Show HN: Smooth – Faster, cheaper browser agent API https://ift.tt/5NerRSZ

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/yKQnk8l ). 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/SUL1H9F ). 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/IcEBKML ) 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/fyXpOvh Docs are here: https://ift.tt/78GJ6qf Demo video: https://youtu.be/18v65oORixQ We'd love feedback :) https://www.smooth.sh/ August 26, 2025 at 08:35PM

Show HN: Enterprise MCP Bridge – Solving the MCP Chaos for IT https://ift.tt/Dd8wzl1

Show HN: Enterprise MCP Bridge – Solving the MCP Chaos for IT Working in IT at a company with a change management process? How are you handling MCPs? Not at all? With very expensive tools not up to the task? How about just making it fit into your current setup! We needed to build this for inxm.ai, and realised this was the perfect time to give back to the community. Enterprise MCP Bridge is Open Source and solves Auth, Multi User, and REST apis by wrapping your existing MCPs. https://ift.tt/xQfzryF August 26, 2025 at 11:21PM

Show HN: Ubon – a solution for the "You're absolutely right" debugging dread https://ift.tt/G45qWkh

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/Rv0NfQ5 August 26, 2025 at 10:57PM

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

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/OShKx1D

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/ybSTdAr... ) 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/pWt1PTm August 26, 2025 at 03:12AM

Show HN: I built an image-based logical Sudoku Solver https://ift.tt/H4spfNL

Show HN: I built an image-based logical Sudoku Solver https://ift.tt/Fh8SRJi August 26, 2025 at 12:09AM

Show HN: RefForge – A WIP modern, lightweight reading list/reference manager https://ift.tt/IOfBG0A

Show HN: RefForge – A WIP modern, lightweight reading list/reference manager Hi HN! I built RefForge, a lightweight, desktop-first reading list and reference manager (WIP). It's a local-first app built with Next.js + Tauri and stores data in a small SQLite DB. I’m sharing it to get feedback on the UX, feature priorities, and architecture before I invest in more advanced features. This is an experimental project where I am trying to build something from scratch using AI and see how far I can build it without writing a single line of code manually. What does it offer? Manage your reading list and references in a simple, project-based UI Local SQLite storage (no cloud; your data stays on your machine) Add / edit / delete references, tag them, rate priority, group by project Built as a Tauri desktop app with a Next.js/React frontend Why did I build it? Existing reference managers can be heavy or opinionated. I wanted a small, fast, local-first tool focused on reading lists and quick citation exports that I can extend with features I need (PDF attachments, DOI lookup, BibTeX export, lightweight sync). Current features Add / edit / delete references Tagging and project organization Priority and status fields Small, searchable local DB (WIP: full-text search planned) Ready-to-extend codebase (TypeScript + React + Tauri + SQLite) https://ift.tt/YspnOqK August 25, 2025 at 10:09PM