The best Hacker News stories from Show from the past day
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Show HN: I built a database GUI with ChatGPT integration
Hey there! I’ve been working on DB Pilot for the last couple of months, and I recently added an AI assistant powered by GPT 3.5 to help you write SQL queries tailored to your DB schema.<p>Simply ask what data you are looking for - GPT will figure out which tables to use, how to join them, and then write a query for you.<p>The AI assistant knows which tables and columns exist in your database, meaning it can write queries specific to your schema.<p>Besides that, it doesn't have access to any actual data from your database though, meaning your data doesn't get exposed to OpenAI.
Show HN: I built a database GUI with ChatGPT integration
Hey there! I’ve been working on DB Pilot for the last couple of months, and I recently added an AI assistant powered by GPT 3.5 to help you write SQL queries tailored to your DB schema.<p>Simply ask what data you are looking for - GPT will figure out which tables to use, how to join them, and then write a query for you.<p>The AI assistant knows which tables and columns exist in your database, meaning it can write queries specific to your schema.<p>Besides that, it doesn't have access to any actual data from your database though, meaning your data doesn't get exposed to OpenAI.
Show HN: I built a database GUI with ChatGPT integration
Hey there! I’ve been working on DB Pilot for the last couple of months, and I recently added an AI assistant powered by GPT 3.5 to help you write SQL queries tailored to your DB schema.<p>Simply ask what data you are looking for - GPT will figure out which tables to use, how to join them, and then write a query for you.<p>The AI assistant knows which tables and columns exist in your database, meaning it can write queries specific to your schema.<p>Besides that, it doesn't have access to any actual data from your database though, meaning your data doesn't get exposed to OpenAI.
Show HN: I built a database GUI with ChatGPT integration
Hey there! I’ve been working on DB Pilot for the last couple of months, and I recently added an AI assistant powered by GPT 3.5 to help you write SQL queries tailored to your DB schema.<p>Simply ask what data you are looking for - GPT will figure out which tables to use, how to join them, and then write a query for you.<p>The AI assistant knows which tables and columns exist in your database, meaning it can write queries specific to your schema.<p>Besides that, it doesn't have access to any actual data from your database though, meaning your data doesn't get exposed to OpenAI.
Show HN: PySaaS – Python SaaS starter kit
Hi HN,<p>Recently, I’ve noticed there’s a decently high barrier to entry in developing competitive, full-stack SaaS applications.<p>Beside the standard, boring features that take months to implement, you typically have to know several languages and frameworks, and be familiar with fancy frontend styling classes.<p>I’m working hard right now to solve this problem by building PySaaS- The 100% pure Python SaaS starter kit.<p>PySaaS is a boilerplate Python codebase that takes care of the fundamental components standard to all SaaS applications.<p>The codebase uses the Pynecone web framework to compile your frontend into a NextJS app, so you never have to touch any HTML, CSS, or Javascript. Pynecone is easy to learn, yet fully flexible and powerful enough for advanced use cases. We implement out-of-the-box functionality for secure Firebase user authentication, Lemon Squeezy subscription management (MoR removes a major tax headache), Notion as a headless blog CMS, and more.<p>Our mission is to help developers and founders save months of development time and focus on building unique features, which will in turn provide more opportunities to generate revenue and give value to customers.<p>And easily do it in pure Python! Frontend. Backend. All in Python.<p>To check out the live demo for free, click the link and then the “See Demo” button.<p>Let me know what you think.
Show HN: PySaaS – Python SaaS starter kit
Hi HN,<p>Recently, I’ve noticed there’s a decently high barrier to entry in developing competitive, full-stack SaaS applications.<p>Beside the standard, boring features that take months to implement, you typically have to know several languages and frameworks, and be familiar with fancy frontend styling classes.<p>I’m working hard right now to solve this problem by building PySaaS- The 100% pure Python SaaS starter kit.<p>PySaaS is a boilerplate Python codebase that takes care of the fundamental components standard to all SaaS applications.<p>The codebase uses the Pynecone web framework to compile your frontend into a NextJS app, so you never have to touch any HTML, CSS, or Javascript. Pynecone is easy to learn, yet fully flexible and powerful enough for advanced use cases. We implement out-of-the-box functionality for secure Firebase user authentication, Lemon Squeezy subscription management (MoR removes a major tax headache), Notion as a headless blog CMS, and more.<p>Our mission is to help developers and founders save months of development time and focus on building unique features, which will in turn provide more opportunities to generate revenue and give value to customers.<p>And easily do it in pure Python! Frontend. Backend. All in Python.<p>To check out the live demo for free, click the link and then the “See Demo” button.<p>Let me know what you think.
Show HN: PySaaS – Python SaaS starter kit
Hi HN,<p>Recently, I’ve noticed there’s a decently high barrier to entry in developing competitive, full-stack SaaS applications.<p>Beside the standard, boring features that take months to implement, you typically have to know several languages and frameworks, and be familiar with fancy frontend styling classes.<p>I’m working hard right now to solve this problem by building PySaaS- The 100% pure Python SaaS starter kit.<p>PySaaS is a boilerplate Python codebase that takes care of the fundamental components standard to all SaaS applications.<p>The codebase uses the Pynecone web framework to compile your frontend into a NextJS app, so you never have to touch any HTML, CSS, or Javascript. Pynecone is easy to learn, yet fully flexible and powerful enough for advanced use cases. We implement out-of-the-box functionality for secure Firebase user authentication, Lemon Squeezy subscription management (MoR removes a major tax headache), Notion as a headless blog CMS, and more.<p>Our mission is to help developers and founders save months of development time and focus on building unique features, which will in turn provide more opportunities to generate revenue and give value to customers.<p>And easily do it in pure Python! Frontend. Backend. All in Python.<p>To check out the live demo for free, click the link and then the “See Demo” button.<p>Let me know what you think.
Show HN: EVA – AI-Relational Database System
Hi friends,<p>We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.<p>The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:<p>+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.<p>+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.<p>Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.<p>We are constantly working on improving EVA and would love to hear your feedback!
Show HN: EVA – AI-Relational Database System
Hi friends,<p>We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.<p>The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:<p>+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.<p>+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.<p>Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.<p>We are constantly working on improving EVA and would love to hear your feedback!
Show HN: EVA – AI-Relational Database System
Hi friends,<p>We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.<p>The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:<p>+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.<p>+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.<p>Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.<p>We are constantly working on improving EVA and would love to hear your feedback!
Show HN: EVA – AI-Relational Database System
Hi friends,<p>We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.<p>The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:<p>+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.<p>+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.<p>Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.<p>We are constantly working on improving EVA and would love to hear your feedback!
Show HN: Frogmouth – A Markdown browser for the terminal
Hi HN,<p>Frogmouth is a TUI to display Markdown files. It does a passable job of displaying Markdown, with code blocks and tables. No image support as yet.<p>It's very browser like, with navigation stack, history, and bookmarks. Works with both the mouse and keyboard.<p>There are shortcuts for viewing README.md files and other Markdown on GitHub and GitLab.<p>License is MIT.<p>Let me know what you think...
Show HN: Frogmouth – A Markdown browser for the terminal
Hi HN,<p>Frogmouth is a TUI to display Markdown files. It does a passable job of displaying Markdown, with code blocks and tables. No image support as yet.<p>It's very browser like, with navigation stack, history, and bookmarks. Works with both the mouse and keyboard.<p>There are shortcuts for viewing README.md files and other Markdown on GitHub and GitLab.<p>License is MIT.<p>Let me know what you think...
Show HN: Frogmouth – A Markdown browser for the terminal
Hi HN,<p>Frogmouth is a TUI to display Markdown files. It does a passable job of displaying Markdown, with code blocks and tables. No image support as yet.<p>It's very browser like, with navigation stack, history, and bookmarks. Works with both the mouse and keyboard.<p>There are shortcuts for viewing README.md files and other Markdown on GitHub and GitLab.<p>License is MIT.<p>Let me know what you think...
Show HN: Frogmouth – A Markdown browser for the terminal
Hi HN,<p>Frogmouth is a TUI to display Markdown files. It does a passable job of displaying Markdown, with code blocks and tables. No image support as yet.<p>It's very browser like, with navigation stack, history, and bookmarks. Works with both the mouse and keyboard.<p>There are shortcuts for viewing README.md files and other Markdown on GitHub and GitLab.<p>License is MIT.<p>Let me know what you think...
Show HN: Jarvis AI – Text, iMessage, and Email ChatGPT
We're back with another ShowHN!<p>When we launched, you could text Jarvis AI using regular SMS messages from your phone.<p>Since launching 3 weeks ago[1], we've introduced two new channels for using ChatGPT.<p><i>Now you can use ChatGPT over SMS text message, iMessage for Apple-enabled devices, and via Email.</i><p>It is super interesting to forward an email to Jarvis AI and see its thoughts on your email thread. It can help you brainstorm or it can suggest a reply to previous emails.<p>10 messages free forever, more volume is free over iMessage and Email for a limited time (until the HN hug of death!). You don't have to sign up or anything to try it. Just send a text to: +1 (855) 676-1two89.<p>Thanks for checking this out. Many exciting features are coming soon to make this more than just a different interface to ChatGPT. We want to make AI accessible to more people, over more channels, with more useful things for your day-to-day.<p>For medical professionals, we added the /diagnose command. Ask Jarvis "/diagnose 32 year old harsh cough" for example.<p>Soon, we are adding features like /invoice for generating invoices and /remember for notes and reminders.<p>[1] Related: See the first HN post when we launched here: <a href="https://news.ycombinator.com/item?id=35466231" rel="nofollow">https://news.ycombinator.com/item?id=35466231</a>
Show HN: All GitHub repos shared on HN
Show HN: A Gentle Introduction to the Fediverse
There's a lot of great explainer sites and articles about the fediverse, but I wanted to make something that serves as a quick introduction for a more casual audience, and lets you dig deeper from there.
Show HN: Pledge Your Human-Made Content
AI generated content is on the rise. How can you be sure what you're reading is 100% human?
Show HN: Pledge Your Human-Made Content
AI generated content is on the rise. How can you be sure what you're reading is 100% human?