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Show HN: Serverless collaborative notion-level note editor using CRDT in GO

Show HN: Serverless collaborative notion-level note editor using CRDT in GO

Show HN: I made a better Perplexity for developers

Hi HN,<p>I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (<a href="https://devv.ai" rel="nofollow">https://devv.ai</a>). In simple terms, it is an AI-powered search engine specifically designed for developers.<p>Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one?<p>We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines (like Google/Bing APIs), but we've taken a different approach.<p>We've created a vertical search index focused on the development domain, which includes:<p>- Documents: These are essentially the single source of truth for programming languages or libraries; I believe many of you are users of Dash (<a href="https://kapeli.com/dash" rel="nofollow">https://kapeli.com/dash</a>) or devdocs (<a href="https://devdocs.io/" rel="nofollow">https://devdocs.io/</a>).<p>- Code: While not natural language, code contains rich contextual information. If you have a question related to the Django framework, nothing is more convincing than code snippets from Django's repository.<p>- Web Search: We still use data from search engines because these results contain additional contextual information.<p>Our reasons for doing this include:<p>- The quality of the index is crucial to the RAG system; its effectiveness determines the output quality of the entire system.<p>- We focus more on the Index (RAG) rather than LLMs because LLMs evolve rapidly; even models performing well today may be superseded by better ones in a few months, and fine-tuning an LLM now has relatively low costs.<p>- All players are currently exploring what kind of LLM product works best; we hope to contribute some different insights ourselves (and plan to open source parts of our underlying infrastructure in return for contributions back into open source communities).<p>Some brief product features:<p>- Three modes: - Fast mode: Offers quick answers within seconds. - Agent mode: For complex queries where Devv Agent infers your question before selecting appropriate solutions. - GitHub mode(currently in beta): Links directly with your own GitHub repositories allowing inquiries about specific codebases.<p>- Clean & intuitive UI/UX design.<p>- Currently only available as web version but Chrome extension & VSCode plugin planned soon!<p>Technical details regarding how we build our Index:<p>- Documents section involves crawling most documentation sources using scripts inspired by devdocs project’s crawler logic then slicing them up according function/symbol dimensions before embedding into vector databases;<p>- Codes require special treatment beyond just embeddings alone hence why custom parsers were developed per language type extracting logical structures within repos such as architectural layouts calling relationships between functions definitions etc., semantically processed via LMM;<p>- Web searches combine both selfmade indices targeting developer niches alongside traditional API based methods. We crawled relevant sites including blogs forums tech news outlets etc..<p>For the Agent Mode, we have actually developed a multi-agent framework. It first categorizes the user's query and then selects different agents based on these categories to address the issues. These various agents employ different models and solution steps.<p>Future Plans:<p>- Build a more comprehensive index that includes internal context (The Devv for Teams version will support indexing team repositories, documents, issue trackers for Q&A)<p>- Fully localized: All of the above technologies can be executed locally, ensuring privacy and security through complete localization.<p>Devv is still in its very early stages and can be used without logging in. We welcome everyone to experience it and provide feedback on any issues; we will continue to iterate on it.<p>[1]: <a href="https://arxiv.org/abs/2005.11401" rel="nofollow">https://arxiv.org/abs/2005.11401</a>

Show HN: I made a better Perplexity for developers

Hi HN,<p>I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (<a href="https://devv.ai" rel="nofollow">https://devv.ai</a>). In simple terms, it is an AI-powered search engine specifically designed for developers.<p>Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one?<p>We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines (like Google/Bing APIs), but we've taken a different approach.<p>We've created a vertical search index focused on the development domain, which includes:<p>- Documents: These are essentially the single source of truth for programming languages or libraries; I believe many of you are users of Dash (<a href="https://kapeli.com/dash" rel="nofollow">https://kapeli.com/dash</a>) or devdocs (<a href="https://devdocs.io/" rel="nofollow">https://devdocs.io/</a>).<p>- Code: While not natural language, code contains rich contextual information. If you have a question related to the Django framework, nothing is more convincing than code snippets from Django's repository.<p>- Web Search: We still use data from search engines because these results contain additional contextual information.<p>Our reasons for doing this include:<p>- The quality of the index is crucial to the RAG system; its effectiveness determines the output quality of the entire system.<p>- We focus more on the Index (RAG) rather than LLMs because LLMs evolve rapidly; even models performing well today may be superseded by better ones in a few months, and fine-tuning an LLM now has relatively low costs.<p>- All players are currently exploring what kind of LLM product works best; we hope to contribute some different insights ourselves (and plan to open source parts of our underlying infrastructure in return for contributions back into open source communities).<p>Some brief product features:<p>- Three modes: - Fast mode: Offers quick answers within seconds. - Agent mode: For complex queries where Devv Agent infers your question before selecting appropriate solutions. - GitHub mode(currently in beta): Links directly with your own GitHub repositories allowing inquiries about specific codebases.<p>- Clean & intuitive UI/UX design.<p>- Currently only available as web version but Chrome extension & VSCode plugin planned soon!<p>Technical details regarding how we build our Index:<p>- Documents section involves crawling most documentation sources using scripts inspired by devdocs project’s crawler logic then slicing them up according function/symbol dimensions before embedding into vector databases;<p>- Codes require special treatment beyond just embeddings alone hence why custom parsers were developed per language type extracting logical structures within repos such as architectural layouts calling relationships between functions definitions etc., semantically processed via LMM;<p>- Web searches combine both selfmade indices targeting developer niches alongside traditional API based methods. We crawled relevant sites including blogs forums tech news outlets etc..<p>For the Agent Mode, we have actually developed a multi-agent framework. It first categorizes the user's query and then selects different agents based on these categories to address the issues. These various agents employ different models and solution steps.<p>Future Plans:<p>- Build a more comprehensive index that includes internal context (The Devv for Teams version will support indexing team repositories, documents, issue trackers for Q&A)<p>- Fully localized: All of the above technologies can be executed locally, ensuring privacy and security through complete localization.<p>Devv is still in its very early stages and can be used without logging in. We welcome everyone to experience it and provide feedback on any issues; we will continue to iterate on it.<p>[1]: <a href="https://arxiv.org/abs/2005.11401" rel="nofollow">https://arxiv.org/abs/2005.11401</a>

Show HN: AI climbing coach – visualize how to climb any route based on your body

I made SABR - an AI model that helps you visualize the beta/technique on any route, based on your body parameters. You can input a video of you climbing any route, in any orientation or lighting condition (it's truly versatile!). SABR then creates a virtual avatar of your body shape and uses it to climb the route you're climbing. Then, you can compare/contrast.<p>You can see the demo here: <a href="https://www.youtube.com/watch?v=cnvNPWoYZz4" rel="nofollow">https://www.youtube.com/watch?v=cnvNPWoYZz4</a><p>Will be open sourcing the model, backend, and frontend codebase soon!

Show HN: AI climbing coach – visualize how to climb any route based on your body

I made SABR - an AI model that helps you visualize the beta/technique on any route, based on your body parameters. You can input a video of you climbing any route, in any orientation or lighting condition (it's truly versatile!). SABR then creates a virtual avatar of your body shape and uses it to climb the route you're climbing. Then, you can compare/contrast.<p>You can see the demo here: <a href="https://www.youtube.com/watch?v=cnvNPWoYZz4" rel="nofollow">https://www.youtube.com/watch?v=cnvNPWoYZz4</a><p>Will be open sourcing the model, backend, and frontend codebase soon!

Show HN: AI climbing coach – visualize how to climb any route based on your body

I made SABR - an AI model that helps you visualize the beta/technique on any route, based on your body parameters. You can input a video of you climbing any route, in any orientation or lighting condition (it's truly versatile!). SABR then creates a virtual avatar of your body shape and uses it to climb the route you're climbing. Then, you can compare/contrast.<p>You can see the demo here: <a href="https://www.youtube.com/watch?v=cnvNPWoYZz4" rel="nofollow">https://www.youtube.com/watch?v=cnvNPWoYZz4</a><p>Will be open sourcing the model, backend, and frontend codebase soon!

Show HN: AI climbing coach – visualize how to climb any route based on your body

I made SABR - an AI model that helps you visualize the beta/technique on any route, based on your body parameters. You can input a video of you climbing any route, in any orientation or lighting condition (it's truly versatile!). SABR then creates a virtual avatar of your body shape and uses it to climb the route you're climbing. Then, you can compare/contrast.<p>You can see the demo here: <a href="https://www.youtube.com/watch?v=cnvNPWoYZz4" rel="nofollow">https://www.youtube.com/watch?v=cnvNPWoYZz4</a><p>Will be open sourcing the model, backend, and frontend codebase soon!

Show HN: I built a non-linear UI for ChatGPT

Hi HN,<p>I built this out of frustration of the evergrowing list of AI models and features to try and to fit my workflow.<p>The visual approach clicks for me so i went with it, it provides more freedom and control of the outcome, because predictable results and increased productivity is what I’m after when using conversational AI.<p>The app is packed with features, my most used are prompt library, voice input and text search, narration is useful too.<p>The app is local-first and works right in the browser, no sign up needed and it's absolutely free to try.<p>BYOAK – bring your own API Keys.<p>Let me know what you think, any feedback is appreciated!

Show HN: I built a non-linear UI for ChatGPT

Hi HN,<p>I built this out of frustration of the evergrowing list of AI models and features to try and to fit my workflow.<p>The visual approach clicks for me so i went with it, it provides more freedom and control of the outcome, because predictable results and increased productivity is what I’m after when using conversational AI.<p>The app is packed with features, my most used are prompt library, voice input and text search, narration is useful too.<p>The app is local-first and works right in the browser, no sign up needed and it's absolutely free to try.<p>BYOAK – bring your own API Keys.<p>Let me know what you think, any feedback is appreciated!

Show HN: I built a non-linear UI for ChatGPT

Hi HN,<p>I built this out of frustration of the evergrowing list of AI models and features to try and to fit my workflow.<p>The visual approach clicks for me so i went with it, it provides more freedom and control of the outcome, because predictable results and increased productivity is what I’m after when using conversational AI.<p>The app is packed with features, my most used are prompt library, voice input and text search, narration is useful too.<p>The app is local-first and works right in the browser, no sign up needed and it's absolutely free to try.<p>BYOAK – bring your own API Keys.<p>Let me know what you think, any feedback is appreciated!

Show HN: I built a non-linear UI for ChatGPT

Hi HN,<p>I built this out of frustration of the evergrowing list of AI models and features to try and to fit my workflow.<p>The visual approach clicks for me so i went with it, it provides more freedom and control of the outcome, because predictable results and increased productivity is what I’m after when using conversational AI.<p>The app is packed with features, my most used are prompt library, voice input and text search, narration is useful too.<p>The app is local-first and works right in the browser, no sign up needed and it's absolutely free to try.<p>BYOAK – bring your own API Keys.<p>Let me know what you think, any feedback is appreciated!

Show HN: I built a non-linear UI for ChatGPT

Hi HN,<p>I built this out of frustration of the evergrowing list of AI models and features to try and to fit my workflow.<p>The visual approach clicks for me so i went with it, it provides more freedom and control of the outcome, because predictable results and increased productivity is what I’m after when using conversational AI.<p>The app is packed with features, my most used are prompt library, voice input and text search, narration is useful too.<p>The app is local-first and works right in the browser, no sign up needed and it's absolutely free to try.<p>BYOAK – bring your own API Keys.<p>Let me know what you think, any feedback is appreciated!

Show HN: I built a Notion for science

I struggle with handwritten notes, but it's always been the primary way to write scientific notes. To do scientific writing on existing keyboard-and-mouse tools generally requires either significant knowledge of the software, the use of screenshotting/exporting from external tools, or (often) both.<p>That's why I made Stempad, which is meant to rival the speed of pen-and-paper for scientific writing. I've currently added 7 different scientific blocks (and several basic blocks too) and plan to continuously add more.<p>Questions are welcome, and preliminary feedback or constructive criticism is really appreciated!<p>(If you're on mobile or would prefer to see a demo, here is one: <a href="https://www.youtube.com/watch?v=U-9j8sFIkS4" rel="nofollow">https://www.youtube.com/watch?v=U-9j8sFIkS4</a>)

Show HN: I made a text to website generator powered by gpt4 and tailwind

Hello HN! A few weeks ago I tried looking for a way to quickly create business sites without spending hours designing or using generic templates. AI seemed to be the answer. After trying multiple AI builders I couldn't find one that was easy to use and didn't use templates. I wanted something truly dynamic a tool that can create a website for anything fully AI driven design decisions based on my description. So I created one. A truly dynamic AI web builder.(Powered by GPT4 with styling done in tailwind css). Try it out here <a href="https://www.instawebai.com" rel="nofollow">https://www.instawebai.com</a>

Show HN: Skyrim – Open-source Large Weather Model (LWM) Zoo

Hi HN, I'm Efe from Secondlaw AI. We are building physics-informed large AI models. Currently, we are focusing on weather modelling.<p>To benchmark SOTA, we had to build a forecasting infra for all available large weather models and we could not find a solid tooling to do so, so we built Skyrim. Within <5 mins and <5 LOC you can run forecasts on par with global weather models that are run on 100K+ CPU HPCs!<p>We are implementing more models & fine-tuning capabilities. Let us know if anything more we can add, also happy to answer any questions!

Show HN: Visual debugger for Rails system tests

Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video<p>This is brand new, so looking for people to start trying it out and leave feedback.

Show HN: Visual debugger for Rails system tests

Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video<p>This is brand new, so looking for people to start trying it out and leave feedback.

Show HN: Visual debugger for Rails system tests

Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video<p>This is brand new, so looking for people to start trying it out and leave feedback.

Show HN: Convert your Containerfile to a bootable OS