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I built this out of frustration as I lead the development of AI features at Yola.com.

Prompt testing should be simple and straightforward. All I wanted was a simple way to test prompts with variables and jinja2 templates across different models, ideally somthing I could open during a call, run few tests, and share results with my team. But every tool I tried hit me with a clunky UI, required login and API keys, or forced a lengthy setup process.

And that's not all.

Then came the pricing. The last quote I got for one of the tools on the market was $6,000/year for a team of 16 people in a use-it-or-loose-it way. For a tool we use maybe 2–3 times per sprint. That’s just ridiculous!

IMO, it should be something more like JSFiddle. A simple prompt playground that does not require you to signup, does not require API keys, and let's experiment instantly, i.e. you just enter a browser URL and start working. Like JSFiddle has. And mainly, something that costs me nothing if I'm or my team is not using it.

Eventually I gave up looking for solution and decided to build it by myself.

Here it is: https://langfa.st

Help me find what's wrong or missing or does not work from you perspctive.

P.S. I did not put any limits or restrictions yet, so test it wisely. Don't make me broke, please.

I’ve been using AI to generate some repetitive frontend (guilty), and while most outputs felt vibe-coded, some results were surprisingly good. So I cleaned it up and made a ranking game out of it with friends, and you can check it out here: https://www.designarena.ai/vote

/vote: Your prompt will be answered by four random, anonymous models. You pick the one you prefer and crown the winner, tournament-style.

/leaderboard: See the current winning models, as dictated by voter preferences.

/play: Iterate quickly by seeing four models respond to the same input and pressing space to regenerate the results you don’t lock-in.

We were especially impressed with the quality of DeepSeek and Grok, and variance between categories (To judge by the results so far, OpenAI is very good for game dev, but seems to suck everywhere else).

We’ve learned a lot, and are curious to hear your comments and questions. Excited to make this better!

We needed a speaker diarization solution that could run every few seconds alongside transcription on iOS and macOS. But native Swift support was either limited or locked behind paid licenses. Since diarization is a common need in speech-to-text workflows, we decided to open source our work and give back to the community.

We initially tried sherpa-onnx, which works, but running both diarization and transcription models slowed down older devices. CPU-only inference just isn’t ideal for near real-time workloads, so we wanted the option to offload segmentation and speaker embedding to the GPU or ANE. Supporting M1 Macs in particular meant pushing more of the workload to the ANE.

Instead of shoehorning the ONNX model into CoreML with C++, we converted the original PyTorch models directly to CoreML. This approach required some monkey-patching in the PyTorch and pyannote code, but the initial benchmarks look promising.

We’d love feedback! We're currently working on adding VAD and integrating Parakeet for transcription, but still wrestling with CoreML model conversion.

Built this Python package because I wanted to run hardware controlling ML stuff and other control algorithms directly connected to industrial I/O hardware without jumping into annoying PLC toolchains (Windows only, licensing hassle, no editor choice, proprietary version control - you name it). For sure its not for ms‑cycle loops, or uptime critical production stuff, but in applications with relaxed timing it allows for fast iteration on the setup - making prototyping a pleasure. Its easy to use, has no dependencies beside Python, its fully type annotated and MIT licensed. Internal it uses ModBus/TCP for hardware communication, the implementation is exposed, so it co-serves as ModBus/TCP client library.

I'd love to hear your use cases, feature ideas and PLC toolchain stories ;)

Docs: https://nonannet.github.io/pyhoff

Hi HN,

I’m a recent CS graduate. During the past few months I wrote BinaryRPC, an open-source RPC framework in modern C++20 focused on low-latency, binary WebSocket messaging.

Why I built it * Wanted first-class session support, pluggable QoS levels and a simple middleware chain (global, specific, multi handler) without extra JSON/XML parsing. * Easy developer experience

A quick feature list * Binary WebSocket frames – minimal overhead * Built-in session layer (login / reconnect / heartbeat) * QoS1 / QoS2 with automatic ACK & retry * Plugin system – rooms, msgpack, etc. can be added in one line * Thread-safe core: RAII + folly

Still early (solo project), so any feedback on design, concurrency model or missing must-have features would help a lot.

Thanks for reading!

also see "Chat Server in 5 Minutes with BinaryRPC": https://medium.com/@efecanerdem0907/building-a-chat-server-i...

Hey HN! I'm Louis, one of the creators of Vibe Kanban.

We started working on this a few weeks ago. Personally, I was feeling pretty useless working synchronously with coding agents. The 2-5 minutes that they take to complete their work often led me to distraction and doomscrolling.

But there's plenty of productive work that we (human engineers) could be doing in that time, especially if we run coding agents in the background and parallelise them.

Vibe Kanban lets you effortlessly spin up multiple coding agents. While some agents handle tasks in the background, you can focus on planning future work or reviewing completed tasks.

After a few weeks of internal dog fooding and sharing it with friends, we've now open-sourced Vibe Kanban, and it's stable enough for day-to-day use.

I'd love to hear your feedback, feel free to open an issue on the github and we'll respond ASAP.

The HN community may find the context of the prompts, organized by each turn in each session, the most useful. See the website/docs/prompts.md and session-X.md files. I also started exploring some workflows for the LLM to execute, organized in the website/docs/tasks/ folder. I found it pretty handy to have the LLM document our work as we went and simply embedded the static site into the executable, along with all the music and logic.

The whole project took me about a day for the backend. The C++ controller itself took only a few turns.

I enjoyed focusing on my son's experience and letting the agent handle the C++, Javascript, and Go code.

I'm still getting started with coding agents, so please do share any tips or tricks to help me with similar projects. I'm most interested in how to work effectively with the agent, like what you see in dev-loop.sh

Hi, I built this iOS App that would let parents create profiles for my children, plan their meals, put their meal preferences, recipes, lunchboxes, export the plan to their calendar, and share links to the timetable with others, and of course an AI helping with the plan and recipe . For now it has a free plan and also paid plan. Initially i built this as a web app but then after feedbacks from close people i developed this iOS app. I would really appreciate your feedback.

Hey HN, Kyle here, one of the co-founders of OpenPipe.

Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement.

One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate.

RULER is a drop-in reward function that works across different tasks without any of that complexity.

It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).

We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! https://openpipe.ai/blog/ruler

Repo: https://github.com/OpenPipe/ART

qwen3-rs is a Rust implementation of the Qwen framework, a high-performance, extensible framework for building server applications. It provides a modular design and support for various data sources, allowing developers to create scalable and efficient server applications.

Pangolin is an open source self-hosted tunneled reverse proxy management server with identity and access control, designed to securely expose private resources through encrypted WireGuard tunnels running in user space.

We made Pangolin so you retain full control over your infrastructure while providing a user-friendly and feature-rich solution for managing proxies, authentication, and access, all with a clean and simple dashboard web UI.

GitHub: https://github.com/fosrl/pangolin

Deployment takes about 5 minutes on a VPS: https://docs.fossorial.io/Getting%20Started/quick-install

Demo by Lawrence Systems (YouTube): https://youtu.be/g5qOpxhhS7M?si=M1XTWLGLUZW0WzTv&t=723

Some use cases:

  - Grant users access to your apps from anywhere using just a web-browser

  - Proxy behind CGNAT

  - One application load balancer across multiple clouds and on-premises

  - Easily expose services on IoT and edge devices for field monitoring

  - Bring localhost online for easy access

  - No port forwarding and hide your public IP for self-hosting

  - Create proxies to multiple different private networks

  - OAuth2/OIDC identity providers

  - Role-based access control

  - Raw TCP and UDP support

  - Resource-specific pin codes, passwords, email OTP

  - Self-destructing shareable links

  - API for automation

  - WAF with CrowdSec and Geoblocking

Hi HN!

I recently built a simple, rechargeable wall lamp that doesn't require any tools, wires, or drilling. It sticks to surfaces using adhesive pads, rotates 360°, and charges via USB-C. The goal was to make lighting *super minimal, renter-friendly, and easy to install*.

The idea came from personal frustration — I live in a rented apartment where I can’t drill holes, and I wanted a modern-looking light I could reposition easily.

I know this isn’t a software product, but I figured some of you might appreciate the problem-solving side of it — designing minimal hardware that’s useful, elegant, and simple. Would love feedback on the product or the landing page:

Happy to answer any questions about the design, battery, lighting specs, remote control logic, etc.

Thanks!

Hi HN!

I'm the developer of rdx, a mildly popular ad-free, privacy and user friendly Reddit client. This time, I made something for a very specific use case: solving chess puzzles with no internet.

Why? Well, my Wi-Fi is terrible in the bathroom—and that's where I do some of my best thinking. I tried printing out “mate in X” puzzles to solve offline, but they weren’t fun without interaction. So I built OffChess.

OffChess is an iPhone/Android app that contains over 100,000 chess puzzles, fully offline and completely ad-free. You can solve puzzles by category (Mate in 1/2/3/4/5, tactics like pins/forks/skewers, or openings like Sicilian/French, etc). You gain or lose points based on how you perform, so there's a light rating system to keep things engaging.

No accounts, no tracking, no monthly subscriptions, no internet required. Just pure, old-school tactical chess training, wherever you are.

You can check out the iPhone/iPad app at https://apps.apple.com/us/app/chess-puzzles-offchess/id67447... or the Android app at https://play.google.com/store/apps/details?id=com.offchess

Would love feedback, bug reports, or suggestions.

Thanks!

The article describes a novel method for block-based diffusion, a technique used in generative models. The proposed approach, called Nano Block Diffusion, aims to improve the performance and efficiency of this method compared to existing approaches.

Hi HN!

I’m the developer of Elara, a tool that automatically scans your code for security issues like misconfigurations, secrets, and risky packages, so you can focus on building without stressing about all this stuff. It’s designed to be simple and fast.

I see so many people launching products online without even knowing what security risks they might have. If you’re a developer or into tech, you know how hard it is to keep systems safe. Yet shockingly it feels like nobody really cares. I want to help folks catch these issues early, before they get burned.

Elara runs multiple security scanners simultaneously, aggregates the results into a single interface, and gives you an actionable to-do list to fix the problems.

It’s super simple to try, just log in with GitHub and see for yourself.

Would really appreciate your feedback!

Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones.

Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more.

Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks and Google AI Edge respectively. However, both are platform-specific and only support specific models from the company. To this end, Cactus:

- Is available in Flutter, React-Native & Kotlin Multi-platform for cross-platform developers, since most apps are built with these today.

- Supports any GGUF model you can find on Huggingface; Qwen, Gemma, Llama, DeepSeek, Phi, Mistral, SmolLM, SmolVLM, InternVLM, Jan Nano etc.

- Accommodates from FP32 to as low as 2-bit quantized models, for better efficiency and less device strain.

- Have MCP tool-calls to make them performant, truly helpful (set reminder, gallery search, reply messages) and more.

- Fallback to big cloud models for complex, constrained or large-context tasks, ensuring robustness and high availability.

It's completely open source. Would love to have more people try it out and tell us how to make it great!

Repo: https://github.com/cactus-compute/cactus

I've been trying to make accessible and beautiful GPIO pinouts since I started one for the Raspberry Pi in 2013 [1]. I've since given the Raspberry Pi Pico [2] and Pico 2 [3] microcontrollers the same treatment when they launched.

Recently I've updated these with a new "Upside-down" view to complement the rear view, giving a pinout in the right orientation to match your project.

The Pico sites are all hand-coded single HTML pages with supporting CSS and minimal JS. They are set up to optionally install as a "Desktop" web app. They also degrade into a somewhat usable table in lieu of CSS and use vector graphics (for the board itself) to be viewable and printable at any size.

Finally, hidden behind "Advanced" is a pinout of the test pads and special function pins!

[1] - https://web.archive.org/web/20130505194305/pi.gadgetoid.com/... [2] - https://pico.pinout.xyz [3] - https://pico2.pinout.xyz

Hi HN!

I've built [CXXStateTree](https://github.com/ZigRazor/CXXStateTree), a modern C++ header-only library to create hierarchical state machines with clean, intuitive APIs.

It supports: - Deeply nested states - Entry/exit handlers - State transitions with guards and actions - Asynchronous transitions with `co_await` (C++20 coroutines) - Optional runtime type identification for flexibility

It's ideal for complex control logic, embedded systems, games, robotics, and anywhere you'd use a finite state machine.

I’d love feedback, use cases, or contributions from the community!

Repo: https://github.com/ZigRazor/CXXStateTree

Hey HN, we're a YC startup building an open-source, privacy-first alternative to Perplexity Comet.

No invite system unlike bunch of others – you can download it today from our website or GitHub: https://github.com/browseros-ai/BrowserOS

--- Why bother building an alternative? We believe browsers will become the new operating systems, where we offload much bunch of our work to AI agents. But these agents will have access to all your sensitive data – emails, docs, on top of your browser history. Open-source, privacy-first alternatives need to exist.

We're not a search or ad company, so no weird incentives. Your data stays on your machine. You can use local LLMs with Ollama. We also support BYOK (bring your own keys), so no $200/month plans.

Another big difference vs Perplexity Comet: our agent runs locally in your browser (not on their server). You can actually watch it click around and do stuff, which is pretty cool! Short demo here: https://bit.ly/browserOS-demo

--- How we built? We patch Chromium's C++ source code with our changes, so we have the same security as Google Chrome. We also have an auto-updater for security patches and regular updates.

Working with Chromium's 15M lines of C++ has been another fun adventure that I'm writing a blog post on. Cursor/VSCode breaks at this scale, so we're back to using grep to find stuff and make changes. Claude code works surprisingly well too.

Building the binary takes ~3 hours on our M4 Max MacBook.

--- Next? We're just 2 people with a lot of work ahead (Firefox started with 3 hackers, history rhymes!). But we strongly believe that a privacy-first browser with local LLM support is more important than ever – since agents will have access to so much sensitive data.

Looking forward to any and all comments!

I was looking around for an MCP server that could connect Anna's Archive to Claude Desktop, as I wanted to be able to search and download books directly through the interface.

I couldn't find any public implementations, so ended up building one myself.

What it does?

- It searches Anna's Archive by keywords. - It downloads books from search results. - It works directly in Claude Desktop through MCP.

Check out the repository's README for detailed installation and configuration instructions.

The code is fully open source and builds run on GitHub Actions for transparency.

I figured I'd share, since I couldn't be the only one wanting this functionality!

Hi HN,

After spending some time with the new `flux kontext dev` model, I realized its most powerful capabilities aren't immediately obvious. Many people might miss its true potential by just scratching the surface.

I went deep and curated a collection of what I think are its most interesting use cases – things like targeted text removal, subtle photo restoration, and creative style transfers.

I felt that simply writing about them wasn't enough. The best way to understand the value is to see it and try it for yourself.

That's why I built FluxKontextLab (https://fluxkontextlab.com).

On the site, I've presented these curated examples with before-and-after comparisons. More importantly, there's an interactive playground right there, so you can immediately test these ideas or your own prompts on your own images.

My goal is to share what this model is capable of beyond the basics.

It's still an early project. I'd love for you to take a look and share your thoughts or any cool results you generate.

Hey HN,

I'm a solopreneur and run a web design agency.

I create open-source apps, but I also work as a freelancer and designer. I was accepting any new freelance project via forms on my agency website.

I was using Typeform, but as time went by and more people submitted forms, it got more and more expensive. That time, I thought to use Google Form, but it was way too blocky and looked very unprofessional on my agency website.

So I thought to build my own forms for my own usage, and it turns out it almost doubled form submissions and inquiry calls.

I was happy, so I thought to build it for everyone and make it open-source.

I added AI functionalities using Vercel AISDK. I can generate forms almost instantly using AI and also added analytics AI so that users can talk with their forms—more like talk with their analytics data.

I've been building this publicly, sharing updates on my X account (preetsuthar17)

I hope this product will be as helpful to you as it was for me. Would love your feedback pls

Preet

I built an app that matches users to exclusive Discord communities based on a 25-question personality quiz. Inspired by HEXACO but with a novel fuzzy-clustering twist, it creates a "harmony genome" to gate access, ensuring tight-knit tribes (e.g., wellness or creative niches). Think Reddit but curated via psych. Launched to test the idea—feedback on algo, niches, or scaling?

The article discusses the concept of 'reviving genius' and explores ways to foster creative potential and innovative thinking in individuals and organizations. It emphasizes the importance of nurturing the right mindset, providing supportive environments, and encouraging continuous learning and experimentation.

The article discusses AsyncMCP, a Python library that simplifies the use of the Multiprocessing and Asyncio libraries, allowing developers to easily create concurrent and asynchronous applications. It provides a high-level interface for running tasks in parallel and managing their execution.

BreakerMachines is a production-ready circuit breaker for Ruby/Rails with built-in async/fiber support, fallback chains, and rich monitoring. Unlike existing gems, it handles modern Ruby's fiber scheduler and avoids dangerous thread timeouts.

Hello HN!

As a long term NYC resident, I have read countless articles on ideas tweaking subway services, but always found them hard to follow without visual aid. So over the long weekend I decided to build one. It has all the basic features: trains would spawn at their origin, stop at stations, and slow down if it gets too close to another. You can also design custom routes by piecing tracks together.

Have fun, and let me know what you think!