The best Hacker News stories from Show from the past week
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Show HN: Openkoda – Open–source, private, Salesforce alternative
Show HN: Openkoda – Open–source, private, Salesforce alternative
Show HN: I made a free app to calibrate your turntable by simply playing a song
Hey there!<p>I made a little app that lets you to calibrate your turntable by putting on any record and tapping a button. It's called Grooved and it uses your phone's microphone to see how fast your platter is going, almost like magic.<p>You can see what it looks like in action here: <a href="https://twitter.com/OKatBest/status/1795453042994680148" rel="nofollow">https://twitter.com/OKatBest/status/1795453042994680148</a><p>The app itself is free without ads, subscriptions, or trackers. It's a tool I built for myself, and I just thought someone else might want to use it too. I have never seen this technology being used before, all other apps require you to either print something and use the camera, or to place your phone on the spinning platter and use the accelerometer.<p>You can grab it on the App Store, and I am working on an Android version I hope to release at some point in June.<p>Would love to hear what you think about it!<p>Ivan_
Show HN: I made a free app to calibrate your turntable by simply playing a song
Hey there!<p>I made a little app that lets you to calibrate your turntable by putting on any record and tapping a button. It's called Grooved and it uses your phone's microphone to see how fast your platter is going, almost like magic.<p>You can see what it looks like in action here: <a href="https://twitter.com/OKatBest/status/1795453042994680148" rel="nofollow">https://twitter.com/OKatBest/status/1795453042994680148</a><p>The app itself is free without ads, subscriptions, or trackers. It's a tool I built for myself, and I just thought someone else might want to use it too. I have never seen this technology being used before, all other apps require you to either print something and use the camera, or to place your phone on the spinning platter and use the accelerometer.<p>You can grab it on the App Store, and I am working on an Android version I hope to release at some point in June.<p>Would love to hear what you think about it!<p>Ivan_
Show HN: Boldly go where Gradient Descent has never gone before with DiscoGrad
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you.<p>We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the single path taken through the program. Our tool offers several backends that handle this situation, giving useful descent directions for optimization by accounting for alternative branches. Besides simulations, this problem arises in many other places, for example in deep learning when trying to combine imperative programs with neural networks.<p>In a nutshell, DiscoGrad applies an (LLVM-based) source-to-source transformation to your C++ program, adding some calls to our header library, which then handles the gradient computation. What sets it apart from similar tools/estimators is that it's fully automatic (no need to come up with a differentiable problem formulation/reparametrization) and that the branching condition can be any function of the program inputs (no need to know upfront what distribution the condition follows).<p>We're currently a team of two working on DiscoGrad as part of a research project, so don't expect to see production-grade code quality, but we do intend for it to be more than a throwaway research prototype. Use cases we've successfully tested include calibrating simulation models of epidemics or evacuation scenarios via gradient descent, and combining simulations with neural networks in an end-to-end trainable fashion.<p>We hope you find this interesting and useful, and we're happy to answer questions!
Show HN: Spot – Simple, cross-platform, reactive desktop GUI toolkit for Go
Hi HN, I’m excited to share Spot, a simple, cross-platform, React-like GUI library for Go. It is just a few days old and has lots of missing features but I'm happy with the results so far, and looking for some design feedback.<p>Spot is designed to be easy to use and provide a consistent API across different platforms (mainly Mac & Linux). It’s inspired by React, but written in Go, aiming to combine the best of both worlds: the easy tooling & performance of Go with a modern, reactive approach to UI development.<p>Key features:<p>- Cross-platform: Leveraging FLTK[1] & Cocoa[2], Spot works on Mac, Linux, and the BSDs with plans for native Windows support in the future.<p>- Reactive UI: Adopts a React-like model for building UIs, making it intuitive for those familiar with reactive frameworks.<p>- Traditional, native widget set: Utilizes native widgets where available to provide a more traditional look and feel.<p>Why I built it:<p>I was searching for a cross-platform GUI toolkit for Go that had a more traditional appearance, and none of the existing options quite met my needs. I then started playing with Gocoa and go-fltk and suddenly I worked on an experiment to see how challenging it would be to build something like React in Go, and it kinda evolved into Spot. ¯\_(ツ)_/¯<p>In 2024, is there a still place for classic desktop GUIs—even with a modern spin?<p>I’d love to hear your thoughts, feedback, and any suggestions for improvement. Also, contributions are very welcome.<p>Thank you for checking it out!<p>[1] <a href="https://github.com/pwiecz/go-fltk">https://github.com/pwiecz/go-fltk</a><p>[2] <a href="https://github.com/roblillack/gocoa">https://github.com/roblillack/gocoa</a>
Show HN: Spot – Simple, cross-platform, reactive desktop GUI toolkit for Go
Hi HN, I’m excited to share Spot, a simple, cross-platform, React-like GUI library for Go. It is just a few days old and has lots of missing features but I'm happy with the results so far, and looking for some design feedback.<p>Spot is designed to be easy to use and provide a consistent API across different platforms (mainly Mac & Linux). It’s inspired by React, but written in Go, aiming to combine the best of both worlds: the easy tooling & performance of Go with a modern, reactive approach to UI development.<p>Key features:<p>- Cross-platform: Leveraging FLTK[1] & Cocoa[2], Spot works on Mac, Linux, and the BSDs with plans for native Windows support in the future.<p>- Reactive UI: Adopts a React-like model for building UIs, making it intuitive for those familiar with reactive frameworks.<p>- Traditional, native widget set: Utilizes native widgets where available to provide a more traditional look and feel.<p>Why I built it:<p>I was searching for a cross-platform GUI toolkit for Go that had a more traditional appearance, and none of the existing options quite met my needs. I then started playing with Gocoa and go-fltk and suddenly I worked on an experiment to see how challenging it would be to build something like React in Go, and it kinda evolved into Spot. ¯\_(ツ)_/¯<p>In 2024, is there a still place for classic desktop GUIs—even with a modern spin?<p>I’d love to hear your thoughts, feedback, and any suggestions for improvement. Also, contributions are very welcome.<p>Thank you for checking it out!<p>[1] <a href="https://github.com/pwiecz/go-fltk">https://github.com/pwiecz/go-fltk</a><p>[2] <a href="https://github.com/roblillack/gocoa">https://github.com/roblillack/gocoa</a>
Show HN: Spot – Simple, cross-platform, reactive desktop GUI toolkit for Go
Hi HN, I’m excited to share Spot, a simple, cross-platform, React-like GUI library for Go. It is just a few days old and has lots of missing features but I'm happy with the results so far, and looking for some design feedback.<p>Spot is designed to be easy to use and provide a consistent API across different platforms (mainly Mac & Linux). It’s inspired by React, but written in Go, aiming to combine the best of both worlds: the easy tooling & performance of Go with a modern, reactive approach to UI development.<p>Key features:<p>- Cross-platform: Leveraging FLTK[1] & Cocoa[2], Spot works on Mac, Linux, and the BSDs with plans for native Windows support in the future.<p>- Reactive UI: Adopts a React-like model for building UIs, making it intuitive for those familiar with reactive frameworks.<p>- Traditional, native widget set: Utilizes native widgets where available to provide a more traditional look and feel.<p>Why I built it:<p>I was searching for a cross-platform GUI toolkit for Go that had a more traditional appearance, and none of the existing options quite met my needs. I then started playing with Gocoa and go-fltk and suddenly I worked on an experiment to see how challenging it would be to build something like React in Go, and it kinda evolved into Spot. ¯\_(ツ)_/¯<p>In 2024, is there a still place for classic desktop GUIs—even with a modern spin?<p>I’d love to hear your thoughts, feedback, and any suggestions for improvement. Also, contributions are very welcome.<p>Thank you for checking it out!<p>[1] <a href="https://github.com/pwiecz/go-fltk">https://github.com/pwiecz/go-fltk</a><p>[2] <a href="https://github.com/roblillack/gocoa">https://github.com/roblillack/gocoa</a>
Show HN: We open sourced our entire text-to-SQL product
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product.<p>The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset.<p>Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by:<p>- Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant<p>- Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset<p>- Executing the SQL against the DB to get a few sample rows and recover from errors<p>- Using an evaluator to assign a confidence score to the generated SQL<p>The repo includes four services <a href="https://github.com/Dataherald/dataherald/tree/main/services">https://github.com/Dataherald/dataherald/tree/main/services</a>:<p>1- Engine: The core service which includes the LLM agent, vector stores and DB connectors.<p>2- Admin Console: a NextJS front-end for configuring the engine and observability.<p>3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend.<p>4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration.<p>Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
Show HN: We open sourced our entire text-to-SQL product
Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product.<p>The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset.<p>Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent which uses Chain of Thought (CoT) reasoning and a number of different tools to generate high accuracy SQL from a given user prompt. The engine achieves this by:<p>- Collecting context at configuration from the database and sources such as data dictionaries and unstructured documents which are stored in a data store or a vector DB and injected if relevant<p>- Allowing users to upload sample NL <> SQL pairs (golden SQL) which can be used in few shot prompting or to fine-tune an NL-to-SQL LLM for that specific dataset<p>- Executing the SQL against the DB to get a few sample rows and recover from errors<p>- Using an evaluator to assign a confidence score to the generated SQL<p>The repo includes four services <a href="https://github.com/Dataherald/dataherald/tree/main/services">https://github.com/Dataherald/dataherald/tree/main/services</a>:<p>1- Engine: The core service which includes the LLM agent, vector stores and DB connectors.<p>2- Admin Console: a NextJS front-end for configuring the engine and observability.<p>3- Enterprise Backend: Wraps the core engine, adding authentication, caching, and APIs for the frontend.<p>4- Slackbot: Integrate Dataherald directly into your Slack workflow for on-the-fly data exploration.<p>Would love to hear from the community on building natural language interfaces to relational data. Anyone live in production without a human in the loop? Thoughts on how to improve performance without spending weeks on model training?
Show HN: Porter Cloud – PaaS with an eject button
Hi HN! Porter Cloud (<a href="https://porter.run/porter-cloud">https://porter.run/porter-cloud</a>) is a Platform as a Service (PaaS) like Heroku, but we make it easy for you to migrate to AWS, Azure, or GCP when you're ready.<p>Like Heroku, Porter takes care of a lot of generic DevOps work for you (like setting up CI/CD, containerizing your applications, autoscaling, SSL certificates, setting up a reverse proxy) and lets you deploy your apps with a few clicks — saving you a lot of time while developing. However, as you probably know, there’s a downside: platforms like this become constraining if and when your app takes off and you need to scale. The time you saved while developing can get pretty expensive once you’re paying for a lot of users — and the platforms tend to try to keep you locked in!<p>Our idea is to give you the best of both worlds: use Porter Cloud for as long as it saves you time and development cost, but at any time you can press the “eject button” to migrate your app to your own AWS, Azure, or GCP account as you please. We make it seamless to break out, so you’re no longer subject to the rigid constraints of a conventional PaaS. You can migrate in a few simple steps outlined here: <a href="https://docs.porter.run/other/eject">https://docs.porter.run/other/eject</a>.<p>A bit of background: we first launched on HN almost 3 years ago with our original product (<a href="https://news.ycombinator.com/item?id=26993421">https://news.ycombinator.com/item?id=26993421</a>, <a href="https://porter.run">https://porter.run</a>), which deploys your applications to your own AWS, Azure, or GCP account with the simple experience of a PaaS.<p>Since then, we’ve helped countless companies migrate from a PaaS to one of the big three cloud providers. Most of them had gotten started on a PaaS in the early days to optimize for speed and ease of use, but ultimately had to go through a painful migration to AWS, Azure, or GCP as they scaled and ran into various constraints on their original PaaS.<p>Interestingly, we learned that many companies that start on a PaaS are fully aware that they’ll have to migrate to one of the big three public clouds [1] at some point. Yet they choose to deploy on a PaaS anyway because outgrowing a cloud platform is a “champagne problem” when you’re focused on getting something off the ground. This, however, becomes a very tangible problem when you need to migrate your entire production infrastructure while serving many users at scale. It’s a “nice problem to have”, until it isn’t.<p>We’ve built Porter Cloud so that the next generation of startups can get off the ground as quickly as possible, with a peace of mind that you can effortlessly move to one of the tried and true hyperscalers when you are ready to scale.<p>We are excited to see what people build on Porter Cloud. If you’ve ever dealt with a migration from a PaaS to one of the big three cloud providers, we’d also love to hear about your experience in the comments. Looking forward to feedback and discussion!<p>[1] By “big three clouds” we mean the lower-level primitives of each cloud provider. We don’t mean their higher level offerings like AWS App Runner, Google Cloud Run, or Azure App Service, since those run into the same PaaS problems described above.
Show HN: Porter Cloud – PaaS with an eject button
Hi HN! Porter Cloud (<a href="https://porter.run/porter-cloud">https://porter.run/porter-cloud</a>) is a Platform as a Service (PaaS) like Heroku, but we make it easy for you to migrate to AWS, Azure, or GCP when you're ready.<p>Like Heroku, Porter takes care of a lot of generic DevOps work for you (like setting up CI/CD, containerizing your applications, autoscaling, SSL certificates, setting up a reverse proxy) and lets you deploy your apps with a few clicks — saving you a lot of time while developing. However, as you probably know, there’s a downside: platforms like this become constraining if and when your app takes off and you need to scale. The time you saved while developing can get pretty expensive once you’re paying for a lot of users — and the platforms tend to try to keep you locked in!<p>Our idea is to give you the best of both worlds: use Porter Cloud for as long as it saves you time and development cost, but at any time you can press the “eject button” to migrate your app to your own AWS, Azure, or GCP account as you please. We make it seamless to break out, so you’re no longer subject to the rigid constraints of a conventional PaaS. You can migrate in a few simple steps outlined here: <a href="https://docs.porter.run/other/eject">https://docs.porter.run/other/eject</a>.<p>A bit of background: we first launched on HN almost 3 years ago with our original product (<a href="https://news.ycombinator.com/item?id=26993421">https://news.ycombinator.com/item?id=26993421</a>, <a href="https://porter.run">https://porter.run</a>), which deploys your applications to your own AWS, Azure, or GCP account with the simple experience of a PaaS.<p>Since then, we’ve helped countless companies migrate from a PaaS to one of the big three cloud providers. Most of them had gotten started on a PaaS in the early days to optimize for speed and ease of use, but ultimately had to go through a painful migration to AWS, Azure, or GCP as they scaled and ran into various constraints on their original PaaS.<p>Interestingly, we learned that many companies that start on a PaaS are fully aware that they’ll have to migrate to one of the big three public clouds [1] at some point. Yet they choose to deploy on a PaaS anyway because outgrowing a cloud platform is a “champagne problem” when you’re focused on getting something off the ground. This, however, becomes a very tangible problem when you need to migrate your entire production infrastructure while serving many users at scale. It’s a “nice problem to have”, until it isn’t.<p>We’ve built Porter Cloud so that the next generation of startups can get off the ground as quickly as possible, with a peace of mind that you can effortlessly move to one of the tried and true hyperscalers when you are ready to scale.<p>We are excited to see what people build on Porter Cloud. If you’ve ever dealt with a migration from a PaaS to one of the big three cloud providers, we’d also love to hear about your experience in the comments. Looking forward to feedback and discussion!<p>[1] By “big three clouds” we mean the lower-level primitives of each cloud provider. We don’t mean their higher level offerings like AWS App Runner, Google Cloud Run, or Azure App Service, since those run into the same PaaS problems described above.
Show HN: HackerNews but for research papers
Hey guys, I love HN! I wanted to extend the same aesthetic and community towards things beyond tech-related news.<p>I thought it would be cool to get the same quality of community gathered around the latest and greatest research coming out.<p>Let me know what you guys think of what I have so far. It's still early so there are probably bugs and other quality issues.<p>If there's any features missing that you'd want let me know.<p>ALSO, if any of you are familiar with the map of the territory of any particular field, please let me know! Would love to pick your brain and to come up with a 'most important papers' section for each field.<p>Thank you!!<p>-stefan
Show HN: HackerNews but for research papers
Hey guys, I love HN! I wanted to extend the same aesthetic and community towards things beyond tech-related news.<p>I thought it would be cool to get the same quality of community gathered around the latest and greatest research coming out.<p>Let me know what you guys think of what I have so far. It's still early so there are probably bugs and other quality issues.<p>If there's any features missing that you'd want let me know.<p>ALSO, if any of you are familiar with the map of the territory of any particular field, please let me know! Would love to pick your brain and to come up with a 'most important papers' section for each field.<p>Thank you!!<p>-stefan
Show HN: Route your prompts to the best LLM
Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think!<p>Here is a quick(ish) screen-recroding explaining how it works: <a href="https://youtu.be/ZpY6SIkBosE" rel="nofollow">https://youtu.be/ZpY6SIkBosE</a><p>Best results when training a custom router on your own prompt data: <a href="https://youtu.be/9JYqNbIEac0" rel="nofollow">https://youtu.be/9JYqNbIEac0</a><p>The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost.<p>The quality for each candidate LLM is predicted ahead of time using a neural scoring function, which is a BERT-like architecture conditioned on the prompt and a latent representation of the LLM being scored. The different LLMs are queried across the batch dimension, with the neural scoring architecture taking a single latent representation of the LLM as input per forward pass. This makes the scoring function very modular to query for different LLM combinations. It is trained in a supervised manner on several open LLM datasets, using GPT4 as a judge. The cost and speed data is taken from our live benchmarks, updated every few hours across all continents. The final "loss function" is a linear combination of quality, cost, inter-token-latency and time-to-first-token, with the user effectively scaling the weighting factors of this linear combination.<p>Smaller LLMs are often good enough for simple prompts, but knowing exactly how and when they might break is difficult. Simple perturbations of the phrasing can cause smaller LLMs to fail catastrophically, making them hard to rely on. For example, Gemma-7B converts numbers to strings and returns the "largest" string when asking for the "largest" number in a set, but works fine when asking for the "highest" or "maximum".<p>The router is able to learn these quirky distributions, and ensure that the smaller, cheaper and faster LLMs are only used when there is high confidence that they will get the answer correct.<p>Pricing-wise, we charge the same rates as the backend providers we route to, without taking any margins. We also give $50 in free credits to all new signups.<p>The router can be used off-the-shelf, or it can be trained directly on your own data for improved performance.<p>What do people think? Could this be useful?<p>Feedback of all kinds is welcome!
Show HN: Openpanel – An open-source alternative to Mixpanel
I have created an open-source alternative to Mixpanel and will explain a bit about why I decided to do this.<p>Mixpanel is a GREAT tool and quite easy to understand (compared to GA4 and similar). I have used Mixpanel extensively for one of my React Native apps, but the last invoice was $300, which was way over my budget. I think I was paying for MTU (monthly tracked users), which was around 7000-10k users.<p>However, a downside of Mixpanel is that it is purely a product analytics tool; you don't get any basic web analytics similar to what GA4 or Plausible offers.<p>Therefore, I have combined the best features of Mixpanel and Plausible to create what I believe is the ultimate experience in an analytics tool (product and web).<p>The focus has always been: it should be easy yet also powerful. This has been a challenging balance, but I think I have managed to keep it somewhat simple.<p>Key Features:
- Privacy-first
- Visualize your events like Mixpanel
- Plausible-like overview
- Self-hostable
- Better support for React Native than Plausible
- Real-time (no delays for events)
Ability to access all individual events and sessions<p>It's currently in beta and completely free during the beta period.<p>Give it a spin: <a href="https://openpanel.dev" rel="nofollow">https://openpanel.dev</a>
Show HN: I built a game to help you learn neural network architectures
Show HN: Pls Fix – Hire big tech employees to appeal account suspensions
I used to work for Facebook and Google and constantly got asked questions like "Hey, my Instagram account got blocked for no reason. Could you help me get it back?". I'd say yes, it would take me 10 min to fill out an internal form and 1 week later the account was back.<p>Even years after leaving, I still get these requests. So I built a marketplace for them. Let me know what you think!
Show HN: Peanut Butter Spinner
Show HN: Peanut Butter Spinner