The best Hacker News stories from All from the past day
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Hurl, the Exceptional Language
The t-test was invented at the Guinness brewery
To the brain, reading computer code is not the same as reading language (2020)
How Home Assistant is being used to protect from missile and drone attacks
What the damaged Svalbard cable looked like
What the damaged Svalbard cable looked like
Google Meet rolls out multi-device adaptive audio merging
Helen Keller on her life before self-consciousness (1908)
Cloudflare took down our website
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>
Abusing Go's Infrastructure
Abusing Go's Infrastructure
Tmux is worse-is-better
Voxel Displacement Renderer – Modernizing the Retro 3D Aesthetic
The hikikomori in Asia: A life within four walls
Google scrambles to manually remove weird AI answers in search
Mp3tag – Universal Tag Editor
Mp3tag – Universal Tag Editor
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?