Show HN: Smooth – Faster, cheaper browser agent API https://ift.tt/RJHU2ZY

Show HN: Smooth – Faster, cheaper browser agent API Hey there HN! We're Antonio and Luca, and we're excited to introduce Smooth, a state-of-the-art browser agent that is 5x faster and 7x cheaper than Browser Use ( https://ift.tt/RzmvVfs ). We built Smooth because existing browser agents were slow, expensive, and unreliable. Even simple tasks could take minutes and cost dollars in API credits. We started as users of Browser Use, but the pain was obvious. So we built something better. Smooth is 5x faster, 7x cheaper, and more reliable. And along the way, we discovered two principles that make agents actually work. (1) Think like the LLM ( https://ift.tt/xj5I489 ). The most important thing is to put yourself in the shoes of the LLM. This is especially important when designing the context. How you present the problem to the LLM determines whether it succeeds or fails. Imagine playing chess with an LLM. You could represent the board in countless ways - image, markdown, JSON, etc. Which one you choose matters more than any other part of the system. Clean, intuitive context is everything. We call this LLM-Ex. (2) Let them write code ( https://ift.tt/UOVe1LA ) Tool calling is limited. If you want agents that can handle complex logic and manipulate objects reliably, you need code. Coding offers a richer, more composable action space. Suddenly, designing for the agent feels more like designing for a human developer, which makes everything simpler. By applying these two principles religiously, we realized you don't need huge models to get reliable results. Small, efficient models can get you higher reliability while also getting human-speed navigation and a huge cost reduction. How it works: 1. Extract: we look at the webpage and extract all relevant elements by looking at the rendered page. 2. Filter and Clean: then, we use some simple heuristics to clean up the webpage. If an element is not interactive, e.g. because a banner is covering it, we remove it. 3. Recursively separate sections: we use several heuristics to represent the webpage in a way that is both LLM-friendly and as similar as possible to how humans see it. We packaged Smooth in an easy API with instant browser spin-up, custom proxies, persistent sessions, and auto-CAPTCHA solvers. Our goal is to give you this infrastructure so that you can focus on what's important: building great apps for your users. Before we built this, Antonio was at Amazon, Luca was finishing a PhD at Oxford, and we've been obsessed with reliable AI agents for years. Now we know: if you want agents to work reliably, focus on the context. Try it for free at https://ift.tt/HBjTN3x Docs are here: https://ift.tt/DvjfBCY Demo video: https://youtu.be/18v65oORixQ We'd love feedback :) https://www.smooth.sh/ August 26, 2025 at 08:35PM

Show HN: Ubon – a solution for the "You're absolutely right" debugging dread https://ift.tt/nIziHo9

Show HN: Ubon – a solution for the "You're absolutely right" debugging dread I used Claude Code heavily while trying to launch an app while being quite sick and my mental focus was not at its best. So I relied 'too much' on Claude Code, and my Supabase keys slipped in a 'hidden' endpoint, causing some emails to be leaked. After some deep introspection, and thinking about the explosion of Lovable, Replit, Cursor, Claude Code vibe-coded apps, I thought about what's the newest newest and most dreadful pain points in the dev arena right now. And I came up with the scenario of debugging some non-obvious errors, where your AI of choice will reply "You're absolutely right! Let me fix that", but never nailing what's wrong in the codebase. So I built Ubon for the last week, listing thoroughly all the pain points I have experienced myself as a software engineer (mostly front-end) for 15 years. Ubon catches the stuff that slips past linters - hardcoded API keys, broken links, missing alt attributes, insecure cookies. The kind of issues that only blow up in production. And now I can use Ubon by adding it to my codebase ("npx ubon scan .", or simply telling Claude Code "install Ubon before commiting"), and it will give outputs that either a developer or an AI agent can read to pinpoint real issues, pinpointing the line and suggested fix. It's open-source, free to use, MIT licensed, and I won't abandon it after 7 days, haha. My hope is that it can become part of the workflow for AI agents or as a complement to linters like ESlint. It makes me happy to share that after some deep testing, it works pretty well. I have tried with dozens of buggy codebases, and also simulated faulty repos generated by Cursor, Windsurf, Lovable, etc. to use Ubon on top of them, and the results are very good. Would love feedback on what other checks would be useful. And if there's enough demand, I am happy to give online demos to get traction of users to enjoy Ubon. https://ift.tt/bleFB57 August 26, 2025 at 10:57PM

Show HN: Stop saving your scans on 3rd party servers https://ift.tt/CAHS6Qi

Show HN: Stop saving your scans on 3rd party servers Hi HN, I built DocsOrb to solve a simple but stressful problem (and my own problem too since many years!): keeping track of important documents like passports, rental contracts, and insurance papers. Too often they're scattered across folders, emails, or piles at home... and you only realize it when you urgently need them. DocsOrb helps you: > Scan documents with auto-crop and enhancements (mobile camera or file upload) > Organize them around life's "moments" (travel, housing, insurance, etc.) > Search quickly using Key Information > AI extracts Key Information so the most important details are always at your fingertips > Export or share in one tap > AI Bulk organize: load up multiple images from your Photos to automatically organize them as documents, put them in the right folders, extract Key Information and also suggest a recommended name and description. Everything stays on your device by default, with optional cloud backup if you want it. Privacy-first, so you're always in control. Tech-wise: it's built with Nuxt + Capacitor, Supabase for structured storage, and a custom scanning flow (to avoid pricey SDK lock-ins). I'd love your feedback: > Does this flow make sense to you? > What's missing in how you manage important documents? > Any suggestions before I go full blast on Marketing? https://docsorb.com/ August 26, 2025 at 06:06AM

Show HN: I built an AI trip planner https://ift.tt/hnqNSDj

Show HN: I built an AI trip planner https://milotrips.com August 26, 2025 at 02:39AM

Show HN: RAG-Guard: Zero-Trust Document AI https://ift.tt/cQVmwdM

Show HN: RAG-Guard: Zero-Trust Document AI Hey HN, I wanted to share something I’ve been working on: *RAG-Guard*, a document AI that’s all about privacy. It’s an experiment in combining Retrieval-Augmented Generation (RAG) with AI-powered question answering, but with a twist — your data stays yours . Here’s the idea: you can upload contracts, research papers, personal notes, or any other documents, and RAG-Guard processes everything locally in your browser. Nothing leaves your device unless you explicitly approve it. ### How It Works - * Zero-Trust by Design*: Every step happens in your browser until you say otherwise. - * Local Document Processing*: Files are parsed entirely on your device. - * Local Embeddings*: We use [all-MiniLM-L6-v2]( https://ift.tt/tN6WRkJ... ) via Transformers.js to generate embeddings right in your browser. - * Secure Storage*: Documents and embeddings are stored in your browser’s encrypted IndexedDB. - * Client-Side Search*: Vector similarity search happens locally, so you can find relevant chunks without sending anything to a server. - * Manual Approval*: Before anything is sent to an AI model, you get to review and approve the exact chunks of text. - * AI Calls*: Only the text you approve is sent to the language model (e.g., Ollama). No tracking. No analytics. No “training on your data.” ### Why I Built This I’ve been fascinated by the potential of RAG and AI-powered question answering, but I’ve always been uneasy about the privacy trade-offs. Most tools out there require you to upload sensitive documents to the cloud, where you lose control over what happens to your data. With RAG-Guard, I wanted to see if it was possible to build something useful without compromising privacy. The goal was to create a tool that respects your data and puts you in control. ### Who It’s For If you’re someone who works with sensitive documents — contracts, research, personal notes — and you want the power of AI without the risk of unauthorized access or misuse, this might be for you. ### What’s Next This is still an experiment, and I’d love to hear your thoughts. Is this something you’d use? What features would make it better? You can check it out here: [ https://mrorigo.github.io/rag-guard/ ] Looking forward to your feedback! https://ift.tt/D6mE35B August 26, 2025 at 03:12AM

Show HN: I built an image-based logical Sudoku Solver https://ift.tt/sna0DuP

Show HN: I built an image-based logical Sudoku Solver https://ift.tt/GnfUjlR August 26, 2025 at 12:09AM

Show HN: I Built a XSLT Blog Framework https://ift.tt/B3eIad7

Show HN: I Built a XSLT Blog Framework A few weeks ago a friend sent me grug-brain XSLT (1) which inspired me to redo my personal blog in XSLT. Rather than just build my own blog on it, I wrote it up for others to use and I've published it on GitHub https://ift.tt/OcH1Kuf (2) Since others have XSLT on the mind, now seems just as good of a time as any to share it with the world. Evidlo@ did a fine job explaining the "how" xslt works (3) The short version on how to publish using this framework is: 1. Create a new post in HTML wrapped in the XML headers and footers the framework expects. 2. Tag the post so that its unique and the framework can find it on build 3. Add the post to the posts.xml file And that's it. No build system to update menus, no RSS file to update (posts.xml is the rss file). As a reusable framework, there are likely bugs lurking in CSS, but otherwise I'm finding it perfectly usable for my needs. Finally, it'd be a shame if XSLT is removed from the HTML spec (4), I've found it quite eloquent in its simplicity. (1) https://ift.tt/s46JEyU (2) https://ift.tt/OcH1Kuf (3) https://ift.tt/j4CAK30 (4) https://ift.tt/1y3QWm6 (Aside - First time caller long time listener to hn, thanks!) https://ift.tt/R7U5G8c August 24, 2025 at 11:08PM

Show HN: Komposer, AI image editor where the LLM writes the prompts https://ift.tt/gZOkMXH

Show HN: Komposer, AI image editor where the LLM writes the prompts A Flux Kontext + Mistral experiment. Upload an image, and let the AIs do the rest of the work. https://www.komposer.xyz/ August 25, 2025 at 12:36AM

Show HN: LoadGQL – a CLI for load-testing GraphQL endpoints https://ift.tt/QMPet6l

Show HN: LoadGQL – a CLI for load-testing GraphQL endpoints Hi HN I’ve been working with GraphQL for a while and always felt the tooling around load testing was lacking. Most tools either don’t support GraphQL natively, or they require heavy setup/config. So I built *LoadGQL* — a single-binary CLI (written in Go) that lets you quickly stress-test a GraphQL endpoint. *What it does today (v1.0.0):* - Run queries against any GraphQL endpoint (no schema parsing required) - Reports median & p95 latency, throughput (RPS), and error rate - Supports concurrency, duration, and custom headers - Minimal and terminal-first by design *Roadmap:* p50/p99 latency, output formats (JSON/CSV), multiple query files. Landing page: [ https://ift.tt/CZ1uPTi ]( https://ift.tt/CZ1uPTi ) I’d love feedback from the HN community: - What metrics matter most to you for GraphQL performance? - Any sharp edges you’d expect in a GraphQL load tester? Thanks for checking it out! https://ift.tt/O5Edpg8 August 24, 2025 at 07:00AM

Show HN: I built aibanner.co to stop spending hours on marketing banners https://ift.tt/LfD0WUP

Show HN: I built aibanner.co to stop spending hours on marketing banners https://www.aibanner.co August 24, 2025 at 05:57AM

Show HN: Python library for fetching/storing/streaming crypto market data https://ift.tt/zcZX52K

Show HN: Python library for fetching/storing/streaming crypto market data https://ift.tt/cEemxVI August 23, 2025 at 09:51PM

Show HN: My First Game Made with My Homemade Engine https://ift.tt/SexoW3h

Show HN: My First Game Made with My Homemade Engine https://reprobate.site/ August 23, 2025 at 03:03AM

Show HN: JavaScript-free (X)HTML Includes https://ift.tt/ORfc12Z

Show HN: AICF – a tiny "what changed" feed for AI/RAG (v0.1 minimal core) https://ift.tt/Qihw7g8

Show HN: AICF – a tiny "what changed" feed for AI/RAG (v0.1 minimal core) I’m proposing AICF (AI Changefeed) — a minimal, web-native way for sites to expose append-only change events. Instead of crawlers or RAG systems re-embedding everything, they can refresh only the sections that changed. Discovery: a /.well-known/ai-changefeed JSON points to a feed. Feed: an append-only NDJSON file with just 4 required fields (id, action, url, time) plus optional hints (anchor, checksum, note). Goal: cut wasted crawling/embedding while keeping docs/pricing/policy pages fresh for AI/agents. Spec & examples here: https://ift.tt/p7L3fxG Would love feedback: is the minimal core (anchors only, no chunks/vectors/push yet) the right starting point? Would you use this in your docs/RAG stack? https://ift.tt/p7L3fxG August 23, 2025 at 01:46AM

Show HN: CopyMagic – The smartest clipboard manager for macOS https://ift.tt/ky6upd4

Show HN: CopyMagic – The smartest clipboard manager for macOS It’s been one month since I launched CopyMagic, a smarter clipboard manager for macOS that makes sure you never lose anything you copy. Instead of digging through endless items, you can type things like “URL from Slack”, “flight information”, or “crypto rate” and it instantly finds what you meant. It’s all completely offline and privacy-first (we don’t even track analytics). https://copymagic.app August 23, 2025 at 12:58AM

Show HN: Playing Piano with Prime Numbers https://ift.tt/57qGj3T

Show HN: Playing Piano with Prime Numbers I decided to turn prime numbers into a mini piano and see what kind of music they could make. Inspired by: https://ift.tt/bDqy1jw Github: https://ift.tt/ATIOiSq https://ift.tt/uYbnzZU August 18, 2025 at 08:44PM

Show HN: Tool shows UK properties matching group commute/time preferences https://ift.tt/Ccyu02T

Show HN: Tool shows UK properties matching group commute/time preferences I came up with this idea when I was looking to move to London with a friend. I quickly learned how frustrating it is to trial-and-error housing options for days on end, just to be denied after days of searching due to some grotesque counteroffer. To add to this, finding properties that meet the budgets, commuting preferences and work locations of everyone in a group is a Sisyphean task - it often ends in failure, with somebody exceeding their original budget or somebody dropping out. To solve this I built a tool ( https://closemove.com/ ) that: - lets you enter between 1-6 people’s workplaces, budgets, and maximum commute times - filters public rental listings and only shows the ones that satisfy everyone’s constraints - shows results in either a list or map view No sign-up/validation required at present. Currently UK only, but please let me know if you'd want me to expand this to your city/country. This currently works best in London (with walking, cycling, driving and public transport links connected), and works decently in the rest of the UK (walking, cycling, driving only). This started as a side project and it still needs improvement. I’d appreciate any feedback! https://closemove.com August 21, 2025 at 12:29AM

Show HN: PlutoPrint – Generate Beautiful PDFs and PNGs from HTML with Python https://ift.tt/hZzlKYU

Show HN: PlutoPrint – Generate Beautiful PDFs and PNGs from HTML with Python Hi everyone, I built PlutoPrint because I needed a simple way to generate beautiful PDFs and images directly from HTML with Python. Most of the tools I tried felt heavy, tricky to set up, or produced results that didn’t look great, so I wanted something lightweight, modern, and fast. PlutoPrint is built on top of PlutoBook’s rendering engine, which is designed for paged media, and then wrapped with a Python API that makes it easy to turn HTML or XML into crisp PDFs and PNGs. I’ve used it for things like invoices, reports, tickets, and even snapshots, and it can also integrate with Matplotlib to render charts directly into documents. I’d be glad to hear what you think. If you’ve ever had to wrestle with generating PDFs or images from HTML, I hope this feels like a smoother option. Feedback, ideas, or even just impressions are all very welcome, and I’d love to learn how PlutoPrint could be more useful for you. https://ift.tt/QCSqKj1 August 21, 2025 at 02:07AM

Show HN: Nestable.dev – local whiteboard app with nestable canvases, deep links https://ift.tt/Zt3YJ0n

Show HN: Nestable.dev – local whiteboard app with nestable canvases, deep links https://ift.tt/8gYLW5K August 20, 2025 at 11:20PM

Show HN: Lemonade: Run LLMs Locally with GPU and NPU Acceleration https://ift.tt/0S3CUos

Show HN: Lemonade: Run LLMs Locally with GPU and NPU Acceleration Lemonade is an open-source SDK and local LLM server focused on making it easy to run and experiment with large language models (LLMs) on your own PC, with special acceleration paths for NPUs (Ryzen™ AI) and GPUs (Strix Halo and Radeon™). Why? There are three qualities needed in a local LLM serving stack, and none of the market leaders (Ollama, LM Studio, or using llama.cpp by itself) deliver all three: 1. Use the best backend for the user’s hardware, even if it means integrating multiple inference engines (llama.cpp, ONNXRuntime, etc.) or custom builds (e.g., llama.cpp with ROCm betas). 2. Zero friction for both users and developers from onboarding to apps integration to high performance. 3. Commitment to open source principles and collaborating in the community. Lemonade Overview: Simple LLM serving: Lemonade is a drop-in local server that presents an OpenAI-compatible API, so any app or tool that talks to OpenAI’s endpoints will “just work” with Lemonade’s local models. Performance focus: Powered by llama.cpp (Vulkan and ROCm for GPUs) and ONNXRuntime (Ryzen AI for NPUs and iGPUs), Lemonade squeezes the best out of your PC, no extra code or hacks needed. Cross-platform: One-click installer for Windows (with GUI), pip/source install for Linux. Bring your own models: Supports GGUFs and ONNX. Use Gemma, Llama, Qwen, Phi and others out-of-the-box. Easily manage, pull, and swap models. Complete SDK: Python API for LLM generation, and CLI for benchmarking/testing. Open source: Apache 2.0 (core server and SDK), no feature gating, no enterprise “gotchas.” All server/API logic and performance code is fully open; some software the NPU depends on is proprietary, but we strive for as much openness as possible (see our GitHub for details). Active collabs with GGML, Hugging Face, and ROCm/TheRock. Get started: Windows? Download the latest GUI installer from https://ift.tt/zgToUDc Linux? Install with pip or from source ( https://ift.tt/zgToUDc ) Docs: https://ift.tt/UtvxoHR Discord for banter/support/feedback: https://ift.tt/DCNpoF8 How do you use it? Click on lemonade-server from the start menu Open http://localhost:8000 in your browser for a web ui with chat, settings, and model management. Point any OpenAI-compatible app (chatbots, coding assistants, GUIs, etc.) at http://localhost:8000/api/v1 Use the CLI to run/load/manage models, monitor usage, and tweak settings such as temperature, top-p and top-k. Integrate via the Python API for direct access in your own apps or research. Who is it for? Developers: Integrate LLMs into your apps with standardized APIs and zero device-specific code, using popular tools and frameworks. LLM Enthusiasts, plug-and-play with: Morphik AI (contextual RAG/PDF Q&A) Open WebUI (modern local chat interfaces) Continue.dev (VS Code AI coding copilot) …and many more integrations in progress! Privacy-focused users: No cloud calls, run everything locally, including advanced multi-modal models if your hardware supports it. Why does this matter? Every month, new on-device models (e.g., Qwen3 MOEs and Gemma 3) are getting closer to the capabilities of cloud LLMs. We predict a lot of LLM use will move local for cost reasons alone. Keeping your data and AI workflows on your own hardware is finally practical, fast, and private, no vendor lock-in, no ongoing API fees, and no sending your sensitive info to remote servers. Lemonade lowers friction for running these next-gen models, whether you want to experiment, build, or deploy at the edge. Would love your feedback! Are you running LLMs on AMD hardware? What’s missing, what’s broken, what would you like to see next? Any pain points from Ollama, LM Studio, or others you wish we solved? Share your stories, questions, or rant at us. Links: Download & Docs: https://ift.tt/zgToUDc GitHub: https://ift.tt/ThmKUPc Discord: https://ift.tt/DCNpoF8 Thanks HN! https://ift.tt/ThmKUPc August 20, 2025 at 01:05AM