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Show HN: Dataherald AI – Natural Language to SQL Engine

Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (<a href="https://github.com/Dataherald/dataherald">https://github.com/Dataherald/dataherald</a>). This engine allows you to set up an API from your structured database that can answer questions in plain English.<p>GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:<p>select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”<p>This pulls data for Los Angeles, CA and Los Angeles, TX without getting columns to differentiate between the two. You can read more about the challenges of enterprise-level text-to-SQL in this blog post I wrote on the topic: <a href="https://medium.com/dataherald/why-enterprise-natural-language-to-sql-is-hard-8849414f41c" rel="nofollow noreferrer">https://medium.com/dataherald/why-enterprise-natural-languag...</a><p>Dataherald comes with “batteries-included.” It has best-in-class implementations of core components, including, but not limited to: a state of the art NL-to-SQL agent, an LLM-based SQL-accuracy evaluator. The architecture is modular, allowing these components to be easily replaced. It’s easy to set up and use with major data warehouses.<p>There is a “Context Store” where information (NL2SQL examples, schemas and table descriptions) is used for the LLM prompts to make the engine get better with usage. And we even made it fast!<p>This version allows you to easily connect to PG, Databricks, BigQuery or Snowflake and set up an API for semantic interactions with your structured data. You can then add business and data context that are used for few-shot prompting by the engine.<p>The NL-to-SQL agent in this open source release was developed by our own Mohammadreza Pourreza, whose DIN-SQL algorithm is currently top of the Spider (<a href="https://yale-lily.github.io/spider" rel="nofollow noreferrer">https://yale-lily.github.io/spider</a>) and Bird (<a href="https://bird-bench.github.io/" rel="nofollow noreferrer">https://bird-bench.github.io/</a>) NL 2 SQL benchmarks. This agent has outperformed the Langchain SQLAgent anywhere from 12%-250%.5x (depending on the provided context) in our own internal benchmarking while being only ~15s slower on average.<p>Needless to say, this is an early release and the codebase is under swift development. We would love for you to try it out and give us your feedback! And if you are interested in contributing, we’d love to hear from you!

Show HN: Dataherald AI – Natural Language to SQL Engine

Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (<a href="https://github.com/Dataherald/dataherald">https://github.com/Dataherald/dataherald</a>). This engine allows you to set up an API from your structured database that can answer questions in plain English.<p>GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:<p>select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”<p>This pulls data for Los Angeles, CA and Los Angeles, TX without getting columns to differentiate between the two. You can read more about the challenges of enterprise-level text-to-SQL in this blog post I wrote on the topic: <a href="https://medium.com/dataherald/why-enterprise-natural-language-to-sql-is-hard-8849414f41c" rel="nofollow noreferrer">https://medium.com/dataherald/why-enterprise-natural-languag...</a><p>Dataherald comes with “batteries-included.” It has best-in-class implementations of core components, including, but not limited to: a state of the art NL-to-SQL agent, an LLM-based SQL-accuracy evaluator. The architecture is modular, allowing these components to be easily replaced. It’s easy to set up and use with major data warehouses.<p>There is a “Context Store” where information (NL2SQL examples, schemas and table descriptions) is used for the LLM prompts to make the engine get better with usage. And we even made it fast!<p>This version allows you to easily connect to PG, Databricks, BigQuery or Snowflake and set up an API for semantic interactions with your structured data. You can then add business and data context that are used for few-shot prompting by the engine.<p>The NL-to-SQL agent in this open source release was developed by our own Mohammadreza Pourreza, whose DIN-SQL algorithm is currently top of the Spider (<a href="https://yale-lily.github.io/spider" rel="nofollow noreferrer">https://yale-lily.github.io/spider</a>) and Bird (<a href="https://bird-bench.github.io/" rel="nofollow noreferrer">https://bird-bench.github.io/</a>) NL 2 SQL benchmarks. This agent has outperformed the Langchain SQLAgent anywhere from 12%-250%.5x (depending on the provided context) in our own internal benchmarking while being only ~15s slower on average.<p>Needless to say, this is an early release and the codebase is under swift development. We would love for you to try it out and give us your feedback! And if you are interested in contributing, we’d love to hear from you!

Show HN: Dataherald AI – Natural Language to SQL Engine

Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (<a href="https://github.com/Dataherald/dataherald">https://github.com/Dataherald/dataherald</a>). This engine allows you to set up an API from your structured database that can answer questions in plain English.<p>GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:<p>select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”<p>This pulls data for Los Angeles, CA and Los Angeles, TX without getting columns to differentiate between the two. You can read more about the challenges of enterprise-level text-to-SQL in this blog post I wrote on the topic: <a href="https://medium.com/dataherald/why-enterprise-natural-language-to-sql-is-hard-8849414f41c" rel="nofollow noreferrer">https://medium.com/dataherald/why-enterprise-natural-languag...</a><p>Dataherald comes with “batteries-included.” It has best-in-class implementations of core components, including, but not limited to: a state of the art NL-to-SQL agent, an LLM-based SQL-accuracy evaluator. The architecture is modular, allowing these components to be easily replaced. It’s easy to set up and use with major data warehouses.<p>There is a “Context Store” where information (NL2SQL examples, schemas and table descriptions) is used for the LLM prompts to make the engine get better with usage. And we even made it fast!<p>This version allows you to easily connect to PG, Databricks, BigQuery or Snowflake and set up an API for semantic interactions with your structured data. You can then add business and data context that are used for few-shot prompting by the engine.<p>The NL-to-SQL agent in this open source release was developed by our own Mohammadreza Pourreza, whose DIN-SQL algorithm is currently top of the Spider (<a href="https://yale-lily.github.io/spider" rel="nofollow noreferrer">https://yale-lily.github.io/spider</a>) and Bird (<a href="https://bird-bench.github.io/" rel="nofollow noreferrer">https://bird-bench.github.io/</a>) NL 2 SQL benchmarks. This agent has outperformed the Langchain SQLAgent anywhere from 12%-250%.5x (depending on the provided context) in our own internal benchmarking while being only ~15s slower on average.<p>Needless to say, this is an early release and the codebase is under swift development. We would love for you to try it out and give us your feedback! And if you are interested in contributing, we’d love to hear from you!

Show HN: Dataherald AI – Natural Language to SQL Engine

Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (<a href="https://github.com/Dataherald/dataherald">https://github.com/Dataherald/dataherald</a>). This engine allows you to set up an API from your structured database that can answer questions in plain English.<p>GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:<p>select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”<p>This pulls data for Los Angeles, CA and Los Angeles, TX without getting columns to differentiate between the two. You can read more about the challenges of enterprise-level text-to-SQL in this blog post I wrote on the topic: <a href="https://medium.com/dataherald/why-enterprise-natural-language-to-sql-is-hard-8849414f41c" rel="nofollow noreferrer">https://medium.com/dataherald/why-enterprise-natural-languag...</a><p>Dataherald comes with “batteries-included.” It has best-in-class implementations of core components, including, but not limited to: a state of the art NL-to-SQL agent, an LLM-based SQL-accuracy evaluator. The architecture is modular, allowing these components to be easily replaced. It’s easy to set up and use with major data warehouses.<p>There is a “Context Store” where information (NL2SQL examples, schemas and table descriptions) is used for the LLM prompts to make the engine get better with usage. And we even made it fast!<p>This version allows you to easily connect to PG, Databricks, BigQuery or Snowflake and set up an API for semantic interactions with your structured data. You can then add business and data context that are used for few-shot prompting by the engine.<p>The NL-to-SQL agent in this open source release was developed by our own Mohammadreza Pourreza, whose DIN-SQL algorithm is currently top of the Spider (<a href="https://yale-lily.github.io/spider" rel="nofollow noreferrer">https://yale-lily.github.io/spider</a>) and Bird (<a href="https://bird-bench.github.io/" rel="nofollow noreferrer">https://bird-bench.github.io/</a>) NL 2 SQL benchmarks. This agent has outperformed the Langchain SQLAgent anywhere from 12%-250%.5x (depending on the provided context) in our own internal benchmarking while being only ~15s slower on average.<p>Needless to say, this is an early release and the codebase is under swift development. We would love for you to try it out and give us your feedback! And if you are interested in contributing, we’d love to hear from you!

Show HN: Dataherald AI – Natural Language to SQL Engine

Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (<a href="https://github.com/Dataherald/dataherald">https://github.com/Dataherald/dataherald</a>). This engine allows you to set up an API from your structured database that can answer questions in plain English.<p>GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:<p>select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”<p>This pulls data for Los Angeles, CA and Los Angeles, TX without getting columns to differentiate between the two. You can read more about the challenges of enterprise-level text-to-SQL in this blog post I wrote on the topic: <a href="https://medium.com/dataherald/why-enterprise-natural-language-to-sql-is-hard-8849414f41c" rel="nofollow noreferrer">https://medium.com/dataherald/why-enterprise-natural-languag...</a><p>Dataherald comes with “batteries-included.” It has best-in-class implementations of core components, including, but not limited to: a state of the art NL-to-SQL agent, an LLM-based SQL-accuracy evaluator. The architecture is modular, allowing these components to be easily replaced. It’s easy to set up and use with major data warehouses.<p>There is a “Context Store” where information (NL2SQL examples, schemas and table descriptions) is used for the LLM prompts to make the engine get better with usage. And we even made it fast!<p>This version allows you to easily connect to PG, Databricks, BigQuery or Snowflake and set up an API for semantic interactions with your structured data. You can then add business and data context that are used for few-shot prompting by the engine.<p>The NL-to-SQL agent in this open source release was developed by our own Mohammadreza Pourreza, whose DIN-SQL algorithm is currently top of the Spider (<a href="https://yale-lily.github.io/spider" rel="nofollow noreferrer">https://yale-lily.github.io/spider</a>) and Bird (<a href="https://bird-bench.github.io/" rel="nofollow noreferrer">https://bird-bench.github.io/</a>) NL 2 SQL benchmarks. This agent has outperformed the Langchain SQLAgent anywhere from 12%-250%.5x (depending on the provided context) in our own internal benchmarking while being only ~15s slower on average.<p>Needless to say, this is an early release and the codebase is under swift development. We would love for you to try it out and give us your feedback! And if you are interested in contributing, we’d love to hear from you!

Show HN: Cosmic Media – Search millions of stock photos and videos

Hello HN,<p>I’m Tony, the CEO of Cosmic (https://www.cosmicjs.com), we provide a headless CMS and API toolkit to create and deliver content to websites and apps. Today, we are releasing Cosmic Media which enables you to search millions of high-quality, royalty-free, stock photos, videos, and vectors from popular online media services: Unsplash, Pexels, Giphy, and Pixabay from one convenient interface. It also includes AI-generated images from OpenAI. Check it out here: https://cosmicmedia.vercel.app<p>We built it to solve our own need to consolidate our existing media extensions, which were individual media extensions using the Unsplash API and Pexels Video API, and we thought, "why not combine them into one"? Rather than search from different stock media websites, seems like it would be nice to aggregate it into one interface. Then we sort of thought about what else might someone want for adding media to their content, so we added DALL-E AI image generation. We've been using it internally and find that it's saved us some time when searching for media to add to our blog posts.<p>We are offering it as both a stand-alone open source tool and as a Cosmic extension which can be added to your projects for easy access during content creation from the Cosmic dashboard. Check out the code and feel free to customize and extend it to suit your needs: https://github.com/cosmicjs/cosmic-media-extension<p>Let me know what you think in the comments.<p>- Tony

Show HN: Cosmic Media – Search millions of stock photos and videos

Hello HN,<p>I’m Tony, the CEO of Cosmic (https://www.cosmicjs.com), we provide a headless CMS and API toolkit to create and deliver content to websites and apps. Today, we are releasing Cosmic Media which enables you to search millions of high-quality, royalty-free, stock photos, videos, and vectors from popular online media services: Unsplash, Pexels, Giphy, and Pixabay from one convenient interface. It also includes AI-generated images from OpenAI. Check it out here: https://cosmicmedia.vercel.app<p>We built it to solve our own need to consolidate our existing media extensions, which were individual media extensions using the Unsplash API and Pexels Video API, and we thought, "why not combine them into one"? Rather than search from different stock media websites, seems like it would be nice to aggregate it into one interface. Then we sort of thought about what else might someone want for adding media to their content, so we added DALL-E AI image generation. We've been using it internally and find that it's saved us some time when searching for media to add to our blog posts.<p>We are offering it as both a stand-alone open source tool and as a Cosmic extension which can be added to your projects for easy access during content creation from the Cosmic dashboard. Check out the code and feel free to customize and extend it to suit your needs: https://github.com/cosmicjs/cosmic-media-extension<p>Let me know what you think in the comments.<p>- Tony

Show HN: Points and Miles Database

Show HN: Points and Miles Database

Show HN: OpenCopilot – Build and embed open-source AI copilots into your product

Hey HN<p>OpenCopilot is an OSS framework which helps devs to build open-source AI Copilots that actually work and embed into their product with ease.<p>Why another LLM framework?<p>Twitter is full of impressive LLM applications but once you peel off the curtains it’s clear that they are just demos. The reason being because building an AI Copilot that goes beyond a Twitter demo can be complex, time-consuming and unreliable.<p>Our team has been in the AI space since 2018 and built numerous LLM apps & copilots. While doing that, we got approached by many startups saying they’d also like to build a copilot for their product but they haven’t been able to get it reliable, fast or cost-effective enough for production use. Thus we built OpenCopilot framework, so devs can intuitively get AI Copilots running in less than 10 minutes and iterate towards a useful Copilot in a single day.<p>We believe every product, company and individual will have their Copilot in the future. Thus, we’d love your feedback, questions and constructive criticism.

Show HN: OpenCopilot – Build and embed open-source AI copilots into your product

Hey HN<p>OpenCopilot is an OSS framework which helps devs to build open-source AI Copilots that actually work and embed into their product with ease.<p>Why another LLM framework?<p>Twitter is full of impressive LLM applications but once you peel off the curtains it’s clear that they are just demos. The reason being because building an AI Copilot that goes beyond a Twitter demo can be complex, time-consuming and unreliable.<p>Our team has been in the AI space since 2018 and built numerous LLM apps & copilots. While doing that, we got approached by many startups saying they’d also like to build a copilot for their product but they haven’t been able to get it reliable, fast or cost-effective enough for production use. Thus we built OpenCopilot framework, so devs can intuitively get AI Copilots running in less than 10 minutes and iterate towards a useful Copilot in a single day.<p>We believe every product, company and individual will have their Copilot in the future. Thus, we’d love your feedback, questions and constructive criticism.

Show HN: FlakeHub – Discover and publish Nix flakes

Show HN: FlakeHub – Discover and publish Nix flakes

Show HN: superwhisper – AI powered offline voice to text for macOS

Hey HN,<p>I built superwhisper out of frustration with the native dictation capabilities of macOS. Inaccurate, required manual punctuation, didnt activate in some contexts or would have audio capture issues.<p>I wanted a replacement that worked offline, had cross language support, was configurable and worked in any application.<p>Under the hood the app is using whisper.cpp, which runs really well on the Apple Silicon chips.<p>You can use the base and standard size models for free, larger models sizes and languages other than english are paid.<p>Let me know what you think! For context, I launched this just one month ago and have been rapidly adding features and making fixes.<p>If you want to follow along with development, I post release info on twitter (<a href="https://x.com/superwhisperapp" rel="nofollow noreferrer">https://x.com/superwhisperapp</a>) or you can subscribe to emails via the form on the website (very bottom).

Show HN: My husband quit his job to build a new social audio app with Flutter

Wife of an app-builder here.<p>My husband quit his full time job at Apple to work full-time last year on a new social audio app where you are anonymous and there are no videos or images - people can only connect with their voice.<p>The app has been up for just less than 2 months, and it was incredible to see 300 users from all around the world leave voice messages of support for each other.<p>It definitely restored my faith in humanity, so much so that I decided to jump on board as a co-founder :)<p>We are on a mission to end loneliness that is gripping our world today, and we hope you also come onboard to get or offer support, or even just to make authentic connections with people around the world.

Show HN: VisionScript, abstract programming language for computer vision

Hello! I'm James and I am working on VisionScript. With VisionScript, I want to empower people -- including everyone without any prior programming experience -- to build cool apps with vision.<p>This weekend, I recorded a demo for VisionScript, in which I made apps that count how many cats are in an image and hides people in a video. Each app was < 10 lines of code.<p><a href="https://vimeo.com/856043804" rel="nofollow noreferrer">https://vimeo.com/856043804</a><p>VisionScript is built for the 10 year old inside of me who would have loved more visual programming languages with which to play. I want to show people the potential of programming and how you can make what you want with computers, whether it be a game that counts cats or an app that monitors how many birds flew past a tree. Those "wow" moments should come as soon as possible in one's learning experience.<p>VisionScript is in active development. I started work on this project in July. There will likely be bugs; this is a passion project. Inspiration comes from Wolfram and Python. Behind the scenes, I am adopting what I am calling "lexical inference", which is to say there is a last state value on which functions infer; the language manages types and state.

Show HN: VisionScript, abstract programming language for computer vision

Hello! I'm James and I am working on VisionScript. With VisionScript, I want to empower people -- including everyone without any prior programming experience -- to build cool apps with vision.<p>This weekend, I recorded a demo for VisionScript, in which I made apps that count how many cats are in an image and hides people in a video. Each app was < 10 lines of code.<p><a href="https://vimeo.com/856043804" rel="nofollow noreferrer">https://vimeo.com/856043804</a><p>VisionScript is built for the 10 year old inside of me who would have loved more visual programming languages with which to play. I want to show people the potential of programming and how you can make what you want with computers, whether it be a game that counts cats or an app that monitors how many birds flew past a tree. Those "wow" moments should come as soon as possible in one's learning experience.<p>VisionScript is in active development. I started work on this project in July. There will likely be bugs; this is a passion project. Inspiration comes from Wolfram and Python. Behind the scenes, I am adopting what I am calling "lexical inference", which is to say there is a last state value on which functions infer; the language manages types and state.

Show HN: The Uncolouring Book

Show HN: The Uncolouring Book

Show HN: The Uncolouring Book