Show HN: A string Enum generator for Go lang https://ift.tt/0zYTRel March 31, 2025 at 05:15AM
Show HN: I built a tool to add noise texture to your images I'm excited to introduce Noise Tools – a simple yet powerful tool that lets you effortlessly add noise textures to your images. Whether you're a designer, artist, or just experimenting with aesthetics, Noise Tools helps you enhance your visuals with just a few clicks. Why I built this? I often found myself needing high-quality noise textures for design projects but struggled to find a quick and easy solution. So, I built Noise Tools to make the process easy for everyone! Features: Generate noise textures instantly Adjust intensity & styles No downloads or complicated settings Would love to hear your thoughts! Try it out and let me know what you think. Check it out here: noisetools.vercel.app https://ift.tt/djxp75f March 27, 2025 at 01:12PM
Show HN: Appear as anyone in video calls like zoom or Google meets Hey everyone! i built this free tool that basically let you appear as literally anyone in video calls. it uses the latest tech in audio driven portrait animation. Would love to have some people test this out and let me know what you think! It's currently available on ubuntu systems. it works best with 4070 or 3080 gpus and up! basically anything with about 30TFLOPS on fp16. It runs totally on your device for 100% privacy too. Just looking for people to test this out and let me know what they think! You can download it at https://www.phazr.ai/ https://www.phazr.ai/ March 30, 2025 at 12:14AM
Show HN: Hexi, modern header-only network binary serialisation for C++ hackers Over the last few years, I've needed an easy way to quickly serialise and deserialise various network protocols safely and efficiently. Most of the libraries that existed at the time were either quite heavy, had less than stellar performance, or were an abstraction level above what I was looking for. I decided to put together my own class to do the job, starting with an easy, low-overhead way to move bytes in and out of arbitrary buffers. Along the way, it picked up useful bits and pieces, such as buffer structures and allocators that made the byte shuffling faster, often being able to do it with zero allocations and zero copies. Safety features came along to make sure that malicious packet data or mistakes in the code wouldn't result in segfaults or vulnerabilities. It's become useful enough to me that I've packaged it up in its own standalone library on the chance that it might be useful to others. It has zero dependencies other than the standard library and has been designed for quick integration into any project within minutes, or seconds with a copy paste of the amalgamated header. It can be used in production code but it's also ideal for for those that want to quickly hack away at binary data with minimal fuss. https://ift.tt/oz8I12y March 28, 2025 at 11:07PM
Show HN: Xorq – open-source Python-first Pandas-style pipelines Hi HN, Dan, Hussain and Daniel here… After years of struggling with data pipelines that worked in notebooks but failed in production, we decided to do something about it. We created xorq to eliminate the constant headaches of SQL/pandas impedance mismatch, runtime debugging, wasteful recomputations and unreliable research-to-production deployments that plague traditional pandas-style pipeline workflows. xorq is built on Ibis and DataFusion. We’d love your feedback and contributions. xorq is [Apache 2.0 licensed]( https://ift.tt/tHArdUx ) to encourage open collaboration. Repo : https://ift.tt/d8bFkQR Docs : https://docs.xorq.dev Roadmap Issues : https://ift.tt/d8bFkQR You can get started `pip install xorq`. Or, if you use nix, you can simply run `nix run github:xorq-labs/xorq` and drop into an IPython shell. Demo video: https://youtu.be/jUk8vrR6bCw Here are some vignettes to look into next: 1. MCP Server + Flight + XGBoost: https://ift.tt/YJGRlZX 2. 1 DuckDB + 2 Writers + 1 Reader: https://ift.tt/Oke2KTi 3. OpenAI UDF: https://ift.tt/iMey25z Some features to note: - Ibis-based multi-engine expression system: effortless engine-to-engine streaming - Cache expressions with `.cache` operator - Portable DataFusion-backed UDF engine with first class support for pandas dataframes - Serialize Expressions to and from YAML - Easily build Flight end-points by composing UDFs thanks for checking this out, and we’re here to answer any questions! https://ift.tt/6lqeOMj March 27, 2025 at 10:57PM
Show HN: Integrate – a Rust crate for numerical integration (+ an mdBook guide) I recently built Integrate, a Rust crate designed to make modular integration in Rust projects smoother and more ergonomic. If you've ever struggled with structuring Rust applications or composing independent modules cleanly, this crate might be useful for you. To make adoption easier, I’ve written a comprehensive mdBook that explains how Integrate works, real-world use cases, and best practices: https://ift.tt/QKydziS I’d love to get feedback from the Rust community! Does this approach make sense? Are there features or pain points you think should be covered? Crate on crates.io: https://ift.tt/6FeKQGy Source code: https://ift.tt/xlHZQif Looking forward to hearing your thoughts! https://ift.tt/QKydziS March 27, 2025 at 05:27AM
Show HN: I made an open source Kubernetes MCP Server to talk with K8s in English A Model Context Protocol (MCP) server for Kubernetes that enables AI assistants like Claude, Cursor, and others to interact with Kubernetes clusters through natural language. ## Features ### Core Kubernetes Operations - [x] Connect to a Kubernetes cluster - [x] List and manage pods, services, deployments, and nodes - [x] Create, delete, and describe pods and other resources - [x] Get pod logs and Kubernetes events - [x] Support for Helm v3 operations (installation, upgrades, uninstallation) - [x] kubectl explain and api-resources support - [x] Choose namespace for next commands (memory persistence) - [x] Port forward to pods - [x] Scale deployments and statefulsets - [x] Execute commands in containers - [x] Manage ConfigMaps and Secrets - [x] Rollback deployments to previous versions - [x] Ingress and NetworkPolicy management - [x] Context switching between clusters ### Natural Language Processing - [x] Process natural language queries for kubectl operations - [x] Context-aware commands with memory of previous operations - [x] Human-friendly explanations of Kubernetes concepts - [x] Intelligent command construction from intent - [x] Fallback to kubectl when specialized tools aren't available - [x] Mock data support for offline/testing scenarios - [x] Namespace-aware query handling ### Monitoring - [x] Cluster health monitoring - [x] Resource utilization tracking - [x] Pod status and health checks - [x] Event monitoring and alerting - [x] Node capacity and allocation analysis - [x] Historical performance tracking - [x] Resource usage statistics via kubectl top - [x] Container readiness and liveness tracking ### Security - [x] RBAC validation and verification - [x] Security context auditing - [x] Secure connections to Kubernetes API - [x] Credentials management - [x] Network policy assessment - [x] Container security scanning - [x] Security best practices enforcement - [x] Role and ClusterRole management - [x] ServiceAccount creation and binding - [x] PodSecurityPolicy analysis - [x] RBAC permissions auditing - [x] Security context validation ### Diagnostics - [x] Cluster diagnostics and troubleshooting - [x] Configuration validation - [x] Error analysis and recovery suggestions - [x] Connection status monitoring - [x] Log analysis and pattern detection - [x] Resource constraint identification - [x] Pod health check diagnostics - [x] Common error pattern identification - [x] Resource validation for misconfigurations - [x] Detailed liveness and readiness probe validation ### Advanced Features - [x] Multiple transport protocols support (stdio, SSE) - [x] Integration with multiple AI assistants - [x] Extensible tool framework - [x] Custom resource definition support - [x] Cross-namespace operations - [x] Batch operations on multiple resources - [x] Intelligent resource relationship mapping - [x] Error explanation with recovery suggestions - [x] Volume management and identification Note: This repo is still in development, use with caution in production. https://ift.tt/8REhu9x March 27, 2025 at 01:07AM
Show HN: Prompteus – Visual workflow builder for shipping better AI features We built Prompteus to help devs build and manage AI features without the mess — no more prompt spaghetti or scattered "hardcoded" AI API calls. Design workflows visually, deploy as APIs, and get built-in caching, logging, rate limits, and model orchestration (OpenAI, Anthropic, Mistral, etc.). It’s like Zapier for LLMs — but dev-friendly. Free up to 50k requests/month. https://prompteus.com March 26, 2025 at 11:20PM
Show HN: Fingernotes – handwritten notes which become their own preview image Hi HN, I've lurked here for ages and decided to come out of the shadows for my latest side project which reached the point where it’s sort of fun to use and hopefully not totally embarrassing to share. Hacking fingernotes.com together over a couple of weeks was a creative outlet when work got stressful. I think of it as digital sticky notes. The goal was to make notes with a personal touch that are easy to write and share. I also wanted them to appear as their own link preview image on supported platforms. That way when you send the link to a note, the person sees the message without following the link. Let me know what you think! I drew inspiration from Apple's quick notes: low latency made scribbling a pleasure, and sending notes to friends felt warm and original compared to a typical exchange. It was also intriguing to see my handwriting printed in a message chat. In a time of rising artificial generation, spreading my clumsy handwriting feels like an act of rebellion. But I dislike the light background in Apple notes, which I don't think you can change when sharing. More importantly, no one sent a note back. With fingernotes the low-friction interaction is meant to make creating notes simple. I also find the image previews aesthetically more pleasing. For implementation, fingernotes are publicly accessible links to collections of strokes that have been persisted to a Cloudflare D1 database and rendered in SVG. Like pen on a sticky note, each stroke is immutable but anyone can add to a note if they have the link. You can't undo strokes, so if you mess up your note just throw it out and start a new one. Having append-only collections avoids handling order of operations when multiple people edit the same note. Hosting it as a Cloudflare worker made it easy to get up and running. There's some latency in Safari on iOS which is absent on desktop. It's noticeable compared to Apple notes and I'm afraid it's a limitation of the browser. https://ift.tt/LbQ83i4 March 23, 2025 at 12:02PM
Show HN: XYMake – Turn Your Posts into LLM-Ready Data I just built XYMake ( https://xymake.com ), a tool that lets you convert any X (Twitter) thread into clean markdown, making your conversations accessible for LLMs, MCPs, or any API. ## What it does: - Transforms any X thread URL into markdown by simply changing "x.com" to "xymake.com" in the URL - OAuth2 login to "free your data" and make your threads available - Auto-generates OG images with token counts and participant info for easy sharing - Serves different content types based on whether the request is from a crawler, browser, or agent ## Why I built it: I believe people should have the right to own and use their own data. While X/Twitter uses our content to train Grok, we should be able to leverage our own conversations for similar purposes. I built this as a proof of concept in one day (what started as a 30-minute experiment turned into a 10-hour flow state). It's built entirely on Cloudflare Workers and uses some interesting techniques to serve different content types to different consumers. ## Technical highlights: - Request identification to serve HTML+OG images to crawlers while providing raw markdown to agents - Preloading OG image generation using ctx.waitUntil for near-instant loading when shared - Optimized OG image rendering across platforms using workers-og Try it out with any X thread - just replace "x.com" with "xymake.com"! Example: https://ift.tt/YhrB3G8 Feedback welcome! This is just the beginning of what's possible when we reclaim our conversational data. https://xymake.com March 25, 2025 at 12:32AM
Show HN: Flappy Gopher with Online Ranking – A Go/WebAssembly Browser Game I implemented a Flappy Bird style game using Go and the Ebitengine (2D Game Engine), compiled to WebAssembly to run in the browser. Tech stack: - Frontend: Go + Ebitengine -> WebAssembly - Backend: Cloudflare Pages Functions - Database: Cloudflare D1 Features: - Daily/weekly/monthly online leaderboards - Jump history recording and verification to prevent cheating - Fully serverless architecture to minimize operational costs Source code: https://ift.tt/JICsgH7 This started as an experimental project to create a game in Go and compile it to WebAssembly for browser play. By leveraging Cloudflare's edge computing, I've been able to deploy it globally with low latency. Feedback and suggestions for improvement are welcome! https://ift.tt/QRrY1Uy March 23, 2025 at 05:42PM
Show HN: DAPS – Prime-Adaptive Search for Discontinuous Optimization Problems I've been working on a global optimization algorithm that uses prime number-based adaptive grid search. It dynamically adjusts resolution by increasing or decreasing prime numbers as "resolution knobs" — allowing it to handle discontinuities, sharp valleys, and chaotic landscapes better than naive grid search. The repo includes Python and PyTorch-compatible versions, benchmarks against grid search, and a research paper. Would love feedback from optimization, ML, or numerical analysis folks. Curious if anyone sees potential applications or improvements. GitHub: https://ift.tt/Q2qaHP1 Paper: https://ift.tt/6tQuxsH.... https://ift.tt/Q2qaHP1 March 23, 2025 at 11:19AM
Show HN: I build a tool that will tell you what to respond in negotations After reading the book Getting to Yes, I really want some tool to help me negotiate more efficiently without having to memorize everything principle. You start by putting in interests of each party, then you can explore different functions: how to respond to the other party, explore objective criteria out there or brainstorm more negotiation options. Still working on it! Leave me feedback if you have any suggestions! https://ift.tt/78R6iQm March 23, 2025 at 03:31AM
Show HN: GoCard – A file-based spaced repetition system built in Go Hi HN! I'm excited to share GoCard, a terminal-based spaced repetition system I built that uses plain Markdown files as its data source. I've always been frustrated with existing spaced repetition tools that lock my knowledge into proprietary formats or require constant internet access. As a developer who lives in terminals and text editors, I wanted something that: 1. Stores cards as plain text files I can edit with any editor 2. Works seamlessly with Git for versioning and sync 3. Runs in a terminal without distractions 4. Has first-class support for code snippets and programming concepts GoCard implements the SM-2 algorithm (the same one used by Anki) but instead of a database, it uses a simple directory structure where: - Each card is a Markdown file with YAML frontmatter - Directories represent decks and subdecks - Everything is editable with standard tools *Key features:* - Distraction-free terminal UI built with BubbleTea - Real-time file watching (edit cards in your editor while reviewing) - Code syntax highlighting for 50+ languages - Vim/Emacs keybindings for efficient navigation - Hierarchical deck organization via directories - Cross-platform (Linux, macOS, Windows) What sets GoCard apart from other SRS tools is its developer-centric approach. Create cards with your favorite editor, organize them with your file manager, version them with Git, and review them in a clean terminal interface. I built this because I wanted a knowledge management system that worked with my developer workflow rather than against it. Making everything file-based means I can apply all my existing text-processing skills and tools. The project is v0.1.0, implemented in Go, and available at: https://ift.tt/z6L92EZ I'd especially appreciate feedback on the UX design and any suggestions for making it more intuitive for terminal users. Has anyone else built similar file-based knowledge tools? What patterns worked well for you? https://ift.tt/z6L92EZ March 23, 2025 at 02:35AM
Show HN: Bonsai – A Competitive Ternary Weight LLM Introducing Bonsai 0.5B, one of the first ternary-weight LLMs to rival full-precision models of similar size, such as Qwen 2.5 0.5B and MobileLLM 0.5B. Trained on just 3.8B tokens, using 1,000x less data than other models, Bonsai redefines what’s possible for ultra-efficient training in low-bit models. Next, we're building larger and more powerful ternary-weight models for the edge. Technical Report: https://ift.tt/El8qo5S... Model (Unpacked): https://ift.tt/vVsIWy2 Reach us: team@deepgrove.ai https://ift.tt/DgmuoBj March 22, 2025 at 12:52AM
Show HN: GizmoSQL – Run DuckDB as a Server with Arrow Flight SQL Hi, I'm Philip Moore - the founder of GizmoData, and creator of GizmoSQL - an Apache Arrow Flight SQL Server - with DuckDB (or SQLite) back-end execution engines. GizmoSQL is a composable SQL server with Arrow Flight SQL, DuckDB, and SQLite - with the intention of making it easy to run DuckDB (or SQLite) as a server - usable by multiple people from a client (remote) computer. It also adds security (authentication) and encryption of traffic with TLS. To run GizmoSQL - see the steps in the README.md - where you can see how easy it is to run the server as well as how to connect via ADBC and JDBC from a remote client - such as DBeaver, Python, etc. The easiest way to run GizmoSQL is via Docker - but there are downloads for Linux and macOS for both x86-64 and arm64 platforms (download links in the README). Why?: As you may know, DuckDB and SQLite are embedded systems - they don't enable client connectivity, and they aren't really designed for concurrency. I've built GizmoSQL to work around that - because I believe the DuckDB engine is very powerful, and I feel like a lot of customers overpay and run distributed compute (i.e. Spark) when they don't really need to. Making it easy to have remote connectivity to DuckDB can make it easier to migrate SQL workloads from Spark or other expensive commercial platforms to this engine - with a much simpler architecture/infrastructure. It is my intention to make GizmoSQL a commercial product - licensed for production use by organizations, but free for developers to code with - evaluate, and test. A little bit of backstory: * I built the initial version of this while working for a former employer - it wasn't their core focus, so they open-sourced that early version. After I left there, I forked the product and have improved it substantially - to support concurrency of both reads and writes, improving security, as well as keeping it up to date with the latest versions of Apache Arrow and DuckDB. * This project evolved from a prototype created by the brilliant Tom Drabas. * It feels a little weird trying to make a commercial product based upon DuckDB, but MotherDuck started it :P - and I've contributed (albeit very little) to the DuckDB and Apache Arrow projects in the form of a couple of PRs. I'm really excited about this project - I have run benchmarks of this product against commercial platforms such as Snowflake and Databricks SQL - and it holds its own running the 22-query TPC-H SF1TB benchmark, especially on cost. See the graph at: https://ift.tt/h1cJ8eb Getting started: Github README: https://ift.tt/vlPk9De... DockerHub: https://ift.tt/8qJx9Mh GizmoSQL homepage: https://ift.tt/h1cJ8eb Phil's Github profile: https://ift.tt/E5tC8QJ Thanks for your time and feedback in advance. https://ift.tt/KclQyXR March 21, 2025 at 12:45AM
Show HN: SpongeCake – open-source SDK for OpenAI computer use agents Hey HN! Wanted to quickly put this together after seeing OpenAI launched their new computer use agent We were excited to get our hands on it, but quickly realized there was still quite a bit of set-up required to actually spin up a VM and have the model do things. So we wanted to put together an easy way to deploy these OpenAI computer use VMs in an SDK format and open source it Hopefully this tooling is helpful to other folks building AI agents! Here’s a link to the repo ( https://ift.tt/XLQ0Zra ) - please try it out and give us a star. If you have any feedback, add it as a comment to this post! Or if you simply just love spongecake, show support for the delicious treat https://ift.tt/XLQ0Zra March 20, 2025 at 10:16PM
Show HN: AgentKit – JavaScript Alternative to OpenAI Agents SDK with Native MCP Hi HN! I’m Tony, co-founder of Inngest. I wanted to share AgentKit, our Typescript multi-agent library we’ve been cooking and testing with some early users in prod for months. Although OpenAI’s Agents SDK has been launched since, we think an Agent framework should offer more deterministic and flexible routing, work with multiple model providers, embrace MCP (for rich tooling), and support the unstoppable and growing community of TypeScript AI developers by enabling a smooth transition to production use cases. This is why we are building AgentKit, and we’re really excited about it for a few reasons: Firstly, it’s simple. We embrace KISS principles brought by Anthropic and HuggingFace by allowing you to gradually add autonomy to your AgentKit program using primitives: - Agents: LLM calls that can be combined with prompts, tools, and MCP native support. - Networks: a simple way to get Agents to collaborate with a shared State, including handoff. - State: combines conversation history with a fully typed state machine, used in routing. - Routers: where the autonomy lives, from code-based to LLM-based (ex: ReAct) orchestration The routers are where the magic happens, and allow you to build deterministic, reliable, testable agents. AgentKit routing works as follows: the network calls itself in a loop, inspecting the State to determine which agents to call next using a router. The returned agent runs, then optionally updates state data using its tools. On the next loop, the network inspects state data and conversation history, and determines which new agent to run. This fully typed state machine routing allows you to deterministically build agents using any of the effective agent patterns — which means your code is easy to read, edit, understand, and debug. This also makes handoff incredibly easy: you define when agents should hand off to each other using regular code and state (or by calling an LLM in the router for AI-based routing). This is similar to the OpenAI Agents SDK but easier to manage, plan, and build. Then comes the local development and moving to production capabilities. AgentKit is compatible with Inngest’s tooling, meaning that you can test agents using Inngest’s local DevServer, which provides traces, inputs, outputs, replay, tool, and MCP inputs and outputs, and (soon) a step-over debugger so that you can easily understand and visually see what's happening in the agent loop. In production, you can also optionally combine AgentKit with Inngest for fault-tolerant execution. Each agent’s LLM call is wrapped in a step, and tools can use multiple steps to incorporate things like human-in-the-loop. This gives you native orchestration, observability, and out-of-the-box scale. You will find the documentation as an example of an AgentKit SWE-bench and multiple Coding Agent examples. It’s fully open-source under the Apache 2 license. If you want to get started: - npm: npm i @inngest/agent-kit - GitHub: https://ift.tt/c8HKfeq - Docs: https://ift.tt/cQIB0Fz We’re excited to finally launch AgentKit; let us know what you think! https://ift.tt/c8HKfeq March 20, 2025 at 10:57PM
Show HN: Codemcp – Claude Code for Claude Pro subscribers – ditch API bills Hi all! I normally work on the PyTorch project but I've been on baby leave for the past month, so I've been playing around with AI as a user rather than a framework implementor. I really liked the agent experience with Claude Code, but I couldn't really justify spending so many dollars on API costs for random side projects. I already pay for a Claude Pro subscription though, and it turns out you can simulate many of Claude Code's features with an MCP. If you have a Pro subscription, check this out! I think it really captures the Claude Code experience quite well, without forcing you to pay for API tokens. https://ift.tt/EofZM3l March 13, 2025 at 11:59PM
Show HN: I built an extension to book Airbnbs directly Hi Hackers, My wife and I have been slow traveling around the world for last 3 years, and our top travel hack that has saved us most amount of money is to book directly with Airbnb host. We usually rent for a month so savings in fees are easily ~$300 - $500 on each stay, i.e. about 15-20%. Not every host offers it we respect that, but there are so many hosts out there that would rather have you book directly via their website and we as guests want the same. The problem is these hosts can’t promote or say that on Airbnb. This inspired me to build OpenBnB.org, where I’ve started collecting direct booking websites of hosts. So far, I have around 1,500 hosts representing 150,000 listings – most of them in USA. The idea is to give guests more options to book, and hosts another channel to distribute their listings. The first solution is a browser extension that makes it really easy to find the direct booking website of hosts when you’re browsing Airbnb. It just a one-time easy install with no sign ups. It: 1) highlights all the directly-bookable listings on the search page, 2) lets you search listings only from hosts with direct booking option and then of course 3) gives you the direct booking link on the listing page. What I like about this solution is it doesn’t require guests to go to a different website; they can just browse Airbnb (largest inventory of short-term rentals) as usual and get more options to book when available. Other than savings, I think there is something about the direct relationship with hosts and guests, without any intermediaries. The cool think is I estimate about 20-33% of Airbnbs can be booked directly! That means I’ve only collected around 6 to 9% of all directly-bookable listings around the world. I’ve seen some other chrome extensions do something similar, but none of them highlight these listings nor let you search listings only with hosts that offer direct booking. What do you think? Try it out and let me know if this is useful. I’m also planning to spin up a website that only has listings from these hosts, kind of like a meta-search engine for vacation rental websites. https://ift.tt/GUVld6F March 20, 2025 at 01:37AM
Show HN: I Built a DEX That Pays You to Take Risks – and You Can Own It Meme coins are a casino, and most DEXs just take your fees and call it a day. I flipped the script with EaglesTrader every trade you make, win or lose, gets you a cashback in SOL—real risk compensation, not just another airdrop gimmick. Here’s the kicker: ownership isn’t locked to VCs or insiders. It’s 100% public. Hold the tokens, and you earn a cut of all platform fees—proportional to your stake. No bots, no gatekeepers, just a community-owned trading engine. Built it on Solana for speed and low costs, sourcing meme coins from Pump.fun and other solana platforms to keep the chaos flowing. The tech’slive SOL cashback’s handled via smart contracts, and fee distribution’s transparent on-chain. Early users are already stacking SOL and tokens. Thoughts on this model? Is risk compensation the future of DeFi, or just a meme coin fever dream? https://ift.tt/2XmiuKJ March 19, 2025 at 11:33PM
Show HN: We built an agentic image editor that preserves the original structure Hi everyone, I’ve been experimenting with app where you can edit images in your camera roll simply by tweaking your photo’s metadata (changing location/time) and our agent will contextually regenerate the photo in that place & time in one shot. There's no prompting involved. One of the hardest problems we’ve seen with these ai image editing/creation tools is that they struggle with preserving the subjects of the original image (faces, genders, number of people, bodies, animals, etc), and I think we’ve gotten a step closer to making it feel more realistic. The gallery has some examples that people have been regenerating. https://ift.tt/Tjsuq8Y Here’s a demo: https://ift.tt/jMnQbAo Feel free to dm me on Twitter: https://twitter.com/sakofchit if you’d like to try out the TestFlight in the meantime Would love to know what y'all think! https://ift.tt/Tjsuq8Y March 19, 2025 at 11:14PM
Show HN: I Made an Escape Room Themed Prompt Injection Challenge We launched an escape room-themed AI Escape Room challenge with prizes of up to $10,000 where you need to convince the escape room supervisor LLM chatbot to give you the key using prompt injection techniques. Hope you like it :) https://ift.tt/A6lqHJb March 19, 2025 at 01:12AM
Show HN: I Created a Fork of Ghost CMS with an AI Editor and Native ECommerce After many months of hard work and innovation, we've built a platform that takes Ghost CMS to the next level. Cartanza integrates native AI-powered content and image creation and native eCommerce functionality directly into the blogging experience. This means you can now: - Generate high-quality blog content and images with AI—no more copy-pasting between tools. - Seamlessly embed eCommerce capabilities, linking products and collections directly into your blog posts. - Manage subscriptions, merchandise, and content marketing all in one place. To see Cartanza in action, check out our demo video on YouTube ( https://youtu.be/CQQDqKjOM-Y ). In the video, I walk you through our platform's key features and show how easy it is to get started with our innovative solution. We're excited to invite bloggers, content creators, and eCommerce enthusiasts to explore Cartanza. Join us as we redefine the blogging experience—where creativity meets commerce, powered by cutting-edge AI. https://cartanza.com/ March 19, 2025 at 12:27AM
Show HN: "Git who" – A new CLI tool for industrial-scale Git blaming I've always wanted a better way to explore the authorship data embedded in a Git commit log. I'm having fun building a CLI tool to do this. It's a bit like the "Contributors" tab on Github that shows you how many commits each contributor has made but much faster and with many more options. If you get a chance to try it out, please let me know. I'd love to hear feedback and suggestions. Thank you! https://ift.tt/YHx4BZN March 19, 2025 at 01:50AM
Show HN: Localscope–Limit scope of Python functions for reproducible execution localscope is a small Python package that disassembles functions to check if they access global variables they shouldn't. I wrote this a few years ago to detect scope bugs which are common in Jupyter notebooks. It's recently come in handy writing jax code ( https://ift.tt/6jwrUDZ ) because it requires pure functions. Thought I'd share. https://ift.tt/S6WNVLK March 17, 2025 at 11:03PM
Show HN: Quickly connect to WiFi by scanning text, no typing needed I travel and work remotely a lot. Every new place—hotels, cafes, coworking spaces—means dealing with a new WiFi network. Sometimes there's a QR code, which is convenient, but usually, it's a hassle: manually finding the right SSID (especially frustrating when hotels have one SSID per room), then typing long, error-prone passwords. To simplify this, I made a small Android app called Wify. It uses your phone's camera to capture WiFi details (network name and password) from printed text, then generates a QR code right on your screen. You can instantly connect using Google Circle to Search or Google Lens. You can also import an image from your gallery instead of using the camera. Currently, it's Android-only since I daily-drive a Pixel 7, and WiFi APIs differ significantly between Android and iOS. Play Store link: https://ift.tt/8ETFei9... I'd appreciate your feedback or suggestions! https://ift.tt/mEiJG73 March 16, 2025 at 07:28PM
Show HN: 10 teams are racing to build a pivotal tracker replacement A lot has changed since the shutdown of pivotal tracker was discussed here. As there were no viable alternatives it seems every month there was a new project popping up. With the last month before the sunsetting approaching, it starts to get exciting who will make it in time, who stays in the race and what the differentiating features of the projects will be. https://bye-tracker.net March 16, 2025 at 07:00PM
Show HN: Nash, I made a standalone note with single HTML file Hello HN, I hope it will posted as well. I made a note in single html file. This does not require a separate membership or installation of the software, and if you download and modify an empty file, you can modify and read it at any time, regardless of online or offline. It can be shared through messengers such as Telegram, so it is also suitable to share contents with long articles and images. It is also possible to host and blog because it is static html file content. https://ift.tt/jD1LXtf March 14, 2025 at 07:21AM
Show HN: Kill SaaS with Open Source KillSaaS is my answer to subscription software in the AI era. I'm building this because I believe small teams can use modern AI tools to create free alternatives to giants like Figma and DocuSign in weeks, not years. We're creating a platform where developers vote on which SaaS to replace, then build it together as open source. wdyt? https://ift.tt/oH0iXJa March 16, 2025 at 02:50AM
Show HN: Basic Memory – Build a knowledge graph from Claude conversations Basic Memory is an open-source tool that enables Claude to build and navigate a persistent knowledge graph based on your conversations. It solves the problem of lost context in AI interactions by storing knowledge in standard Markdown files on your computer. I built this because I found myself constantly repeating information to LLMs and wanted a system where my knowledge grew naturally through conversations while maintaining complete control over my data. Demo video: https://ift.tt/4FQHyPv Key features: - Continue conversations exactly where you left off without repetition - All knowledge stays in local Markdown files you can edit anytime - Works with Claude Desktop via the Model Context Protocol - Seamless integration with Obsidian for visualization and editing - Fully open source (AGPL) The system works by creating structure from simple markdown patterns: - Observations with categories: `- [category] fact #tag` - Relations between documents: `- relation_type [[WikiLink]]` or plain `[[Wikilinks]]` - These patterns emerge naturally during conversations When you chat with Claude, you can simply say "Let's continue our conversation about X" and it will build context from your knowledge base, without needing to upload files every time. GitHub: https://ift.tt/HbPA2Md Docs: https://ift.tt/lFn4v9a Website: https://ift.tt/Zl1Fp86 Requires Claude Desktop or other MCP host and Python 3.12+ I'd love feedback from the HN community, particularly from those interested in knowledge management or AI applications. https://ift.tt/HbPA2Md March 15, 2025 at 11:49PM
Show HN: Psyllium, a Ruby Gem to make Fibers behave more like Threads Hi everyone! I created this small Ruby Gem to add some convenient methods to the Fiber class to make it easier to use in the same way a Thread object can be used. This was born out of my frustration that the current implementation of the Fiber class makes it difficult to retrieve the final value of a block passed to a Fiber, especially when creating a fiber via the `schedule` class method. I appreciate any feedback anyone has. https://ift.tt/Z32hXRE March 15, 2025 at 12:09AM
Show HN: OCR Benchmark Focusing on Automation OCR/Document extraction field has seen lot of action recently with releases like Mixtral OCR, Andrew Ng's agentic document processing etc. Also there are several benchmarks for OCR, however all testing for something slightly different which make good comparison of models very hard. To give an example, some models like mixtral-ocr only try to convert a document to markdown format. You have to use another LLM on top of it to get the final result. Some VLM’s directly give structured information like key fields from documents like invoices, but you have to either add business rules on top of it or use some LLM as a judge kind of system to get sense of which output needs to be manually reviewed or can be taken as correct output. No benchmark attempts to measure the actual rate of automation you can achieve. We have tried to solve this problem with a benchmark that is only applicable for documents/usecases where you are looking for automation and its trying to measure that end to end automation level of different models or systems. We have collected a dataset that represents documents like invoices etc which are applicable in processes where automation is needed vs are more copilot in nature where you would need to chat with document. Also have annotated these documents and published the dataset and repo so it can be extended. Here is writeup: https://ift.tt/a6ejtim Dataset: https://ift.tt/crNBC5o Github: https://ift.tt/rRJVzgs Looking for suggestions on how this benchmark can be improved further. https://ift.tt/a6ejtim March 13, 2025 at 02:19AM
Show HN: Pi Labs – AI scoring and optimization tools for software engineers Hey HN, after years building some of the core AI and NLU systems in Google Search, we decided to leave and build outside. Our goal was to put the advanced ML and DS techniques we’ve been using in the hands of all software engineers, so that everyone can build AI and Search apps at the same level of performance and sophistication as the big labs. This was a hard technical challenge but we were very inspired by the MVC architecture for Web development. The intuition there was that when a data model changes, its view would get auto-updated. We built a similar architecture for AI. On one side is a scoring system, which encapsulates in a set of metrics what’s good about the AI application. On the other side is a set of optimizers that “compile” against this scorer - prompt optimization, data filtering, synthetic data generation, supervised learning, RL, etc. The scoring system can be calibrated using developer, user or rater feedback, and once it’s updated, all the optimizers get recompiled against it. The result is a setup that makes it easy to incrementally improve the quality of your AI in a tight feedback loop: You update your scorers, they auto-update your optimizers, your app gets better, you see that improvement in interpretable scores, and then you repeat, progressing from simpler to more advanced optimizers and from off-the-shelf to calibrated scorers. We would love your feedback on this approach. https://build.withpi.ai has a set of playgrounds to help you quickly build a scorer and multiple optimizers. No sign in required. https://code.withpi.ai has the API reference and Notebook links. Finally, we have a Loom demo [1]. More technical details Scorers: Our scoring system has three key differences from the common LLM-as-a-judge pattern. First, rather than a single label or metric from an LLM judge, our scoring system is represented as a tunable tree of metrics, with 20+ dimensions which get combined into a final (non-linear) weighted score. The tree structure makes scores easily interpretable (just look at the breakdown by dimension), extensible (just add/remove a dimension), and adjustable (just re-tune the weights). Training the scoring system with labeled/preference data adjusts the weights. You can automate this process with user feedback signals, resulting in a tight feedback loop. Second, our scoring system handles natural language dimensions (great for free-form, qualitative questions requiring NLU) alongside quantitative dimensions (like computations over dates or doc length, which can be provided in Python) in the same tree. When calibrating with your labeled or preference data, the scorer learns how to balance these. Third, for natural language scoring, we use specialized smaller encoder models rather than autoregressive models. Encoders are a natural fit for scoring as they are faster and cheaper to run, easier to fine-tune, and more suitable architecturally (bi-directional attention with regression or classification head) than similar sized decoder models. For example, we can score 20+ dimensions in sub-100ms, making it possible to use scoring everywhere from evaluation to agent orchestration to reward modeling. Optimizers: We took the most salient ML techniques and reformulated them as optimizers against our scoring system e.g. for DSPy, the scoring system acts as its validator. For GRPO, the scoring system acts as its reward model. We’re keen to hear the community’s feedback on which techniques to add next. Overall stack: Playgrounds next.js and Vercel. AI: Runpod and GCP for training GPUs, TRL for training algos, ModernBert & Llama as base models. GCP and Azure for 4o and Anthropic calls. We’d love your feedback and perspectives: Our team will be around to answer questions and discuss. If there’s a lot of interest, happy to host a live session! - Achint, co-founder of Pi Labs [1] https://ift.tt/Dgok0t4 https://ift.tt/oKT5wYL March 14, 2025 at 07:07PM
Show HN: Bubbles, a vanilla JavaScript web game Hey everybody, you might remember my older game, Lander! It made a big splash on Hacker News about 2 years ago. I'm still enjoying writing games with no dependencies. I've been working on Bubbles for about 6 months and would love to see your scores. If you like it, you can build your own levels with my builder tool: https://ift.tt/jTkDaIU and share the levels here or via Github. https://ift.tt/gNfZPm7 March 13, 2025 at 11:18PM
Show HN: Time Portal – Get dropped into history, guess where you landed Hi HN! I love imagining the past, so I made Time Portal, a game where you are dropped into a historical event and see AI video footage from that moment. You have to guess where you are in time and on the map. It’s like GeoGuessr (and heavily inspired by it!) but for historical events. The videos are all created with AI. It’s a pipeline of Flux (images), Kling (video), and mmaudio (audio). The videos aren’t always historically accurate to the last detail. They might incorporate elements of folklore or have details from popular beliefs about the way things looked rather than the latest academic research on how they looked. I’m thinking a lot about how to make the game more interactive. One thing that makes Geoguessr so fun for me is that you can move infinitely and always find more details to help you pinpoint the location. I want Time Portal to have a similar quality. I have a few ideas to try soon that will hopefully make the game more interactive and infinite. https://ift.tt/l3YatFV March 13, 2025 at 01:53AM
Show HN: Translate Japanese Manga and Korean Manhwa with Chrome Extension If you are a manga or manhwa lover, you must understand the feeling of waiting for your favourite series being translated into English or sometimes your native language. Now, you can translate them in real-time with Fakey Chrome extension! https://ift.tt/GFYNKzH March 9, 2025 at 07:55PM
Show HN: We built a Plug-in Home Battery for the 99.7% of us without Powerwalls Hi HN! I’m Cole Ashman, founder of Pila Energy. I’ve spent my career working on home energy systems—first as an engineer on Tesla’s Powerwall, where I focused on the Backup Gateway, Solar Inverter, and metering systems. More recently, I led Product at SPAN, where we built the Smart Electrical Panel and integrated with most major home solar, EV, and battery systems. Pila ( https://pila.energy/ ) is a home battery that plugs into a standard wall outlet, provides smart backup power, energy shifting, and grid services. It’s more than a power bank—it’s a distributed energy system that can scale across multiple rooms, entire buildings, and work together in real time as a coordinated system. We built Pila to be local first with an open API to allow developers to build use cases on top of our hardware (Home Assistant, etc). Big batteries like Tesla Powerwall and Enphase are great if you own a home and can afford a $10K+ electrical project, but they require permanent installation, electricians, and panel upgrades—which makes them inaccessible for renters, apartments, and cost-conscious homeowners. Over 50% of the cost of installing a Powerwall isn’t even the battery itself—it’s soft costs: labor, permitting, etc. We wanted to create an entry point for more people to access energy security at home. How does it work? Plug Pila into any 120V wall outlet, and power passes through to connected devices and appliances. The inverter, LFP battery, BMS, grid disconnection, controller, and wireless connectivity are all built in. (details at https://ift.tt/FxDUNRq ) When an outage happens, the onboard inverter detects the power loss within 20ms and automatically disconnects from the grid (islanding). Whether you’re home or away, backup kicks in instantly. A built-in cellular radio ensures you get a notification even if your home WiFi is out. Pila is 1.6kWh. That will backup a standard fridge for over a day. One key challenge we faced with a distributed architecture was coordination between batteries, for things like solar-following and managing real-time draw from your utility connection. Unlike large garage systems, where you can run a wired CAN bus, our batteries are spread across the home. We’re solving this with a sub-GHz wireless mesh network—self-healing, coordinator-less, and designed to make setup and expansion as simple as plugging in another unit. Long-term, we’d love to open up this protocol to provide a more reliable communication layer for energy products in noisy built environments—reducing reliance on consumer Wi-Fi. We want to deliver the value you’d expect from a whole-home battery like Powerwall, in a plug-in format. That means going beyond a basic lead acid UPS with real home energy management, useful insights about power use, power larger loads like sump pumps, and even deliver grid services. Most portable batteries are missing the functionality that makes a home battery useful: no bidirectional power, no integration with solar or smart home systems, and no ability to manage home energy dynamically. They tend to be boxy, ruggedized, meant to be moved around, not seamlessly integrated into your living space. On top of that, many use e-mobility battery chemistries, which are great for delivering high power on demand but wear out faster when cycled daily for home energy use. As a renter myself, I started Pila because these awesome energy products aren’t accessible enough. And frankly, generators are loud, expensive, and a pain to deal with. Even many Powerwall owners I’ve talked to say they really care about keeping the fridge, WiFi, and a sump pump running—so why does energy resilience have to be so complicated and expensive? As the grid struggles to keep up with demand, we believe modular, renter-friendly batteries can make home energy resilience more accessible. What's been your experience with home batteries? What recent power outages have you had, and how were you affected? https://pilaenergy.com March 11, 2025 at 09:18PM
Show HN: Hot Design – Like Hot Reload, but a Runtime Visual Designer Hi HN, Nick here, from the open-source Uno Platform team. You are likely familiar with Hot Reload , pioneered by Flutter. We’ve taken that concept further and built Hot Design , let me introduce it to you. Architecturally, Hot Design idea is simple: 1. In your IDE, pause the live, running app at runtime, turning it into a designer. 2. Modify the UI directly on the designer —add elements, adjust layouts, tweak bindings etc. 3. Resume the app without restarting or losing state. We built Hot Design to address the frustration of slow iteration cycles when building and tweaking UI or debugging data bindings in apps targeting multiple platforms. Here’s a detailed explanation and a video of Hot Design in action: https://ift.tt/mNz5Kk1 I can see potential criticism: It will get killed by AI, it’s another abstraction over code, it is .NET etc. Happy to respond to those comments if they come; we put a lot of thought into Hot Design and would love to hear it challenged! Nick https://ift.tt/mNz5Kk1 March 11, 2025 at 07:40AM
Show HN: Chrome Extension for ChatGPT to organize conversations into folders Hi HN, I'm Alex, a full-stack developer from Toronto, Canada. I recently built a Chrome extension that organizes ChatGPT conversations into folders, allowing users to sort and save important information for easy reference. The idea for this extension came from a friend who highlighted the lack of good (and affordable) ChatGPT organizers. Many existing tools were either low-quality or overpriced, so I decided to create one that was both reliable and accessible. I built the extension using plain JavaScript and developed a backend with Express to handle Google authentication. For storage, I used MongoDB, enabling all users with an account to save their folder structures and conversation data. Initially, I planned to charge $5 per month to cover costs since originally this extension was intended as a portfolio project addressing a real-world problem. However, just as I finished the main functionality and was about to implement payments, ChatGPT announced an official feature similar to one my extension was providing. Rather than continue competing in a market with an "official" solution, I decided to stop development. But I didn't want my work to go to waste, so I chose to release it for free, motivated by a desire to share it with the community. I made some changes to eliminate the backend. Now the extension stores all folder structures and content locally in Chrome storage. Luckily, I had some old code to reuse for this. The extension is now live on the Chrome Web Store. This project introduced me to a lot of new challenges with technologies I hadn’t used before, but I’m grateful for the experience and the skills I gained along the way. I hope you find it useful! Links to the extension and its website: https://ift.tt/itznvfe... https://ift.tt/1XDOlYi If you have any questions or suggestions, feel free to reach out in the comments or via email at georgepozdman@gmail.com. https://ift.tt/1XDOlYi March 11, 2025 at 04:41AM
Show HN: SQLite vs. GoatDB: Surprising Benchmark Results for a New Realtime NoDB We introduced GoatDB just three weeks ago and have been blown away by the community’s response. Your feedback and excitement genuinely exceeded our expectations—so first and foremost, thank you from all of us! For anyone just hearing about it: GoatDB is a real-time, version-controlled NoDB for Deno and React that’s edge-native, meaning it requires only minimal backend infrastructure without heavy server components. It’s designed for prototyping, self-hosting, single-tenant apps, and even ultra-light multi-tenant setups if you want to keep your backend minimal. One of the biggest requests we heard was, “Where are the benchmarks?” We’re thrilled to share them now. The numbers tell an interesting story: in some tests, our distributed-commit-graph architecture can be significantly slower than SQLite; in others, it’s surprisingly faster. This is what happens when you put synchronization and collaboration first (instead of disk I/O). But let’s be crystal clear: GoatDB isn’t a drop-in SQLite replacement. It has a fundamentally different architecture designed for real-time distributed scenarios and cryptographic auditing, so it comes with its own unique tradeoffs. Key Takeaways: - Simple reads and incremental queries can be blazingly fast , especially with concurrency and real-time syncing. - Opening large repositories can take longer if everything stays in memory (we’re exploring a zero-copy format to address that). - It’s not just a SQLite wrapper—this is a fundamentally different approach with its own unique tradeoffs. We’ve documented how to run these same benchmarks in our documentation if you’re curious. Once again, thank you so much for the excitement and support. We’re a small team on a mission to reimagine what a lightweight database can do, and your feedback keeps us inspired. We can’t wait to see what you build with GoatDB! Checkout our Github Repo: https://ift.tt/6deXMam https://ift.tt/P9ZzREm March 10, 2025 at 10:34PM
Show HN: I built a Figma plugin for quick data calculations I lead design on a B2B SaaS product. It's quite data-heavy in places. Using placeholder content in data tables, checkout summaries and dashboards is a big no-no for us. It might seem like using random numbers saves time at first, but sooner or later there's documentation to write and plenty of clarifications to be made. It throws off customers during interviews – "hey, that's not really my sales target!". It confuses stakeholders at review time– "what's this data point supposed to be?" I built a Figma calculator plugin for my team so that they spend less time doing mental maths. It calculates sums, differences averages and percentages, and makes it easy to use real-looking data in designs. https://ift.tt/DwWGPlc March 10, 2025 at 07:11PM
Show HN: Evolving Agents Framework Hey HN, I've been working on an open-source framework for creating AI agents that evolve, communicate, and collaborate to solve complex tasks. The Evolving Agents Framework allows agents to: Reuse, evolve, or create new agents dynamically based on semantic similarity Communicate and delegate tasks to other specialized agents Continuously improve by learning from past executions Define workflows in YAML, making it easy to orchestrate agent interactions Search for relevant tools and agents using OpenAI embeddings Support multiple AI frameworks (BeeAI, etc.) Current Status & Roadmap This is still a draft and a proof of concept (POC). Right now, I’m focused on validating it in real-world scenarios to refine and improve it. Next week, I'm adding a new feature to make it useful for distributed multi-agent systems. This will allow agents to work across different environments, improving scalability and coordination. Why? Most agent-based AI frameworks today require manual orchestration. This project takes a different approach by allowing agents to decide and adapt based on the task at hand. Instead of always creating new agents, it determines if existing ones can be reused or evolved. Example Use Case: Let’s say you need an invoice analysis agent. Instead of manually configuring one, our framework: Checks if a similar agent exists (e.g., a document analyzer) Decides whether to reuse, evolve, or create a new agent Runs the best agent and returns the extracted information Here's a simple example in Python: import asyncio from evolving_agents.smart_library.smart_library import SmartLibrary from evolving_agents.core.llm_service import LLMService from evolving_agents.core.system_agent import SystemAgent async def main(): library = SmartLibrary("agent_library.json") llm = LLMService(provider="openai", model="gpt-4o") system = SystemAgent(library, llm) result = await system.decide_and_act( request="I need an agent that can analyze invoices and extract the total amount", domain="document_processing", record_type="AGENT" ) print(f"Decision: {result['action']}") # 'reuse', 'evolve', or 'create' print(f"Agent: {result['record']['name']}") if __name__ == "__main__": asyncio.run(main()) Next Steps Validating in real-world use cases and improving agent evolution strategies Adding distributed multi-agent support for better scalability Full integration with BeeAI Agent Communication Protocol (ACP) Better visualization tools for debugging Would love feedback from the HN community! What features would you like to see? Repo: https://ift.tt/iYA13Oq https://ift.tt/iYA13Oq March 9, 2025 at 10:21PM
Show HN: Simple Certificate Decoder Tool Sometimes I need to quickly check certificates, especially key details like SANs, expiration dates, issuer info, etc. I know there are dozens (if not hundreds) of certificate decoders out there already, but I built my own—mostly for fun, but also because I prefer tools that are clean, simple, and straightforward to use. Would appreciate your feedback! https://ift.tt/YherBq8 March 8, 2025 at 11:09PM
Show HN: Story Jam, a music composition tool for Storytellers https://ift.tt/nVi9PZj Hello! My name is Cortland Mahoney. I'm a music researcher, software engineer, and producer. I made Story Jam. This doc is intended to inform you of not just the product, but the centuries of work that have led up to its implementation. Are you tired of the barriers in traditional music composition? Story Jam is here to break them down. Designed for anyone with creative ideas — from poets to film directors — our tool offers a new way to create and edit chord progressions, powered by cutting-edge music theory. *Who Story Jam is for: Storytellers* Story Jam makes music composition accessible and meaningful to anybody, with or without musical training. It is designed for those who crave musical control but struggle with traditional composition methods. This includes film directors, slam poets, and self-taught musicians. Story Jam is not music production software. Do not expect fancy sounds or synthesizers. It's purely a composition tool, designed to spark your creative process. *Try it out!* The demo is free on the homepage, no login required! This is an MVP, so it has an "introductory" feature set. Feature requests welcome; help me build the product you want. *The chord progression suggestion logic* This service is built on a novel new music theory I have developed called Monic Theory. Monic Theory is a rigorous proof for music. Not "Western music": music. Monic Theory describes the tonal space of any conventional music on earth (except noise music. For that just use `Math.random()`). It describes the static and transient function of chords, instantaneously and differentially over time. This model enables empirical measurement of chords and the relationship between chords. (hint: It is nothing you have seen in Xenharmonic Alliance. This is a new approach I have been developing over the past 10 years.) Therefore, Monic Theory enables us to describe (or "predict" if you will) a chord progression to invoke a certain feeling. *Music Composition* Three people who helped set up the environment for Monic Theory are composers Paul Hindemith and Harry Partch , and music theorist Heinrich Schenker. These folks independently contributed new ideas to music composition and analysis. All of these people lived without access to rapid computation. This is critical for the Partch case, as he computed many tables of frequencies by hand to support his compositional technique. Partch recognized the human-math-music relation in "Genesis of a Music." He includes in this text some samples of his hand-computed tables of frequency values of overtones and (importantly) undertones which support the basis is technique. Partch's techniques were so far-fetched that he had to construct new instruments to perform his scores. Similarly, I had to build a digital synthesizer to render the output of Monic Theory. (See: https://ift.tt/rhp023Y ). *About me* I was a working composer and violinist from 2007 until 2017, and I have been a software engineer for the past 7.5 years. I was a volunteer organizer for Livecode.NYC, an NYC livecode community; and am the volunteer creator of Data Dancers, Atlanta's livecode community. I am passionate about algorithmic art and have provided about a dozen workshops over four years on the topic. https://ift.tt/UN518ig thank you for reading. May the flow of Spices be with you :) naltroc March 5, 2025 at 11:16PM
Show HN: Microvoxel FPS game test in browser Microvoxel streaming FPS game test with half a kilometer scanned play area made of 1.5cm voxels. In-browser with no signup - just click and you're in. You can shoot holes in anything, dig up new items out of the ground, and generate 3D objects from images you drag into the 3D view from your desktop or other browser windows. All changes are persistent, and you can see/shoot/build with other users in there at the same time, or build/destroy things that have been have made in the past. https://ift.tt/8oEZVei March 5, 2025 at 10:37PM
Show HN: Time travel debugging AI for more reliable vibe coding Hi HN, I'm the CEO at https://replay.io . We've been building a time travel debugger for web apps for several years now (previous HN post: https://ift.tt/Lx3Ef7Y ) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand what's going on in the app, but AIs struggle here. With a recording of the app its behavior becomes a giant database for querying using RAG. We've been giving Claude tools to explore and understand what happens in a Replay recording, from basic stuff like seeing console messages to more advanced analysis of React, control dependencies, and dataflow. For now this is behind a chat API ( https://ift.tt/dBkL4An ). We recently launched Nut ( https://nut.new ) as an open source project which uses this tech for building apps through prompting (vibe coding), similar to e.g. https://bolt.new and https://v0.dev . We want Nut to fix bugs effectively (cracking nuts, so to speak) and are working to make it a reliable tool for building complete production grade apps. It's been pretty neat to see Nut fixing bugs that totally stump the AI otherwise. Each of the problems below has a short video but you can also load the associated project and try it yourself. - Exception thrown from a catch block unmounts the entire app: https://ift.tt/Qogy4Y6 - A settings button doesn't work because its modal component isn't always created: https://ift.tt/4QxFImC - An icon is really tiny due to sizing constraints imposed by other elements: https://ift.tt/hTuJWOP - Loading doesn't finish due to a problem initializing responsive UI state: https://ift.tt/21aQ7L3 - Infinite rendering loop caused by a missing useCallback: https://ift.tt/ARpODNJ Nut is completely free. You get some free uses or can add an API key, and we're also offering unlimited free access for folks who can give us feedback we'll use to improve Nut. Email me at hi@replay.io if you're interested. For now Nut is best suited for building frontends but we'll be rolling out more full stack features in the next few weeks. I'd love to know what you think! https://nut.new March 5, 2025 at 12:23AM
Show HN: Vidformer – Drop-In Acceleration for Cv2 Video Annotation Scripts Hi HN, this is a project I've been working on as part of my PhD. Vidformer is a system that makes video annotation or transformation scripts practically instant. Traditional scripts that render full videos can take minutes—Vidformer speeds this up by optimizing execution and using on-demand rendering, so results appear immediately instead of waiting for entire videos to render. It works as a drop-in replacement for OpenCV's cv2, meaning most scripts can adopt it by simply changing "import cv2" to "import vidformer.cv2 as cv2"—no need to rewrite code or sacrifice flexibility. Vidformer is written in Rust and uses FFmpeg libraries for low-level video access. Under the hood, Vidformer runs code with symbolic references to frames and tracks frame modifications to build a declarative representation of the task. Then, when rendering, it can transparently distribute the workload across many cores and efficiently use additional memory for caching frames. Further, it can expose a Video on Demand endpoint and only render segments once requested; this lets playback begin instantly. Repo: https://ift.tt/fd5BiHI The "Open in Colab" notebook is a great place to start. Would love to hear feedback! https://ift.tt/fd5BiHI March 4, 2025 at 11:05PM
Show HN: Firebender, a simple coding agent for Android Engineers Hey HN, I made a simple coding agent plugin in Android Studio called Firebender. Here’s an unedited 5-minute video where it writes tests for an Android app and iterates against the Gradle task output on its own ( https://ift.tt/5sg46l0 ). You can use the plugin for free, no sign up needed, on the jetbrains marketplace. The agent can edit multiple files, run gradle tasks like tests, and use the output to improve its changes. At the end, it reports a git diff of all changes that can be accepted or rejected. Under the hood, the agent relies on Claude 3.7 sonnet and a fast code apply model to speed up edits. We built tools to give deeper access throughout the IDE like IntelliJ’s graph representation of kotlin/java code, “everywhere search” for classes, and have more integrations planned. The goal is for the agent to have access to all the IDE goodies that we engineers take for granted, to improve the agent's responses and ability to gather correct context. In order to improve the agent, there are internal evals like “tasks” and simulate the IDE which serves as a gym for the agent. This is heavily inspired by SWE-bench. Whenever tools, prompts, subagents, or models are changed, this gym helps find regressions quickly. Building the UI was surprisingly hard. I had the great pleasure of becoming proficient in Java Swing (released in ‘96 by Netscape) to get this done right. Things like markdown streaming, or streaming git diffs are prone to layout flickering where Swing tries to recalculate where elements should go. We had to write our own markdown parsing and rendering engine that repaints Swing components only when changed portions of the markdown nodes. The UI tends to focus on simplifying reviewing AI changes, something I have a feeling we’ll be doing much more in the coming years. If you’re an Android engineer, please let me know if you run into any bugs or want anything improved in the plugin! https://ift.tt/5sg46l0 March 3, 2025 at 11:18PM
Show HN: Mmar – open-source, zero-dependancy, cross-platform HTTP tunneling Hey HN! For the past couple of months, I've been working on and off on a cool project I'm excited to share. mmar (pronounced "ma-mar") is an open-source, zero dependency, cross platform and self-hostable HTTP tunnel built in Go. It allows you to easily expose your localhost to the world on a public URL. You can easily create an HTTP tunnel right away for free on a randomly generated subdomain on "*.mmar.dev" if you don't feel like self-hosting. This isn't something new, in fact there's quite a few of alternative HTTP tunneling tools out there. mmar is my attempt to optimize for a super easy developer experience and simplified implementation. None the less, I had a blast building it and I think developers could find it pretty useful. Additionally, I documented the whole process of building mmar through devlogs. You can read about the thought process and implementation details here ( https://ift.tt/n5yRSiO ). If I would suggest one devlog to read, I highly recommend devlog 5 ( https://ift.tt/SI9rROK ). I describe how I built a (very) basic DNS server just to run simulation tests for mmar (a bit of an overkill, but a fantastic learning experience). I dive deep into the DNS protocol and explain why I needed to implement it. Finally, I would love to hear your thoughts and feedback. If you try mmar out, let me know! https://ift.tt/zIw09E4 March 3, 2025 at 01:28AM
Show HN: Crop images into square, circle, heart, oval for free I developed an Image Cropper, which supports cropping images into square, circle, heart, and oval shapes. It also supports customizing the width and height for arbitrary cropping, which is very simple. https://cropimage.co March 2, 2025 at 10:10PM
Show HN: I built an app to convert ChatGPT Deep Research to PDFs with footnotes Whilst ChatGPT Deep Research is very useful for generating in-depth reports, it's time consuming to copy, reformat the text (thousands of words) and clean referenced hyperlinks for use in a professional context. Out of frustration, I built deep research docs to help save time by automating the reformatting, cleaning links, footnote references, and conversion to shareable PDF format. Hopefully this helps you save time to focus on meaningful work. Let me know your feedback. https://ift.tt/JIyCK0Y March 1, 2025 at 06:22PM
Show HN: Built a "Story Chatbot Arena" to Crowdsource AI Story Preferences Hi HN, I’m a university student interested in Gen AI, and over the holidays, I built a project in public: Who Rates the Rater? – a crowdsourced dataset for benchmarking AI-generated storytelling. The Idea Inspired by Chatbot Arena, this lets users compare AI-generated stories and provide feedback. The goal is to refine LLMs for creative writing using real human preferences. How It Works - Live Demo: https://ift.tt/2zFvPnL - Tech Stack: Built with Streamlit + Supabase - Open Source: https://ift.tt/AYeOC1c Get Involved - Try it & star the repo if you find it interesting - Bug reports & feature requests welcome on Twitter - Follow me for future AI & storytelling projects Would love to hear your thoughts! https://ift.tt/AYeOC1c March 1, 2025 at 11:19PM
Show HN: Betting game puzzle (Hamming neighbor sum in linear time) In Spain, there's a betting game called La Quiniela: https://ift.tt/FTyvAZs Players predict the outcome of 14 football matches (home win, draw, away win). You win money if you get at least 10 correct, and the prize amount depends on the number of winners. Since all bets are public, the number of winners and the corresponding payouts can be estimated for each of the 3^14 possible outcomes. We can also estimate their probabilities using bookmaker odds, allowing us to compute the expected value for each prediction. As a side project, I wanted to analyze this, but ran into a computational bottleneck: to evaluate a prediction, I had to sum the values of all its Hamming neighbors up to distance 4. That’s nearly 20,000 neighbors per prediction (1+28+364+2912+16016=19321): S_naive = sum from k=0 to r of [(d! / ((d-k)! * k!)) * (q-1)^k] (d=14, q=3, r=4) This took days to run in my first implementation. Optimizing and doing it with matrices brought it down to 20 minutes—still too slow (im running it in GAS with 6 minutes limit). For a while, I used a heuristic: start from a random prediction, check its 28 nearest neighbors, move to the highest-value one, and repeat until no improvement is possible within distance 3. It worked surprisingly well. But I kept thinking about how to solve the problem properly. Eventually, I realized that partial sums could be accumulated efficiently by exploiting overlaps: if two predictions A and B share neighbors, their shared neighbors can be computed once and reused. This is achieved through a basic transformation that I implemented using reshape, roll, and flatten (it is probably not the most efficient implementation but it is the clearest), which realigns the matrix by applying an offset in dimension i. This transformation has two key properties that enable reducing the number of summations from 19,321 to just 101: - T(T(space, d1), d2) = T(T(space, d2), d1) - T(space1, d) + T(space2, d) = T(space1+space2, d) Number of sums would be the result of this expression: S_PSA = 1 + (d - (r-1)/2) * r * (q-1) I've generalized the algorithm for any number of dimensions, elements per dimension, and summation radius. The implementation is in pure NumPy. I have uploaded the code to colab, github and an explanation in my blog. Apparently, this falls under Hamming neighbor summation, but I haven't found similar approaches elsewhere (maybe I'm searching poorly). If you know or you've worked on something similar, I'd love to hear your thoughts! colab: https://ift.tt/MoVN6Af... github: https://ift.tt/4KqaUNE blog: https://ift.tt/V7PAsRM... March 1, 2025 at 02:03AM
Show HN: News-briefing-generator – Local LLM-powered news digest Hey HN, I created a tool to generate personalized news briefings from RSS/Atom feeds using local LLMs through Ollama. It currently works in two modes: fully automatic or with an interactive review where you can select which "main topics of the day" to include in your briefing. The result is a HTML document with summaries for each topic. https://ift.tt/mjiPnNb February 28, 2025 at 10:45PM
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