Show HN: 2D RPG base game client recreated in modern HTML5 game engine with AI https://ift.tt/hwGnBOK

Show HN: 2D RPG base game client recreated in modern HTML5 game engine with AI When I was much younger, I used to play a Korean MMORPG called Helbreath, and I also hosted a bunch of private servers for it. I eventually moved on, but I always loved the game’s aesthetics, its 2D nature, and its atmosphere. That may just be nostalgia talking. The community maintained private server and client, which to my knowledge were based on leaked official files, were written in fairly archaic C++. If you’re interested in the original sources, I’ve included the main client and server files, Client.cpp and Server.cpp, in the reference folder. I always felt that if the project was rewritten in something more modern and better structured, a lot more could be done with it. But rewriting an MMORPG client and server from scratch is not exactly the kind of thing you do on a whim. That said, there was a guy who got pretty far with a C# rewrite and an XNA-based client, though that project is now also discontinued. Now that AI has become quite capable, I decided to see how far I could get by hooking up the original assets in a modern HTML5 game engine. I wanted HTML5 because I figured a nearly 30 year old 2D game should run just fine in a browser. I ended up choosing Phaser 3 for a few reasons. Mainly, it's 2D only, free, HTML5 first (JS/TS), and code-first, which mattered because I wanted good Cursor integration for AI assistance. Another thing I liked was its integration with React, which let me build the UI using browser technologies and render the UI at native resolution on top of the WebGL canvas, rather than building the UI inside the game engine itself, which runs at 1024x576 resolution. The original game ran at 640x480. After about 1.5 months of talking to AI on evenings and weekends, and roughly $200 worth of Cursor usage later, I finished hooking up the original assets in a modern game engine that seems to run just fine in a browser. By "base game client", I mean that it's not fully hooked up in terms of how the full (MMO)RPG should function, but it does include all the original assets and core mechanics needed to provide a solid foundation if you want to build your own 2D (MMO)RPG on top of it. Continuing to build with AI should also work just fine, since this is how I managed to get that far. The asset library is quite rich, if you ask me, but there is one caveat: these assets are not in the public domain. They are still the property of someone, or some entity, that inherited the IP from the original developer, which is no longer in business. You can read more about that on the GitHub page. https://ift.tt/QXvUtTr March 11, 2026 at 01:39AM

Show HN: Don't share code. Share the prompt https://ift.tt/YpKt6Sd

Show HN: Don't share code. Share the prompt Hey HN, I'm Mario. I recently talked to a colleague about AI, agents and how software development will change in the future. We were wondering why we should even share code anymore when AI agents are already really good at implementing software, just through prompts. Why can't everyone get customized software with prompts? "Share the prompt, not the code." Well, I thought, great idea, let's do that. That's why I built Open Prompt Hub: https://ift.tt/8N02X7M . Think GitHub just for prompts. The idea is simple: Users can upload prompts that can then be used by you and your AI tools to generate a script, app, or web service (or prime their agent for a certain task): Just past it into your agent or ide and watch it build for you. If the prompt does not 100% covers your usecase, fork it, tweak it, et voila: tailor-made software ready to use! The prompts are simple markdown files with a frontematter block for meta information. (The spec can be found here: https://ift.tt/6YNf8PC ) They versioned, have information on which AI models build it successfuly and have instructions on how the AI agent can test the resulting software. Users can mention with which models they have successfully or unsuccessfully executed a prompt (builds or fail). This helps in assessing whether a prompt provides reliable output or not. Want to create a open prompt file? Here is the prompt for it which will guide you through: https://ift.tt/2wbYg6J Security! Always a topic when dealing with AI and prompts? I've added several security checks that look at every prompt for injections and malicious behavior. Statistical analysis as well as two checks against LLMs for behaviour classification and prompt injection detection. It's an MVP for now. But all the mentioned features are already included. If this sounds good, let me know. Try a prompt, fork it, or tell me what you'd change in the spec or security scanner. I'm really curious about what would make you trust and reuse prompts. Or if you like the general idea... https://ift.tt/w8usVTa March 11, 2026 at 12:29AM

Show HN: A retention mechanic for learning that isn't Duolingo manipulation? https://ift.tt/Rs4vNc1

Show HN: A retention mechanic for learning that isn't Duolingo manipulation? i've spent the last few years shipping learning products at scale - Andrew Ng's AI upskilling platform, my MIT Media Lab spinoff focused on AI coaching. the retention problem was the same everywhere. people would engage with content once and not return. not because the content was bad - rather because there was no mechanism/motivation to make it a habit. the standard industry answer is gamification — streaks, points, badges. Duolingo has shown this works for language. but I'm skeptical it generalizes. duolingo's retention is built on a very specific anxiety loop that feels increasingly manipulative and doesn't translate well to topics like astrophysics or reading dense research papers. i've been building Daily - 5 min/day structured social learning on any topic, personalized by knowledge level. Eerly and small (20 users). the interesting design question i keep running into: what actually drives someone to return to learn something they want to learn but don't need to learn? no external accountability, no credential at the end, no job pressure. pure intrinsic motivation is notoriously hard to sustain. my current hypothesis: the return trigger isn't gamification, it's social - knowing someone else is learning the same thing, or that someone will notice if you stop. testing this in month 1. has anyone built in this space or thought carefully about the retention mechanic for purely intrinsic learning? curious what the HN crowd has seen work. https://ift.tt/1f7Nczd March 10, 2026 at 05:56AM

Show HN: The Mog Programming Language https://ift.tt/Ca2WUqB

Show HN: The Mog Programming Language https://moglang.org March 9, 2026 at 11:27PM

Show HN: Proxly – Self-hosted tunneling on your own domain in 60 second https://ift.tt/WzI13ur

Show HN: Proxly – Self-hosted tunneling on your own domain in 60 second Proxly is a self-hosted tunneling tool that exposes local services through subdomains on your own VPS. npm install -g @a1tem/proxly, run proxly, and the interactive wizard sets up your first tunnel. No bandwidth caps, no session limits. Built it because frp's config is painful and ngrok's free tier is too limited. Open source, MIT licensed. GitHub: https://ift.tt/CKOlHzQ March 8, 2026 at 03:34PM

Show HN: Tessera – MCP server that gives Claude persistent memory and local RAG https://ift.tt/wX9Sa6Y

Show HN: Tessera – MCP server that gives Claude persistent memory and local RAG https://ift.tt/9KZYdrP March 7, 2026 at 11:12PM

Show HN: Mog, a programming language for AI agents https://ift.tt/Kv2WOgp

Show HN: Mog, a programming language for AI agents I wrote a programming language for extending AI agents, called Mog. It's like a statically typed Lua. Most AI agents have trouble enforcing their normal permissions in plugins and hooks, since they're external scripts. Mog's capability system gives the agent full control over I/O, so it can enforce whatever permissions it wants in the Mog code. This is even true if the plugin wants to run bash -- the agent can check each bash command the Mog code emits using the exact same predicate it uses for the LLM's direct bash tool. Mog is a statically typed, compiled, memory-safe language, with native async support, minimal syntax, and its own compiler written in Rust and its own runtime, also written in Rust, with `extern "C"` so the runtime can easily be embedded in agents written in different languages. It's designed to be written by LLMs. Its syntax is familiar, it minimizes foot-guns, and its full spec fits in a 3200-token file. The language is quite new, so no hard security guarantees are claimed at present. Contributions welcome! https://gist.github.com/belisarius222/203ac5edbc3306c34bf0481f451d4003 March 7, 2026 at 12:16AM