Show HN: SoloDB – A document database build on top of SQLite with JSONB https://ift.tt/R3iG5te

Show HN: SoloDB – A document database build on top of SQLite with JSONB https://ift.tt/SAIg6O8 June 1, 2025 at 12:39AM

Show HN: A site for YC rejection stories https://ift.tt/FxOX9Kr

Show HN: A site for YC rejection stories Got rejected from YC a few times, so I built a site to collect lessons, reflections, and what people would do differently next time. Thought it could be helpful since most founders apply more than once anyway :) Hope it's helpful: https://ift.tt/kJcgRPn https://ift.tt/kJcgRPn June 1, 2025 at 02:15AM

Show HN: LaminarFlow – AI-native, open-source finance platform for startups https://ift.tt/jaBGgk4

Show HN: LaminarFlow – AI-native, open-source finance platform for startups I'm Yash, and I'm building LaminarFlow — an AI-native, open-source platform to help startups, founders, SMBs, manage their fincial ops more efficiently. It brings together financial insights, banking, invoicing, payment tracking, time tracking, and banking-style reconciliation — all powered by an AI agent that automates the boring stuff. We’re building this as an open startup, sharing everything publicly, and keeping it fully open-source (MIT). Would love your feedback and thoughts! https://www.lamflo.xyz May 31, 2025 at 09:31PM

Show HN: Icepi Zero – The FPGA Raspberry Pi Zero Equivalent https://ift.tt/R2v86Ha

Show HN: Icepi Zero – The FPGA Raspberry Pi Zero Equivalent I've been hacking away lately, and I'm now proud to show off my newest project - The Icepi Zero! In case you don't know what an FPGA is, this phrase summarizes it perfectly: "FPGAs work like this. You don't tell them what to do, you tell them what to BE." You don't program them, but you rewrite the circuits they contain! So I've made a PCB that carries an ECP5 FPGA, and has a raspberry pi zero footprint. It also has a few improvements! Notably the 2 USB b ports are replaced with 3 USB C ports, and it has multiple LEDs. This board can output HDMI, read from a uSD, use a SDRAM and much more. I'm very proud the product of multiple weeks of work. (Thanks for the pcb reviews on r/PrintedCircuitBoard ) (All the sources on github under an open source license :D) PS. See some more pics on reddit https://ift.tt/KtGqsEJ... https://ift.tt/iZ8mQ9j May 28, 2025 at 08:31PM

Show HN: Asdf Overlay – High performance in-game overlay library for Windows https://ift.tt/Lu2wdtH

Show HN: Asdf Overlay – High performance in-game overlay library for Windows I am making a open source overlay library. Game overlay is for rendering contents on top of game screen. Representative examples are Discord and Steam in-game overlay. They are complicated because it has to hook rendering part of a game. Asdf overlay provides easy to use interfaces for rendering on top of game screen. I recognize game performance degradation due to overlay rendering, so GPU shared texture was used to avoid CPU framebuffer copy. Asdf Overlay is capable of rendering full screen overlay without noticeable performance loss. https://ift.tt/wnOdixS May 31, 2025 at 01:27AM

Show HN: OTelWasm, a PoC for a WebAssembly Based OpenTelemetry Collector Plugins https://ift.tt/THP71Bz

Show HN: OTelWasm, a PoC for a WebAssembly Based OpenTelemetry Collector Plugins https://ift.tt/tn47MuU May 30, 2025 at 10:53PM

Show HN: JsonPP, a Functional JSON Superset https://ift.tt/A1NEclH

Show HN: JsonPP, a Functional JSON Superset Json plus plus or Json pre-processor, whichever you prefer. A turing complete, unit tested joke of a language with just the slightest glint of usefulness. https://ift.tt/Bugxb8s May 30, 2025 at 01:10AM

Show HN: Technical Interviews Built for 2025 https://ift.tt/ZpNQrte

Show HN: Technical Interviews Built for 2025 Hey HN, The way we hire engineers made sense in 2015. But in 2025, when engineers use AI tools daily, we're still testing algorithm memorization on whiteboards.. That's why we're building DevDay. DevDay is built for the new reality of modern engineering work: candidates collaborate with AI teammates, delegate tasks to AI agents, and solve problems using the tools they'd actually use on the job (LLMs, git, and Slac(k) for team communication). The old interview playbook is fundamentally broken: - Whiteboard anxiety tests don't predict performance - Take-home tests and virtual paired programming get gamed with ChatGPT - Algorithm memorization has zero correlation with debugging prod issues (what you actually deal with in your day to day work) Here is what we are not: X Another LeetCode clone with AI buzzwords X Replacing engineers with AI X "Disrupting" hiring with magic algorithms What it actually does: - Tests AI collaboration skills (AI teammates, delegate task to agents, coding assistant integrations) - Simulates real team environments and workflows - Shows problem-solving approach, collaboration and behavioral skills, not memorized solutions - Assesses how candidates think and communicate Questions for HN because we are genuinely curious: - Do you assess engineers who work with AI daily? If yes, how do you do it today? - What would technical interviews look like if designed today within your organisation? - Are we testing skills that matter in 2025? Link: trydevday.com P.S. - Yes, someone will suggest "just pair program" or "check their GitHub." Great for small teams, doesn't scale when hiring 10+ engineers monthly. May 30, 2025 at 12:03AM

Show HN: Compliant-LLM: Audit AI Agents for Compliance with NIST AI RMF https://ift.tt/d4jbUax

Show HN: Compliant-LLM: Audit AI Agents for Compliance with NIST AI RMF We're excited to launch compliant-llm: an open-source toolkit that helps infosec and compliance teams audit AI agents against regulatory frameworks like NIST AI RMF, ISO 42001, and OWASP Top 10. Infosec and compliance teams are now responsible for tracking security and compliance risks of a growing number of AI agents across external and internal apps and third-party vendors. compliant-llm gives you a way to: - Define and run comprehensive red-teaming tests for AI agents - Maps test outcomes to compliance frameworks like NIST AI RMF - Generate detailed audit logs and documentation - Integrate with Azure, OpenAI, Anthropic, or wherever you host your models - With an open-source, self-hosted solution Install and launch the red-teaming dashboard locally: pip install compliant-llm compliant-llm dashboard This opens an interactive UI for running AI compliance checks and analyzing results. We’re at v0.1, and would love your feedback. Tell us about the compliance or AI risk issues you’re facing, and we’ll prioritize what matters most. https://ift.tt/z7wxV6O May 29, 2025 at 09:52PM

Show HN: Octogen: e-commerce capabilities for agents https://ift.tt/Eg9fCiV

Show HN: Octogen: e-commerce capabilities for agents Hi HN, We just released a public beta of e-commerce capabilities for AI agents — aimed at developers building shopping agents or personal assistants. It’s early and buggy, but we’d love your feedback. Try a live demo here: https://ift.tt/Gbcdfzk --- ## Why we built this We believe the biggest *technical* bottleneck in building consumer e-commerce agents is fragmented product data and inconsistent schemas across online stores. So we created a high-fidelity yet unified interface for *e-commerce catalog + checkout*, regardless of the underlying platform. --- ## What it does We currently offer two core capabilities: ### 1. Unified product catalog (for LLM-style search) - Octogen automatically wrangles any ecommerce site into a common schema — a superset of `schema.org/Product`. - It works across platforms and is available today for hundreds of sites. - You can request new stores — ~95% are processed fully autonomously. - Useful for agents doing RAG-based product search with rich attribute awareness. ### 2. Agentic checkout (closed beta) - Works on *any ecommerce site* using virtual cards (Visa only for now). - Enables agents to complete checkout flows much faster than browser-based "computer agents." - We're working on support for additional vaults/wallets/payment APIs. --- If you’re working on agentic commerce, autonomous checkout, or personal AI shoppers — we’d love your feedback and ideas. More at: https://octogen.ai https://octogen.ai May 29, 2025 at 01:55AM

Show HN: Every problem and solution in Beyond Cracking the Coding Interview https://ift.tt/ugP2o7l

Show HN: Every problem and solution in Beyond Cracking the Coding Interview Hey HN, I'm Aline, founder of interviewing.io and one of the authors of Beyond Cracking the Coding Interview (the official sequel to CTCI). We just compiled every problem (and solution) in the book and made them available for free. There are ~230 problems in total. Some of them are classics like n-queens, but almost all are new and not found in the original CTCI. You can read through the problems and solutions, or you work them with our AI Interviewer, which is also free. I'd recommend doing AI Interviewer before you read the solutions, but you can do it in whichever order you like. (When you first get into AI Interviewer, you can configure which topics you want problems on, and at what difficulty level, and you can add topics and change difficulty levels as you go.) Here's the link: https://ift.tt/PEnDz1v... (You'll have to create an account if you don't already have one, but there's nothing else you need to do to access all the things.) May 29, 2025 at 12:06AM

Show HN: Burner Bitcoin – a simple hardware wallet for gifting Bitcoin https://ift.tt/KwtQ0Df

Show HN: Burner Bitcoin – a simple hardware wallet for gifting Bitcoin Hello HN! Late last year we launched Burner, a low cost Ethereum hardware wallet for gifting crypto. It's a card-sized wallet that generates and stores cryptocurrency keys with no need to manage seed phrases or private keys. It operates entirely through your smartphone browser and NFC. We've produced tens of thousands of Burners to date, primarily for gifting and partner collaborations (e.g. conference handouts). Today we're launching our most requested feature: a Bitcoin compatible Burner. Burner Bitcoin features nearly identical hardware to the Ethereum variant -- a secure element chip with NFC capabilities in colorful/translucent plastic -- however, we made the decision to split the Bitcoin and Ethereum versions into two products rather than creating a single monolithic wallet. This is because we're seeing that both end users and business customers want to use Burner as a tool to give specific assets rather than gifting a universal hardware wallet. In order to generate transactions on Bitcoin, Burner directly signs transactions using it's internal private key. You can add entropy that is hashed alongside entropy from Burner's RNG during the generation process of this key, but the private is never revealed in plaintext. We will also indicate through the interface whether or not a key has been used to sign a message or not, a helpful indicator so you can be confident funds have never been withdrawn from a gifted Burner. Like the original Burner, we offer open libraries to interface with the wallet so you can sign transactions and messages fully offline if you want to use it for cold storage rather than as a “cash like” wallet. Our long term vision is to make buying small amounts of various cryptocurrencies for those who are crypto curious as easy as buying a restaurant or retailer gift card. https://ift.tt/wAWRBOQ May 28, 2025 at 11:01PM

Show HN: Free mammogram analysis tool combining deep learning and vision LLM https://ift.tt/6QfjX5p

Show HN: Free mammogram analysis tool combining deep learning and vision LLM I've built Neuralrad Mammo AI, a free research tool that combines deep learning object detection with vision language models to analyze mammograms. The goal is to provide researchers and medical professionals with a secondary analysis tool for investigation purposes. Important Disclaimers: - NOT FDA 510(k) cleared - this is purely for research investigation - Not for clinical diagnosis - results should only be used as a secondary opinion - Completely free - no registration, no payment, no data retention What it does: 1. Upload a mammogram image (JPEG/PNG) 2. AI identifies potential masses and calcifications 3. Vision LLM provides radiologist-style analysis 4. Interactive viewer with zoom/pan capabilities You can try it with any mass / calcification mammo images, e.g. by searching Google: mammogram images mass Key Features: - Detects and classifies masses (benign/malignant) - Identifies calcifications (benign/malignant) - Provides confidence scores and size assessments - Generates detailed analysis using vision LLM - No data storage - images processed and discarded Use Cases: - Medical research and education - Second opinion for researchers - Algorithm comparison studies - Teaching tool for radiology training - Academic research validation The system is designed specifically for research investigation purposes and to complement (never replace) professional medical judgment. I'm hoping this can be useful for the medical AI research community and welcome feedback on the approach. Address: https://ift.tt/OfoB0ti https://ift.tt/OfoB0ti May 27, 2025 at 10:13PM

Show HN: XOff an open source Chrome extension to change X links to Xcancel https://ift.tt/y6RYhMV

Show HN: XOff an open source Chrome extension to change X links to Xcancel Basic, but works afaics. https://ift.tt/sTLEJ47 May 27, 2025 at 01:42AM

Show HN: Cleaner Markdown lang and static site generator (Shrimple) https://ift.tt/Zv7Shwq

Show HN: Cleaner Markdown lang and static site generator (Shrimple) https://ift.tt/rBzwngM May 27, 2025 at 12:23AM

Show HN: Yuno – P2P Google Docs for Entire Codebases (No Servers) https://ift.tt/turH2l4

Show HN: Yuno – P2P Google Docs for Entire Codebases (No Servers) I built Yuno to fix distributed team collaboration: Real-time sync for all project files (not just open tabs) Try it now: npx yuno create -r my-hackathon npx yuno init -r my-hackathon -p my-hackathon-password -u Bob Would this replace your current workflow? What’s missing? No central servers → no latency/file limits Tech stack: node, p2p. More information, documentation, etc., can be found on the website (yuno.ws) https://yuno.ws May 26, 2025 at 09:32PM

Show HN: Generate SVGs with AI https://ift.tt/oNAPJCg

Show HN: Generate SVGs with AI https://vectorart.ai May 26, 2025 at 02:17AM

Show HN: Self hosted bangs and go links with jmp https://ift.tt/KxBjvfS

Show HN: Self hosted bangs and go links with jmp https://ift.tt/2pQeHgn May 25, 2025 at 11:12PM

Show HN: DaedalOS – Desktop Environment in the Browser https://ift.tt/MOBLysd

Show HN: DaedalOS – Desktop Environment in the Browser Demo: https://dustinbrett.com Hey HN! I've been building my passion project daedalOS for over 4 years now. The original idea was to give visitors to my website the experience as if they had remotely connected to my personal machine. To do this I decided I would attempt to recreate as much of the functionality as possible. My hope is to keep working on this project for the rest of my life and continue to evolve it's capabilities as technologies progress. Thanks for checking it out! https://ift.tt/kJsb96q May 25, 2025 at 11:06PM

Show HN: 1min Workouts for People Who Sit All Day https://ift.tt/pNucSRU

Show HN: 1min Workouts for People Who Sit All Day I am a software developer and in the last few months after recently becoming a father I was barely finding time for a proper workout. Recently I was reading about new research on Snack Exercises and how beneficial mini workouts of less than 2mins every so often, during the day are to our body. So, I decided to build an iOS App for me and others to help with this. The app generates a list of exercises that I need to tick to complete daily or loose my streak. The algorithm takes into account muscle groups and balancing the exercises to hit most main muscles. I also stayed going through all exercises and adding a couple of alternative exercises in case I don't feel like the recommended exercise. Since I'm not a trainer I commissioned professional exercise posture video guides and animations by an exercise expert which I attached to each exercise. I uploaded the app on the app store for free and no ads. If this is something that interests you, I want to hear how you balance a long day on your desk vs exercise. https://shortreps.com May 25, 2025 at 03:41AM

Show HN: Can AI Help Designers Ideate Better? We Spent 5 Wks Finding Out https://ift.tt/ZNKG9mp

Show HN: Can AI Help Designers Ideate Better? We Spent 5 Wks Finding Out We set out to answer a simple but deep question: Can AI actually practically help product designers improve during the discovery and ideation phase of the design process? So we spent 5 weeks running an experiment. We mapped every tool we use for discovery: Mobbin, Dribbble, Pinterest, Twitter, Behance We broke down typical design thinking and brainstorming workflows We reviewed every prototyping or idea-capturing tool we’ve used Then we tried building lightweight AI workflows with various LLM tools and frameworks Result: Yes. Used well, AI can significantly improve design thinking — especially for junior/mid-level designers — by offering faster idea generation, design critiques, and creative merges. Out of that research, we built Moonchild: A discovery-stage design ideation tool that: Generates thoughtful UI concepts from minimal prompts Allows asking design questions and getting structured critique Merges styles, flows, and interaction patterns from multiple directions Outputs great Figma-ready screens and UX flows, fast Try it (private beta): https://moonchild.ai Use code 'hackernews' for early access. Would love feedback — especially from product designers, PMs, and UX folks doing early-stage work. May 25, 2025 at 02:06AM

Show HN: F2 – Cross-Platform CLI Batch Renaming Tool https://ift.tt/MVlh2bA

Show HN: F2 – Cross-Platform CLI Batch Renaming Tool Hey HN! I'm excited to share f2, a command-line tool I built for fast and flexible bulk renaming of files. It's cross-platform (Linux, macOS, Windows), executes a dry-run by default, supports undo, and provides great flexibility in file renaming with several built-in variables and Exiftool integration. I hope you find it useful! https://ift.tt/b86gyYT May 24, 2025 at 10:49PM

Show HN: Advanced Chunking in JavaScript/TypeScript with Chonkie https://ift.tt/wBeA7Xv

Show HN: Advanced Chunking in JavaScript/TypeScript with Chonkie Hi HN, We’re Shreyash and Bhavnick. We built Chonkie, an open-source library for advanced chunking and embedding of text and code. It was previously Python-only, but we just released a TypeScript version: https://ift.tt/1cIRZLN Many AI projects in JS/TS (like those using Vercel's AI SDK or Mastra) rely on basic text splitters. But better chunking = better retrieval = better performance. That’s what Chonkie is built for. Current native chunkers (in TS): - Code Chunker – handles Python, TypeScript, etc. - Recursive Chunker – rule-based, hierarchical splitting - Token Chunker – split by token count (fully customizable) - Sentence Chunker – split on sentence boundaries. Delimiters are customizable, so it works for multiple languages. All chunkers support custom tokenizers, chunk overlap, delimiters, and more. Coming soon in native TS (already available via the API client): - Semantic Chunker – splits texts wherever it detects a shift in meaning. - SDPM Chunker – merges semantically similar disjoint chunks - Late Chunker – generates context-aware embeddings for each chunk - Slumber Chunker – LLM-refined recursive chunks. Significantly reduces token usage (and thus cost) while maximizing chunk quality. - Embeddings Refinery - Embed chunks with any embedding model - Overlap Refinery – Create overlaps between consecutive chunks for better context preservation. Chonkie is free, open-source, and MIT licensed. GitHub: https://ift.tt/1cIRZLN We’d love your feedback, ideas, or contributions. Thanks! May 24, 2025 at 03:03AM

Show HN: I made an infinite gallery of AI-generated 3D skeuomorphic icons https://ift.tt/vw0kLrO

Show HN: I made an infinite gallery of AI-generated 3D skeuomorphic icons https://ift.tt/kJujWVt May 24, 2025 at 12:52AM

Show HN: Pi Co-pilot – Evaluation of AI apps made easy https://ift.tt/s45yd39

Show HN: Pi Co-pilot – Evaluation of AI apps made easy Hey HN — 2 months ago we shared our first product with the HN community ( https://ift.tt/saSBeEu ). Despite receiving lots of traffic from HN, we didn’t see any traction or retention. One of our major takeaways was that our product was too complicated. So we’ve spent the last 2 months iterating towards a much more focused product that tries to do just one thing really well. Today, we’d like to share our second launch with HN. Our original idea was to help software engineers build high-quality LLM applications by integrating their domain knowledge into a scoring system, which could then drive everything from prompt tuning to fine-tuning, RL, and data filtering. But what we quickly learned (with the help of HN – thank you!) is that most people aren’t optimizing as their first, second, or even third step — they’re just trying to ship something reasonable using system prompts and off-the-shelf models. In looking to build a product that’s useful to a wider audience, we found one piece of the original product that most people _did_ notice and want: the ability to check that the outputs of their AI apps look good. Whether you’re tweaking a prompt, switching models, or just testing a feature, you still need a way to catch regressions and evaluate your changes. Beyond basic correctness, developers also wanted to measure more subtle qualities — like whether a response feels friendly. So we rebuilt the product around this single use case: helping developers define and apply subjective, nuanced evals to their LLM outputs. We call it Pi Co-pilot. You can start with any/all of the below: - a few good/bad examples - a system prompt, or app description - an old eval prompt you wrote The co-pilot helps you turn that into a scoring spec — a set of ~10–20 concrete questions that probe the output against dimensions of quality you care about (e.g. “is it verbose?”, “does it have a professional tone?”, etc). For each question, it selects either: - a fast encoder-based model (trained for scoring) – Pi scorer. See our original post [1] for more details on why this is a good fit for scoring compared to the “LLM as a judge” pattern. - or generates Python functions when that makes more sense (word count, regex etc.) You iterate over examples, tweak questions, adjust scoring behavior, and quickly reach a spec that reflects your actual taste — not some generic benchmark or off-the-shelf metrics. Then you can plug the scoring system into your own workflow: Python, TypeScript, Promptfoo, Langfuse, Spreadsheets, whatever. We provide easy integrations with these systems. We took inspiration from tools like v0 and Bolt: natural language on the left, structured artifacts on the right. That pattern felt intuitive — explore conversationally, and let the underlying system crystallize it into things you can inspect and use (scoring spec, examples and code). Here is a loom demo of this: https://ift.tt/cznKDyj We’d appreciate feedback from the community on whether this second iteration of our product feels more useful. We are offering $10 of free credits (about 25M input tokens), so you can try out the Pi co-pilot for your use-cases. No sign-in required to start exploring: https://withpi.ai Overall stack: Co-pilot next.js and Vercel on GCP. Models: 4o on Azure, fine tuned Llama & ModernBert on GCP. Training: Runpod and SFCompute. – Achint (co-founder, Pi Labs) https://withpi.ai/ May 22, 2025 at 07:31PM

Show HN: DockFlow – Switch between multiple macOS Dock layouts instantly https://ift.tt/F74JhSg

Show HN: DockFlow – Switch between multiple macOS Dock layouts instantly I built DockFlow after constantly rearranging my macOS Dock when switching between coding, designing, or writing tasks. macOS only supports one Dock layout, and every context switch felt like wasted time and broken focus. DockFlow solves this by letting you save multiple Dock presets and switch between them instantly, all without complex setup or bloat. Key features: - Save and switch between multiple Dock configurations - Assign custom hotkeys to change layouts instantly - Add apps, folders, files, or links to your Dock - Insert visual spacers to group items - Lightweight and macOS-native (no permissions or custom dock) - Supports macOS 13.5 and above - Includes CLI tools and Shortcuts integration *Launch Price:* €4.99 Price increases to €9.99 on June 1 No subscriptions. One-time payment for lifetime access. Try it here: https://ift.tt/wYQfkzV Happy to hear your feedback or questions! Let me know what you think Hope more people will find this app helpful. https://ift.tt/wYQfkzV May 23, 2025 at 12:00AM

Show HN: Super (YC W18) - Turn company data into answers & agents for your team https://ift.tt/dfYzLxW

Show HN: Super (YC W18) - Turn company data into answers & agents for your team Hey there, Chris here We're known for our straightforward yet powerful Knowledge Base, Slite(YCW18).We launched our AI-powered search in Feb 2023 and after getting great response and usage, we dove deeper into solving the challenge of knowledge retrieval in daily work. That's why we're now launching our second major product, Super( https://www.super.work ). Super seamlessly connects your existing tools, providing accurate answers, streamlined workflows, automated digests, and much more. You might wonder: Why not just link your apps together using something like an MCP? The problem is that MCPs can't handle complex knowledge retrieval effectively. MCPs are basically LLMs equipped with API toolbelts. If you've ever tried asking a complicated question through an MCP, one that needs data from multiple different tools, you've likely faced frustrating delays. MCPs slowly make API calls one after another, causing long waits while they collect data from each endpoint. By contrast, Super quickly searches through all the data that actually matters from all of your tools simultaneously. This means you'll get your accurate answer in seconds, not minutes. The limitations of MCP-based solutions become clear when you try to deploy them reliably within a team. They either won't index your critical content effectively, won't do it fast enough, or won't cover all your tools at once. Properly chunking, embedding, querying, and filtering data from various sources is still essential. MCPs triggering APIs can't match this integrated approach for speed and accuracy. Moreover, Super understands the value of running multiple tasks simultaneously through LLMs. For example, one step may involve identifying search filters, while another simultaneously uses an LLM to aggregate and refine information. This parallel process quickly shapes the final, accurate answer for users. Additionally, MCPs aren't designed for enterprise-grade use. Businesses need standardized experiences, fine-grained user permissions, and consistent access controls across multiple tools. Super addresses these requirements by indexing data beforehand while still respecting each user's access permissions. Super offers: - Perplexity-like search experience on your team data - A growing selection of integrations with popular data sources - Customizable AI assistants tailored to your specific needs - An extension to embed Super directly into external websites you're already using - A clear path for your company to adopt AI strategically, rather than letting individual employees scatter across different, incompatible tools. And of course... It does comes with its MCP, which makes your agentic workflows actually able to properly tap on your data. Here's a quick video showing Super in action: https://www.youtube.com/watch?v=L5A6BRW90K4 Have you hit such walls with standard MCPs? Have you try building your own solutions? https://super.work May 21, 2025 at 09:18PM

Show HN: Appwrite Sites – the open-source vercel alternative https://ift.tt/OVHC6Qn

Show HN: Appwrite Sites – the open-source vercel alternative https://ift.tt/hD3dVYB May 19, 2025 at 07:23PM

Show HN: I made "Who's Hiring?" searchable using GPT and Metabase https://ift.tt/4DcTubP

Show HN: I made "Who's Hiring?" searchable using GPT and Metabase I vibe-coded a small project that turns the “Ask HN: Who is hiring?” thread into searchable job data using OpenAI, PostgreSQL, and Metabase. It pulls the thread using the Hacker News API, uses GPT to extract fields like company, role, location, salary, and contact, stores everything in PostgreSQL, and spins up Metabase so you can explore and search the results. Right now it runs locally, but would anyone be interested if I built this out a bit more and made a public dashboard? https://ift.tt/3V0rd1u May 21, 2025 at 11:07PM

Show HN: Chatsh – A Conversational CLI Blending Terminal Ops with Real-Time Chat https://ift.tt/jxMdEGB

Show HN: MCP Server for Document Processing via Natural Language https://ift.tt/6L0MxD1

Show HN: MCP Server for Document Processing via Natural Language Hey, I'm Nick from Nutrient, I want to share our newly released MCP Server that enables document workflows using natural language — things like redacting, merging, signing, converting formats, or extracting data. While many MCP servers have traditionally been developer-focused, we recognized that the technology could be highly effective in promoting the adoption of tools that are often hidden from end-user interfaces. We’re really interested to see if this side of the protocol could continue to mature. One thing we struggled with was the inability to receive documents from the client (no client -> server resource support), thus we had to resort to file system support. (See this GitHub discussion: https://ift.tt/J01gC4Y... ) I’d love to know the communities thoughts on this. It’s designed for Claude Desktop on macOS, but since it’s built on the Model Context Protocol, it’d be interesting to hear of other MCP client use cases. So feel free to reach out! GitHub: https://ift.tt/M1IOP7x NPM: https://ift.tt/9QISeTs More details + demo video: https://ift.tt/TvEzXw2 All thoughts, feedback, and issue reports welcome! :) https://ift.tt/M1IOP7x May 19, 2025 at 11:56PM

Show HN: Llm.fm – an AI-generated, satirical radio show in the browser https://ift.tt/AmTHjxs

Show HN: Llm.fm – an AI-generated, satirical radio show in the browser I've spent the past few weeks building *LLM.FM*, a GTA-inspired Radio Show where every topic, song, and advertisement is generated entirely by AI. I loved the absurd style of the radio stations in the Grand Theft Auto games, and I wanted to see how close I could get with todays AI stack. While it isn't quite as vulgar, I think it's still a unique experience! How it works * GPT-4.1 writes a "high-level" show overview given a desired structure, this is done to keep coherence between segments * Individual segment transcripts are generated with the provided outline * ElevenLabs turns the into voices and returns word-level timestamps for a live transcript * Segments are converted to HLS chunks, then all chunks are merged with audio padding; Song are pre-generated Suno songs based on ChatGPT generated song prompts * Next.js + Vercel for the web app; Railway with workers to handle new show generation The show is fully free - new episodes release at 8 AM, 12 PM, and 4 PM ET daily. https://www.llm.fm/ May 19, 2025 at 09:32PM

Show HN: Vaev – A browser engine built from scratch (It renders google.com) https://ift.tt/joiwJ2M

Show HN: Vaev – A browser engine built from scratch (It renders google.com) We’ve been working on Vaev, a minimal web browser engine built from scratch. It supports HTML/XHTML, the CSS cascade, @page rules for pagination, and print-to-PDF rendering. It even handles calc(), var(), and percentage units—and yes, it renders Google.com (mostly). This is an experimental project focused on learning and exploration. Networking is basic ( http:// and file:// only), and grid layouts aren’t supported yet, but we’re making progress fast. We’d love your thoughts and feedback. https://ift.tt/16FKwkt May 19, 2025 at 12:54AM

Show HN: Racketmeter – Measure Badminton String Tension Using Sound Frequency https://ift.tt/PkL0u36

Show HN: Racketmeter – Measure Badminton String Tension Using Sound Frequency Racketmeter lets badminton players measure string tension using the sound frequency produced when tapping the racket strings. It's 100% free, works in your browser on mobile and desktop, and requires no sign-up or installation. I built it to solve a personal problem. I started playing badminton regularly in 2016 and quickly learned that players often ask stringers to string rackets at specific tensions (like 22 or 26 lbs). But after a few stringing jobs, I began to feel like the tension was inconsistent. Other players told me they just tap the strings and go by ear where "sharper sound meant higher tension." One day while tuning my guitar, I could see exact sound frequencies on my tuner app. That’s when it clicked. It should be possible to build a tuner for badminton strings as well! I searched online and found some tension-frequency data shared by professional stringers, but it wasn’t clean or comprehensive. So I visited 5 or 6 local stringers, gave them a frequency measuring app, and asked them to record racket head size, string thickness, tension, and sound frequency for each job. Some asked for a small payment, but most helped for free. Within a week, I had over 200 solid data points. I trained a simple regression model using that data and validated it with newly strung rackets. It turned out to be surprisingly accurate. I shared it with friends and fellow players, and it started to spread in badminton forums. There was another app that launched a few months later with big celebrity endorsements, but it was less accurate, harder to use, and required in-app purchases. Mine wasn't built to compete, but it ended up being more useful. I originally released it as a mobile app, but constant changes in Google Play policies kept taking it down. So I rebuilt it as a simple browser-based tool. Would love feedback, suggestions for improvements, or ideas on how to sustain it without cluttering it with ads or paywalls. Let me know what you think. https://ift.tt/MCrlzF5 May 19, 2025 at 12:08AM

Show HN: Buckaroo – The data table UI for Notebooks https://ift.tt/yUftLlp

Show HN: Buckaroo – The data table UI for Notebooks Buckaroo is my open source project. It is a dataframe viewer that has the basic features we expect in a modern table - scroll, search, sort. In addition there are summary stats, and histograms available. Buckaroo support Pandas and Polars dataframes and works on Jupter, Marimo, VSCode and Google Colab notebooks. All of this is extensible. I think of Buckaroo as a framework for building table UIs, and an initial data exploration app built on top of that framework. AG-Grid is used for the core table display and it has been customized with a declarative layer so you don't have to pass JS functions around for customizations. On the python side there is a framework for adding summary stats (with a small DAG for dependencies). There is also an entire Low Code UI for point and click selection of common commands (drop column). The lowcode UI also generates a python function that accomplishes the same tasks. This is built on top of JLisp - a small lisp interpreter that reads JSON flavored lisp. Auto Cleaning looks at columns and heuristically suggests common cleaning operations. The operations are added to the lowcode UI where they can be edited. Multiple cleaning strategies can be applied and the best fit retained. Autocleaning without a UI and multiple strategies is very opaque. Since this runs heuristically (not with an LLM), it’s fast and data stays local. I'm eager to hear feedback from data scientists and other users of dataframes/notebooks. https://ift.tt/CIBU3FK May 18, 2025 at 10:56PM

Show HN: Blacklight – secret scanner for code, databases, drives, and slack https://ift.tt/bfKDqzk

Show HN: Blacklight – secret scanner for code, databases, drives, and slack We often ran pattern matching searches for secrets and keys across codebases, databases etc. Therefore, we thought about converting that workflow into a tool that we could just easily generate a SARIF report and share with our customers. Blacklight is a powerful secret, key, and sensitive data scanning tool that helps you detect and prevent sensitive information leaks in your codebase, databases, cloud storage, and communication platforms. The idea is that one can add their custom rules around their governance and compliance requirements. The platform comes with 114 matching criteria, but this can be extended easily. https://ift.tt/SzbPCWh May 18, 2025 at 01:40AM

Show HN: I built a knife steel comparison tool https://ift.tt/ClDLQ7h

Show HN: I built a knife steel comparison tool Hey HN! I'm a bit of a knife steel geek and got tired of juggling tabs to compare stats. So, I built this tool: https://ift.tt/3iC8due It lets you pick steels (like the ones in the screenshot) and see a radar chart comparing their edge retention, toughness, corrosion resistance, and ease of sharpening on a simple 1-10 scale. [Maybe attach the screenshot here if HN allows, or link to it] It's already been super handy for me, and I thought fellow knife/metallurgy enthusiasts here might find it useful too. Would love to hear your thoughts or any steel requests! Cheers! https://ift.tt/3iC8due May 18, 2025 at 12:13AM

Show HN: Inconveniently operating my computer with voice and hand gestures https://ift.tt/jCrM70t

Show HN: Inconveniently operating my computer with voice and hand gestures Introducing Iron OS: it's like a regular computer, but much more inconvenient Created with threejs, rosebud AI, web speech API, and mediapipe computer vision Any feedback would be appreciated! I've been having fun experimenting with computer vision and voice control lately. https://twitter.com/measure_plan/status/1923452731248795856 May 17, 2025 at 02:16AM

Show HN: Samurai Interview – a mock interview simulator https://ift.tt/UPQukAO

Show HN: Samurai Interview – a mock interview simulator https://ift.tt/fTepuFE May 16, 2025 at 11:59PM

Show HN: wghttp – An HTTP server for managing WireGuard devices https://ift.tt/45iHQsC

Show HN: wghttp – An HTTP server for managing WireGuard devices Unix socket default, opinionated behavior, swagger UI, lightweight server. https://ift.tt/5F9earC May 16, 2025 at 11:18PM

Show HN: Workflow Use – Deterministic, self-healing browser automation (RPA 2.0) https://ift.tt/A3XuC9N

Show HN: Workflow Use – Deterministic, self-healing browser automation (RPA 2.0) Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use ( https://ift.tt/Nxg4hT2 ), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these high-frequency tasks. So we just started working on Workflow Use: - You show the browser what to do (by manually recording steps; show don’t tell). - An LLM converts these recordings into deterministic scripts with variables (scripts include AI steps as well, where it’s 100% agentic) - Scripts run reliably, 10x faster, and ~90% cheaper than Browser Use. - If a step breaks, workflow will fallback to Browser Use and agentically run the step. (This self-healing functionality is still very early.) This project just kicked off, so lots of things will break, it’s definitely not production-ready yet, and plenty of stuff is still missing (like a solid editor and proper self-healing). But we wanted to share early, get feedback, and figure out what workflows you’d want to automate this way. Try it out and let us know what you think! https://ift.tt/Asf8LVQ May 16, 2025 at 11:05PM

Show HN: Convert JSON Schema to SQL DDL https://ift.tt/VEjQGq6

Show HN: Convert JSON Schema to SQL DDL While doing research for an architectural change at work, I couldn’t find a nice npm library that let’s you create SQL tables from a JSON Schema. That’s how I decided to create one myself. https://ift.tt/9MktTJh May 16, 2025 at 04:19AM

Show HN: AsianMOM – WebGPU Vision-LLM app that roasts you like ur mom in-browser https://ift.tt/zWM6FPb

Show HN: AsianMOM – WebGPU Vision-LLM app that roasts you like ur mom in-browser Randomly got inspired yesterday seeing SmolVLM working on WebGPU and had the silly idea for this project. it's not perfect and super limited because of the current limitations of WebML (and admittedly, because I suck at prompting, but that's why it's Open Source haha) but it is 1.5B WORTH OF AI (SmolVLM 500M and LLama 3.2 1B) working RIGHT IN YOUR BROWSER with you not having to install anything! In fact, the whole thing is actually just an index.html that you can install and even use directly! It might be a little bit slow on first try (takes about 3 mins) when it installs models, but it caches it so it's way faster the second time (also, it's available offline after it's cached haha) Works on any modern web browser It may be a funny little project, but it's genuinely taught me so much about WebML and Vision models, and the technologies we're getting with WebML will 100% democratize AI access and make it way simpler and easier to be used everywhere :p GH Repo in case you're interested: https://ift.tt/5raewZQ https://ift.tt/o4Gpr8k May 16, 2025 at 02:20AM

Show HN: Turn OpenAPI documents to LLM friendly Markdown https://ift.tt/7A0l4Km

Show HN: Turn OpenAPI documents to LLM friendly Markdown https://ift.tt/3yUmOQa May 16, 2025 at 12:44AM

Show HN: Undetectag, track stolen items with AirTag https://ift.tt/ZEejJ0n

Show HN: Undetectag, track stolen items with AirTag I developed a device that turns an Airtag on and off at specific intervals. Current Airtags are detectable right away and cannot be used to track stolen property. That device allows you to hide an Airtag in your car, for example, and someone that steals your car will not be able to use some app to detect it. The Airtag will also not warn the thief of its presence. After some hours, the Airtag turns on again and you can find out its location. It’s not foolproof, as the timing has to be right, but still useful. What do you think? https://undetectag.com/ May 15, 2025 at 10:46PM

Show HN: Family Folder – Help your family remember everything, organise anything https://ift.tt/o32uMCO

Show HN: Family Folder – Help your family remember everything, organise anything Hi Show HN, I’m both nervous and excited to share what I’ve been working on in the early mornings and late evenings over the past few months: Family Folder – a tool to help you and your loved ones stay connected, simplify planning, and never miss a moment. This is mostly a solo project—though I’ve leaned on ChatGPT and Upwork when I hit the limits of my technical skills. I love learning, and this has been a crash course in programming, DevOps, design, UX, and everything in between. The idea came directly from my own experience: trying to keep on top of family life, from newborns to supporting my mum’s memory, birthdays, childcare logistics, and where the insurance documents are stored. Existing tools felt too generic, too corporate, or too messy. I wanted something built for families. Stack: • Ruby on Rails 7 (via Jumpstart Pro) • PostgreSQL • Hosted on Heroku (EU region) • S3 (EU) for file uploads • (Coming soon: iOS app & AI assistant) Family Folder is private by design—you only see what you’re invited to. It’s meant to be simple enough for parents or siblings to actually use, but structured enough to avoid chaos. If this sounds useful—or if you’ve ever tried to manage a family using group chats or shared docs—I’d love your feedback. What would make something like this truly work for your family? Thanks for taking a look! – Tony https://ift.tt/RmEZFkj https://ift.tt/RmEZFkj May 15, 2025 at 01:57AM

Show HN: Doxxer – CLI tool for dynamic SemVer versioning using tags https://ift.tt/LsMS0zP

Show HN: Doxxer – CLI tool for dynamic SemVer versioning using tags Hi, first time poster here! Wanted to share a small CLI utility in Rust: doxxer! It is a tool for working with Git repositories, more specifically, extracting and calculating current/upcoming semantic versions for your repo's tags. It is heavily inspired by the output from "git describe --tags". Why use anything else then? The output is not fully SemVer compliant and therefore I was modifying it in all my projects separately, which I wanted to avoid. Single binary, single predictable output. It does not currently have pre-built binaries, so you have to install it via cargo, but it's in the roadmap! https://ift.tt/5tGMXHU May 14, 2025 at 10:10PM

Show HN: LTXV 13B Distilled – Generate 5s Videos in Under 10s https://ift.tt/THsEMrl

Show HN: LTXV 13B Distilled – Generate 5s Videos in Under 10s Hey HN, after our 13B release we've been working on a faster version of our open-source video model and we're excited to share it. We started with a 13B base model that already had competitive generation speeds (e.g. 55s for a 5s video on an H100 — faster than any other model out there). But we wanted to push it further to allow everyone to quickly iterate over their video generations. So we built a distilled version focused on speed without sacrificing temporal or spatial coherence. With the Distilled model, you can now generate 5-second 720p videos in about 9.5 seconds on an H100, and around 1.5 minutes on a consumer GPU like an RTX 5090. Because the base and distilled models are interoperable, you can mix and match them in a single rendering pipeline. That gives you three modes: - Distilled Pipeline: Fastest. Uses just 4–8 steps end to end. Ideal for rapid iteration and experimentation (~9.5 seconds on H100, ~1.5 min on RTX 5090). - Mixed Pipeline: Starts with the base model to capture accurate motion, physics, and detail, then switches to distilled at higher resolutions for faster rendering. A good middle ground. (~20s on H100, ~2.5 min on RTX 5090) - Base Pipeline: Full-fidelity generation from start to finish. Best quality, best for final renders. (~43s on H100) All pipelines are compatible with our multiscale rendering system, which allows you to first render a lower resolution video and upscale it as needed. The project is open source and available on GitHub ( https://ift.tt/qykL0Sx ) and Hugging Face ( https://ift.tt/RU0SKpT ). You can also enjoy our Comfy integration here ( https://ift.tt/9FwQu26 ) and our open source LTXV trainer ( https://ift.tt/IPtdrLZ ). Would love to hear your thoughts — especially around performance, integration into your own tools or workflows, or any creative uses you're exploring. We're actively working on tuning and adding new configurations, and early feedback is super helpful. https://ift.tt/1fQivKD May 15, 2025 at 12:38AM

Show HN: Put macros.menu/ in front of any restaurant menu URL https://ift.tt/KPMF148

Show HN: Put macros.menu/ in front of any restaurant menu URL I’ve been tracking my macros every day since January 1st. Weighing and measuring at home is a breeze but eating out is a total pain. I built this tool for myself but a lot of likeminded people have loved it. Please note macros are estimated by gen AI. Image menus not supported yet. https://macros.menu May 14, 2025 at 02:19AM

Show HN: Helixdb – Open-source vector-graph database for AI applications (Rust) https://ift.tt/dvSOx03

Show HN: Helixdb – Open-source vector-graph database for AI applications (Rust) Hey HN, we want to share HelixDB ( https://ift.tt/hCqXIPT ), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://ift.tt/ef3j1tH . Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g. key-value stores vs. node-and-edge tables). However, many AI-driven applications need both similarity and relationship queries. For example, you might use vector-based semantic search to retrieve relevant legal documents, and then use graph traversal to identify relationships between cases. Developers of such apps have the quandary of needing to build on top of two different databases—a vector one and a graph one—plus you have to link them together and sync the data. Even then, your two databases aren't designed to work together—for example, there’s no native way to perform joins or queries that span both systems. You’ll need to handle that logic at the application level. Helix started when we realized that there are ways to integrate vector and graph data that are both fast and suitable for AI applications, especially RAG-based ones. See this cool research paper: https://ift.tt/71LitOe . After reading that and some other papers on graph and hybrid RAG, we decided to build a hybrid DB. Our aim was to make something better to use from a developer standpoint, while also making it fast as hell. After a few months of working on this as a side project, our benchmarking shows that we are on par with Pinecone and Qdrant for vectors, and our graph is up to three orders of magnitude faster than Neo4j. Problems where a hybrid approach works particularly well include: - Indexing codebases: you can vectorize code-snippets within a function (connected by edges) based on context and then create an AST (in a graph) from function calls, imports, dependencies, etc. Agents can look up code by similarity or keyword and then traverse the AST to get only the relevant code, which reduces hallucinations and prevents the LLM from guessing object shapes or variable/function names. - Molecule discovery: Model biological interactions (e.g., proteins → genes → diseases) using graph types and then embed molecule structures to find similar compounds or case studies. - Enterprise knowledge management: you can represent organisational structure, projects, and people (e.g., employee → team → project) in graph form, then index internal documents, emails, or notes as vectors for semantic search and link them directly employees/teams/projects in the graph. I naively assumed when learning about databases for the first time that queries would be compiled and executed like functions in traditional programming. Turns out I was wrong, but this creates unnecessary latency by sending extra data (the whole written query), compiling it at run time, and then executing it. With Helix, you write the queries in our query language (HelixQL), which is then transpiled into Rust code and built directly into the database server, where you can call a generated API endpoint. Many people have a thing against “yet another query language” (doubtless for good reason!) but we went ahead and did it anyway, because we think it makes working with our database so much easier that it’s worth a bit of a learning curve. HelixQL takes from other query languages such as Gremlin, Cypher and SQL with some extra ideas added in. It is declarative while the traversals themselves are functional. This allows complete control over the traversal flow while also having a cleaner syntax. HelixQL returns JSON to make things easy for clients. Also, it uses a schema, so the queries are type-checked. We took a crude approach to building the original graph engine as a way to get an MVP out, so we are now working on improving the graph engine by making traversals massively parallel and pipelined. This means data is only ever decoded from disk when it is needed, and parts of reads are all processed in parallel. If you’d like to try it out in a simple RAG demo, you can follow this guide and run our Jupyter notebook: https://ift.tt/YXVtk2C... Many thanks! Comments and feedback welcome! https://ift.tt/hCqXIPT May 14, 2025 at 12:26AM

Show HN: AG-UI Protocol – Bring Agents into Frontend Applications https://ift.tt/amivDPu

Show HN: AG-UI Protocol – Bring Agents into Frontend Applications https://ift.tt/JtWswak May 13, 2025 at 11:09PM

Show HN: Lumoar – Free SOC 2 tool for SaaS startups https://ift.tt/p3EHXcf

Show HN: Lumoar – Free SOC 2 tool for SaaS startups We built Lumoar to help small SaaS teams get SOC 2-ready without paying thousands for Big 4 consultants or dealing with bloated compliance platforms. As a startup ourselves, we faced the usual issues: long security questionnaires, confusing audit requirements, and expensive tools that felt overkill. Lumoar is a simpler alternative: - Generate compliant SOC 2 policies automatically - Track your controls and progress in a clean dashboard - Upload evidence and get plain-language recommendations - Designed for engineers and founders, not compliance pros It's free to start — you can generate policies and explore the dashboard without a sales call or demo. Would love to hear what blockers you’ve faced with SOC 2 and what other frameworks you’re thinking about (e.g., ISO 27001, GDPR). All feedback is welcome. https://www.lumoar.com May 13, 2025 at 02:05AM

Show HN: The missing inbox for GitHub pull requests https://ift.tt/0MLcQHu

Show HN: The missing inbox for GitHub pull requests Mergeable is an improved inbox for GitHub pull requests. They can be organized according to your rules into any number of sections, each section being defined as an arbitrary search query. Data is refreshed periodically, and is kept locally in the browser. Mergeable is an open source project, which can be self-hosted very easily. A free public instance is also available to get started very quickly. https://ift.tt/L8Gwv4V May 13, 2025 at 12:29AM

Show HN: Pure Go QuickJS JavaScript engine (Golang) https://ift.tt/SoJY8X3

Show HN: Pure Go QuickJS JavaScript engine (Golang) Package quickjs is a pure Go embeddable Javascript engine. It supports the ECMA script 14 (ES2023) specification including modules, asynchronous generators, proxies and BigInt. https://ift.tt/oKrj1I3 May 12, 2025 at 07:43PM

Show HN: Airweave – Let agents search any app https://ift.tt/ZWiYScr

Show HN: Airweave – Let agents search any app Hey HN, we're Lennert and Rauf. We’re building Airweave ( https://ift.tt/9cket8o ), an open-source tool that lets agents search and retrieve data from any app or database. Here’s a general intro: https://www.youtube.com/watch?v=EFI-7SYGQ48 , and here’s a longer one that shows more real-world use cases, examples of how Airweave is used by Cursor (0:33) and Claude desktop (2:04), etc.: https://youtu.be/p2dl-39HwQo A couple of months ago we were building agents that interacted with different apps and were frustrated when they struggled to handle vague natural language requests like "resolve that one Linear issue about missing auth configs", "if you get an email from an unsatisfied customer, reimburse their payment in Stripe", or "what were the returns for Q1 based on the financials sheet in gdrive?", only to have the agent inefficiently chain together loads of function calls to find the data or not find it at all and hallucinate. We also noticed that despite the rise of MCP creating more desire for agents to interact with external resources, the majority of agent dev tooling focused on function calling and actions instead of search. We were annoyed by the lack of tooling that enabled agents to semantically search workspace or database contents, so we started building Airweave first as an internal solution. Then we decided to open-source it and pursue it full time after we got positive reactions from coworkers and other agent builders. Airweave connects to productivity tools, databases, or document stores via their APIs and transforms their contents into searchable knowledge bases, accessible through a standardized interface for the agent. The search interface is exposed via REST or MCP. When using MCP, Airweave essentially builds a semantically searchable MCP server on top of the resource. The platform handles the entire data pipeline from connection and extraction to chunking, embedding, and serving. To ensure knowledge is current, it has automated sync capabilities, with configurable schedules and change detection through content hashing. We built it with support for white-labeled multi-tenancy to provide OAuth2-based integration across multiple user accounts while maintaining privacy and security boundaries. We're also actively working on permission-awareness (i.e., RBAC on the data) for the platform. So happy to share learnings and get insights from your experiences. looking forward to comments! https://ift.tt/9cket8o May 12, 2025 at 10:34PM

Show HN: MCP CLI Adapter – run scripts as MCP tools https://ift.tt/NaAvHG7

Show HN: MCP CLI Adapter – run scripts as MCP tools The MCP CLI Adapter is a tool that allows LLMs to safely execute command-line tools through the Model Context Protocol (MCP). It provides a secure bridge between LLMs and operating system commands. https://ift.tt/mguUVjp May 12, 2025 at 05:28AM

Show HN: Sqlitemap, a persistent map implementation backed by SQLite for C++ https://ift.tt/ciso1lN

Show HN: AI-powered batch photo editor for real estate photographers https://ift.tt/Uxv5ONa

Show HN: AI-powered batch photo editor for real estate photographers I got tired of repetitive editing tasks, so I built a tool that simplifies bulk edits using text prompts and AI workflows. Now I can quickly handle things like virtual staging, changing backgrounds, adding/removing objects, adjusting brightness and exposure, color corrections, boosting contrast and clarity, fixing distortions, batch color grading and much more! But most importantly, I can do this to all selected images, tens, hundreds or more. I'm particularly interested in feedback on the workflow and UI from photographers/editors who handle large volumes of images. I've increased the free plan credits to 40 so you can edit up to 40 images, if you'd like to help me trial it out. Otherwise I'm happy to answer any questions about the implementation or roadmap. https://4ditor.com/ May 9, 2025 at 08:08AM

Show HN: PLAttice, for assembling structures much larger than the 3D printer bed https://ift.tt/a2RxM0h

Show HN: PLAttice, for assembling structures much larger than the 3D printer bed Struts, nodes, and pins are reversibly assembled into fully 3D printed lattices, trusses, and tree-like structures spanning up to a few meters. I used the system to build a stand for an overhead table lamp which supports a ~1 m cantilevered arm using a tensioned floor-to-ceiling column. If you want to give it a try, find the *.stl files at the bottom of the page; figure ~1 kg of PLA and ~1 day of print time per meter of box truss; pay attention to print orientation; plz respect the license; and definitely print the pin trimming jig. https://ift.tt/wpl7D5X May 11, 2025 at 03:18AM

Show HN: Sprigman – Pac-Man Recreated in a Limited Tile Based JavaScript Engine https://ift.tt/v7ECITP

Show HN: Sprigman – Pac-Man Recreated in a Limited Tile Based JavaScript Engine Sprig is a tile based game engine made by Hack Club that's like Scratch's older brother but has it's own set of unique limitations. All sprites need to be made in Bitmap and uses a midi like event sequencer to get any music into it, since it's meant for a different set of hardware, you can only use very basic controls too. I made a version of PacMan inside it that has randomly generated mazes every time and mostly works well! https://ift.tt/h2WqKRS May 8, 2025 at 03:23AM

Show HN: MiniLMs – A website-repo for tiny language models and learning resource https://ift.tt/W82MpoU

Show HN: MiniLMs – A website-repo for tiny language models and learning resource I've been tinkering with how small language models can be while still doing something interesting. This led me to create MiniLMs: a website and open-source repository where I'm experimenting with minimalist LLM architectures and sharing my learning process. The current main focus is "SYNEVA," a small Markov chain chatbot I worked on – some with pattern matching to simple neural nets, always aiming for a tiny <3kb footprint. You can try it (and some older versions) on the site. The project also includes: Study resources I'm gathering on NNs, LLMs, and AI history. the current models are HTML/JS. Definitely a learning-in-public kind of thing. Website & Demos: https://minilms.kuber.studio/ GitHub Repository (MIT License): https://github.com/Kuberwastaken/MiniLMs-PROJECT Curious to hear your thoughts or any cool tiny AI projects you've seen or want to see. https://minilms.kuber.studio/ May 10, 2025 at 11:01PM

Show HN: Oliphaunt – A Native Mastodon Client for macOS https://ift.tt/YAUD5Rg

Show HN: Oliphaunt – A Native Mastodon Client for macOS I’ve been building Oliphaunt, a native Mastodon client for macOS, as a solo project — designed to be fast, lightweight and feel right at home on the Mac. It’s not built with Catalyst or Electron framework. Key features: • Native macOS UI using AppKit with some SwiftUI integration (not a web wrapper) • Core Data for local caching • Responsive, keyboard-friendly interface • UX tailored for desktop-class Mac computers • Supports multiple accounts, cross-instance timelines and search You can try it via TestFlight (macOS 14+ Sonoma): https://ift.tt/h6PlXYO Feedback is welcome here, on GitHub, or via TestFlight: https://ift.tt/pJRAWIs https://ift.tt/h6PlXYO May 9, 2025 at 11:21PM

Show HN: BlenderQ – A TUI for managing multiple Blender renders https://ift.tt/PFTHidr

Show HN: BlenderQ – A TUI for managing multiple Blender renders Hi HN, I’m a solo content-creator/Blender user and developed this tool as an easy way to manage and render multiple Blender renders locally. The TUI portion is written in TypeScript because it gave me a good way to build the front end that allowed for some complex components in a language that I was intimately familiar with, and the portions that interact with Blender are actually Python scripts. https://ift.tt/7IJpcNV May 9, 2025 at 11:16PM

Show HN: Extreme Router – A High-Performance, Plugin-Driven JavaScript Router https://ift.tt/xC9VPsq

Show HN: Extreme Router – A High-Performance, Plugin-Driven JavaScript Router Extreme Router is a radix-tree-based, plugin-driven JavaScript router designed for speed and extensibility. It features: Blazing-fast lookups using an optimized radix tree. Extensible plugin system to customize route matching. Universal compatibility across Node.js, Bun, Deno, and browsers. Zero dependencies, lightweight yet powerful. Built-in support for parameters, wildcards, regex patterns, and optional segments. Benchmarked for high performance under stress. Ideal for high-traffic applications, custom routing needs, and scalable architectures. Check it out! Open to feedback, contributions, and ideas. https://ift.tt/JjY1geh May 9, 2025 at 10:38PM

Show HN: Hydra (YC W22) – Serverless Analytics on Postgres https://ift.tt/mWQ4cXs

Show HN: Hydra (YC W22) – Serverless Analytics on Postgres Hi HN, Hydra cofounders (Joe and JD) here ( https://www.hydra.so/ )! We enable realtime analytics on Postgres without requiring an external analytics database. Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database. (A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.) For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: ( https://ift.tt/aLqKTxr... ). We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables. Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database. What is serverless? Serverless means the database automatically provisions and isolates dedicated compute resources for each query process. The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time. How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process. why is hydra so fast at analytics? ( https://ift.tt/wJLgD26 ) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql. example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable. Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project. https://www.hydra.so/ May 9, 2025 at 10:24PM

Show HN: I created an open source AI research assistant https://ift.tt/q3rw6Rd

Show HN: I created an open source AI research assistant Imagine typing ANY question and getting a perfectly organized answer with sources, summaries, AND a knowledge graph... instantly! That's the power of the AI Research Assistant we're building TODAY using Motia, the backend framework that's changing the game for AI agents! This isn't just another tutorial – it's a peek into the future of AI workflows. See how Motia's event-driven magic lets you chain together complex tasks: Accepting your query, Generating search ideas with Gemini, Scouring the web, Deep-diving into content analysis, Extracting key concepts, Delivering a full research report. We'll start coding LIVE (well, almost!) and you'll see just how FAST and SIMPLE it is to get a powerful agent running. https://www.youtube.com/watch?v=uyo3dfR9DDc May 8, 2025 at 11:48PM

Show HN: Checking Pope's election results with smoke test script for chimney https://ift.tt/4Op0oZD

Show HN: Checking Pope's election results with smoke test script for chimney This Playwright test script uses AI to test if there's smoke coming out of the Sistine Chapel chimney and whether that smoke is white. The test only passes if the smoke is white. Currently, set to use Google Gemini Flash 2.0, but you can switch it to use other LLM providers/models by setting the environment variable in the Github actions workflow: https://ift.tt/UJ25lIT... I've set it to run every minute during the Papal Conclave election times - https://ift.tt/rLwyKeh... https://ift.tt/KuQcA5Y May 8, 2025 at 11:13PM

Show HN: Cutting the Fat from Stream Processing: Meet ZephFlow (Open Source) https://ift.tt/B2t3lRH

Show HN: Cutting the Fat from Stream Processing: Meet ZephFlow (Open Source) Hey HN, Apache Flink is a beast for heavy-duty stream processing – no doubt about it. We've used it, admired its power, and it heavily influenced our thinking. But let's be honest: for a lot of common, everyday stream processing tasks, Flink (and similar frameworks) can feel like bringing a bazooka to a knife fight. The operational overhead and complexity for simpler, stateless jobs often outweigh the benefits. That's why we built ZephFlow. What is ZephFlow? ZephFlow is a stream processing framework built for lightweight operations and simplicity. It distills the core principles of high-performance, low-latency data processing but specifically targets stateless or simpler stateful workloads. This focus allows ZephFlow to be significantly more lightweight and easier to operate compared to more comprehensive platforms like Apache Flink. A single ZephFlow job can run as a lean, efficient process, making it highly versatile. Why We Think ZephFlow Hits the Mark Today (and in 2025): - Stop Overspending on Resources: Run your streaming tasks efficiently, whether on modest cloud instances or even edge devices, without paying for idle complexity. - Slash Operational Headaches: Deploying and managing ZephFlow is refreshingly simple. Spend your time on logic, not on wrestling with the infrastructure. - Ship Faster: If you need to get a streaming pipeline from idea to production quickly, ZephFlow's lean nature makes for rapid iteration. - Actually Versatile: It's proven great for us on real-time ETL, transforming logs and telemetry on the fly, prepping data for ML models, and even as a responsive backend for APIs. Our Philosophy: Simplicity Scales (Development, if Not Yet a Cluster). Many common real-time data tasks don't require distributed state management across a massive cluster from day one. ZephFlow lets you build and test your pipelines on a single instance with remarkable ease. While Flink is the go-to for truly complex distributed state, ZephFlow offers a nimble, focused alternative for a wide array of everyday streaming needs. We've been dogfooding ZephFlow for our own log and telemetry processing, and the sheer speed from coding to live has been a breath of fresh air. What's Next? We Need Your Brain. We're now sketching out how to scale ZephFlow deployments horizontally while obsessively preserving that core simplicity. For the kind of stateless or less-complex stateful streaming you do, what distributed features would genuinely make your life easier (and which ones are just noise)? We'd love your feedback in the comments. And if this sounds interesting, check out the project and maybe star it if you like where we're headed: https://ift.tt/S0aGXbO Thanks for reading! https://ift.tt/S0aGXbO May 8, 2025 at 10:50PM

Show HN: A coilgun I built when I was 16 https://ift.tt/bBCWuL3

Show HN: A coilgun I built when I was 16 I made it when I was 15 or 16, so it's nothing amazing—just thought I'd share https://ift.tt/bhp8MXO May 8, 2025 at 09:10PM

Show HN: Picostrap5 A free bootstrap-based WordPress theme on GitHub https://ift.tt/AILfsyU

Show HN: Picostrap5 A free bootstrap-based WordPress theme on GitHub https://ift.tt/kaEVcrR May 8, 2025 at 03:52AM

Show HN: I vibe-coded Product Hunt, but for Videos https://ift.tt/8qPC6WL

Show HN: I vibe-coded Product Hunt, but for Videos https://tubehunt.co May 8, 2025 at 03:04AM

Show HN: Cloi – free local debugging agent in your terminal https://ift.tt/uH4krRP

Show HN: Cloi – free local debugging agent in your terminal Hey everyone! For the past two weeks my friend and I have been heads-down building Cloi, a fully local debugging agent that runs right in your terminal. You probably know the drill—every AI coding tool asks for API keys, subscriptions, and uploads your entire codebase to the cloud. Cloi does none of that: it runs entirely on your machine, with no cloud, no API keys, no subscriptions, and zero data leaving your system. What Cloi does: - Contextual error capture: Grabs your stack trace, local files, and environment to understand the issue. - Local LLM inference: Spins up Ollama on your box and generates targeted fixes—no external servers. - Safe patch application: Presents you with diffs and only applies changes when you explicitly approve. - Model‐agnostic: Ships with Phi-4 out of the box (surprisingly capable for its size!), but you can swap in any Ollama model you’ve installed. Why we built it: - Maintain full control over your code and data—ideal for security-sensitive projects - Avoid recurring subscription fees and cloud vendor lock-in - Keep your development flow entirely offline when you need it Highlights: We hit 202 stars in just 5 days, which tells us we're not the only ones who wanted this! Cloi is plug-and-play (just install and run), and we designed it to be completely unopinionated, meaning you can you whatever Ollama model you want. Get it now: npm install -g @cloi-ai/cloi If you find Cloi useful, we’d really appreciate a star on GitHub. Try it out, let us know what you think, and happy debugging! — Gabriel Cha & Mingyou Kim https://ift.tt/ROxUjFY May 8, 2025 at 12:25AM

Show HN: 100.st – Dev utilities I built for format conversions and encoding https://ift.tt/XmRL6a4

Show HN: 100.st – Dev utilities I built for format conversions and encoding I built this because I needed one place for all my dev tasks. Handles JSON/YAML/XML conversions, UUID gen, case transforms, and encoding tools. Built with Next.js/Chakra UI frontend, NGINX on a vps, Cloudflare for dns and cache. All client-side processing for privacy. What tools should I add next? Andrei https://100.st May 5, 2025 at 01:53AM

Show HN: Kevin-32B – how to do multi-turn RL on writing CUDA kernels https://ift.tt/q4LzhTR

Show HN: Kevin-32B – how to do multi-turn RL on writing CUDA kernels Hey – we just published a blog post about Kevin-32B = K(ernel D)evin. It's to our knowledge the first open-source model that's RL-trained on CUDA kernels. Our goal was to demonstrate multi-turn RL using GRPO. We used 180 Python->CUDA conversion tasks from the KernelBench dataset. The results were surprisingly strong! We were able to outperform top reasoning model like o3 & o4-mini. We're sharing our training setup and learnings in the blogpost. Also the model is on HuggingFace: https://ift.tt/eL7zYWS https://ift.tt/tCEXgKG May 7, 2025 at 02:48AM

Show HN: X402 – an open standard for internet native payments https://ift.tt/aYrnBhI

Show HN: X402 – an open standard for internet native payments Hi HN – excited to announce x402, initially developed by Coinbase (YC 12) x402 lets any HTTP API charge per request without issuing API keys or storing credit cards. Buyers (humans or AI agents) keep funds in their own wallet and dynamically discover compatible endpoints, call them as usual, and automatically pay a microtransaction in USDC or other tokens to settle. 90 second demo: https://www.youtube.com/watch?v=PV-L2AfLhJg Problem: Every time we want to use a new API we have to: find the service online create a developer account, copy a secret key into env vars, pre-fund or hand over a credit card This flow blocks agents even more. They can’t solve CAPTCHAs or enter credit cards. It also hurts sellers: fraud, chargebacks, onboarding friction, and marketing to humans are huge pain points. Why buyers care Zero setup – Hit a new endpoint immediately. Runtime discovery – Because every x402 service exists in a common registry, an agent can search, compare, and invoke in one loop. Self-assembling agents become practical. Easily create proxy servers – Want an endpoint that isn’t supported? You can use our proxy server template to spin up an x402-compatible instance yourself using traditional API keys, and monetize it for others wanting access. Why sellers care Reach incremental demand – Long-tail bots, side projects, one-off scripts, all of which too small for an account/signup flow, can now pay you. Micropayments without fraud – All payments settle onchain, nothing for stolen credit cards or chargebacks to reverse. Embedded distribution – instead of marketing to humans, create a compelling service meeting demand for agents and watch the requests roll in. How we got here Last year we launched AgentKit (wallets for AI agents). Tens of thousands of agents now hold onchain balances, but they can’t pay for most web services. We revived the long-unused HTTP 402 (“Payment Required”) status code and wrote a spec to make it real. Marc Andresseen calls the lack of native value transfer “the original sin of the internet,” and we see x402 as the absolution of this sin. How it works: x402 specifies a standard response body to accompany a 402 status code. This response body contains machine understandable instructions for how to pay. Payments are signature based an included as an `X-PAYMENT` header in a subsequent request to the same API endpoint. The accepting server can verify and settle payment themselves, or delegate the onchain settlement to what we call a facilitator. This means you don't have to touch crypto as a developer, you can just integrate a middleware and start receiving stablecoin payments in as little as 1 line of code. Because x402 natively traverses your existing client / server requests, it can be implemented in any language, and doesn't require webhooks, or any other complex integration. Its literally this simple: `paymentMiddleware("0xYourAddress", {"/your-endpoint": "$0.01"})` Ask HN API providers – does the one-line integration fit your stack? What’s missing? Agent / infra builders – if a service isn’t available is the proxy server template sufficient? File issues, PRs welcome Everyone – poke holes in the trust and fee model; we’d love to iterate with your feedback Curious to learn more? Check out our documentation and repo for more information, and please don’t hesitate to reach out to get onboard. https://ift.tt/2DMYdPn https://x402.org https://ift.tt/uapfLFz... https://www.x402.org/ May 7, 2025 at 01:24AM

Show HN: Feedsmith — Fast parser & generator for RSS, Atom, OPML feed namespaces https://ift.tt/TIUgZVX

Show HN: Feedsmith — Fast parser & generator for RSS, Atom, OPML feed namespaces Hi HN! While working on a project that involves frequently parsing a lot of feeds, I needed a fast JavaScript-based parser to extract specific fields from feed namespaces. Existing Node packages were either too slow or merged all feed formats, losing namespace information. So I decided to write it myself and created this NPM package with a simple API. Feedsmith supports all feed formats and many popular namespaces, including: Podcast, Media, iTunes, Dublin Core, and more. It can also parse and generate OPML files. I am currently adding support for more namespaces and feed generation for RSS, Atom and RDF. The library grew into something bigger than I initially anticipated, so I also started creating a dedicated documentation website to describe all the features. https://ift.tt/1dZjQCs May 7, 2025 at 01:03AM

Show HN: Tkintergalactic - Declarative Tcl/Tk UI Library for Python https://ift.tt/QBW7kdG

Show HN: Tkintergalactic - Declarative Tcl/Tk UI Library for Python https://ift.tt/OUhHVNd May 6, 2025 at 01:02AM

Show HN: I built a mini macOS app to reveal my yearly subscription spending https://ift.tt/ltFv2s9

Show HN: I built a mini macOS app to reveal my yearly subscription spending I built a macOS app to track my subscriptions after realizing I was spending over $XXXX annually on services I barely used. I wanted a simple, privacy-focused tool to help me stay on top of recurring charges without relying on third-party integrations or sharing financial data. Key Features: – Visual Calendar: See all upcoming charges in a monthly view. – Custom Notifications: Set reminders for upcoming payments. – Highlights: Flag subscriptions as annual, trial, or one-time. – Statistics: View projected yearly spending, average monthly costs, and peak spending months. – Custom Categories: Organize subscriptions with user-defined categories. – Multi-Currency Support: Convert prices on the fly to your preferred currency. – Status Management: Mark subscriptions as active or canceled, with accurate updates in your stats. – Quick Addition: Start typing a service name, and the app auto-suggests logos, categories, and colors. – Import and export data The app is free to use with some limitations. I’m currently working on additional features and would appreciate any feedback or suggestions from the community. https://ift.tt/wXmK1r0 May 6, 2025 at 01:01AM

Show HN: TextQuery – Query CSV, JSON, XLSX Files with SQL https://ift.tt/fKeUBFg

Show HN: TextQuery – Query CSV, JSON, XLSX Files with SQL https://textquery.app/ May 5, 2025 at 11:59PM

Show HN: DistilKitPlus, a distillation framework between any LLMs https://ift.tt/L7iyA9Y

Show HN: DistilKitPlus, a distillation framework between any LLMs Over the past few months, I have built a distillation toolkit that supports cross-tokenizer distillation (e.g., distilling from LLaMA to Qwen vocab, or others). This approach has worked well on reasoning datasets like AIME, and we’ve validated on models like Phi and Qwen. We’ve also integrated Modal for quick deployment (with $30/month credits to try it out). Would love any feedback! GitHub: https://ift.tt/C1zdBKG Docs: https://ift.tt/2Xrw1Gb https://ift.tt/C1zdBKG May 5, 2025 at 11:12PM

Show HN: routr - a fast local replacement for DuckDuckGo bangs https://ift.tt/45Fl7Jk

Show HN: routr - a fast local replacement for DuckDuckGo bangs https://ift.tt/SzVbNYU May 5, 2025 at 12:16AM

Show HN: EZ-TRAK Satellite Hand Tracking Suite https://ift.tt/1H8wpsy

Show HN: EZ-TRAK Satellite Hand Tracking Suite EZ-TRAK is a comprehensive satellite tracking suite designed for amateur radio operators, weather satellite enthusiasts, and educational purposes. The software interfaces with an EZ-TRAK BLE device which is mounted to a lightweight foldable portable satellite dish antenna to hand track satellites in real-time, providing azimuth and elevation data for optimal antenna positioning. https://ift.tt/XfPGuOo May 4, 2025 at 11:10PM

Show HN: Open-lmake, a scalable, reliable build system with auto dep-tracking https://ift.tt/sBzcy2d

Show HN: Open-lmake, a scalable, reliable build system with auto dep-tracking Hello Hacker News, I often hear people saying "all build-systems suck", an opinion I have been sharing for years, and this is the motivation for this project. I finally got the opportunity to make it open-source, and here it is. In a few words, it is like make, except it can be comfortably used even in big projects using HPC (with millions of jobs, thousands of them running in parallel). The major differences are that: - dependencies are automatically tracked (no need to call gcc -M and the like, no need to be tailored to any specific tool, it just works) by spying disk activity - it is reliable : any modification is tracked, whether it is in sources, included files, rule recipe, ... - it implements early cut-off, i.e. it tracks checksums, not dates - it is fully traceable (you can navigate in the dependency DAG, get explanations for decisions, etc.) And it is very light weight. Configuration (Makefile) is written in Python and rules are regexpr based (a generalization of make's pattern rules). And many more features to make it usable even in awkward cases as is common when using, e.g., EDA tools. Give it a try and enjoy :-) https://ift.tt/UnzZ0v5 May 3, 2025 at 11:11PM

Show HN: I made a toast that shows what visitors are doing in real-time https://ift.tt/Q7VKzEH

Show HN: I made a toast that shows what visitors are doing in real-time Hey HN, A couple years ago, I switched from my corporate 9 to 5 job to become a Tech Educator. Starting with little social proof was tough, I only had testimonials from past colleagues. Existing social proof tools were charging $75/month i.e. ($900/year) and were too complex to use. This is why my partner and I built ProofyBubble for my Next.js Course Early Access Launch. We saw a real jump in revenue the moment we added ProofyBubble to show off our website traffic, waitlist signups, incoming sales, and past sales. I've since used ProofyBubble in all my products - my newsletter subscribers grew, sales increased, and I launched my course with tons of social proof. I hope it helps you as much as it helped me. Would love your feedback please. https://ift.tt/akuGj0o May 2, 2025 at 07:51AM

Show HN: I built a synthesizer based on 3D physics and launched the product https://ift.tt/DAdvbt9

Show HN: I built a synthesizer based on 3D physics and launched the product I've been working on the Anukari 3D Physics Synthesizer for a little over two years now. It's one of the earliest virtual instruments to rely on the GPU for audio processing, which has been incredibly challenging and fun. In the end, predictably, the GUI for manipulating the 3D system actually ended up being a lot more work than the physics simulation. So far I am only selling it direct on my website, which seems to be working well. I hope to turn it into a sustainable business, and ideally I'd have enough revenue to hire folks to help with it. So far it's been 99% a solo project, with (awesome) contractors brought in for some of the stuff that I'm bad at, like the 3D models and making instrument presets/videos. The official launch announcement video is here: https://www.youtube.com/watch?v=NYX_eeNVIEU But if you REALLY want to see what it can do, check out what Mick Cormick did with in on the first day: https://ift.tt/4gXTnhH I've kept a fairly detailed developer log about my progress on the project since October 2023, which might be of interest to the hardcore technical folks here: https://ift.tt/W6o05zD I also gave a talk at Audio Developer Conference 2023 (ADC23) that goes deep into a couple of the problems I solved for Anukari: https://www.youtube.com/watch?v=lb8b1SYy73Q https://anukari.com May 3, 2025 at 01:12AM

Show HN (YC S25): Well – MCP AI-Based Collection of Invoices https://ift.tt/TGbLF48

Show HN (YC S25): Well – MCP AI-Based Collection of Invoices Hi HN, we’re the cofounders of Lago:an AI-agent powered Chrome extension that becomes every founder’s best friend when accounting season hits. Well automates supplier invoice collection and pipes the data directly into your accounting tools, ERP, or dashboards — with zero effort. Why We’re Building Well: Automating the Missing Half of Payments Our website is at https://wellapp.ai/ and our Github is here: https://ift.tt/njfKDXN . --- Our Background We’re a team of infrastructure builders with deep roots in European fintech. Over the last decade, we built and scaled two core-banking platforms — from scratch — across IbanFirst, Fintecture, and Qonto (serving 600K+ SMEs). Payments, compliance, scalability: we lived it, pushed it, broke it, and rebuilt it. Across all those years, one thing became obvious: while moving money became fast, standardized, and predictable, handling supplier invoices remained chaos. --- The Insight Payments today are a solved problem. Protocols like EMV, SEPA Instant, and SWIFT create reliable, instant settlement flows. Whether you tap a card or wire funds across borders, the infrastructure just works. But invoices? Every supplier, every service, every vendor still does it differently. Some email you a PDF. Some force you to log into portals. Some send nothing at all unless you chase them manually. There’s no protocol, no standards, no rails — just friction. In a world where payment is protocolized, *invoice management is broken*. --- The Problem This chaos isn’t just annoying — it’s operationally expensive: - Solopreneurs and small teams waste hours every month just retrieving invoices. - Accounting tools often rely on manual drag-and-drop uploads. - Poor invoice tracking means poor treasury visibility — contributing to the 57% of bankruptcies in France caused by cash flow mismanagement. We’ve seen it first-hand working closely with thousands of startups and SMEs at Qonto. Adoption of accounts payable solutions is painfully low — not because people don’t need them, but because getting started still requires too much manual work. No one wants to forward emails, upload PDFs, or manage inbox rules just to track basic finances. --- Why Now Two forces are converging: - Regulation: EU-wide electronic invoicing mandates are coming, shifting the landscape. - Technology: AI now allows us to automate where legacy RPA (robotic process automation) approaches failed. We believe the next true disruption in finance won’t come from payment rails. It will come from *bridging the invoice gap* — automatically, invisibly, and at scale. --- What We’re Building Well is an AI-powered infrastructure designed to automate supplier invoice collection — no matter the format, no matter the source. Here’s what Well does: • Captures invoices from portals, emails, and attachments — fully automated. • Extracts structured data (amounts, vendors, dates) with high accuracy. • Securely syncs invoice data into your accounting SaaS, ERP, or dashboards. No manual uploads. No password sharing. No chasing. Just a protocolized pipeline — like payments, but for supplier invoices. --- What’s Next - Extending Well’s reach across 1,000+ SaaS vendors. - Deepening integrations into accounting, ERP, and spend management ecosystems. - Building invisible finance workflows where invoices just...appear where you need them. We’re starting with solopreneurs, indie hackers, and lean startup teams — but our ambition is to make invoice chaos obsolete for businesses of any size. If you’re tired of chasing supplier invoices instead of growing your business, we’d love to hear from you. Learn more at https://wellapp.ai/ May 2, 2025 at 10:44PM

Show HN: Exhibit and Site on Mechanisms for Students https://ift.tt/V5tTLuE

Show HN: Exhibit and Site on Mechanisms for Students Just finished a super-nerdy amateur hobby project: An exhibit and website to show kids how cool mechanisms are! Sadly, kids don't get much tangible experience with machines anymore. Ideally, this exhibit will inspire some to explore engineering, even if they are not "book learners". The website provides content to back up the exhibit, with videos and 3D printing files. The project is inspired by engineering exhibits from the past. Check out the research page for more. The project will be open-sourced to enable people to make their own and extend it. If you want to collaborate, LMK. --Steve https://ift.tt/vKCRHlu May 2, 2025 at 11:37PM

Show HN: Robot Unlock – an open-ended programming game/zachlike https://ift.tt/PAoU5zM

Show HN: Robot Unlock – an open-ended programming game/zachlike Hello, In 2010 I made an open-ended programming game based on Befunge and Brainf*k. I was young and didn't know what I was doing - coding it in AutoIT of all things and using borderless windows for sprites. Nevertheless, it was a full game and some people actually played it, sharing solutions with each other. I took it as a sign that the game had some potential - I appreciated this very much at the time. It was zachlike at its core, except that it came out earlier than SpaceChem and the term hadn't been coined yet. Years passed, I worked in the game industry, had some fun, learned a few things and eventually burned out. Meanwhile, Zachtronics kept making games and managed to define a genre, proving that there indeed was a market for such games. I'm very happy about that. Now I want to have a shot at going indie and almost 15 years later I'm launching the sequel to my 2010 game. One of my playtesters has been at it for 26 hours so I know it can be a real nerd sniper. It's a game for the type of person who loves quirky languages and optimizing their programs under extreme constraints. I have been hanging out on HN for a long time and thought some in this community might like the game. I want to keep doing this and I will as long as I can afford it. Looking forward to your questions and feedback. https://ift.tt/sFgmLav https://ift.tt/uVyToge May 1, 2025 at 10:38PM

Show HN: Runnem – A CLI to manage local services with screen and YAML https://ift.tt/Obko72X

Show HN: Runnem – A CLI to manage local services with screen and YAML Hi all! This is my second ever Show HN post, my first was back in 2020. I built runnem, a CLI for managing local services in projects with multiple components like frontend, backend, and database. I have a few projects going at once, each with several services. When I come back to one after a few weeks to fix a bug or make a change, I always forget how to spin everything up. Which command runs what? What depends on what? runnem solves that for me: - runnem up and runnem down to start or stop everything - Handles service dependencies automatically - Uses screen under the hood (I'm keen on screen) - Manages logs, rotates them, and cleans up ports - Simple YAML config per project (runnem.yaml) I didn’t research alternatives deeply. I’ve used Tilt before, mainly with Docker, but wanted something lightweight and terminal-native for projects with mixed stacks. Also, did I mention that I like screen? :) I haven’t tested it much with services that run inside Docker, or local databases (I use neon.tech), or unusual screen setups. There may be edge cases it doesn’t handle yet. It would be great if people find issues I can patch over time. It's made my dev workflow a bit smoother. I'd love to hear what you think! Docs: https://runnem.com GitHub: https://ift.tt/6WESvw9 PyPI: https://ift.tt/EdsmVQL https://runnem.com May 1, 2025 at 10:40PM

Show HN: Tgfeed – convert public Telegram channels into RSS or Atom feeds https://ift.tt/5puQ4mC

Show HN: Tgfeed – convert public Telegram channels into RSS or Atom feeds I’m using it to read Telegram channels without ads and in the same place as everything else (Miniflux), hosting it on my home server. Maybe you’ll find it useful too. As a bonus, here’s a Solarized Light theme for Miniflux RSS reader I vibed with AI : https://ift.tt/Bz3QhnO... https://ift.tt/tjGXC53 May 1, 2025 at 11:03PM

Show HN: Mechanical Computer Kit (Roons) https://ift.tt/ksxnhwq

Show HN: Mechanical Computer Kit (Roons) I built a mechanical computer kit: https://ift.tt/Ky4japV tl;dr: it's a cellular automaton on a "loom" of alternating bars, using contoured tiles to guide marbles through logic gates. It's not just "Turing complete, job done"; I've tried to make it actually practical. Devices are compact, e.g. you can fit a binary adder into a 3cm square. It took me nearly two years and dozens of different approaches. There's a sequence of interactive tutorials to try out, demo videos, and a janky simulator. I've also sent out a few prototype kits and have some more ready to go. Please ask me anything, I will talk about this for hours. -- Jesse https://ift.tt/pTt31O6 May 1, 2025 at 10:57PM