The best Hacker News stories from Show from the past day
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Show HN: Selecta – Tune Your Own Spotify Recommendation Algorithm
I posted here a while back with this app I'd been working on. In short:<p>It allows you to talk to Spotify's recommendations API to specify musical features you'd like recommendations similar to.<p>It's loads of fun to mess around with, and I've given it an overhaul to include many more dimensions ito. musical features, as well as the ability to log in with Spotify, and save things you find to a playlist. You don't need a subscription to Spotify to use the app either.<p>Thanks to @Mockapapella for the tagline when I posted the first version: <a href="https://news.ycombinator.com/item?id=35907215">https://news.ycombinator.com/item?id=35907215</a>
Show HN: Emaction – GitHub styled emoji reaction for blogs
Just For Fun.
Show HN: DankGPT – Chat with Your Documents
Uses hybrid semantic search (combination of dense embeddings and sparse vectors) to retrieve high quality answers across your documents.<p>Features<p>- Significantly faster than competition (Process a 200 page PDF in <5s)<p>- Much better answer quality<p>- Fast summarization tool<p>- Beta API for end to end extractive document QA (hello@dankgpt.com)<p>Try it out (no login)<p>- Llama 2 paper <a href="https://www.dankgpt.com/chat/346f444d-e286-4671-b157-540f4cb819ca" rel="nofollow noreferrer">https://www.dankgpt.com/chat/346f444d-e286-4671-b157-540f4cb...</a><p>- Scott Aaronson Quantum Information Science lectures <a href="https://www.dankgpt.com/chat/cc491d72-dc7b-4ace-8e26-60026ae29c02" rel="nofollow noreferrer">https://www.dankgpt.com/chat/cc491d72-dc7b-4ace-8e26-60026ae...</a><p>- Berkshire 2022 Annual Report <a href="https://www.dankgpt.com/chat/068bf85f-b372-46a4-a164-6096f8c4c5fd" rel="nofollow noreferrer">https://www.dankgpt.com/chat/068bf85f-b372-46a4-a164-6096f8c...</a><p>Why not host it yourself?<p>- You definitely can! DankGPT is intended as a quick way to ask questions about a research paper, or help students with answering questions from their lecture slides, with an easy way to share your chatbot.
Show HN: Fplyr – Adult Entertainment Tool for playing moaning sounds and music
Fplyr is a background audio sample and music player specialized in playing moaning sounds and relaxing music for adult entertainment purpose.<p>With fplyr you can define audio samples like lustful moans and (if you like) rubber clothing squeeching which are extracted from your video files and played back in a defined random fashion on multiple audio tracks.<p>Of course you can define totally different sound setting. It depends on which video files you have in your collection and which one of these you wanna hear.<p>What are your favorite sounds you would like to mix in?<p>~<p>Please also visit my other projects for your entertainment pleasure:<p>Want to organize your collection of adult images and videos? -> fapel-system [1]
Want to browse all your adult images on a single huge canvas? -> rugivi [2]<p>[1] <a href="https://github.com/pronopython/fapel-system">https://github.com/pronopython/fapel-system</a>
[2] <a href="https://github.com/pronopython/rugivi">https://github.com/pronopython/rugivi</a>
Show HN: Pebble Finance – Personalize Your Investments Today
Hello HN,<p>We are Pebble Finance! Our newly launched product offers a custom indexing solution, empowering investors to personalize their investment portfolios.<p># The product<p><i>What our product is now:</i>
We are creating a platform for users to use natural language to search for their interests and personalize their portfolio.<p>Search is a combination of using OpenAI LLM models with Langchain, OpenSearch index, and some traditional NLP techniques. Search is divided into several tools which are selected based on the user’s input query. Based on the complexity of the input query, a single tool or a combination of tools can be used to return a list of companies for the user’s portfolio. Also based on the complexity of query search times can vary from less than a second to up to 15 seconds.<p><i>List of tools</i><p>- Themes - General themes or concepts such as type of company<p>- Index/ETFs - Search for common ETFs such as S&P 500, Russell1000, and Russell3000
Location - Currently we can search for US states and countries (we are going to be adding in US cities soon as well)<p>- Fundamental Metrics - Filter companies based on fundamental metrics such as market cap, revenue, and pe ratio<p>- Supplier Data - Identify companies based on their supplier relationships<p><i>What we would like to add:</i><p>We are looking to add more tools to add more functionality for users. Some tools we are looking to add are based around news integration, historic stock price, and expanding our sources of data<p>We are also trying to improve search speed while keeping quality of results consistent so we are looking at new ways to parse queries such as with fine tuning transformer models and looking at more traditional NLP techniques.<p><i>Challenges we have come across</i><p>We are seeing issues with trading off between speed and quality of search results. Many of the queries can take up to 15 seconds based on the complexity. Also saw issues where LLMs are producing hallucinations which led to incorrect results. We have mitigated this issue by using OpenAI’s davinci-003 model versus gpt3.5-turbo, but this also increases overall cost.<p><i>Some Example Queries</i><p>- I want to invest in eco friendly companies<p>- Faang without the n<p>- Electric cars without elon musk<p>- Californian companies<p>- Aapl<p>- Top marketcap companies<p>- ev companies with marketcap above 10 billion<p>- s&p without apple<p># Feedback<p>Please try out our search capabilities on pebble.finance/themes and check the results!<p>We would really love honest feedback on search results, suggestions on improving results/speed, future tools, and/or general thoughts!<p>Thanks for reading!<p>The Pebble Finance Team
Show HN: Pebble Finance – Personalize Your Investments Today
Hello HN,<p>We are Pebble Finance! Our newly launched product offers a custom indexing solution, empowering investors to personalize their investment portfolios.<p># The product<p><i>What our product is now:</i>
We are creating a platform for users to use natural language to search for their interests and personalize their portfolio.<p>Search is a combination of using OpenAI LLM models with Langchain, OpenSearch index, and some traditional NLP techniques. Search is divided into several tools which are selected based on the user’s input query. Based on the complexity of the input query, a single tool or a combination of tools can be used to return a list of companies for the user’s portfolio. Also based on the complexity of query search times can vary from less than a second to up to 15 seconds.<p><i>List of tools</i><p>- Themes - General themes or concepts such as type of company<p>- Index/ETFs - Search for common ETFs such as S&P 500, Russell1000, and Russell3000
Location - Currently we can search for US states and countries (we are going to be adding in US cities soon as well)<p>- Fundamental Metrics - Filter companies based on fundamental metrics such as market cap, revenue, and pe ratio<p>- Supplier Data - Identify companies based on their supplier relationships<p><i>What we would like to add:</i><p>We are looking to add more tools to add more functionality for users. Some tools we are looking to add are based around news integration, historic stock price, and expanding our sources of data<p>We are also trying to improve search speed while keeping quality of results consistent so we are looking at new ways to parse queries such as with fine tuning transformer models and looking at more traditional NLP techniques.<p><i>Challenges we have come across</i><p>We are seeing issues with trading off between speed and quality of search results. Many of the queries can take up to 15 seconds based on the complexity. Also saw issues where LLMs are producing hallucinations which led to incorrect results. We have mitigated this issue by using OpenAI’s davinci-003 model versus gpt3.5-turbo, but this also increases overall cost.<p><i>Some Example Queries</i><p>- I want to invest in eco friendly companies<p>- Faang without the n<p>- Electric cars without elon musk<p>- Californian companies<p>- Aapl<p>- Top marketcap companies<p>- ev companies with marketcap above 10 billion<p>- s&p without apple<p># Feedback<p>Please try out our search capabilities on pebble.finance/themes and check the results!<p>We would really love honest feedback on search results, suggestions on improving results/speed, future tools, and/or general thoughts!<p>Thanks for reading!<p>The Pebble Finance Team
Show HN: Subset – Spreadsheet building blocks on an infinite canvas
Hey HN! We’re excited to show you Subset (<a href="https://subset.so" rel="nofollow noreferrer">https://subset.so</a>)! Subset is a spreadsheet on an infinite canvas where you can drop in spreadsheet building blocks and connect them together to easily build good looking analytical tools.<p>My cofounder and I both worked in finance before this. We quit our jobs and learned to code to build the spreadsheet we always wanted.<p>We got frustrated with how much time was wasted recreating the same thing from scratch or trying to extract something reusable from a previous spreadsheet. We wanted a spreadsheet with more composability and a community of building blocks to work off of.<p>Why infinite canvas? Reusability is not really a first class concept in a traditional endless spreadsheet grid [1]. A canvas felt more intuitive. You can communicate data flow, better understand inputs/outputs, and quickly make data presentable.<p>Subset is browser based and real-time multiplayer. It is free to use. We haven’t figured out a pricing model yet, but we imagine it’ll be a freemium SaaS model.<p>We’ve spent a lot of time on getting the core spreadsheet functionality as close to Sheets/Excel as possible, but there’s a lot more to do here. This list could be endless, but let us know if we need anything in particular.<p>Here’s a couple templates to explore:<p>Splitting a bill that includes sharing items - <a href="https://subset.so/templates/how-to-split-a-bill" rel="nofollow noreferrer">https://subset.so/templates/how-to-split-a-bill</a>
Calculating the cap rate and cash yield on real estate investments - <a href="https://subset.so/templates/quick-real-estate-deal-calculator" rel="nofollow noreferrer">https://subset.so/templates/quick-real-estate-deal-calculato...</a><p>We love spreadsheets and we think this combination of a canvas + reusable blocks has the potential to solve some of the biggest challenges with spreadsheets themselves.<p>Let us know what you think!<p>— AJ and Jason<p>[1] <a href="https://www.microsoft.com/en-us/research/uploads/prod/2020/04/joharizadeh_2020_gridlets.pdf" rel="nofollow noreferrer">https://www.microsoft.com/en-us/research/uploads/prod/2020/0...</a>
Show HN: Subset – Spreadsheet building blocks on an infinite canvas
Hey HN! We’re excited to show you Subset (<a href="https://subset.so" rel="nofollow noreferrer">https://subset.so</a>)! Subset is a spreadsheet on an infinite canvas where you can drop in spreadsheet building blocks and connect them together to easily build good looking analytical tools.<p>My cofounder and I both worked in finance before this. We quit our jobs and learned to code to build the spreadsheet we always wanted.<p>We got frustrated with how much time was wasted recreating the same thing from scratch or trying to extract something reusable from a previous spreadsheet. We wanted a spreadsheet with more composability and a community of building blocks to work off of.<p>Why infinite canvas? Reusability is not really a first class concept in a traditional endless spreadsheet grid [1]. A canvas felt more intuitive. You can communicate data flow, better understand inputs/outputs, and quickly make data presentable.<p>Subset is browser based and real-time multiplayer. It is free to use. We haven’t figured out a pricing model yet, but we imagine it’ll be a freemium SaaS model.<p>We’ve spent a lot of time on getting the core spreadsheet functionality as close to Sheets/Excel as possible, but there’s a lot more to do here. This list could be endless, but let us know if we need anything in particular.<p>Here’s a couple templates to explore:<p>Splitting a bill that includes sharing items - <a href="https://subset.so/templates/how-to-split-a-bill" rel="nofollow noreferrer">https://subset.so/templates/how-to-split-a-bill</a>
Calculating the cap rate and cash yield on real estate investments - <a href="https://subset.so/templates/quick-real-estate-deal-calculator" rel="nofollow noreferrer">https://subset.so/templates/quick-real-estate-deal-calculato...</a><p>We love spreadsheets and we think this combination of a canvas + reusable blocks has the potential to solve some of the biggest challenges with spreadsheets themselves.<p>Let us know what you think!<p>— AJ and Jason<p>[1] <a href="https://www.microsoft.com/en-us/research/uploads/prod/2020/04/joharizadeh_2020_gridlets.pdf" rel="nofollow noreferrer">https://www.microsoft.com/en-us/research/uploads/prod/2020/0...</a>
Show HN: Subset – Spreadsheet building blocks on an infinite canvas
Hey HN! We’re excited to show you Subset (<a href="https://subset.so" rel="nofollow noreferrer">https://subset.so</a>)! Subset is a spreadsheet on an infinite canvas where you can drop in spreadsheet building blocks and connect them together to easily build good looking analytical tools.<p>My cofounder and I both worked in finance before this. We quit our jobs and learned to code to build the spreadsheet we always wanted.<p>We got frustrated with how much time was wasted recreating the same thing from scratch or trying to extract something reusable from a previous spreadsheet. We wanted a spreadsheet with more composability and a community of building blocks to work off of.<p>Why infinite canvas? Reusability is not really a first class concept in a traditional endless spreadsheet grid [1]. A canvas felt more intuitive. You can communicate data flow, better understand inputs/outputs, and quickly make data presentable.<p>Subset is browser based and real-time multiplayer. It is free to use. We haven’t figured out a pricing model yet, but we imagine it’ll be a freemium SaaS model.<p>We’ve spent a lot of time on getting the core spreadsheet functionality as close to Sheets/Excel as possible, but there’s a lot more to do here. This list could be endless, but let us know if we need anything in particular.<p>Here’s a couple templates to explore:<p>Splitting a bill that includes sharing items - <a href="https://subset.so/templates/how-to-split-a-bill" rel="nofollow noreferrer">https://subset.so/templates/how-to-split-a-bill</a>
Calculating the cap rate and cash yield on real estate investments - <a href="https://subset.so/templates/quick-real-estate-deal-calculator" rel="nofollow noreferrer">https://subset.so/templates/quick-real-estate-deal-calculato...</a><p>We love spreadsheets and we think this combination of a canvas + reusable blocks has the potential to solve some of the biggest challenges with spreadsheets themselves.<p>Let us know what you think!<p>— AJ and Jason<p>[1] <a href="https://www.microsoft.com/en-us/research/uploads/prod/2020/04/joharizadeh_2020_gridlets.pdf" rel="nofollow noreferrer">https://www.microsoft.com/en-us/research/uploads/prod/2020/0...</a>
Show HN: Litellm – Simple library to standardize OpenAI, Cohere, Azure LLM I/O
I built this library because langchain was too bloated and I needed a simple abstraction to call multiple LLM APIs. litellm has two functions - completion(), embedding()
Show HN: Litellm – Simple library to standardize OpenAI, Cohere, Azure LLM I/O
I built this library because langchain was too bloated and I needed a simple abstraction to call multiple LLM APIs. litellm has two functions - completion(), embedding()
Show HN: I built a multiplayer Gameboy
Still very much a work in progress, but really wanted to share this even in it's early state. Had heaps of fun building it to learn more about WebRTC.
Show HN: I built a multiplayer Gameboy
Still very much a work in progress, but really wanted to share this even in it's early state. Had heaps of fun building it to learn more about WebRTC.
Show HN: I built a multiplayer Gameboy
Still very much a work in progress, but really wanted to share this even in it's early state. Had heaps of fun building it to learn more about WebRTC.
Show HN: Shell AI – My Aggressively Minimal Open Source Assistant
Show HN: Experiment with Hugging Face models in a single notebook interface
Customize Django Admin Interface
Show HN: Continue – Open-source coding autopilot
Hi HN, we’re Nate and Ty, co-founders of Continue, an open-source autopilot for software development built to be deeply customizable and continuously learn from development data. It consists of an extended language server and (to start) a VS Code extension.<p>Our GitHub is <a href="https://github.com/continuedev/continue">https://github.com/continuedev/continue</a>. You can watch a demo of Continue and download the extension at <a href="https://continue.dev">https://continue.dev</a><p>— — —<p>A growing number of developers are replacing Google + Stack Overflow with Large Language Models (LLMs) as their primary approach to get help, similar to how developers previously replaced reference manuals with Google + Stack Overflow.<p>However, existing LLM developer tools are cumbersome black boxes. Developers are stuck copy/pasting from ChatGPT and guessing what context Copilot uses to make a suggestion. As we use these products, we expose how we build software and give implicit feedback that is used to improve their LLMs, yet we don’t benefit from this data nor get to keep it.<p>The solution is to give developers what they need: <i>transparency, hackability,</i> and <i>control</i>. Every one of us should be able to reason about what’s going on, tinker, and have control over our own development data. This is why we created Continue.<p>— — —<p>At its most basic, Continue removes the need for copy/pasting from ChatGPT—instead, you collect context by highlighting and then ask questions in the sidebar or have an edit streamed directly to your editor.<p>But Continue also provides powerful tools for managing context. For example, type ‘@issue’ to quickly reference a GitHub issue as you are prompting the LLM, ‘@README.md’ to reference such a file, or ‘@google’ to include the results of a Google search.<p>And there’s a ton of room for further customization. Today, you can write your own<p>- slash commands (e.g. ‘/commit’ to write a summary and commit message for staged changes, ‘/docs’ to grab the contents of a file and update documentation pages that depend on it, ‘/ticket’ to generate a full-featured ticket with relevant files and high-level instructions from a short description)<p>- context sources (e.g. GitHub issues, Jira, local files, StackOverflow, documentation pages)<p>- templated system message (e.g. “Always give maximally concise answers. Adhere to the following style guide whenever writing code: {{ /Users/nate/repo/styleguide.md }}”)<p>- tools (e.g. add a file, run unit tests, build and watch for errors)<p>- policies (e.g. define a goal-oriented agent that works in a write code, run code, read errors, fix code, repeat loop)<p>Continue works with any LLM, including local models using ggml or open-source models hosted on your own cloud infrastructure, allowing you to remain 100% private. While OpenAI and Anthropic perform best today, we are excited to support the progress of open-source as it catches up (<a href="https://continue.dev/docs/customization#change-the-default-llm">https://continue.dev/docs/customization#change-the-default-l...</a>).<p>When you use Continue, you automatically collect data on how you build software. By default, this development data is saved to `.continue/dev_data` on your local machine. When combined with the code that you ultimately commit, it can be used to improve the LLM that you or your team use (if you allow).<p>You can read more about how development data is generated as a byproduct of LLM-aided development and why we believe that you should start collecting it now: <a href="https://medium.com/@continuedev/its-time-to-collect-data-on-how-you-build-software-197d12a020d5" rel="nofollow noreferrer">https://medium.com/@continuedev/its-time-to-collect-data-on-...</a><p>Continue has an Apache 2.0 license. We plan to make money by offering organizations a paid development data engine—a continuous feedback loop that ensures the LLMs always have fresh information and code in their preferred style.<p>— — —<p>We’d love for you to try out Continue and give us feedback! Let us know what you think in the comments : )
Show HN: Continue – Open-source coding autopilot
Hi HN, we’re Nate and Ty, co-founders of Continue, an open-source autopilot for software development built to be deeply customizable and continuously learn from development data. It consists of an extended language server and (to start) a VS Code extension.<p>Our GitHub is <a href="https://github.com/continuedev/continue">https://github.com/continuedev/continue</a>. You can watch a demo of Continue and download the extension at <a href="https://continue.dev">https://continue.dev</a><p>— — —<p>A growing number of developers are replacing Google + Stack Overflow with Large Language Models (LLMs) as their primary approach to get help, similar to how developers previously replaced reference manuals with Google + Stack Overflow.<p>However, existing LLM developer tools are cumbersome black boxes. Developers are stuck copy/pasting from ChatGPT and guessing what context Copilot uses to make a suggestion. As we use these products, we expose how we build software and give implicit feedback that is used to improve their LLMs, yet we don’t benefit from this data nor get to keep it.<p>The solution is to give developers what they need: <i>transparency, hackability,</i> and <i>control</i>. Every one of us should be able to reason about what’s going on, tinker, and have control over our own development data. This is why we created Continue.<p>— — —<p>At its most basic, Continue removes the need for copy/pasting from ChatGPT—instead, you collect context by highlighting and then ask questions in the sidebar or have an edit streamed directly to your editor.<p>But Continue also provides powerful tools for managing context. For example, type ‘@issue’ to quickly reference a GitHub issue as you are prompting the LLM, ‘@README.md’ to reference such a file, or ‘@google’ to include the results of a Google search.<p>And there’s a ton of room for further customization. Today, you can write your own<p>- slash commands (e.g. ‘/commit’ to write a summary and commit message for staged changes, ‘/docs’ to grab the contents of a file and update documentation pages that depend on it, ‘/ticket’ to generate a full-featured ticket with relevant files and high-level instructions from a short description)<p>- context sources (e.g. GitHub issues, Jira, local files, StackOverflow, documentation pages)<p>- templated system message (e.g. “Always give maximally concise answers. Adhere to the following style guide whenever writing code: {{ /Users/nate/repo/styleguide.md }}”)<p>- tools (e.g. add a file, run unit tests, build and watch for errors)<p>- policies (e.g. define a goal-oriented agent that works in a write code, run code, read errors, fix code, repeat loop)<p>Continue works with any LLM, including local models using ggml or open-source models hosted on your own cloud infrastructure, allowing you to remain 100% private. While OpenAI and Anthropic perform best today, we are excited to support the progress of open-source as it catches up (<a href="https://continue.dev/docs/customization#change-the-default-llm">https://continue.dev/docs/customization#change-the-default-l...</a>).<p>When you use Continue, you automatically collect data on how you build software. By default, this development data is saved to `.continue/dev_data` on your local machine. When combined with the code that you ultimately commit, it can be used to improve the LLM that you or your team use (if you allow).<p>You can read more about how development data is generated as a byproduct of LLM-aided development and why we believe that you should start collecting it now: <a href="https://medium.com/@continuedev/its-time-to-collect-data-on-how-you-build-software-197d12a020d5" rel="nofollow noreferrer">https://medium.com/@continuedev/its-time-to-collect-data-on-...</a><p>Continue has an Apache 2.0 license. We plan to make money by offering organizations a paid development data engine—a continuous feedback loop that ensures the LLMs always have fresh information and code in their preferred style.<p>— — —<p>We’d love for you to try out Continue and give us feedback! Let us know what you think in the comments : )
Show HN: Continue – Open-source coding autopilot
Hi HN, we’re Nate and Ty, co-founders of Continue, an open-source autopilot for software development built to be deeply customizable and continuously learn from development data. It consists of an extended language server and (to start) a VS Code extension.<p>Our GitHub is <a href="https://github.com/continuedev/continue">https://github.com/continuedev/continue</a>. You can watch a demo of Continue and download the extension at <a href="https://continue.dev">https://continue.dev</a><p>— — —<p>A growing number of developers are replacing Google + Stack Overflow with Large Language Models (LLMs) as their primary approach to get help, similar to how developers previously replaced reference manuals with Google + Stack Overflow.<p>However, existing LLM developer tools are cumbersome black boxes. Developers are stuck copy/pasting from ChatGPT and guessing what context Copilot uses to make a suggestion. As we use these products, we expose how we build software and give implicit feedback that is used to improve their LLMs, yet we don’t benefit from this data nor get to keep it.<p>The solution is to give developers what they need: <i>transparency, hackability,</i> and <i>control</i>. Every one of us should be able to reason about what’s going on, tinker, and have control over our own development data. This is why we created Continue.<p>— — —<p>At its most basic, Continue removes the need for copy/pasting from ChatGPT—instead, you collect context by highlighting and then ask questions in the sidebar or have an edit streamed directly to your editor.<p>But Continue also provides powerful tools for managing context. For example, type ‘@issue’ to quickly reference a GitHub issue as you are prompting the LLM, ‘@README.md’ to reference such a file, or ‘@google’ to include the results of a Google search.<p>And there’s a ton of room for further customization. Today, you can write your own<p>- slash commands (e.g. ‘/commit’ to write a summary and commit message for staged changes, ‘/docs’ to grab the contents of a file and update documentation pages that depend on it, ‘/ticket’ to generate a full-featured ticket with relevant files and high-level instructions from a short description)<p>- context sources (e.g. GitHub issues, Jira, local files, StackOverflow, documentation pages)<p>- templated system message (e.g. “Always give maximally concise answers. Adhere to the following style guide whenever writing code: {{ /Users/nate/repo/styleguide.md }}”)<p>- tools (e.g. add a file, run unit tests, build and watch for errors)<p>- policies (e.g. define a goal-oriented agent that works in a write code, run code, read errors, fix code, repeat loop)<p>Continue works with any LLM, including local models using ggml or open-source models hosted on your own cloud infrastructure, allowing you to remain 100% private. While OpenAI and Anthropic perform best today, we are excited to support the progress of open-source as it catches up (<a href="https://continue.dev/docs/customization#change-the-default-llm">https://continue.dev/docs/customization#change-the-default-l...</a>).<p>When you use Continue, you automatically collect data on how you build software. By default, this development data is saved to `.continue/dev_data` on your local machine. When combined with the code that you ultimately commit, it can be used to improve the LLM that you or your team use (if you allow).<p>You can read more about how development data is generated as a byproduct of LLM-aided development and why we believe that you should start collecting it now: <a href="https://medium.com/@continuedev/its-time-to-collect-data-on-how-you-build-software-197d12a020d5" rel="nofollow noreferrer">https://medium.com/@continuedev/its-time-to-collect-data-on-...</a><p>Continue has an Apache 2.0 license. We plan to make money by offering organizations a paid development data engine—a continuous feedback loop that ensures the LLMs always have fresh information and code in their preferred style.<p>— — —<p>We’d love for you to try out Continue and give us feedback! Let us know what you think in the comments : )