Show HN: DAPS – Prime-Adaptive Search for Discontinuous Optimization Problems https://ift.tt/cXQBSFG

Show HN: DAPS – Prime-Adaptive Search for Discontinuous Optimization Problems I've been working on a global optimization algorithm that uses prime number-based adaptive grid search. It dynamically adjusts resolution by increasing or decreasing prime numbers as "resolution knobs" — allowing it to handle discontinuities, sharp valleys, and chaotic landscapes better than naive grid search. The repo includes Python and PyTorch-compatible versions, benchmarks against grid search, and a research paper. Would love feedback from optimization, ML, or numerical analysis folks. Curious if anyone sees potential applications or improvements. GitHub: https://ift.tt/ykSGO0Y Paper: https://ift.tt/2max4Mt.... https://ift.tt/ykSGO0Y March 23, 2025 at 11:19AM

Show HN: I build a tool that will tell you what to respond in negotations https://ift.tt/08mPQDN

Show HN: I build a tool that will tell you what to respond in negotations After reading the book Getting to Yes, I really want some tool to help me negotiate more efficiently without having to memorize everything principle. You start by putting in interests of each party, then you can explore different functions: how to respond to the other party, explore objective criteria out there or brainstorm more negotiation options. Still working on it! Leave me feedback if you have any suggestions! https://ift.tt/G4uzdP6 March 23, 2025 at 03:31AM

Show HN: I Made a Language to Be JavaScript's Nanny https://ift.tt/OoW7Qql

Show HN: I Made a Language to Be JavaScript's Nanny I'm working on a language called Chicory. It's yet-another compiles to JS(X) language. I'd value any feedback. See also https://ift.tt/XNZfliQ https://ift.tt/1QoZFmy March 23, 2025 at 01:39AM

Show HN: GoCard – A file-based spaced repetition system built in Go https://ift.tt/IeDU2Xg

Show HN: GoCard – A file-based spaced repetition system built in Go Hi HN! I'm excited to share GoCard, a terminal-based spaced repetition system I built that uses plain Markdown files as its data source. I've always been frustrated with existing spaced repetition tools that lock my knowledge into proprietary formats or require constant internet access. As a developer who lives in terminals and text editors, I wanted something that: 1. Stores cards as plain text files I can edit with any editor 2. Works seamlessly with Git for versioning and sync 3. Runs in a terminal without distractions 4. Has first-class support for code snippets and programming concepts GoCard implements the SM-2 algorithm (the same one used by Anki) but instead of a database, it uses a simple directory structure where: - Each card is a Markdown file with YAML frontmatter - Directories represent decks and subdecks - Everything is editable with standard tools *Key features:* - Distraction-free terminal UI built with BubbleTea - Real-time file watching (edit cards in your editor while reviewing) - Code syntax highlighting for 50+ languages - Vim/Emacs keybindings for efficient navigation - Hierarchical deck organization via directories - Cross-platform (Linux, macOS, Windows) What sets GoCard apart from other SRS tools is its developer-centric approach. Create cards with your favorite editor, organize them with your file manager, version them with Git, and review them in a clean terminal interface. I built this because I wanted a knowledge management system that worked with my developer workflow rather than against it. Making everything file-based means I can apply all my existing text-processing skills and tools. The project is v0.1.0, implemented in Go, and available at: https://ift.tt/BlvT6zj I'd especially appreciate feedback on the UX design and any suggestions for making it more intuitive for terminal users. Has anyone else built similar file-based knowledge tools? What patterns worked well for you? https://ift.tt/BlvT6zj March 23, 2025 at 02:35AM

Show HN: Hyperbrowser MCP Server – Connect AI agents to the web through browsers https://ift.tt/cavY5hi

Show HN: Hyperbrowser MCP Server – Connect AI agents to the web through browsers Hi HN! Excited to share our MCP Server at Hyperbrowser - something we’ve been working on for a few days. We think it’s a pretty neat way to connect LLMs and IDEs like Cursor / Windsurf to the internet. Our MCP server exposes seven tools for data collection and browsing: 1. `scrape_webpage` - Extract formatted (markdown, screenshot etc) content from any webpage 2. `crawl_webpages` - Navigate through multiple linked pages and extract LLM-friendly formatted content 3. `extract_structured_data` - Convert messy HTML into structured JSON 4. `search_with_bing` - Query the web and get results with Bing search 5. `browser_use_agent` - Fast, lightweight browser automation with the Browser Use agent 6. `openai_computer_use_agent` - General-purpose automation using OpenAI’s CUA model 7. `claude_computer_use_agent` - Complex browser tasks using Claude computer use You can connect the server to Cursor, Windsurf, Claude desktop, and any other MCP clients with this command `npx -y hyperbrowser-mcp` and a Hyperbrowser API key. We're running this on our cloud browser infrastructure that we've been developing for the past few months – it handles captchas, proxies, and stealth browsing automatically. Some fun things you can do with it: (1) deep research with claude desktop, (2) summarizing the latest HN posts, (3) creating full applications from short gists in Cursor, (3) automating code review in cursor, (4) generating llms.txt for any website with windsurf, (5) ordering sushi from windsurf (admittedly, this is just for fun - probably not actually going to do this myself). We're building this server in the open and would love feedback from anyone building agents or working with web automation. If you find bugs or have feature requests, please let us know! One big issue with MCPs in general is that the installation UX sucks and auth credentials have to be hardcoded. We don’t have a solution to this right now but Anthropic seems to be working on something here so excited for that to come out. Love to hear any other complaints / thoughts you have about the server itself, Hyperbrowser, or the installation experience. You can check us out at https://hyperbrowser.ai or check out the source code at https://ift.tt/3APZL6J https://ift.tt/3APZL6J March 20, 2025 at 10:31PM

Show HN: Reaching inbox zero has never been more fun https://ift.tt/4pijzYU

Show HN: Reaching inbox zero has never been more fun The average person receives around 120+ emails per day. This makes it almost impossible to stay on top of your emails. This is where InboxSwipe comes in. InboxSwipe is the easiest and most fun way to clean up your Gmail inbox. We present all your emails in a tinder style card view where you can swipe away to take several actions on your email (deleting, unsubscribing, marking as read, etc.) You can choose from 10+ actions and assign it to any swipe gesture you want. We also have AI powered reply feature so that you can respond and discard emails with just a click of a button. Try it out and let me know what you think. https://ift.tt/EPRB95S March 21, 2025 at 11:22PM

Show HN: GizmoSQL – Run DuckDB as a Server with Arrow Flight SQL https://ift.tt/Ptcl47v

Show HN: GizmoSQL – Run DuckDB as a Server with Arrow Flight SQL Hi, I'm Philip Moore - the founder of GizmoData, and creator of GizmoSQL - an Apache Arrow Flight SQL Server - with DuckDB (or SQLite) back-end execution engines. GizmoSQL is a composable SQL server with Arrow Flight SQL, DuckDB, and SQLite - with the intention of making it easy to run DuckDB (or SQLite) as a server - usable by multiple people from a client (remote) computer. It also adds security (authentication) and encryption of traffic with TLS. To run GizmoSQL - see the steps in the README.md - where you can see how easy it is to run the server as well as how to connect via ADBC and JDBC from a remote client - such as DBeaver, Python, etc. The easiest way to run GizmoSQL is via Docker - but there are downloads for Linux and macOS for both x86-64 and arm64 platforms (download links in the README). Why?: As you may know, DuckDB and SQLite are embedded systems - they don't enable client connectivity, and they aren't really designed for concurrency. I've built GizmoSQL to work around that - because I believe the DuckDB engine is very powerful, and I feel like a lot of customers overpay and run distributed compute (i.e. Spark) when they don't really need to. Making it easy to have remote connectivity to DuckDB can make it easier to migrate SQL workloads from Spark or other expensive commercial platforms to this engine - with a much simpler architecture/infrastructure. It is my intention to make GizmoSQL a commercial product - licensed for production use by organizations, but free for developers to code with - evaluate, and test. A little bit of backstory: * I built the initial version of this while working for a former employer - it wasn't their core focus, so they open-sourced that early version. After I left there, I forked the product and have improved it substantially - to support concurrency of both reads and writes, improving security, as well as keeping it up to date with the latest versions of Apache Arrow and DuckDB. * This project evolved from a prototype created by the brilliant Tom Drabas. * It feels a little weird trying to make a commercial product based upon DuckDB, but MotherDuck started it :P - and I've contributed (albeit very little) to the DuckDB and Apache Arrow projects in the form of a couple of PRs. I'm really excited about this project - I have run benchmarks of this product against commercial platforms such as Snowflake and Databricks SQL - and it holds its own running the 22-query TPC-H SF1TB benchmark, especially on cost. See the graph at: https://ift.tt/7Yb1Tyx Getting started: Github README: https://ift.tt/RJLBZ3E... DockerHub: https://ift.tt/ygeYBPs GizmoSQL homepage: https://ift.tt/7Yb1Tyx Phil's Github profile: https://ift.tt/b1rAzoD Thanks for your time and feedback in advance. https://ift.tt/vbU1exo March 21, 2025 at 12:45AM