Show HN: SurveyMoji – An easy way to get realtime feedback with a link/QR code https://ift.tt/qeIsOUR

Show HN: SurveyMoji – An easy way to get realtime feedback with a link/QR code Hi HN - I've been working on this site on and off for awhile. It started out as a personal tool and fun thing to hack on in my spare time. I recently added some UI improvements and a landing page and thought maybe it would be nice for other people to use as well. The idea was to create the fastest/easiest way to share a link to collect realtime feedback from a person's phone during a presentation or video call. Would love to hear any feedback, especially on UI design as it is the first time I've tried to create something that has a modern SaaS look to it. Built with go/htmx/alpinejs https://surveymoji.com November 30, 2024 at 12:47PM

Show HN: Jinbase – Multi-model transactional embedded database https://ift.tt/E8splCv

Show HN: Jinbase – Multi-model transactional embedded database Hi HN ! Alex here. I'm excited to show you Jinbase ( https://ift.tt/aDlxuBK ), my multi-model transactional embedded database. Almost a year ago, I introduced Paradict [1], my take on multi-format streaming serialization. Given its readability, the Paradict text format appears de facto as an interesting data format for config files. But using Paradict to manage config files would end up cluttering its programming interface and making it confusing for users who still have choices of alternative libraries (TOML, INI File, etc.) dedicated to config files. So I used Paradict as a dependency for KvF (Key-value file format) [2], a new project of mine that focuses on config files with sections. With its compact binary format, I thought Paradict would be an efficient dependency for a new project that would rely on I/O functions (such as Open, Read, Write, Seek, Tell and Close) to implement a minimalistic yet reliable persistence solution. But that was before I learned that "files are hard" [3]. SQLite with its transactions, BLOB data type and incremental I/O for BLOBs seemed like the right giant to stand on for my new project. Jinbase started small as a key-value store and ended up as a multi-model embedded database that pushes the boundaries of what we usually do with SQLite. The first transition to the second data model (the depot) happened when I realized that the key-value store was not well suited for cases where a unique identifier is supposed to be automatically generated for each new record, saving the user the burden of providing an identifier that could accidentally be subject to a collision and thus overwrite an existing record. After that, I implemented a search capability that accepts UID ranges for the depot store, timespans (records are automatically timestamped) for both the depot and key-value stores and GLOB patterns and number ranges for string and integer keys in the key-value store. The queue and stack data models emerged as solutions for use cases where records must be consumed in a specific order. A typical record would be retrieved and deleted from the database in a single transaction unit. Since SQLite is used as the storage engine, Jinbase supports the relational model de facto. For convenience, all tables related to Jinbase internals are prefixed with "jinbase_", making Jinbase a useful tool for opening legacy SQLite files to add new data models that will safely coexist with the ad hoc relational model. All four main data models (key-value, depot, queue, stack) support Paradict-compatible data types, such as dictionaries, strings, binary data, integers, datetimes, etc. Under the hood, when the user initiates a write operation, Jinbase serializes (except for binary data), chunks, and stores the data iteratively. A record can be accessed not only in bulk, but also with two levels of partial access granularity: the byte-level and the field-level. While SQLite's incremental I/O for BLOBs is designed to target an individual BLOB column in a row, Jinbase extends this so that for each record, incremental reads cover all chunks as if they were a single unified BLOB. For dictionary records only, Jinbase automatically creates and maintains a lightweight index consisting of pointers to root fields, which then allows extracting from an arbitrary record the contents of a field automatically deserialized before being returned. The most obvious use cases for Jinbase are storing user preferences, persisting session data before exit, order-based processing of data streams, exposing data for other processes, upgrading legacy SQLite files with new data models and bespoke data persistence solutions. Jinbase is written in Python, is available on PyPI and you can play with the examples on the README. Let me know what you think about this project. [1] https://ift.tt/q791EVj [2] https://ift.tt/c7BTNza [3] https://ift.tt/Ub5OE7k https://ift.tt/aDlxuBK November 30, 2024 at 03:25AM

Show HN: wazero compiler ported to 4 new OSes https://ift.tt/1xazPdY

Show HN: wazero compiler ported to 4 new OSes Release 1.8.2 of wazero, the zero dependency WebAssembly runtime for Go, brings the amd64 compiler to 4 new OSes: NetBSD, DragonFly BSD, illumos and Solaris. The compiler also supports Linux, FreeBSD, macOS and Windows, on amd64 and arm64. This didn't require any changes to the compiler, just enabling it after setting up tests to validate that it already worked. Now the HN hook: noticeably absent is OpenBSD, which I failed to get working, even after taking W^X into account (we already had that for arm64 on macOS). If you wanna help, please drop us a note! https://ift.tt/2od5byr December 1, 2024 at 01:48AM

Show HN: Built This in 3 Hours Using Bolt (No React Knowledge) https://ift.tt/M546932

Show HN: Built This in 3 Hours Using Bolt (No React Knowledge) It's blowing my mind that I could build an app from scratch in 3 hours and deploy it with the click of a button - without writing a single line of code myself CacheNotes is a browser-based note-taking app that saves your notes, threads, and AI conversations securely in your browser's local storage 100% free. No login required. You get, - simple, minimal note taking app in your browser - no login required - you can connect your claude api key for the ai integration - visualise notes and twitter threads Check it out! Would love some feedback :) https://ift.tt/e1DS7ZC November 30, 2024 at 02:09AM

Show HN: I built an extension to contact Airbnb hosts direcly https://ift.tt/U3I6gtG

Show HN: I built an extension to contact Airbnb hosts direcly Hey HN, Over the years, I’ve found myself frustrated with the extra fees Airbnb. While they provide a lot of convenience, the service fees often stack up to a point where they overshadow any potential savings. I started wondering if there was a way to connect directly with property hosts and skip the middleman entirely. That curiosity led me to build getaway.direct, a free Chrome extension that helps travelers save money. It works like this: 1. You browse listings on Airbnb. 2. The extension scans for direct booking links, host websites, or social media profiles where you can reach out to the host directly. 3. It shows those results instantly, so you can compare prices, avoid service fees, and book smarter. The main idea is to provide transparency. A lot of hosts already have their own websites but rely on platforms like Airbnb for visibility. This tool helps surface those direct options, which can often save you 10-20% per booking. I’d love to hear your thoughts: Would something like this be helpful to you? Also, I’m curious to get feedback on ways to improve the tool—whether it’s adding more integrations, improving usability, or something else entirely. Thanks for reading! Let me know what you think. https://ift.tt/KFUeNR5 November 29, 2024 at 06:18PM

Show HN: A tool for kids to practice arithmetic https://ift.tt/hM7JjbZ

Show HN: A tool for kids to practice arithmetic https://ift.tt/ILxztfV November 30, 2024 at 01:25AM

Show HN: Managed Redux Toolkit: Autogenerate Your RTK Definitions https://ift.tt/VLifx6o

Show HN: Managed Redux Toolkit: Autogenerate Your RTK Definitions Redux Toolkit (RTK) provides a lot of primitives to build performant frontend applications. Celestial generates RTK code for your backend (with additional features), exposing an intuitive hook-based API. So you can have all the benefits of RTK without having to familiarise yourself with the underlying framework. Essentially, we write the backend wiring code for your frontend. You get simple-to-use hooks, which implement advanced backend sync & state management patterns. You also get a fast, snappy application out-of-the box. You can also think about it like this: Celestial generates a unified state layer that contains your server state and any other state that you decide to store. It exposes hooks to interact with the state, while constantly keeping client state in sync with the backend. Your UI simply plugs into the state layer using a hook-based API to manage data flow for your web app. UI development can broadly be categorised as (1) interacting with the backend to fetch data and push updates, and (2) writing and styling the UI. The vision is to automate (1) end-to-end, allowing devs to focus on (2). Like how Firebase/Supabase (when they began) abstracted away the implementation of chat. An analogy can be found in the data world: before the data analyst can build a data dashboard, the data needs to be supplied to the dashboarding tool from various sources. This data fetching is generic enough to be abstracted away. The analyst simply writes SQL on their dashboarding tool to build their dashboard, without worrying about bringing the data in. They assume that the data will be present in a predefined structure and will update at a regular cadence. In the frontend domain, sophisticated tools for data fetching and updating the backend exist. For ex. RTK, RTK Query, TanStack Query, SWR. But a developer still has to tie everything together themself. IMO, this can be automated, more so because of generative AI. The idea is to abstract away all details of backend-interaction so the frontend developer can manipulate the data & develop the UI as if the data rests in in-memory data structures on the frontend itself. I am assuming that taking away the burden of backend interaction (and all its various nuances) would make life easy/save time for some devs and would empower other devs by giving them tools to do more. Please feel free to validate or invalidate this assumption :) All comments & feedback welcomed. Feedback is the fuel that drives my work! https://ift.tt/AJiRyjG November 29, 2024 at 03:44PM

Show HN: A word guessing game based on text vector embeddings and cos-similarity https://ift.tt/G8NoIM4

Show HN: A word guessing game based on text vector embeddings and cos-similarity Try to find the secret word that computer holds, by guessing and getting feedback in form of how similar your guess to the secret is. The fewer attempts the better. There is also a hint and a give-up button. Thank you, please give it a try ) https://ift.tt/uzyirDT November 29, 2024 at 06:11AM

Show HN: iOS Theremin Simulator with Hand Tracking (Beta) https://ift.tt/IbjNGHu

Show HN: iOS Theremin Simulator with Hand Tracking (Beta) https://ift.tt/s1JIjmF November 29, 2024 at 01:53AM

Show HN: MyDuck Server – Supercharge MySQL and Postgres Analytics with DuckDB https://ift.tt/FPS15ba

Show HN: MyDuck Server – Supercharge MySQL and Postgres Analytics with DuckDB Hello HN! We're excited to announce MyDuck Server, an open-source project that seamlessly integrates the analytical power of DuckDB with your existing MySQL & Postgres databases. *Backstory* Currently, there are no fully satisfactory open-source OLAP solutions for MySQL & Postgres. In the MySQL ecosystem, HeatWave offers close integration, but it's a proprietary, commercial product from Oracle. The Postgres community has seen promising DuckDB-based extensions emerge, including the official pg_duckdb. However, extensions can introduce isolation concerns in mission-critical environments. Consequently, many organizations resort to setting up complex and costly data movement pipelines using tools like Debezium, Flink, or other commercial solutions to replicate data from MySQL & Postgres to OLAP systems (e.g., Snowflake, BigQuery, ClickHouse) or Lakehouses (e.g., Delta Lake + Spark). This approach introduces significant operational overhead and expense. Another emerging strategy is the zero-ETL approach, increasingly advocated by cloud providers. This model simplifies data integration by allowing the cloud provider to manage ETL pipelines, while necessitating reliance on specific cloud ecosystems and services. *Key features* MyDuck Server offers a real-time analytical replica that leverages DuckDB's native columnar storage and processing capabilities. It operates as a separate server, ensuring isolation and minimizing impact on your primary database. Key features include: - Easy Zero-ETL: Built-in real-time replication from MySQL & Postgres with no complex pipelines to manage. It feels like a standard MySQL replica or Postgres standby. With the Docker image, passing a connection string is enough. - MySQL & Postgres Protocol Compatibility: We take this seriously and are working to make this project integrate well with the existing ecosystem around MySQL & Postgres. Currently, it is already possible to connect to MyDuck with standard MySQL & PostgreSQL clients in many programming languages. - HTAP Support: A standard database proxy can be deployed in front of a MySQL/Postgres primary and its MyDuck replica to route write operations to the primary and read operations to the replica. It just works. - DuckDB SQL & Columnar I/O over Postgres Protocol: It's unnecessary to restrict ourselves to MySQL/Postgres's SQL expressiveness and row-oriented data transfer. The Postgres port accepts all DuckDB-valid SQL queries, and you can retrieve query results in columnar format via `COPY (SELECT ...) TO STDOUT (FORMAT parquet/arrow)`. - Standalone Mode: It does not need to be run as a replica. It can also act as a primary server that brings DuckDB into server mode and accepts updates from multiple connections, breaking DuckDB's single-process limitation. *Relevant Previous HN Threads* - pg_duckdb [1] ( https://ift.tt/dHCotxu ) is the official Postgres extension for DuckDB. It uses DuckDB as an execution engine to accelerate analytical queries by scanning Postgres tables directly. - pg_mooncake [2] ( https://ift.tt/1Vu8jhU ) is a Postgres extension that adds columnstore tables for PG. It uses pg_duckdb under the hood but stores data in Lakehouse formats (Iceberg & Delta Lake). - BemiDB [3] ( https://ift.tt/DVF3bl9 ) is also a DuckDB-based Postgres replica. Unlike us, they focus on storing data in Lakehouse format. We believe MyDuck Server offers a compelling solution for those seeking high-performance analytics on their MySQL & Postgres data without the complexities and costs of traditional approaches. We're eager to hear your feedback and answer any questions you might have. Let me know what you think! [0] https://ift.tt/D3uWb2P [1] https://ift.tt/G49uUDp [2] https://ift.tt/mYIjQtP [3] https://ift.tt/tPUsFlw https://ift.tt/D3uWb2P November 28, 2024 at 09:50PM

Show HN: AirPrint Bridge: Enable AirPrint for Non-AirPrint Printers on macOS https://ift.tt/b4sSEla

Show HN: AirPrint Bridge: Enable AirPrint for Non-AirPrint Printers on macOS Lightweight, open-source, and fully automated, AirPrint Bridge uses macOS's built-in tools to bridge the gap without relying on external software. Perfect for reviving your trusty old printer! https://ift.tt/2NW7aTO November 28, 2024 at 01:11AM

Show HN: AutoPiP – Safari extension for automatic Picture-in-Picture mode https://ift.tt/IYNqJjW

Show HN: AutoPiP – Safari extension for automatic Picture-in-Picture mode AutoPiP is a Safari extension that automatically enables Picture-in-Picture mode when switching tabs and disables it when returning to the video tab. No clicks needed - just seamless multitasking while watching videos. Key features: • Automatic PiP activation when switching tabs • Smart detection for active videos only • Seamless disable on tab return • Zero configuration needed GitHub: https://ift.tt/G7yFhUI https://ift.tt/G7yFhUI November 27, 2024 at 05:36PM

Show HN: Type Safe TypeScript/JS ORM for SQLite, MySQL and PostgreSQL https://ift.tt/CzapK8l

Show HN: Type Safe TypeScript/JS ORM for SQLite, MySQL and PostgreSQL https://litdb.dev November 27, 2024 at 09:34PM

Show HN: I created a lightweight JavaScript library to visualize JSON as a graph https://ift.tt/LqYKBr1

Show HN: I created a lightweight JavaScript library to visualize JSON as a graph https://ift.tt/UzMWacH November 27, 2024 at 05:41AM

Show HN: Clean Your Mac with a Script https://ift.tt/G6NCJrA

Show HN: Clean Your Mac with a Script I wanted to clean old temporary files and caches from my macOS with a script instead of using a shady paid app, so I created a simple script for that. Pull requests are very welcome for other unused files to clean up storage space! https://ift.tt/UY561tK November 27, 2024 at 04:49AM

Show HN: Drafting AI – Human-in-the-loop AI automation for ops teams' email https://ift.tt/YLriaOk

Show HN: Drafting AI – Human-in-the-loop AI automation for ops teams' email Hi HN, I'm Jared from Drafting AI ( https://ift.tt/X9wRVjH ). We're a Chrome extension that helps operations and customer support teams work through their inboxes more efficiently. Here's a quick demo video: https://www.youtube.com/watch?v=PCDqAMaYx2Q We believe AI can be deployed more widely when you mitigate hallucinations through human-in-the-loop review. This led to two decisions: 1. A Chrome Extension is ideal for presenting actions in third-party apps for human review. When AI suggests issuing a refund via Shopify, what better way to show it than through Shopify's own refund interface? 2. AI should "draft" responses but not submit them. For instance, it’ll open Shopify admin and fill in refund details, but won't click "submit." This gives the user full control to approve, modify, or discard the AI's suggestions. We believe this "drafting" UX of pre-filling form fields will become standard for AI. It feels like GitHub Copilot's autocomplete UX, but for forms. You can add Drafting ( https://ift.tt/X9wRVjH ) to your internal tools today to speed up email-based workflows. It's perfect for any process that follows the pattern of: 1) read email, 2) use internal tool to handle it, 3) reply to email. For an example, consider a customer support rep for an e-commerce. A customer emails about poor product quality. The CS team has configured Drafting AI to process refunds through Shopify for serious quality complaints. The rep opens the email in Gmail or Front and clicks the Drafting AI button. A window appears with Shopify's refund form already filled out, plus a reply draft ready in their email. After reviewing both, they approve and send. We’ve seen that without Drafting AI, a rep can spend 5 minutes just writing the reply email; half their time on a 10 minute ticket. With Drafting AI, reviewing and approving takes seconds. Other use cases include: freight and logistics ops (TMS) coordinating with carriers, travel agents making trip adjustments (GDS), and HR teams processing payroll and benefits requests (HRIS). These and other job roles can sometimes be thought of as using a person as a bridge between email and internal tools. As a Chrome extension, we integrate with all your existing internal tools as long as they’re web-based, whether they’re SaaS (Shopify, Salesforce), nocode/lowcode (Retool, Airtable), or custom apps (React, Rails, Django) Try it at https://ift.tt/X9wRVjH or reply here with questions! I'm happy to personally onboard the first 20 teams that reach out. https://ift.tt/X9wRVjH November 26, 2024 at 11:23PM

Show HN: We Built "Wikipedia Link Previews" for YouTube Videos https://ift.tt/ziZw4De

Show HN: We Built "Wikipedia Link Previews" for YouTube Videos Hey HN! I’m excited to share Strawberry, Chrome extension that provides instant summaries of YouTube videos when you hover over thumbnails—think “Wikipedia link previews” but for YouTube. I love YouTube—it’s genuinely enriched my life over the years. But I often find myself getting pulled into endless rabbit holes due to catchy thumbnails. So I made Strawberry to help navigate YouTube more mindfully. Right now we have 3 features: - Hover any thumbnail for a summary (including Homepage, Search, Suggested vids, Shorts) - Detailed video summaries - Chat with videos Eager to hear your thoughts, and especially how it could be more useful for you! https://ift.tt/9BeA4Rh November 26, 2024 at 11:51PM

Show HN: WeSQL – An Innovative MySQL That Stores All Data on S3 https://ift.tt/3pAkW8u

Show HN: WeSQL – An Innovative MySQL That Stores All Data on S3 https://ift.tt/c2JFrOP November 26, 2024 at 01:09AM

Show HN: Neon-PostgREST – Build a Supabase-Like API/SDK DevX over Neon https://ift.tt/8iuJI0D

Show HN: Neon-PostgREST – Build a Supabase-Like API/SDK DevX over Neon Hey folks - one thing I find a bit frustrating using non-supabase Postgres providers is the lack of ability to perform DB queries via an SDK/API. I decided to build a quick sample of how to combine PostgREST + Neon to recreate this dev experience. https://ift.tt/UcyjJ4s November 26, 2024 at 12:42AM

Show HN: Minimal, customizable new tab for Chrome/Firefox https://ift.tt/CAVJpHm

Show HN: Minimal, customizable new tab for Chrome/Firefox Hello HN! Flowtide is a project I have been working on for about 2 months now. It is a customizable new tab page for Firefox or Chrome. By default, it is configured to have a minimal amount of features, but it can be configured to include a clock, to-do list, or even soundscapes. Install: https://flowtide.app/ GitHub: https://ift.tt/8EYVKhL https://ift.tt/GLZVbWt November 25, 2024 at 10:51PM

Show HN: QuackHouse, Database in the browser, using WASM and DuckDB https://ift.tt/bd57Ylk

Show HN: QuackHouse, Database in the browser, using WASM and DuckDB I'm building a privacy focused analytics tool, using WebAssembly and DuckDB. You can upload your files (CSV, JSON and Parquet), and interact with them as where they a SQL Server. Your data never leaves your computer, however I do track page views and visitors using Plausible. The next steps are to add forecasting and segmentation, as well as some data visualisation capabilities. I would love to hear your opinion. All code for the repo is available here: https://ift.tt/kMTWORm https://ift.tt/o7qmVyl November 25, 2024 at 04:12AM

Show HN: My weekend project to end Go/TypeScript boilerplate hell https://ift.tt/5HRTUW7

Show HN: My weekend project to end Go/TypeScript boilerplate hell https://ift.tt/of9qlty November 24, 2024 at 04:54PM

Show HN: Over 600 CSS Animations to Code https://ift.tt/jvHPXKh

Show HN: Over 600 CSS Animations to Code Hey HN, We’re excited to share Gradienty's CSS Animation Generator designed to make web animations intuitive and accessible for developers and designers at any level. Whether you’re new to CSS animations or a seasoned pro looking to save time, Gradienty equips you with the tools to create beautiful animations with zero coding headaches. Key Features: 1. 600+ Pre-Built Animations: From subtle hover effects to complex keyframe sequences, all categorized for easy navigation. 2. Visual Editor with Live Preview: See your animations in action as you tweak timing, easing, delay, and iterations. 3. Responsive Design Previews: Test animations across layouts for desktop and mobile compatibility. 4. Multiple Preview Objects: Visualize animations on text, buttons, boxes, circles, and more. 5. One-Click Code Export: Generate production-ready CSS with proper vendor prefixes, ready to drop into your project. 6. Zero-Dependency Animations: Works flawlessly across all modern browsers. Why We Built This: As developers, we often found animations to be either overly complex to implement manually or limited by pre-made libraries. Gradienty bridges this gap by offering both flexibility and ease of use, helping you create animations that look and perform great—without sacrificing development time. Who It’s For: Beginners: Experiment with animations visually without writing a single line of code. Designers: Focus on creativity while leaving the technical aspects to the generator. Developers: Save time with ready-to-use animations that can be customized and exported instantly. What’s Next: We’re working on adding community features like user-created animation libraries, animation presets for specific design systems, and integration guides for popular frameworks. We’d love to hear your feedback! Check it out here: Gradienty Let us know what you think or if there are features you’d love to see! https://ift.tt/pZeW4UP November 24, 2024 at 02:37AM

Show HN: I'm making a spiritual successor to Ski Free that runs in a browser https://ift.tt/gYoEUS2

Show HN: I'm making a spiritual successor to Ski Free that runs in a browser https://ift.tt/4eNg65E November 24, 2024 at 12:15AM

Show HN: EnvCloak: lightweight, env file encryption – ready for CICD pipelines https://ift.tt/nsFtbD3

Show HN: EnvCloak: lightweight, env file encryption – ready for CICD pipelines When someone here told me to focus on something more useful than reinventing the wheel. So. EnvCloak, a lightweight and simple tool for securely managing sensitive environment files. The design focuses on simplicity - just a few intuitive commands using the Click Python library. I assume seamless integration with CI/CD workflows. The aim is to provide a streamlined solution without the need for clunky tools. If you're tired of complex configurations or bloated alternatives, this might be worth a look! I would appreciate any feedback, feature ideas or input on how to improve this solution. I'd love to hear your thoughts! Regards! https://ift.tt/W7uJcE0 November 24, 2024 at 12:59AM

Show HN: AI bot that automatically processes unstructured documents https://ift.tt/QcrFkxP

Show HN: AI bot that automatically processes unstructured documents Hi HN! We’re excited to share what we’ve been working on—a bot that automates the tedious task of processing unstructured documents from emails and entering them into ERPs. After many iterations, we’ve achieved 99.8% accuracy in extracting and mapping data from invoices, POs, and other documents. One surprising takeaway from this journey: building the AI was only 10% of the challenge! The real work came from handling edge cases, integrating seamlessly with various ERPs, and creating a reliable pipeline for real-world documents with messy formats. We’d love your feedback, thoughts, or questions about how we built this, the challenges we faced, or anything else. Let us know what you think! Thanks for checking it out! https://ift.tt/yhs2f6V November 23, 2024 at 08:20AM

Show HN: Open-Source Pull Request AI Reviewer https://ift.tt/WlXhSd0

Show HN: Open-Source Pull Request AI Reviewer Hey HN, Over the last year, I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building Presubmit.ai, an open-source AI reviewer that runs as a Github Action right when you open a Pull Request. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I save 80hours (~10 working days) per year. Unlike similar SaaS solutions, the goal is not to replace the human reviewer but to highlight obvious mistakes early, spot security vulnerabilities and give more context about the change. I like to think of it as a “pre-reviewer”. Some of its features are: * Line-by-line comments * PR summarization * Title generation on request * Responds to review comments It supports all major LLMs, but I’ve found Anthropic's Claude works best for this use case. Please give it a try and share your feedback! https://ift.tt/v6MDbWV https://ift.tt/v6MDbWV November 22, 2024 at 09:58PM

Show HN: Pull Request Reviewed by LLM https://ift.tt/dfnEyND

Show HN: Pull Request Reviewed by LLM This year I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building an LLM-based prereviewer that should take a first pass before the actual human reviewer. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I save 80hours (~10 working days) per year. Linked above is an example of a PR where I'm testing the AI reviewer and it showcase how it can detect bugs, suggest best practices about token validity, generate summary and title, and even chat with me in review comments. The AI reviewer is a simple Github action that runs everytime I open or synchronize a pull request and you can see the source code at https://ift.tt/v6MDbWV . https://ift.tt/9shPtWK November 23, 2024 at 12:59AM

Show HN: Shop on Amazon with Crypto https://ift.tt/NdOo0xZ

Show HN: Shop on Amazon with Crypto Hey folks! I'm building an app to let anyone shop on Amazon with crypto. How it works: 1. Add "baggins.ai/" before any Amazon product URL 2. Fill in your shipping details 3. Pay with your preferred wallet (BTC, ETH, DOGE, USDC, etc.) Features: - Supports 1-2 day Prime shipping - Uses Coinbase Commerce for secure transactions Here's an early version for y'all to test: https://www.baggins.ai/ Keen to get feedback on the UX and feature requests! My LI to show that it’s not a scam :) https://ift.tt/dCSfUAn https://ift.tt/Mq65GUd November 22, 2024 at 11:29PM

Show HN: VS Code extensions that display CGM blood glucose levels in status bar https://ift.tt/M9WnrZo

Show HN: VS Code extensions that display CGM blood glucose levels in status bar As a Type 1 diabetic, I need to continuously monitor my blood glucose levels. I’ve implemented a couple of Visual Studio Code extensions that retrieve the latest blood glucose readings from your CGM and display them in your VS Code status bar. One VS code extension uses the Nightscout CGM to retrieve the blood glucose readings. It requires users to run the Nightscout application on a hosted server. A nice benefit of Nightscout application is that it works with all the major CGM devices. However, a slight drawback of this option is that it requires a hosted third party software (Nightscout) for proper functionality. I’ve also implemented a Visual Studio code extension for those (like myself) that use the Freestyle Libre CGM. This version connects directly to LibreLinkUp to retrieve the latest blood glucose readings and display them in your VS code status bar. This removes the dependency for the intermediary Nightscout application. If you are or know any software engineers living with diabetes, these tools might be helpful with diabetes management. These are tools I built for myself that help me manage my diabetes. They are completely free and open source. I am not selling anything. Users of the tools can use them without any restrictions or connection to me. I am genuinely trying to help others in the community that are software engineers and might find this helpful. If you try out any of these extensions, I’d love to hear back from you. Any feedback on improvements are very welcome and appreciated. - https://ift.tt/OuVd8Rp... - https://ift.tt/pao7265... November 22, 2024 at 01:49AM

Show HN: My Remote Teaching Station (Mk IV) https://ift.tt/j65C1ZH

Show HN: My Remote Teaching Station (Mk IV) The remote teaching station has been evolving for the last four years. The Mk IV is the most advanced and most attractive version so far. https://ift.tt/ZmJM7SK November 21, 2024 at 11:37PM

Show HN: An AI that reliably builds full-stack apps by preventing LLM mistakes https://ift.tt/atB7RQf

Show HN: An AI that reliably builds full-stack apps by preventing LLM mistakes Hey HN! Previous CERN physicist turned hacker here. We've developed a way to make AI coding actually work by systematically identifying and fixing places where LLMs typically fail in full-stack development. Today we're launching as Lovable (previously gptengineer.app) since it's such a big change. The problem? AI writing code typically make small mistakes and then get stuck. Those who tried know the frustration. We fixed most of this by mapping out where LLMs fail in full-stack dev and engineering around those pitfalls with prompt chains. Thanks to this, in all comparisons I found with: v0, replit, bolt etc we are actually winning, often by a wide margin. What we have been working on since my last post ( https://ift.tt/PV45spZ ): > Handling larger codebases. We actually found that using small LLMs works much better than traditional RAG for this. > Infra work to enable instant preview (it spins up dev environments quickly thanks to microVMs and idle pools of machines) > A native integration with Supabase. This enables users to build full-stack apps (complete with auth, db, storage, edge functions) without leaving our editor. Interesting project as an example: https://ift.tt/lQn9mcR – a clone of our product, built with our AI. Looks like a perfect copy and works (click "edit with lovable" to get to a recursive editor...) Going forward, we're shipping improvements weekly, focusing on making it faster, even more reliable and adding visual editing experience similar to figma. If you want to try it has a completely free tier for now at lovable.dev Would love your thoughts on where this could go and what you'd want to build with it. And what it means for the future of software engineering... https://lovable.dev November 22, 2024 at 12:40AM

Show HN: Rebuild of Blossom, an open-source social robot https://ift.tt/TWg43Hr

Show HN: Rebuild of Blossom, an open-source social robot From the post: Blossom is an open-source robot platform for human-robot interaction (HRI) research that I developed during my PhD. I’ve used Blossom for research in design, machine learning, and telepresence; others have made Blossoms for their own research purposes. I have continued working on “rebuilding” the entire platform: I redesigned the inner frame as a model kit, complete with Gunpla-inspired runners and instructions, and refactored the codebase as r0b0, a Python library for communicating between hardware peripherals and software applications. In preparation to present Blossom at Maker Faire Coney Island, I refined the telepresence interface and enabled conversational interaction with a language model. The new repository is available on GitHub and includes documentation for construction. https://msgtn.xyz/rebuild_of_blossom November 21, 2024 at 12:28AM

Show HN: Agentic Evaluators for Agentic Workflows (Starting with RAG) https://ift.tt/62YUaqi

Show HN: Agentic Evaluators for Agentic Workflows (Starting with RAG) Hey all! Thought this group might find this interesting - new approach to evaluating RAG pipelines using 'agents as a judge'. We got excited by the findings in this paper ( https://ift.tt/Y4qxz1M ), about agents producing evaluations closer to human-evaluators, especially for multi-step workflows. Our first use case was RAG pipelines, specifically evaluating if your agent MISSED pulling any important chunks from the source document. While many RAG evaluators determine if your model USED its chunk in the output, there's no visibility on if your model grabbed all the right chunks in the first place. We thought we'd test the 'agent as judge', with a new metric called 'potential sources missed', to help evaluate if your agents are missing any important chunks from the source of truth. Curious what you all think! https://ift.tt/DCgRhQv November 20, 2024 at 11:57PM

Show HN: Pesto, a PST viewer made for archivists https://ift.tt/jM7RLxb

Show HN: Pesto, a PST viewer made for archivists In the past year, I have built a PST viewer that lets you analyze, visualize, and search multiple PST files. The project was born from the issues that archivists in France face when dealing with Outlook mailboxes (PST files). In public administration, archivists must collect office data (mostly from Microsoft products) generated during administrative operations. This data will be used by researchers in the future to document and report how public administration works and what the decision-making process was. A lot of information is conveyed through emails, and in most French administrations, they use Outlook. Therefore, archivists must collect and handle PST files but lack proper tools. To work with and analyze PST files, they can only use Outlook, which is slow and cumbersome given the size of the PST files and the limited power of their computers. My application is called Pesto, and it can be tried for free here: https://ift.tt/21bfeIo . I wanted to provide the best performance possible for archivists. Pesto is designed to work with PST files of any size (tens of gigabytes) and on low-budget office laptops. To achieve this, I chose Rust for its performance (and also because I wanted to learn and practice Rust). The main challenge was implementing the PST specification in Rust to read and exploit the files effectively. For the curious, the stack is: Rust, Tauri, Tokio, Serde, TypeScript, React, Vite, and Axum for the website. I have been in contact with archivists from the main French ministries. There has been some word-of-mouth interest and weak signals, but adoption has been slow, and I can’t make a living from it yet. So I am looking for an opportunity to work with small, motivated, user-centric teams. If you’re interested, I’d be happy to meet for coffee in Paris. Have fun with Pesto. Jean-Baptiste Assouad Creator of Pesto https://ift.tt/sonScRa https://ift.tt/I8qbgoh November 20, 2024 at 07:20PM

Show HN: archgw: open-source, intelligent proxy for AI agents, built on Envoy https://ift.tt/rITOmYS

Show HN: archgw: open-source, intelligent proxy for AI agents, built on Envoy Hi HN! This is Adil, Salman, Co and Shuguang and we're excited to introduce archgw [1], an open source intelligent proxy for agents built on Envoy [2]. Arch moves the critical but crufty work around safety, observability, and routing of prompts outside business logic. Arch is a uniquely intelligent infrastructure primitive, engineered with purpose-built fast LLMs [3] for tasks like intent detection over multi-turn, parameter identification and extraction, triggering single/multiple function calls, and offers convenience features to auto dispatch LLM calls for summarization based on data from your APIs via system prompts configured in archgw. Today, the approach to build a smart production-ready agent is weaving together a large set of mono-functional opinionated libraries, adding extra layers like LLM-based preprocessing to determine things like relevance and safety of the user's prompt (e.g. applying governance and guardrails). Once past that stage, developers must extract relevant information from the user prompt to determine intent, extract parameters as necessary, package relevant tools calls to an LLM to trigger a backend API to execute particular domain-specific task. etc. After all that is done then only are developers ready to trigger an LLM call for summarization and must manage upstream error handling and retry logic themselves. Not to mention, if they want to experiment with multiple LLMs or move between LLM versions, they have to write crufty undifferentiated code. This entire experience is slow, error prone, cumbersome, and not specifically unique. Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized search and intent models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that several rules based mono-functional tools should be converged into a multi-functional infrastructure primitive designed for prompts and agents. We built archgw on the highly popular, battle-tested open source proxy Envoy and re-imagined it for prompts and agents. For this we had to build blazing fast LLMs [3] that can handle crufty, ahead-in-the-request-path type of work in handling and processing prompts that are sent to an agent, so that developers can focus on what matters most: building fast personalized agents without the unnecessary prompt engineering and systems integration work needed to get there. Here are some additional details about the open source project. arghw is written in rust, and the request path has three main parts: * Listener subsystem which handles downstream (ingress) and upstream (egress) request processing. * Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard primitive and identifies where to forward the conversation to via its prompt_target primitive. * Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4] Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7]. [1] https://ift.tt/pB2Jdv1 [2] https://ift.tt/LFcsvWB [3] https://ift.tt/DsPrAiB... [4] https://ift.tt/TeHC7b0... [5] https://www.youtube.com/watch?v=I4Lbhr-NNXk [6] https://ift.tt/Fs35rCl [7] https://ift.tt/obulcN6 https://ift.tt/pB2Jdv1 November 20, 2024 at 02:26AM

Show HN: Fireproof – local-first database with Git-like encrypted sync https://ift.tt/NPC3wze

Show HN: Fireproof – local-first database with Git-like encrypted sync Hi, HN! As a cofounder of Couchbase, I pioneered mobile sync, and I’ve always wanted to bring the speed and reliability of local-first data to the web, incubating PouchDB among other efforts. I learned the constraints of real world financial applications at McKinsey & Company FinLab, and Merkle integrity research at Protocol Labs taught me smart contract data structures. As part of the JavaScript community (and early hosting provider for NPM) I’ve been waiting, and now with the availability of APIs like Passkeys and Origin Private Filesystem, I’m happy to say the browser is ready to support embedded databases. Front-ends are a lot easier to write when your database handles live sync for you, but the existing solutions rely on heavyweight cloud APIs instead of putting the smarts at the edge, where it belongs. I started from a different set of constraints, and arrived at a lightweight embedded database that uses a git-like data model to offer cryptographic causal consistency across browsers, edge functions, and anywhere TypeScript runs. It’s designed to make building full-featured apps as simple as calling `db.put({ hello: "world" })` and syncing them as easy as calling `connect(db, remote)`. People are using Fireproof for AI character chat[1], personal finance[2], and hedge funds[3], and we aim to be simple enough for novice coders to build enterprise-critical apps. Fireproof makes product owners dangerous, because just a little bit of code can define an application’s workflow and data model. See the code sample below. The reactive APIs[4] are designed for live collaboration so your user interfaces update automatically, making it an easy way to add query collaboration to legacy dashboards, or write new interactive tools for your team. Merkle CRDTs[5] provide multi-writer safety while maintaining tamperproof data provenance, conflict tracking, and deterministic merges. The storage engine writes content-addressed encrypted files that can be synced via commodity backends like S3 or Cloudflare[], without sacrificing data integrity. Our contributors include legends like Damien Katz, Meno Abels, Mikeal Rogers, and Alan Shaw. Fireproof is open source (Apache/MIT) and we know there are rough edges, so we hope this post stirs up collaborators![6] Please `npm install @fireproof/core` and give us feedback[7]. We are on the stable side of beta, so it’s a great time for the adventurous to join. I’m excited to see all the apps people write now that it’s easy! [1] https://ift.tt/1PJ5CND [2] https://ift.tt/GftnzoB... [3] https://ift.tt/vjuQ6qF... [4] https://ift.tt/5q26nvY [5] https://ift.tt/dLV4vnz... [6] https://ift.tt/n2cP7Ad [7] https://ift.tt/Gj63CUB https://ift.tt/rDzE9Rq November 19, 2024 at 10:19PM

Show HN: Nosia – Privacy-Focused AI to Run Models on Your Own Data and Device https://ift.tt/rJ81kYb

Show HN: Nosia – Privacy-Focused AI to Run Models on Your Own Data and Device What happens when you wait months after the official release of ChatGPT, with all the media buzz, before you actually try it for the first time? What happens when your first question to ChatGPT is about its carbon footprint, including Scope 3 emissions, and whether OpenAI complies with the Paris Agreement? What happens when, back in 2013, you almost left tech to become a beekeeper, but then returned to the field driven by passion and a vision for doing things differently? What happens when you believe in extending the life of terminals and servers, recycling, and reusing hardware, instead of succumbing to programmed obsolescence or deleting old emails? What happens when you believe in the power of the French and European tech ecosystem to provide digital solutions that respect GDPR and uphold core values? What happens when you stand for data sovereignty, empowering organizations to protect their data and act independently? What happens when you champion open-source and collective intelligence as key drivers of innovation? What happens when you work with code, systems, networks, and cybersecurity, and approach your work like a craftsman, building something meaningful? Introducing Nosia – a platform that allows you to run an AI model directly on your own data and device, from small models (SLM) to large models (LLM). It's designed to be easy to install and use, empowering you to take control of your AI needs while respecting privacy, sustainability, and autonomy. https://ift.tt/8cLAxjQ November 19, 2024 at 06:14AM

Show HN: CSV Table – Proper GUI for View and Edit CSV, JSON https://ift.tt/rqkVN13

Show HN: CSV Table – Proper GUI for View and Edit CSV, JSON https://csvtable.com November 18, 2024 at 11:34PM

Show HN: FastGraphRAG – Better RAG using good old PageRank https://ift.tt/5WES2IP

Show HN: FastGraphRAG – Better RAG using good old PageRank Hey there HN! We’re Antonio, Luca, and Yuhang, and we’re excited to introduce Fast GraphRAG, an open-source RAG approach that leverages knowledge graphs and the 25 years old PageRank for better information retrieval and reasoning. Building a good RAG pipeline these days takes a lot of manual optimizations. Most engineers intuitively start from naive RAG: throw everything in a vector database and hope that semantic search is powerful enough. This can work for use cases where accuracy isn’t too important and hallucinations are tolerable, but it doesn’t work for more difficult queries that involve multi-hop reasoning or more advanced domain understanding. Also, it’s impossible to debug it. To address these limitations, many engineers find themselves adding extra layers like agent-based preprocessing, custom embeddings, reranking mechanisms, and hybrid search strategies. Much like the early days of machine learning when we manually crafted feature vectors to squeeze out marginal gains, building an effective RAG system often becomes an exercise in crafting engineering “hacks.” Earlier this year, Microsoft seeded the idea of using Knowledge Graphs for RAG and published GraphRAG - i.e. RAG with Knowledge Graphs. We believe that there is an incredible potential in this idea, but existing implementations are naive in the way they create and explore the graph. That’s why we developed Fast GraphRAG with a new algorithmic approach using good old PageRank. There are two main challenges when building a reliable RAG system: (1) Data Noise: Real-world data is often messy. Customer support tickets, chat logs, and other conversational data can include a lot of irrelevant information. If you push noisy data into a vector database, you’re likely to get noisy results. (2) Domain Specialization: For complex use cases, a RAG system must understand the domain-specific context. This requires creating representations that capture not just the words but the deeper relationships and structures within the data. Our solution builds on these insights by incorporating knowledge graphs into the RAG pipeline. Knowledge graphs store entities and their relationships, and can help structure data in a way that enables more accurate and context-aware information retrieval. 12 years ago Google announced the knowledge graph we all know about [1]. It was a pioneering move. Now we have LLMs, meaning that people can finally do RAG on their own data with tools that can be as powerful as Google’s original idea. Before we built this, Antonio was at Amazon, while Luca and Yuhang were finishing their PhDs at Oxford. We had been thinking about this problem for years and we always loved the parallel between pagerank and the human memory [2]. We believe that searching for memories is incredibly similar to searching the web. Here’s how it works: - Entity and Relationship Extraction: Fast GraphRAG uses LLMs to extract entities and their relationships from your data and stores them in a graph format [3]. - Query Processing: When you make a query, Fast GraphRAG starts by finding the most relevant entities using vector search, then runs a personalized PageRank algorithm to determine the most important “memories” or pieces of information related to the query [4]. - Incremental Updates: Unlike other graph-based RAG systems, Fast GraphRAG natively supports incremental data insertions. This means you can continuously add new data without reprocessing the entire graph. - Faster: These design choices make our algorithm faster and more affordable to run than other graph-based RAG systems because we eliminate the need for communities and clustering. Suppose you’re analyzing a book and want to focus on character interactions, locations, and significant events: from fast_graphrag import GraphRAG DOMAIN = "Analyze this story and identify the characters. Focus on how they interact with each other, the locations they explore, and their relationships." EXAMPLE_QUERIES = [ "What is the significance of Christmas Eve in A Christmas Carol?", "How does the setting of Victorian London contribute to the story's themes?", "Describe the chain of events that leads to Scrooge's transformation.", "How does Dickens use the different spirits (Past, Present, and Future) to guide Scrooge?", "Why does Dickens choose to divide the story into \"staves\" rather than chapters?" ] ENTITY_TYPES = ["Character", "Animal", "Place", "Object", "Activity", "Event"] grag = GraphRAG( working_dir="./book_example", domain=DOMAIN, example_queries="\n".join(EXAMPLE_QUERIES), entity_types=ENTITY_TYPES ) with open("./book.txt") as f: grag.insert(f.read()) print(grag.query("Who is Scrooge?").response) This code creates a domain-specific knowledge graph based on your data, example queries, and specified entity types. Then you can query it in plain English while it automatically handles all the data fetching, entity extractions, co-reference resolutions, memory elections, etc. When you add new data, locking and checkpointing is handled for you as well. This is the kind of infrastructure that GenAI apps need to handle large-scale real-world data. Our goal is to give you this infrastructure so that you can focus on what’s important: building great apps for your users without having to care about manually engineering a retrieval pipeline. In the managed service, we also have a suite of UI tools for you to explore and debug your knowledge graph. We have a free hosted solution with up to 100 monthly requests. When you’re ready to grow, we have paid plans that scale with you. And of course you can self host our open-source engine. Give us a spin today at https://circlemind.co and see our code at https://ift.tt/qsHIajV We’d love feedback :) [1] https://ift.tt/wHJa4TO... [2] Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the Mind: Predicting Fluency with PageRank. Psychological Science, 18(12), 1069–1076. https://ift.tt/7wmVvdc [3] Similarly to Microsoft’s GraphRAG: https://ift.tt/C3vGKgn [4] Similarly to OSU’s HippoRAG: https://ift.tt/nNtuY8s https://ift.tt/bm8jrVi https://ift.tt/qsHIajV November 19, 2024 at 12:43AM

Show HN: EnkiTask – Simple Task Management for Freelancers, Pay Only for Tasks https://ift.tt/tOA58kj

Show HN: EnkiTask – Simple Task Management for Freelancers, Pay Only for Tasks Hi HN, I’m excited to share EnkiTask, a simple project management tool we built for freelancers and indie hackers. We wanted something with: - No subscriptions or recurring payments (pay only for tasks). - Unlimited team members at no extra cost. - A clean, intuitive interface built with Agile/Scrum methodologies in mind. We started using it internally, and after positive feedback from friends, decided to make it public. Would love your thoughts! Check it out here: [ https://enkitask.com](https://enkitask.com) November 18, 2024 at 11:22PM

Show HN: Store and render ASCII diagrams in Obsidian https://ift.tt/tdzUGxy

Show HN: Store and render ASCII diagrams in Obsidian Obsidian plug-in that allows you to create and store ASCII diagrams in your notes. It can be used to visualise diagrams, flowcharts, complex tables, Gantt charts and more in technical documentation, that will be rendered as a nice SVG graphics. https://ift.tt/nIyROBM November 12, 2024 at 09:03AM

Show HN: I made Picle (a.k.a. Catchphrase x Wordle x AI) https://ift.tt/y7UBuZA

Show HN: I made Picle (a.k.a. Catchphrase x Wordle x AI) Love to hear what you think! Thank you! https://picle.fi/ November 17, 2024 at 10:08PM

Show HN: Novus – local HTTPS proxy for a delightful developer experience https://ift.tt/G9wOThK

Show HN: Novus – local HTTPS proxy for a delightful developer experience https://ift.tt/Hha2XJ9 November 17, 2024 at 10:52PM

Show HN: I built an app for anyone to design their own kitchen https://ift.tt/TNd8Coc

Show HN: I built an app for anyone to design their own kitchen https://ift.tt/kZKSAzp November 17, 2024 at 06:19AM

Show HN: I built a(nother) house optimized for LAN parties https://ift.tt/6mYauiK

Show HN: I built a(nother) house optimized for LAN parties https://lanparty.house/ November 16, 2024 at 10:52PM

Show HN Memory for AI Agents https://ift.tt/9PI2aF6

Show HN Memory for AI Agents https://ift.tt/Y7Ckv6w November 16, 2024 at 10:03PM

Show HN: Ore – Lightweight, Generic and Simple DI Container for Go https://ift.tt/JFesLCD

Show HN: Ore – Lightweight, Generic and Simple DI Container for Go https://ift.tt/RZpN3UF November 16, 2024 at 09:39PM

Show HN: Scooter – Interactive find and replace in the terminal https://ift.tt/YfVolrM

Show HN: Scooter – Interactive find and replace in the terminal https://ift.tt/eCfPbm4 November 15, 2024 at 11:45PM

Show HN: Windsurf – Agentic IDE https://ift.tt/n3YhXyQ

Show HN: Windsurf – Agentic IDE At Codeium, we've been building AI-powered extensions for a while (we first launched our VSCode extension with autocomplete a little over 2 years ago!), but we've always thought there would come a day where we would hit the limits of what could be achieved within existing IDEs, so we decided to build our own: Windsurf (yes, it's yet another VSCode fork :) We've stuffed a lot of cool features into Windsurf—a super fast autocomplete model, an inline diff generation experience that feels truly native, but we're most proud of Cascade, which is an evolution of the sidebar chat experience that many other extensions have. Cascade can perform deep reasoning on your existing codebase, access a vast array of tools that allow it to run terminal commands and find relevant files, and it's omniscient of all the actions that the user has taken independent of invoking the AI. (You can for example, start implementing a change manually and just ask Cascade to "continue"). We've been using Cascade internally at Codeium on our actual production codebase, and we're getting actual value from it. We hope everyone here does too! You can find a bunch of demos of Cascade on our website but I want to show one that I made myself using Cascade to solve an interesting cryptography challenge: https://youtu.be/LbYepFmVB20 Cascade was able to explain the problem to me, install some libraries needed to interact with the challenge, give me some pointers towards a solution, and implement an attack that I described to it all by itself. https://ift.tt/iEo2fWr November 14, 2024 at 12:09AM

Show HN: The Billy Bass I put on the Internet https://ift.tt/HzGuFkC

Show HN: The Billy Bass I put on the Internet This one is just for fun. A couple years back I put together this tool to help me make synced up open/close mouth animations. End goal was always to "put it on the internet" like so many other things I remember when I was a bit younger. You can't drop this into a livestream these days without getting banned because people can't play nice, so I made it to record and distribute videos privately. I only code like this as a hobby, so it is a bit rough (I am a hardware guy), but these days you can get pretty far without knowing a lot. Enjoy https://ift.tt/N5HwcUR November 14, 2024 at 07:47PM

Show HN: Memoripy – AI Memory Layer with Short- & Long-Term Memory, Clustering https://ift.tt/lnMC0TV

Show HN: Memoripy – AI Memory Layer with Short- & Long-Term Memory, Clustering Hey HN! I built Memoripy, a memory layer for AI that adds short-term, long-term, and semantic memory capabilities to enhance LLM applications. It helps AI systems retain and prioritize past interactions, adapt over time, and respond with greater context and personalization. Memoripy uses semantic clustering to retrieve relevant memories, along with adaptive memory decay and reinforcement, so interactions stay fresh and context-aware. It’s designed for easy integration with OpenAI, Ollama, and other platforms—giving your AI applications dynamic memory management with minimal setup. Would love to hear your thoughts and feedback! https://ift.tt/TzIgkMp November 15, 2024 at 03:42AM

Show HN: Xfer, a data-transfer language https://ift.tt/cQgknlT

Show HN: Xfer, a data-transfer language Xfer is an experimental language I've been defining for a little while. It's intended to be a strictly-typed alternative to Json that offers a few other features, like comments, nested elements, placeholder substitution, and metadata. It's in VERY early days, so I'd love to hear your suggestions and feedback. https://ift.tt/myeoVFD November 12, 2024 at 06:05PM

Show HN: Nango Auth – Free OAuth for 250 APIs https://ift.tt/kZfzlS2

Show HN: Nango Auth – Free OAuth for 250 APIs Nango Auth is a free, source-available implementation of (O)Auth for 250+ APIs. Auth with external APIs is surprisingly difficult. OAuth is a mess[0], many APIs require parameters other than an API key or access token, and platforms like Stripe or GitHub even create their own custom auth protocols. We first launched Nango Auth on HN 1.5 years ago[1] when it only supported 40 OAuth APIs. Since then, it has expanded beyond OAuth into an end-to-end solution for API authentication: - Supports 250+ APIs[2] and almost a dozen auth modes: OAuth 2.0, OAuth 1.0, API key, Basic auth, and a half-dozen custom formats - Pre-built UI to help users pick & connect the API (including guides to find their API key and other required parameters) - Automatic credentials validation on connect - Secure, encrypted credentials storage & automatic token refresh - Detects expired/broken access tokens - Very detailed logs for quick & easy debugging More than 300 companies use Nango Auth in production for their product integrations: From fast-growing AI startups (Respell, Beam, Levity) to established SaaS players (Typeform, Semgrep, Electric) and even public companies. We also run a Slack Community[3] with 2,000+ engineers building product integrations. Nango Auth is a part of Nango[4], our source-available product integrations platform. While Nango itself is a paid product, our auth product (Nango Auth) is free for unlimited use, forever. It’s our small way of giving back to the community. You can either self-host Nango Auth or use our free cloud option. We hope Nango Auth can be helpful for your next integration, and look forward to your feedback! Demo video: https://ift.tt/rotpPsj GitHub Repo: https://ift.tt/P0ogvXp Landing page: https://ift.tt/H2vMjBT [0]: https://ift.tt/MtoSuV2 [1]: https://ift.tt/byasUER [2]: https://ift.tt/XgbmdTx [3]: https://nango.dev/slack [4]: https://www.nango.dev https://ift.tt/H2vMjBT November 13, 2024 at 11:51PM

Show HN: Konga Beat – A custom track editor for Donkey Konga 2 and 3 https://ift.tt/n0VlqOv

Show HN: Konga Beat – A custom track editor for Donkey Konga 2 and 3 Howdy HN! For those who don't know, back in the early 2000s, Nintendo and Namco developed a series of music rhythm games for the GameCube featuring Donkey Kong called Donkey Konga: https://ift.tt/CyNGiM6 The Donkey Konga games borrowed heavily from Taiko no Tatsujin (another music rhythm game by Namco). However, instead of taiko drums, the player would use DK Bongos to jam along with music from different eras and genres. Long story short, I figured out how to add custom tracks to some of the Donkey Konga games (Donkey Konga 2 and 3) but found the entire process cumbersome, so I decided to make a dedicated editor. It was a lot of fun to make, and I hope others get some enjoyment out of it too! https://ift.tt/e6NoIX9 November 13, 2024 at 11:39PM

Show HN: Stretch My Time Off – An Algorithm to Optimize Your Vacation Days https://ift.tt/ftdSzbq

Show HN: Stretch My Time Off – An Algorithm to Optimize Your Vacation Days Hey HN! I built StretchMyTimeOff as a quick experiment using Cursor (Anysphere's AI code editor) and GPT-4o to see how far AI could go in building a simple, functional site. What it does: The site helps you get the most out of your vacation by suggesting optimal days to take off around national holidays, maximizing long breaks with minimal vacation days, anywhere in the world and for any calendar year. It's an idea I've had for a while, and building the algorithm with GPT was a fun challenge. Any feedback or ideas I'm all ears :) https://ift.tt/GKsj7AH November 13, 2024 at 01:11AM

Show HN: Danswer APIs – Open-source APIs for building RAG apps over company docs https://ift.tt/6qGTCOM

Show HN: Danswer APIs – Open-source APIs for building RAG apps over company docs Hi HN! I’m one of the creators of Danswer, the open-source enterprise search project that you may have seen on HN a bit under a year ago ( https://ift.tt/jrB5GCl ). Really excited to be back to post about our new APIs :) Over the last half-year the #1 thing we’ve heard from our open-source community is the desire to build custom applications on top of the core connections with common apps + search + assistants. While this has always technically been possible, it hasn’t been easy for two main reasons: (1) inability to use APIs with auth-enabled w/o grabbing cookies from the browser and (2) a lack of simple APIs. Well no longer! For (1), we now provide a UI to generate API keys and for (2) we provide a pure “search” endpoint, a “chat” endpoint, and an implementation of OpenAI's Assistants API. With these, we take care of all the boring stuff (integrating with 35+ tools, constantly keeping docs up-to-date, and handling permissions) and provide an opinionated starting point for the generic hard stuff (really good hybrid-search + factoring in metadata, recency, etc.), so that you can focus on the specific business logic of the app. Back when I was at Alation, on-call for our team could have been made so much easier by a small, custom app that auto-responded to every alert based on Confluence, Slack, and a few Drive folders. Danswer’s APIs would power the core of this, and all I’d be left with are app-specific things like routing alerts to the right team member based on the document ownership of the cited documents. For an example of what it looks like to use the APIs, check out this script here: https://ift.tt/CYycxLw... ! If you're curious what's happening under the hood, check out our docs: https://ift.tt/8txBCFL . Thanks, excited to hear your thoughts! https://ift.tt/JkG6P3O November 12, 2024 at 10:17PM

Show HN: Visual inference exploration and experimentation playground https://ift.tt/nVHiI1P

Show HN: Visual inference exploration and experimentation playground https://ift.tt/pNFE6R9 November 12, 2024 at 08:03AM

Show HN: Next.js GitHub Markdown Blog https://ift.tt/b9kLu0d

Show HN: Next.js GitHub Markdown Blog This project allows you to easily add a nice looking, mobile-friendly blog to your existing NextJS app without a lot of heavy dependencies or complex stuff to configure. You just set up a repo on GitHub with some markdown files with some metadata at the top, and it will automatically retrieve and convert the markdown to rich html/css blog posts that works well with SEO. I made it for my own webapp because I wanted something simple and easy and didn't want to deal with a complex library/system that does tons of stuff I don't care about. https://ift.tt/cu3D1e9 November 13, 2024 at 12:10AM

Show HN: Flash Kitty – Archive of Adobe/Macromedia Flash Movies from Flash Kit https://ift.tt/39h0Tti

Show HN: Flash Kitty – Archive of Adobe/Macromedia Flash Movies from Flash Kit After realizing a few months ago that the current flashkit owners didn't really back up any of the user submitted movies, and getting some Flash nostalgia, I created this working Flash Kit archive using data from the Wayback Machine/Internet Archive. It uses Raffle so you can watch the submitted movies in a modern browser without needing plugins. It's not curated so you'll find a variety of things, some things are really creative and can be used for inspiration. https://ift.tt/gaFfG8W November 12, 2024 at 12:41AM

Show HN: I built a Claude AI chat interface to bypass platform limits https://ift.tt/ZMhXGWn

Show HN: I built a Claude AI chat interface to bypass platform limits I got frustrated with Claude's official platform limitations - conversations disappearing, no customization options, and clunky file handling. So I built my own interface over a weekend! GitHub: https://ift.tt/oNbRdwk What it does: - Saves all conversations locally (SQLite + Drizzle ORM) - Lets you customize Claude's personality per chat - Handles file attachments properly + caching input tokens - Dark/light mode - No conversation limits - Markdown & code syntax highlighting Tech stack is pretty simple: - Nuxt 3 for the frontend - Anthropic's SDK for Claude integration - @nuxt/ui for components - Local SQLite DB Happy to answer any questions! https://ift.tt/oNbRdwk November 11, 2024 at 08:16PM

Show HN: Managed foundation models for time-series forecasting https://ift.tt/3WqeECt

Show HN: Managed foundation models for time-series forecasting https://sulie.co/ November 11, 2024 at 04:26PM

Show HN: I made a tiny device for automatically recording digital pianos https://ift.tt/2bHmkI3

Show HN: I made a tiny device for automatically recording digital pianos Hey HN! A few years ago, I left my cushy big tech job to make hardware. And made the device I always wanted - an automatic piano recorder! I usually play piano improvisationally, and manually hitting record never meshed well with that. But there are always moments I wish I recorded, and now they are! Hopefully it scratches a similar itch for some of you as well! A few of the tech details: * built on an esp32-s3 * custom injection molded enclosure * BLE comms, sd card storage, DS1302 RTC * android & ios apps with Flutter * Shadertoy vfx support for video sharing - Chip https://jamcorder.com/ November 8, 2024 at 06:41AM

Show HN: I made a minimalistic AI calendar creator to accelerate daily planning https://ift.tt/5u4tQsx

Show HN: I made a minimalistic AI calendar creator to accelerate daily planning Hey Hacker News! To test implementing AI (and paywalls tbh) in software for the first time, I made this simple calendar creator (no mail address required) that lets a person create a calendar based on his needs and availability and export it to his calendar app, pdf or receive a daily newsletter with his daily tasks (and more). After wrestling with different models (f.e. with huggingface) and encountering different problems, I decided to use ChatGPT's models, tune them a little and see how the result works out. ChatGPT's low price enables me to give users the opportunity to create calendars for free and without a mail address required. Demo video: https://youtu.be/UeZmqXcU5fY?feature=shared The website/tool includes: - Creating your calendar based on a title, date range and a description - Editing and "polishing" the calendar until getting a desired result - Exporting the calendar to your calendar app (can be Google Calendar, Apple Calendar, Microsoft Outlook, etc...) - Exporting the calendar to PDF - Signing up for a daily newsletter that contains a user's daily tasks, reminders, special ai notes and more! Some technical stuff: - I am using firestore database and paypal payments - I am always using the latest-fastest ChatGPT AI model (as generation-time is really important here) - The website is created with Nuxt 3 (Nuxt for vuejs is what Next is for reactjs) and designed with tailwindcss https://ift.tt/1H6KzpB November 11, 2024 at 12:12AM

Show HN: httpclock. A useless way to tell the time with HTTP status codes… https://ift.tt/yjdfW75

Show HN: httpclock. A useless way to tell the time with HTTP status codes… I made this a few months back as a fun little project and it has absolutely no utility whatsoever. The server returns the current time in the request's status code — i.e., as long as the time can be mapped to a valid HTTP status code (e.g., 4:04pm -> 404 Not found) the server returns that status code for the request. Example: https://ift.tt/Pf9k4aE The httpclock.com domain detects timezone by IP and redirects to the respective subdomain such as pt.httpclock.com (but will fall back to UTC) A browser will show a blank page for most of the day given the number of valid status codes, but you can check other timezone subdomains, e.g. `curl -I https://ift.tt/g3dtvMz ` Note: "Invalid" times will return 999 with no content. Anyway, I hope you enjoy this very useless clock. I plan to post the code on GitHub soon since I may as well. Thanks! https://httpclock.com November 10, 2024 at 10:05PM

Show HN: BladeEvader – Move Your Head to Evade Blades https://ift.tt/4n5lqPV

Show HN: BladeEvader – Move Your Head to Evade Blades https://ift.tt/I8rJh2H November 10, 2024 at 08:52PM

Show HN: Akas: A Micropodcasting App https://ift.tt/O6SZIne

Show HN: Akas: A Micropodcasting App App store: https://ift.tt/5wtc2KU... Play store: https://ift.tt/wGekxho... Site link: https://akashq.com Invitation code: BVN55L https://www.akashq.com/ November 10, 2024 at 01:45AM

Show HN: Arch GW – Distributed gateway for agents, engineered with small LLMs https://ift.tt/PHj4uwQ

Show HN: Arch GW – Distributed gateway for agents, engineered with small LLMs Hi HN My name is Salman and I work on Arch GW - the intelligent gateway designed to protect, observe, and personalize LLM applications with your APIs. https://ift.tt/ac9nu6J Our team built Envoy Proxy at Lyft, and re-imagined it with the belief that: Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests including secure handling, intelligent routing, robust observability, and integration with backend (API) systems for personalization – all outside business logic. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling "backend" APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way. Core Features: * Built on Envoy: Arch runs alongside application servers, and builds on top of Envoy's proven HTTP management and scalability features to handle ingress and egress traffic related to prompts and LLMs. * Function Calling for fast Agentic and RAG apps. Engineered with purpose-built LLMs to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts. * Prompt Guard: Arch centralizes prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code. * Traffic Management: Arch manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability. * Standards-based Observability: Arch uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates. We are just getting started, and would love feedback and contribution from the community https://ift.tt/6yxriRg November 9, 2024 at 10:25PM

Show HN: Svgg – Turn raw SVG into image link https://ift.tt/tEgWuR7

Show HN: Svgg – Turn raw SVG into image link https://svgg.link November 9, 2024 at 10:24PM

Show HN: 0x4c – Information Collections https://ift.tt/0iqK4CI

Show HN: 0x4c – Information Collections https://0x4c.quest November 9, 2024 at 10:10PM

Show HN: Mdsf, a Codeblock Formatter for Markdown https://ift.tt/iurzBVa

Show HN: Mdsf, a Codeblock Formatter for Markdown https://ift.tt/KP0voAQ November 8, 2024 at 08:23PM

Show HN: Intelligent Browser for Web Automation Powered by AI https://ift.tt/kyC0S7z

Show HN: Intelligent Browser for Web Automation Powered by AI Hey HN this is Daniel, I'm excited to share Airtop with the community! Airtop enables AI agents to seamlessly browse and automate any website — even ones with complex authentication like MFA or OAuth. Whether you need to scrape, interact with dynamic content, or automate tasks across multiple browsers, Airtop's cloud-based infrastructure makes it scalable and accessible. Why Airtop? — AI-Driven Web Automation: Automate interactions with any site through natural language commands even if they include human-in-the-loop scenarios. — Authentication Ready: Airtop handles websites requiring authentication, allowing bots to perform complex tasks. — Scalability: Easily manage and deploy tasks, whether it's one browser or a thousand. — Compliance: Airtop ensures your data is handled with the highest security, availability, and privacy standards, giving you peace of mind while automating sensitive tasks. You can check it all out for free and play with our Studio (in the dashboard) to see how the API and prompts work. I would love to hear your thoughts and feedback! https://www.airtop.ai/ November 7, 2024 at 11:00PM

Show HN: BemiDB – Postgres read replica optimized for analytics https://ift.tt/W09gUpy

Show HN: BemiDB – Postgres read replica optimized for analytics https://ift.tt/KSQ6CYG November 7, 2024 at 11:25PM

Show HN: MLGarden, a tool/toy to build and train simple neural networks visually https://ift.tt/XFEe2Ly

Show HN: MLGarden, a tool/toy to build and train simple neural networks visually I made this tool to get some better intuition on how neural networks/backpropagation worked, but I'm really unsure what to do with it now, so I've put it up on github, and I wrote a little primer on backprop and neural networks to showcase it. Really curious to hear any thoughts you might have, or anything I got wrong in the write up! https://ift.tt/P2kaOq9 November 7, 2024 at 09:55PM

Show HN: GitQuill – free cross-platform GUI for Git, inspired by GitKraken https://ift.tt/FBnHU6G

Show HN: GitQuill – free cross-platform GUI for Git, inspired by GitKraken https://ift.tt/Ikuan2j November 7, 2024 at 03:43AM

Show HN: Take a Moment and Breathe https://ift.tt/M3OdX6E

Show HN: Take a Moment and Breathe A simple web app to help your breathing exercises. If you submit your email, I'll also ask what personalised scenes you might like to meditate to. First 5 free but will have to work out a payment system after that... https://breaths.life/ November 7, 2024 at 04:52AM

Show HN: Last CLI tool you will need https://ift.tt/xwrnBYZ

Show HN: Last CLI tool you will need https://ift.tt/evLy9CZ November 7, 2024 at 02:08AM

Show HN: Whirlwind – Async concurrent hashmap for Rust https://ift.tt/CVDs756

Show HN: NewsCatcher's Hyperlocal News API – Granular City-Level News Feeds https://ift.tt/087xPsz

Show HN: NewsCatcher's Hyperlocal News API – Granular City-Level News Feeds We’ve launched NewsCatcher’s Hyperlocal News API to provide city-level news feeds for market analysis, localized apps, and data analytics. Why We Built It: Aggregating hyperlocal news is complex. Local news comes from diverse sources, many covering national topics, making it hard to isolate local events like transit updates, crime, extreme weather alerts, or infrastructure projects. Searching by location alone can lead to irrelevant results, especially with city names that overlap or match common words (e.g., Orange, Lake). What It Does: Our API provides precise, location-based news by processing 185K articles daily and tagging each with its associated_town down to the town level with 92% accuracy. Covering 31,000 U.S. locations, it uses advanced NLP to accurately associate location names, even when they’re common words. It enables you to: - Access near-real-time, location-focused news feeds - Filter by topic (e.g., crime, sports), keyword, and date - Cluster articles covering similar events and topics - Get sentiment score for a headline and article body - Recognize and extract entities Key Features: - AI-powered location recognition and validation - Advanced querying with boolean operators and proximity search - Multiple town association methods for precise location matching - NLP-enriched content for deeper analysis - Articles clustering - Multi-language support - High volume data retrieval (up to 1000 articles per request) For a demo, try our 15 public RSS feeds: https://ift.tt/AfR6IZN . The API provides JSON output for easy integration. Tech Specs: - RESTful, JSON-based API - API key authentication - Scalable for high-volume queries We’d appreciate any feedback or questions on use cases. November 5, 2024 at 11:57PM

Show HN: I wrote an open-source browser alternative for Computer Use for any LLM https://ift.tt/NXRbvTu

Show HN: I wrote an open-source browser alternative for Computer Use for any LLM Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general SaaS tool yes, but I think a lot of people are going to try to make their own web automation agents from scratch, so the idea is to provide groundwork/library for the hard part so that not everyone has to repeat these steps: - parse html in a LLM friendly way (clickable items + screenshots) - provide a nice function calls for everything inside the browser - create reusable agent classes What this is NOT? An all knowing AI agent that can solve all your problems. The vision: create repeatable tasks on the web just by prompting your agent and not care about the hows. To better showcase the power of text extraction we made a few demos such as: - Applying for multiple software engineering jobs in San Francisco - Opening new tabs to search for images of Albert Einstein, Oprah Winfrey, and Steve Jobs - Finding the cheapest one-way flight from London to Kyrgyzstan for December 25th I’d be interested in feedback on how this tool fits into your automation workflows. Try it out and let me know how it performs on your end. We are Gregor & Magnus and we built this in 5 days. https://ift.tt/fSRy5Xa November 5, 2024 at 10:51PM

Show HN: A free multiviewer to watch major news events like elections https://ift.tt/Ni7vZIp

Show HN: A free multiviewer to watch major news events like elections Hey HN! I'm a Product Manager at the FT in the UK, and VidGrid is something I've been working on for years in my own time. It's just got a major update just in time for the election. The idea is a way to watch lots of different news streams at the same time, as if you're in a control room or news gallery for the very latest info. I tried to prioritise usability and speed. All streams are third-party sourced (from broadcaster's own links). You can now sign up for an account to save/favourite your own streams, and it has better keyboard and drag and drop support. I've also fixed a ton of bugs from the last version. Happy to answer questions about it, or about news media at this time of year in general! Enjoy, and have a good evening. https://vidgrid.tk.gg November 5, 2024 at 09:13PM

Show HN: Krixik – Easily sequence small/specialized AI models (pip-installable) https://ift.tt/VSdy8x9

Show HN: Krixik – Easily sequence small/specialized AI models (pip-installable) Our initial intention was to experiment with a smartbook idea, but we couldn't find a good prototyping tool for small/specialized AI models. That rabbit hole took us through vector databases, through infra for small AI, and finally here. We’re particularly proud of the accessibility/simplicity of our code syntax. Krixik’s model library is limited to eleven model types (modules) and a few dozen models, but we will significantly expand it. Other enhancements, like a local client and several 3rd-party integrations, are also planned. You require API credentials to try Krixik. You can quickly get them through this form: https://ift.tt/M8EJqzk And here’s a demo video (our YouTube channel also has a couple dozen example videos in it): https://youtu.be/WpSSYLfvfdM https://ift.tt/BI8CqSV November 4, 2024 at 10:03PM

Show HN: HN Push – Web push notifications for top stories on Hacker News https://ift.tt/ltOFiRz

Show HN: HN Push – Web push notifications for top stories on Hacker News I created HN Push to help reduce the urge to refresh Hacker News constantly. Since I rely on HN for real-time tech news, I wanted an efficient way to stay informed without having to check the site. Receiving summaries with Apple Intelligence was an added bonus. The source code is available on GitHub[^1]. [^1]: https://ift.tt/tavTMEx https://www.hnpush.com November 4, 2024 at 08:47PM

Show HN: Cmaxz – weather radar map viewer https://ift.tt/PxQcOId

Show HN: Cmaxz – weather radar map viewer https://ift.tt/XOrk43Q November 4, 2024 at 09:12PM

Show HN: I wrote a techno-thriller on AI and need feedback (no signup) https://ift.tt/iB1gjGQ

Show HN: I wrote a techno-thriller on AI and need feedback (no signup) https://ift.tt/FNAwSRo November 3, 2024 at 11:44PM

Show HN: Outcheckr- Webpage outbound link enumerator with threading https://ift.tt/Ym9pbdh

Show HN: Outcheckr- Webpage outbound link enumerator with threading Outcheckr enumerates all outbound links present in a webpage, now faster with multi-threading support. https://ift.tt/9ZiztKk November 3, 2024 at 11:22PM

Show HN: I built a shadcn visual playground https://ift.tt/wotmh1c

Show HN: I built a shadcn visual playground I'm excited to introduce Robust Design Systems – a tool to make creating and managing design systems easier than ever. With the visual component editor, you can customize shadcn component variants without touching any code. When you're done, simply export the code instantly! I'd love to hear your feedback and see how I can make it even better! https://ift.tt/Qorw79U November 3, 2024 at 10:15PM

Show HN: X.infer-Framework agnostic computer vision inference https://ift.tt/K5ax8Hi

Show HN: X.infer-Framework agnostic computer vision inference I spent the past 3 weeks building x.infer, a Python package that lets you run computer vision inference on a framework of choice. It currently supports models from transformers, Ultralytics, Timm, vLLM and Ollama. Combined, this covers over 1000+ computer vision models. You can easily add your own model. Repo - https://ift.tt/9b46EMg Colab quickstart - https://ift.tt/Zf6LmJE... Why did I make this? It's mostly just for fun. I wanted to practice some design pattern principles I picked up from the past. The code is still messy though but it works. Also, I enjoy playing around with new vision models, but not so much learning about the framework it's written with. I'm working on this during my free time. Contributions/feedback are more than welcome! Hope this also helps you (especially newcomers) to experiment and play around with new vision models. https://ift.tt/9b46EMg November 2, 2024 at 10:41PM

Show HN: I created a minimalist pastebin clone using Go, Htmx, and PostgreSQL https://ift.tt/piyjtdN

Show HN: I created a minimalist pastebin clone using Go, Htmx, and PostgreSQL https://ift.tt/tmzXOsY November 1, 2024 at 09:26PM

Show HN: Exploring Russian Election Interference with Graphiti https://ift.tt/j8oRpeT

Show HN: Exploring Russian Election Interference with Graphiti Hi HN, we're Jack and Daniel from Zep. We've built a visual exploration tool and AI assistant for analyzing Russian election interference in the run-up to next week's US elections. The Explorer uses Graphiti, Zep's open-source temporal Knowledge Graph library. Graphiti autonomously creates dynamic, temporally-aware knowledge graphs representing complex, evolving relationships between entities. To offer users a detailed view of Russian state operations and related topics, we populated the graph with over 50+ sources. These include US DOJ indictments, research by US and foreign governments, research by non-governmental organizations, and media articles. Using the graph, you can explore which US organizations and individuals are implicated in these efforts and learn how private entities (such as OpenAI, Meta, and others) have responded. Two operations stand out in this dataset: Doppelganger and Tenet Media. - Doppelganger: A campaign that used AI-generated content and fake accounts to spread anti-Ukraine propaganda and influence Western public opinion. - Tenet Media: a media company secretly funded by RT (Russian state television), paid popular American influencers to create pro-Russian content across social media platforms. The Doppelganger operation's multinational scope required us to draw from both US and European sources to capture its full impact. For Tenet Media, we used diverse sources, ranging from federal government reports to Variety magazine's industry coverage, to detail the company's collapse and its broader implications for the media landscape. To build the app quickly, we used FastHTML and LangGraph, a fun combination. The linked article explains how we built the Explorer and some of the challenges we faced, particularly around ingesting data. Explorer: https://ift.tt/CSqvo6A How we built it: https://ift.tt/hJbL5UB... We've spent hours delving into the data. We hope you find it as fascinating as we did. Let us know what you think! https://ift.tt/7TcjwX4 November 1, 2024 at 10:45PM

Show HN: Ratarmount 1.0.0 – Rapid access to large archives via a FUSE filesystem https://ift.tt/nC6V9Ay

Show HN: Ratarmount 1.0.0 – Rapid access to large archives via a FUSE filesystem Hi HN, Since my first posted introduction of ratarmount [0], 2 years have gone by and many features have been added. To summarize, ratarmount enables working with archived contents exposed as a filesystem without the data having to be extracted to disk: pip install ratarmount ratarmount archive.tar mounted ls -la mounted I started this project after noticing the slowness of archivemount with large TAR files and wondering how this could be because the file contents exist at some offset in the archive file and it should not be difficult to read that data. Turns out, that part was not difficult, however packaging everything nicely, adding tests, and adding many more formats and features such as union mounting and recursive mounting, are the things keeping me busy on this project until today. Since the last Show HN, a libarchive, SquashFS, fsspec, and many more backends have been added, so that it now should be able to read every format that archivemount can and some more, and even read them remotely. However, performance for any use case besides bzip2/gzip-compressed TARs may vary even though I did my best. Personally, I am using it view to packed folders with many small files that do not change anymore. I pack these folders because else copying to other hard drives takes much longer. I'm also using it when I want to avoid the command line. I have added ratarmount as a Caja user script for mounting via right-click. This way, I can mount an archive and then copy the contents to another drive to effectively do the extraction and copying in one step. Initially, I have also used it to train on the ImageNet TAR archive directly. I probably should have released a 1.0.0 some years ago because I have kept the command line interface and even the index file format compatible as best as possible between the several 0.x versions already. Some larger future features on my wishlist are: - A new indexed_lz4 backend. This should be doable inside my indexed_bzip2 [1] / rapidgzip [2] backend library. - A custom ZIP and SquashFS reader accelerated by rapidgzip and indexed_bzip2 to enable faster seeking inside large files inside those archives. - I am eagerly awaiting the Linux Kernel FUSE BPF support [3], which might enable some further latency reductions for use cases with very small files / very small reads, at least in the case of working with uncompressed archives. I have done comparisons for such archives (100k images a 100 KiB) and noticed that direct access via the Python library ratarmountcore was roughly two times faster than access via ratarmount and FUSE. Maybe I'll even find the time to play around with the existing unmerged FUSE BPF patch set. [0] https://ift.tt/Dp2z14e [1] https://ift.tt/wNuIR16 [2] https://ift.tt/rwQtInm [3] https://ift.tt/CPWxDTl https://ift.tt/NA4dhHZ November 1, 2024 at 10:25PM