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We are Bailey and Robbie and we are working on Klaus (https://klausai.com/): hosted OpenClaw that is secure and powerful out of the box.

Running OpenClaw requires setting up a cloud VM or local container (a pain) or giving OpenClaw root access to your machine (insecure). Many basic integrations (eg Slack, Google Workspace) require you to create your own OAuth app.

We make running OpenClaw simple by giving each user their own EC2 instance, preconfigured with keys for OpenRouter, AgentMail, and Orthogonal. And we have OAuth apps to make it easy to integrate with Slack and Google Workspace.

We are both HN readers (Bailey has been on here for ~10 years) and we know OpenClaw has serious security concerns. We do a lot to make our users’ instances more secure: we run on a private subnet, automatically update the OpenClaw version our users run, and because you’re on our VM by default the only keys you leak if you get hacked belong to us. Connecting your email is still a risk. The best defense I know of is Opus 4.6 for resilience to prompt injection. If you have a better solution, we’d love to hear it!

We learned a lot about infrastructure management in the past month. Kimi K2.5 and Mimimax M2.5 are extremely good at hallucinating new ways to break openclaw.json and otherwise wreaking havoc on an EC2 instance. The week after our launch we spent 20+ hours fixing broken machines by hand.

We wrote a ton of best practices on using OpenClaw on AWS Linux into our users’ AGENTS.md, got really good at un-bricking EC2 machines over SSM, added a command-and-control server to every instance to facilitate hotfixes and migrations, and set up a Klaus instance to answer FAQs on discord.

In addition to all of this, we built ClawBert, our AI SRE for hotfixing OpenClaw instances automatically: https://www.youtube.com/watch?v=v65F6VBXqKY. Clawbert is a Claude Code instance that runs whenever a health check fails or the user triggers it in the UI. It can read that user’s entries in our database and execute commands on the user’s instance. We expose a log of Clawbert’s runs to the user.

We know that setting up OpenClaw is easy for most HN readers, but I promise it is not for most people. Klaus has a long way to go, but it’s still very rewarding to see people who’ve never used Claude Code get their first taste of AI agents.

We charge $19/m for a t4g.small, $49/m for a t4g.medium, and $200/m for a t4g.xlarge and priority support. You get $15 in tokens and $20 in Orthogonal credits one-time.

We want to know what you are building on OpenClaw so we can make sure we support it. We are already working with companies like Orthogonal and Openrouter that are building things to make agents more useful, and we’re sure there are more tools out there we don’t know about. If you’ve built something agents want, please let us know. Comments welcome!

Hi HN, I forked chromium and built agent-browser-protocol (ABP) after noticing that most browser-agent failures aren’t really about the model misunderstanding the page. Instead, the problem is that the model is reasoning from a stale state.

ABP is designed to keep the acting agent synchronized with the browser at every step. After each action (click, type, etc), it freezes JavaScript execution and rendering, then captures the resulting state. It also compiles the notable events that occurred during that action loop, such as navigation, file pickers, permission prompts, alerts, and downloads, and sends that along with a screenshot of the frozen page state back to the agent.

The result is that browser interaction starts to feel more like a multimodal chat loop. The agent takes an action, gets back a fresh visual state and a structured summary of what happened, then decides what to do next from there. That fits much better with how LLMs already work.

A few common browser-use failures ABP helps eliminate: * A modal appears after the last Playwright screenshot and blocks the input the agent was about to use * Dynamic filters cause the page to reflow between steps * An autocomplete dropdown opens and covers the element the agent intended to click * alert() / confirm() interrupts the flow * Downloads are triggered, but the agent has no reliable way to know when they’ve completed

As proof, ABP with opus 4.6 as the driver scores 90.5% on the Online Mind2Web benchmark. I think modern LLMs already understand websites, they just need a better tool to interact with them. Happy to answer questions about the architecture, forking chrome or anything else in the comments below.

Try it out: `claude mcp add browser -- npx -y agent-browser-protocol --mcp` (Codex/OpenCode instructions in the docs)

Demo video: https://www.loom.com/share/387f6349196f417d8b4b16a5452c3369

Demo: https://aether.saphal.me GitHub: https://github.com/saphalpdyl/Aether

Aether is a multi-BNG (Broadband Network Gateway) ISP infrastructure lab built almost from scratch that emulates IPoE IPv4 subscriber management end-to-end. It supports IPoE/Ipv4 networks and runs a python-based vBNG with RADIUS AAA, per-subscriber traffic shaping, and traffic simulation emulated on Containerlab. It is also my first personal networking project, built roughly over a month.

Motivations behind the project

I'm a CS sophomore. About three years ago, I was assigned, as an intern, to build a OSS/BSS platform for a regional ISP by myself without mentoring. Referencing demo.splynx.com , I developed most of the BSS side ( bookkeeping, accounting, inventory management ), but, in terms of networking, I managed to install and setup RADIUS and that was about it. I didn't have anyone to mentor me or ask questions to, so I had given up then.

Three years later, I decided to try cracking it again. This project is meant to serve as a learning reference for anyone who's been in that same position i.e staring at closed-source vendor stacks without proper guidance. This is absolutely not production-grade, but I hope it gives someone a place to start.

Architecture overview

The core component, the BNG, runs on an event-driven architecture where state changes are passed around as messages to avoid handling mutexes and locks. The session manager is the sole owner of the session state. To keep it clean and predictable, the direBNG never accepts external inputctly. The one exception is the Go RADIUS CoA daemon, which passes CoA messages in via IPC sockets. Everything the BNG produces(events, session snapshots) gets pushed to Redis Streams, where the bng-ingestor picks them up, processes them, and persists them.

Simulation and meta-configs

I am generating traffic through a simulator node that mounts the host's docker socket and runs docker exec commands on selected hosts. The topology.yaml used by Containerlab to define the network topology grows bigger as more BNG's and access nodes are added. So aether.config.yaml, a simpler configuration, is consumed by the configuration pipeline to generate the topology.yaml and other files (nginx.conf, kea-dhcp.conf, RADIUS clients.conf etc.)

Known Limitations

- Multiple veth hops through the emulated topology add significant overhead. Profiling with iperf3 (-P 10 -t 10, 9500 MTU, 24 vCPUs) shows BNG→upstream at ~24 Gbit/s, but host→BNG→upstream drops to ~3.5 Gbit/s. The 9500 MTU also isn't representative of real ISP deployments. This gets worse when the actual network is reintroduced capping my throughput to 1.6 Gbits/sec in local. - The circuit ID format (1/0/X) is non-standard. I simplified it for clarity. - No iBGP or VLAN support. - No Ipv6 support. I wanted to target IPv4 networks from the start to avoid getting too much breadth without a lot of depth.

Nearly everything I know about networking (except some sections from AWS) I learned building this. A lot was figured out on the fly, so engineers will likely spot questionable decisions in the codebase. I'd genuinely appreciate that feedback.

Questions

- Currently, the circuit where the user connects is arbitrarily decided by the demo user. In a real system with thousands of circuits, it'd be very difficult to properly assess which circuit the customer might connect to. When adding a new customer to a service, how does the operator decide, based on customer's location, which circuit to provide the service to ?

The article describes go-mongosh, a Go-based driver for the MongoDB database that provides a user-friendly shell interface for interacting with MongoDB instances. The tool aims to simplify MongoDB operations and offers features like auto-completion, history tracking, and support for various MongoDB operations.

Thesys just open-sourced their generative UI rendering engine. Interesting timing given where Google a2ui and Vercel's json-render are headed. The difference worth noting: a2ui and json-render both treat JSONL as the contract between the LLM and the renderer. Thesys is betting that's the wrong primitive. Their engine uses a code-like syntax (OpenUI Lang) instead — LLM writes it, renderer executes it. The argument is that LLMs are fundamentally better at generating code than generating structured data, so you get cleaner output and ~67% fewer tokens. The broader vision seems to be a model-agnostic, design-system-agnostic layer that sits between any LLM and your actual UI components. You bring your own components and design tokens, the engine handles translating LLM output into rendered interfaces — charts, forms, tables, cards. Generative UI as a category is still figuring out what the right abstraction is. This is a concrete stake in the ground against JSON-as-spec.

Loquix is an open-source platform for building conversational AI applications. It provides a modular architecture, pre-built components, and tools for developing, testing, and deploying natural language processing models and chatbots.

Hey HN,

I built StreamHouse, an open-source streaming platform that replaces Kafka's broker-managed storage with direct S3 writes. The goal: same semantics, fraction of the cost.

How it works: Producers batch and compress records, a stateless server manages partition routing and metadata (SQLite for dev, PostgreSQL for prod), and segments land directly in S3. Consumers read from S3 with a local segment cache. No broker disks to manage, no replication factor to tune — S3 gives you 11 nines of durability out of the box.

What's there today: - Producer API with batching, LZ4 compression, and offset tracking (62K records/sec) - Consumer API with consumer groups, auto-commit, and multi-partition fanout (30K+ records/sec) - Kafka-compatible protocol (works with existing Kafka clients) - REST API, gRPC API, CLI, and a web UI - Docker Compose setup for trying it locally in 5 minutes

  What's not there yet:
  - Battle-tested production deployments (I'm the only user so far)
  - Connectors for consumers to immediately connect to (i.e clickhouse, elastic search etc)
  

Written in Rust, 15 crates thus far. Apache 2.0 licensed.

GitHub: https://github.com/gbram1/streamhouse How it works blog on my main website: https://streamhouse.app/how-it-works

Happy to answer questions about the architecture, tradeoffs, or what I learned building this.

Instead of hard-coding payroll rules, PayrollEngine models business logic as composable Regulation layers — versioned JSON/YAML config + runtime C# (Roslyn). Layers inherit and override like CSS cascade: national law → industry → company.

v0.10.0-beta.1 shipped earlier this week alongside the new docs site (payrollengine.org).

The most interesting new example: MultiCountryPayroll — DE/FR/NL sharing a single base regulation, with a split employee whose contract crosses borders mid-period. The regulation handles it without a single country-specific code path.

Other additions: - Payrun Preview: in-memory calculation, no DB writes - Async payrun jobs: HTTP 202, bounded queue, webhook on completion - Parallel employee processing with per-employee state isolation

Stack: .NET 10, SQL Server, Docker, Roslyn

GitHub: https://github.com/Payroll-Engine/PayrollEngine Docs: https://payrollengine.org

Hi HN. This week Meta acquired Moltbook (agent social network), OpenAI acquired Promptfoo (agent testing), and Mandiant's founder raised $190M for Armadin. Agent infrastructure is clearly where things are heading.

We built AgentSign -- a zero trust engine for AI agents. The problem: agents are operating without any identity infrastructure. Moltbook went viral for fake posts because there was zero verification on who or what was posting.

AgentSign gives every agent a cryptographic identity certificate, signs every action into an execution chain, and runs runtime code attestation before anything executes. There's also an MCP Trust Layer for agent-to-MCP server verification, and a Stripe-powered Trust Gate for agent payments.

5 subsystems: identity certs, execution chain verification, runtime code attestation, output tamper detection, and cryptographic trust scoring.

Free and open source. Built in London.

SDK: https://github.com/razashariff/agentsign-sdk

Happy to answer questions.

This article introduces Ory Lumen, a semantic search engine that uses the Claude language model to provide more relevant and accurate search results. The article explains how Ory Lumen combines natural language processing and machine learning to understand the context and meaning behind user queries, improving the search experience.

I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants.

The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole.

The whole thing was developed on 2x RTX 4090s in my basement. I'm now running current models (GLM-4.7, Qwen3.5, MiniMax M2.5) on a dual GH200 rig (see my other post). Code and new models coming soon.

Happy to answer questions.

Tap on the canvas to create silly faces.

Discover new brushes by combining them on the canvas.

Used to be a React Native app from the pre-AI ages. Recently ported with AI to be just a wordpress plugin.

I was frustrated with photo sharing sites. Apple's iCloud shared albums take 20+ seconds to load, and everything else comes with ads, cumbersome UIs, or social media distractions. I just want to share photos with friends and family: fast, mobile-friendly, distraction-free.

So I built DD Photos. You export photos from whatever you already use (Lightroom, Apple Photos, etc.) into folders, run `photogen` (a Go CLI) to resize them to WebP and generate JSON indexes, then deploy the SvelteKit static site anywhere that serves files. Apache, S3, whatever. No server-side code, no database.

Built over several weeks with heavy use of Claude Code, which I found genuinely useful for this kind of full-stack project spanning Go, SvelteKit/TypeScript, Apache config, Docker, and Playwright tests. Happy to discuss that experience too.

Live example: https://photos.donohoe.info Repo: https://github.com/dougdonohoe/ddphotos

I built Joha, a free browser-based drawing playground built around preset shape tools.

You can click or drag to quickly generate individual shapes like waves, stars, layered squares, particles, textured strokes, and ring patterns, then combine them into larger compositions.

It’s designed for fast visual exploration and composition rather than precise vector editing.

Under the hood, it’s built with Vue 3, Vite, and p5.js for the drawing engine.

Hello HN, we're Jeet and Husain from Modulus (https://modulus.so) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into:

- Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo.

- Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again.

Modulus shares memory across agents and repositories so they can understand your entire system.

It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents.

Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus.

Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context.

If you wanna see a quick demo before trying out, here is our launch post - https://x.com/subhajitsh/status/2024202076293841208

We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus.

Hey HN, I made a site where you can draw on street-level panoramas. Your drawings persist and other people can see them in real time.

Strokes get projected onto the 3D panorama so they wrap around buildings and follow the geometry, not just a flat overlay. Uses WebGL2 for rendering, Mapillary for the street imagery.

The idea is for it to become a global canvas, anyone can leave a mark anywhere and others stumble onto it.

Ash is a macOS sandbox that restricts AI coding agents. It limits access to files, networks, processes, IO devices, and environment variables. You can use Ash with any CLI coding agent by wrapping it in a single command: `ash run -- <agent>`. I typically use it with Claude to stay safe while avoiding repetitive prompts: `ash run -- claude --dangerously-skip-permissions`.

Ash restricts resources via the Endpoint Security and Network Extension frameworks. These frameworks are significantly more powerful than the sandbox-exec tool.

Each session is driven by a policy file. Any out-of-policy action is denied by default. You can audit denials in the GUI app, which lets you view out-of-policy actions and retroactively add them to your policy file.

Ash also comes with tools for building policies. You can use an "observation session" to watch the typical behavior of a coding agent and capture that behavior in a policy file for future sandbox sessions. Linting, formatting, and rule merging are all built into the Ash CLI to keep your policy files concise and maintainable.

Download Ash at https://ashell.dev

LiteParser is a fast, embeddable SQLite parser designed for embedded systems and mobile apps. It provides a simple API for executing SQL queries and managing database operations, making it a lightweight and efficient solution for a variety of applications.

The article discusses the Kitty Graphics Mode, an Emacs extension that provides a powerful and customizable graphics rendering engine for Emacs. It allows users to create and manipulate visual graphics directly within the Emacs environment, enabling a wide range of applications and integrations.

ClaudeRank is a web-based platform that provides personalized career recommendations and job search tools. The platform uses machine learning algorithms to analyze users' skills, experience, and preferences to suggest suitable job opportunities and career paths.

Hi, Ted here, creator of Mog.

- Mog is a statically typed, compiled, embedded language (think statically typed Lua) designed to be written by LLMs -- the full spec fits in 3,200 tokens. - An AI agent writes a Mog program, compiles it, and dynamically loads it as a plugin, script, or hook. - The host controls exactly which functions a Mog program can call (capability-based permissions), so permissions propagate from agent to agent-written code. - Compiled to native code for low-latency plugin execution -- no interpreter overhead, no JIT, no process startup cost. - The compiler is written in safe Rust so the entire toolchain can be audited for security. Even without a full security audit, Mog is already useful for agents extending themselves with their own code. - MIT licensed, contributions welcome.

Motivations for Mog:

1. Syntax Only an AI Could Love: Mog is written for AIs to write, so the spec fits easily in context (~3200 tokens), and it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c. It's also why there's no implicit type coercion, which I've found over the decades to be an annoying source of runtime bugs. There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.

When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.

2. Capabilities-Based Permissionsl: There's a paradox with existing security models for AI agents. If you give an agent like OpenClaw unfettered access to your data, that's insecure and you'll get pwned. But if you sandbox it, it can't do most of what you want. Worse, if you run scripts the agent wrote, those scripts don't inherit the permissions that constrain the agent's own bash tool calls, which leads to pwnage and other chaos. And that's not even assuming you run one of the many OpenClaw plugins with malware.

Mog tries to solve this by taking inspiration from embedded languages. It compiles all the way to machine code, ahead of time, but the compiler doesn't output any dangerous code (at least it shouldn't -- Mog is quite new, so that could still be buggy). This allows a host program, such as an AI agent, to generate Mog source code, compile it, and load it into itself using dlopen(), while maintaining security guarantees.

The main trick is that a Mog program on its own can't do much. It has no direct access to syscalls, libc, or memory. It can basically call functions, do heap allocations (but only within the arena the host gives it), and return something. If the host wants the Mog program to be able to do I/O, it has to supply the functions that the Mog program will call. A core invariant is that a Mog program should never be able to crash the host program, corrupt its state, or consume more resources than the host allows.

This allows the host to inspect the arguments to any potentially dangerous operation that the Mog program attempts, since it's code that runs in the host. For example, a host agent could give a Mog program a function to run a bash command, then enforce its own session-level permissions on that command, even though the command was dynamically generated by a plugin that was written without prior knowledge of those permission settings.

(There are a couple other tricks that PL people might find interesting. One is that the host can limit the execution time of the guest program. It does this using cooperative interrupt polling, i.e. the compiler inserts runtime checks that check if the host has asked the guest to stop. This causes a roughly 10% drop in performance on extremely tight loops, which are the worst case. It could almost certainly be optimized.)

3. Self Modification Without Restart: When I try to modify my OpenClaw from my phone, I have to restart the whole agent. Mog fixes this: an agent can compile and run new plugins without interrupting a session, which makes it dynamically responsive to user feedback (e.g., you tell it to always ask you before deleting a file and without any interruption it compiles and loads the code to... actually do that).

Async support is built into the language, by adapting LLVM's coroutine lowering to our Rust port of the QBE compiler, which is what Mog uses for compilation. The Mog host library can be slotted into an async event loop (tested with Bun), so Mog async calls get scheduled seamlessly by the agent's event loop. Another trick is that the Mog program uses a stack inside the memory arena that the host provides for it to run in, rather than the system stack. The system tracks a guard page between the stack and heap. This design prevents stack overflow without runtime overhead.

Lots of work still needs to be done to make Mog a "batteries-included" experience like Python. Most of that work involves fleshing out a standard library to include things like JSON, CSV, Sqlite, and HTTP. One high-impact addition would be an `llm` library that allows the guest to make LLM calls through the agent, which should support multiple models and token budgeting, so the host could prevent the plugin from burning too many tokens.

I suspect we'll also want to do more work to make the program lifecycle operations more ergonomic. And finally, there should be a more fully featured library for integrating a Mog host into an AI agent like OpenClaw or OpenAI's Codex CLI.

Hi everyone, I am Kumar, co-founder of Dench (https://denchclaw.com). We were part of YC S24, an agentic workflow company that previously worked with sales floors automating niche enterprise tasks such as outbound calling, legal intake, etc.

Building consumer / power-user software always gave me more joy than FDEing into an enterprise. It did not give me joy to manually add AI tools to a cloud harness for every small new thing, at least not as much as completely local software that is open source and has all the powers of OpenClaw (I can now talk to my CRM on Telegram!).

A week ago, we launched Ironclaw, an Open Source OpenClaw CRM Framework (https://x.com/garrytan/status/2023518514120937672?s=20) but people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw (https://denchclaw.com).

OpenClaw today feels like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode was the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt.

That is how we think about DenchClaw. We are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world.

Demo: https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43

  npx denchclaw

On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill.

The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. Thank you DuckDB team!

It creates a new OpenClaw profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA.

Think of it as Cursor for your Mac (also works on Linux and Windows) which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do linkedin outreach using MY browser.

DenchClaw finds your Chrome Profile and copies it fully into its own, so you won’t have to log in into all your websites again. DenchClaw sees what you see, does what you do. It’s an everything app, that sits locally on your mac.

Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use.

We would love you all to break it, stress test its CRM capabilities, how it streams subagents for lead enrichment, hook it into your Apollo, Gmail, Notion and everything there is. Looking forward to comments/feedback!

Agent Kanban has 4 main features:

GitOps & team friendly kanban board integration inside VS Code Structured plan / todo / implement via @kanban commands Leverages your existing agent harness rather than trying to bundle a built in one .md task format provides a permanent (editable) source of truth including considerations, decisions and actions, that is resistant to context rot

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 the AI has become quite capable, I decided to see how far I could get by hooking up 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.

react-tourlight is the modern React tour library. Zero dependencies, WCAG 2.1 AA accessible, under 5 kB gzipped. The one that works with React 19.

I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number.

I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :)

source code: https://github.com/tonytonyjan/crypto_flora

Why I built Skir: https://medium.com/@gepheum/i-spent-15-years-with-protobuf-t...

Quick start: npx skir init

All the config lives in one YML file.

Website: https://skir.build

GitHub: https://github.com/gepheum/skir

Would love feedback especially from teams running mixed-language stacks.

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.