneurx

2026-06-16 原文 →
AI 资讯

How to Build an AI Coding Stack Without Going Broke in 2026

A solo developer with a $200/month budget can now access the same AI coding power that cost enterprises $50,000/month just two years ago. The secret isn't one tool — it's knowing how to mix and match three different access models to get frontier output at budget prices. I've been running this exact stack for months. Here's the breakdown. The Three Ways to Access AI Coding Models Before we talk strategy, understand your three options. Each has a wildly different cost profile. Option 1: Self-Hosted Open Models With models like GLM-5.2 hitting near-Claude Opus quality under MIT license, self-hosting is finally viable. The math is straightforward. Hardware cost: A dedicated GPU server (RTX 4090 or A100) runs $300–$800/month. An H100 rental starts at $1.99/hour on platforms like RunPod. Break-even point: According to cost analysis from multiple providers, self-hosting becomes cheaper than APIs at roughly 5–10 million tokens per month for premium-tier models [1]. Below that volume, you're paying for idle hardware. The catch: You need DevOps skills. Model deployment, quantization, monitoring, failover — it's real infrastructure work. If you save $500 on compute but burn out managing GPUs on weekends, you lost money. Best for: Teams with predictable, high-volume workloads and existing DevOps capability. Think 100M+ tokens/month where savings hit $5M+ annually [2]. Option 2: Pay-Per-Token APIs The default starting point. You pay exactly for what you use. Current pricing (early 2026, per 1M tokens): GPT-4o: $2.50 input / $10.00 output Claude 3.5 Sonnet: $3.00 input / $15.00 output Gemini 1.5 Pro: $1.25 input / $5.00 output DeepSeek V3: $0.27 blended (yes, really) Together AI (Llama 70B): $0.88 blended [1] The pricing floor crashed when DeepSeek V3 arrived at $0.27/M tokens with GPT-4-class quality. Open-source models routed through providers like Together AI or Cerebras ($6–12/M tokens at 969 tok/s) give you more options than ever. The trap: Pricing scales linearly forever. A

2026-06-16 原文 →
AI 资讯

I built a browser-based desktop environment (IP Linux) with React, TypeScript and Vite

I built a browser-based desktop environment (IP Linux) with React, TypeScript and Vite I have been working on a project called IP Linux : a browser-based desktop environment that runs as a static web app. Live site: https://ip-os-linux.vercel.app/ GitHub Repository: https://github.com/ikerperez12/IP-OS-LINUX It is not a real Linux distribution, and it does not run native binaries. The idea is different: I wanted to explore how far a polished desktop-like experience can go inside a normal browser tab. The result is a small web OS-style environment with: A splash / entry screen A desktop with icons, folders, and widgets A top panel with system controls A dock and app launcher Resizable and draggable windows Virtual workspaces Snap assist A global search / Spotlight-style command palette Local-first apps (Files, Terminal, settings, player) Reactive wallpapers Glass UI and visual effects Why I built it Most web demos are landing pages, dashboards, or small single-purpose apps. I wanted to build something that feels more like an environment. I was interested in questions like: Can a web app feel physical and desktop-like? How should windows behave inside a browser viewport? How far can local-first storage go before a backend is actually needed? How do you organize many small apps without making the UI messy? IP Linux became a way to test all of that in one project. The app includes a catalog of built-in apps and tools: Files, Terminal, Browser, Settings, App Store, Music Player, Matrix Rain, games, developer tools, productivity apps, and visual utilities. The virtual file system and user preferences are stored locally in the visitor's browser with IndexedDB/localStorage. There is no backend, no account system, and no required environment variables for the public release. Would love to get feedback on the interaction design, responsiveness, or features!

2026-06-15 原文 →
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