The best Hacker News stories from Show from the past day
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Show HN: Synthical – Like HN, but for Science
Show HN: Synthical – Like HN, but for Science
Show HN: Synthical – Like HN, but for Science
Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations
Hello HN!<p>I've been thinking about the idea of a LLM thats a clone of me - instead of generating replies to be a helpful assistant, it generates replies that are exactly like mine. The concept's appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige), and I think with LLMs, there might actually be a possibility of us doing something like this!<p>I've just released a fork of the facebookresearch/llama-recipes which allows you to fine-tune a Llama model on your personal WhatsApp conversations. This adaptation can train the model (using QLoRA) to respond in a way that's eerily similar to your own texting style.<p>What I've figured out so far:<p>Quick Learning: The model quickly adapts to personal nuances, emoji usage, and phrases that you use. I've trained just 1 epoch on a P100 GPU using QLoRA and 4 bit quantization, and its already captured my mannerisms<p>Turing Tests: As an experiment, I asked my friends to ask me 3 questions, and responded with 2 candidate responses (one from me and one from llama). My friends then had to guess which candidate response was mine and which one was Llama's. Llama managed to fool 10% of my friends, but with more compute, I think it can do way better.<p>Here's the GitHub repository: <a href="https://github.com/Ads-cmu/WhatsApp-Llama/">https://github.com/Ads-cmu/WhatsApp-Llama/</a><p>Would love to hear feedback, suggestions, and any cool experiences if you decide to give it a try! I'd love to see how far we can push this by training bigger models for more epochs (I ran out of compute credits)
Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations
Hello HN!<p>I've been thinking about the idea of a LLM thats a clone of me - instead of generating replies to be a helpful assistant, it generates replies that are exactly like mine. The concept's appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige), and I think with LLMs, there might actually be a possibility of us doing something like this!<p>I've just released a fork of the facebookresearch/llama-recipes which allows you to fine-tune a Llama model on your personal WhatsApp conversations. This adaptation can train the model (using QLoRA) to respond in a way that's eerily similar to your own texting style.<p>What I've figured out so far:<p>Quick Learning: The model quickly adapts to personal nuances, emoji usage, and phrases that you use. I've trained just 1 epoch on a P100 GPU using QLoRA and 4 bit quantization, and its already captured my mannerisms<p>Turing Tests: As an experiment, I asked my friends to ask me 3 questions, and responded with 2 candidate responses (one from me and one from llama). My friends then had to guess which candidate response was mine and which one was Llama's. Llama managed to fool 10% of my friends, but with more compute, I think it can do way better.<p>Here's the GitHub repository: <a href="https://github.com/Ads-cmu/WhatsApp-Llama/">https://github.com/Ads-cmu/WhatsApp-Llama/</a><p>Would love to hear feedback, suggestions, and any cool experiences if you decide to give it a try! I'd love to see how far we can push this by training bigger models for more epochs (I ran out of compute credits)
Show HN: WhatsApp-Llama: A clone of yourself from your WhatsApp conversations
Hello HN!<p>I've been thinking about the idea of a LLM thats a clone of me - instead of generating replies to be a helpful assistant, it generates replies that are exactly like mine. The concept's appeared in fiction numerous times (the talking paintings in Harry Potter that mimic the person painted, the clones in The Prestige), and I think with LLMs, there might actually be a possibility of us doing something like this!<p>I've just released a fork of the facebookresearch/llama-recipes which allows you to fine-tune a Llama model on your personal WhatsApp conversations. This adaptation can train the model (using QLoRA) to respond in a way that's eerily similar to your own texting style.<p>What I've figured out so far:<p>Quick Learning: The model quickly adapts to personal nuances, emoji usage, and phrases that you use. I've trained just 1 epoch on a P100 GPU using QLoRA and 4 bit quantization, and its already captured my mannerisms<p>Turing Tests: As an experiment, I asked my friends to ask me 3 questions, and responded with 2 candidate responses (one from me and one from llama). My friends then had to guess which candidate response was mine and which one was Llama's. Llama managed to fool 10% of my friends, but with more compute, I think it can do way better.<p>Here's the GitHub repository: <a href="https://github.com/Ads-cmu/WhatsApp-Llama/">https://github.com/Ads-cmu/WhatsApp-Llama/</a><p>Would love to hear feedback, suggestions, and any cool experiences if you decide to give it a try! I'd love to see how far we can push this by training bigger models for more epochs (I ran out of compute credits)
Show HN: I built a Python web framework
been working on this for nearly a year
Show HN: I built a Python web framework
been working on this for nearly a year
Show HN: I built a Python web framework
been working on this for nearly a year
Show HN: Find jobs at top AI startups
Hello HN, I am one of the creators of WorkInAI, and I'm excited to share our project with the community and gather valuable feedback.<p>WorkInAI is a job aggregation platform for positions at leading AI startups. We have compiled over 350 job listings from more than 20 top AI startups, including companies like OpenAI, Anthropic, Cohere, and more. We created this platform in response to a friend's frustration with trying to find suitable AI startup roles in London. He used to check various company career pages frequently to see if any new opportunities had arisen -- so we built this to aggregate jobs in a single place.<p>We're launching this MVP early to gather feedback, whether it's feature requests or suggestions for adding new startups to our list. We value your thoughts and input on our product and idea.<p>Thanks!
Show HN: Find jobs at top AI startups
Hello HN, I am one of the creators of WorkInAI, and I'm excited to share our project with the community and gather valuable feedback.<p>WorkInAI is a job aggregation platform for positions at leading AI startups. We have compiled over 350 job listings from more than 20 top AI startups, including companies like OpenAI, Anthropic, Cohere, and more. We created this platform in response to a friend's frustration with trying to find suitable AI startup roles in London. He used to check various company career pages frequently to see if any new opportunities had arisen -- so we built this to aggregate jobs in a single place.<p>We're launching this MVP early to gather feedback, whether it's feature requests or suggestions for adding new startups to our list. We value your thoughts and input on our product and idea.<p>Thanks!
Show HN: Dweets.com – Microblogging for Devs
Built this to microblog everything I am learning as a dev. It's an MVP, lots to improve on! Would love some feedback. (:<p>Also am looking for a job (preferably remote since I'm in SG), 1.5 years in as a Jr Dev at a local startup, self-taught by building stuff. I'll be on Twitter if you're interested! @raymondmoay
Show HN: Vanna AI – Open-sourced text-to-SQL in Python
Hey there HN!<p>We've just open-sourced Vanna – a Python package that allows you to transform questions into SQL. We've leveraged LLMs to enable you to "ask" databases what you need, bypassing the need to "write" complex SQL.<p>Quick Overview:<p>- "Train" using DDL statements, documentation, or known correct SQL statements.<p>- "Ask" questions in natural language and receive SQL, tables, and charts in return.<p>- Open Source Flexibility: Swap storage mechanisms, customize LLMs, and choose your databases.<p>- Local or Hosted: Operate everything locally or use our hosted version for free (including complimentary LLM calls).<p>- Use it wherever Python is applicable. We provide code examples for integration in Jupyter, Streamlit, Slack, and more.<p>We would greatly appreciate your feedback, insights on issues, and contributions:<p><a href="https://github.com/vanna-ai/vanna">https://github.com/vanna-ai/vanna</a>
Show HN: Vanna AI – Open-sourced text-to-SQL in Python
Hey there HN!<p>We've just open-sourced Vanna – a Python package that allows you to transform questions into SQL. We've leveraged LLMs to enable you to "ask" databases what you need, bypassing the need to "write" complex SQL.<p>Quick Overview:<p>- "Train" using DDL statements, documentation, or known correct SQL statements.<p>- "Ask" questions in natural language and receive SQL, tables, and charts in return.<p>- Open Source Flexibility: Swap storage mechanisms, customize LLMs, and choose your databases.<p>- Local or Hosted: Operate everything locally or use our hosted version for free (including complimentary LLM calls).<p>- Use it wherever Python is applicable. We provide code examples for integration in Jupyter, Streamlit, Slack, and more.<p>We would greatly appreciate your feedback, insights on issues, and contributions:<p><a href="https://github.com/vanna-ai/vanna">https://github.com/vanna-ai/vanna</a>
Show HN: Rivet – open-source AI Agent dev env with real-world applications
We just launched Rivet, the open-source visual AI programming environment! We built Rivet, because we were building complex AI Agent applications at Ironclad. It unlocked our abilities here, and we're excited to make available to the entire community.<p>Backstory: A few months ago, inspired by things like LangChain and LlamaIndex, we started building an AI agent that could work with legal contracts. Unfortunately, we couldn't just use retrieval augmented generation (RAG), because a lot of contracts are basically identical (many chunks with near-identical embeddings), except for a few key details. So, we turned to things like ReAct and AutoGPT for inspiration.<p>At first, things went great. We were adding agent capabilities, doing chain-of-thought prompting.<p>But then we hit a wall.<p>The agent became too complex. We had debugger breakpoints on almost every line of code, but we still had no idea where the agent was breaking. Every change we made destabilized something else. After two weeks of fumbling, I decided to end the project.<p>But one of my teammates, Andy, didn't give up.<p>The following week, he showed me v0 of Rivet. He'd used it to refactor and improve our existing agent. I was skeptical... it just seemed like a visual programming environment, and I was not a fan. But I gave it a shot, and suddenly found myself able to add new skills to the agent, debug brittle areas with ease, and update prompts with confidence.<p>Rivet is a game-changer. And more than that, it makes building with LLMs super fun.<p>What exactly makes it different?<p>First, the debugger is incredible. You have to experience it to believe it. You can update a graph, and then immediately run it, and see where it succeeded or failed. Even better: you can attach Rivet as a remote debugger, and watch your agent graphs execute in your app.<p>Second, visual programming is actually a game-changer for prompting LLMs. I don't know why exactly, but it's way easier to understand and organize your work when you have an extra dimension to work with.<p>Finally, Rivet is built to be embedded into a larger application (TypeScript for now, but we've also found a way to run it in Python). Beyond importing Rivet as a dependency, you can also define "external functions" dynamically at run-time. It feels pretty sketchy to give a LLM a key and unfettered access to an API. With Rivet, you can give it access to a specific set of defined functions, potentially pre-scoped to the access level you want.<p>...Sorry that was long. If you read this whole thing, thank you!<p>We're really excited to hear what you think! We just launched our first Rivet-based application at Ironclad, and we've been working with companies like Sourcegraph, Attentive, AssemblyAI, Bento, and Willow to make Rivet useful for others.
Show HN: Rivet – open-source AI Agent dev env with real-world applications
We just launched Rivet, the open-source visual AI programming environment! We built Rivet, because we were building complex AI Agent applications at Ironclad. It unlocked our abilities here, and we're excited to make available to the entire community.<p>Backstory: A few months ago, inspired by things like LangChain and LlamaIndex, we started building an AI agent that could work with legal contracts. Unfortunately, we couldn't just use retrieval augmented generation (RAG), because a lot of contracts are basically identical (many chunks with near-identical embeddings), except for a few key details. So, we turned to things like ReAct and AutoGPT for inspiration.<p>At first, things went great. We were adding agent capabilities, doing chain-of-thought prompting.<p>But then we hit a wall.<p>The agent became too complex. We had debugger breakpoints on almost every line of code, but we still had no idea where the agent was breaking. Every change we made destabilized something else. After two weeks of fumbling, I decided to end the project.<p>But one of my teammates, Andy, didn't give up.<p>The following week, he showed me v0 of Rivet. He'd used it to refactor and improve our existing agent. I was skeptical... it just seemed like a visual programming environment, and I was not a fan. But I gave it a shot, and suddenly found myself able to add new skills to the agent, debug brittle areas with ease, and update prompts with confidence.<p>Rivet is a game-changer. And more than that, it makes building with LLMs super fun.<p>What exactly makes it different?<p>First, the debugger is incredible. You have to experience it to believe it. You can update a graph, and then immediately run it, and see where it succeeded or failed. Even better: you can attach Rivet as a remote debugger, and watch your agent graphs execute in your app.<p>Second, visual programming is actually a game-changer for prompting LLMs. I don't know why exactly, but it's way easier to understand and organize your work when you have an extra dimension to work with.<p>Finally, Rivet is built to be embedded into a larger application (TypeScript for now, but we've also found a way to run it in Python). Beyond importing Rivet as a dependency, you can also define "external functions" dynamically at run-time. It feels pretty sketchy to give a LLM a key and unfettered access to an API. With Rivet, you can give it access to a specific set of defined functions, potentially pre-scoped to the access level you want.<p>...Sorry that was long. If you read this whole thing, thank you!<p>We're really excited to hear what you think! We just launched our first Rivet-based application at Ironclad, and we've been working with companies like Sourcegraph, Attentive, AssemblyAI, Bento, and Willow to make Rivet useful for others.
Show HN: Rivet – open-source AI Agent dev env with real-world applications
We just launched Rivet, the open-source visual AI programming environment! We built Rivet, because we were building complex AI Agent applications at Ironclad. It unlocked our abilities here, and we're excited to make available to the entire community.<p>Backstory: A few months ago, inspired by things like LangChain and LlamaIndex, we started building an AI agent that could work with legal contracts. Unfortunately, we couldn't just use retrieval augmented generation (RAG), because a lot of contracts are basically identical (many chunks with near-identical embeddings), except for a few key details. So, we turned to things like ReAct and AutoGPT for inspiration.<p>At first, things went great. We were adding agent capabilities, doing chain-of-thought prompting.<p>But then we hit a wall.<p>The agent became too complex. We had debugger breakpoints on almost every line of code, but we still had no idea where the agent was breaking. Every change we made destabilized something else. After two weeks of fumbling, I decided to end the project.<p>But one of my teammates, Andy, didn't give up.<p>The following week, he showed me v0 of Rivet. He'd used it to refactor and improve our existing agent. I was skeptical... it just seemed like a visual programming environment, and I was not a fan. But I gave it a shot, and suddenly found myself able to add new skills to the agent, debug brittle areas with ease, and update prompts with confidence.<p>Rivet is a game-changer. And more than that, it makes building with LLMs super fun.<p>What exactly makes it different?<p>First, the debugger is incredible. You have to experience it to believe it. You can update a graph, and then immediately run it, and see where it succeeded or failed. Even better: you can attach Rivet as a remote debugger, and watch your agent graphs execute in your app.<p>Second, visual programming is actually a game-changer for prompting LLMs. I don't know why exactly, but it's way easier to understand and organize your work when you have an extra dimension to work with.<p>Finally, Rivet is built to be embedded into a larger application (TypeScript for now, but we've also found a way to run it in Python). Beyond importing Rivet as a dependency, you can also define "external functions" dynamically at run-time. It feels pretty sketchy to give a LLM a key and unfettered access to an API. With Rivet, you can give it access to a specific set of defined functions, potentially pre-scoped to the access level you want.<p>...Sorry that was long. If you read this whole thing, thank you!<p>We're really excited to hear what you think! We just launched our first Rivet-based application at Ironclad, and we've been working with companies like Sourcegraph, Attentive, AssemblyAI, Bento, and Willow to make Rivet useful for others.
Show HN: Conway's Game of Life in TypeScript's type system
TypeScript playground: <a href="https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmYcCiBHArgQwDYDOAPABoA0cAmgHxwC8AUHHABREAq1LAlPbe3FAxgAOwAmBOCTgB+OAEY4ALjgAmXkNESmrDl151+gkMPGTKshcrW85MKFlQqAZvgLAA3AwZIUcAMLAeHj0cADkIGFwAD7hAHRhPsioAEIQOFBioYHBANoAugXevqgAavgAlmIAkuKgRACCUFDGphJwWCIA1iIQAO4iBbSMzGggAMZ4WGLARF3AiBDOcE1QFGOT07Pzi8urFAAGACQA3iJYALYARsBQAL4H1LSaZnDHJxUizrdwAHKtWkk52utweOjkvx0KhEwAAbrdvAB6RFwAAiEGABBEYXgAAscOI8KgYQBzHAwCrwuDQOAzcYVC74ODjHDuAgMZFwaowMKSAm9RCfEkICBwfGE1AwXHEuE-FnuCgEUXVMIXDoEIVwAC0ilZcBwcHcIgpMJCsPwjjgnwIwhwYiSfgAylgrvIiP8Xu1gTc1gEAa8AAwFUJDeg6eyWz2STC4QjuigB2ixGNueNwb23Wjg7XyKF-f3tXJxYv+BOFMJEkQkqVhfI6Zhyfy5Cuiau42v16zO11poslsvUYrJFZiMRuj0mQFwAAKEAIBHKU0xFFn88XjkkUfTlx9FH8BckQfyIfywx0KbjvwoGagz0nrwjwGzOrzE7akibLarNbrzAbcD7OJSzgANy0rNtawPGc5wXC1MU7f9AOA0Dm3An8EOsHVmE5MAoFlY0rTqEBqSweAljgK4IE6bQ-2sBpR3HFcYPXZcAOLICByHPxqgIAAZSlZjSDIxEdGByVQLchMyChpC3G8KAsOSd0zMNmFk+92n8CA8FqGYQAAeWcIgpJEsThEHf9FI0yQACV+l00BDOM9JMlE8SLL-OQTLc4RckoQoSGPLcIkSWj-0fDCXDcJ9aKiwgYusVx4q41AtM6GB2AcWZVigglEAKPcoKPE8z2YHKt1yT5vhaXiKHYqqfhs39LF4qCItoxsqONTLHCIGyKCQzjYoCLqMqyvq9w8lRPzQ9s6wdVKgjwHzBJc0zxKgkyZKgrSdKIpzvLM4BqAUqC7L6ByDKMw73NKkb0p62Zck7Hj+PhZzhJWihuzdEgTrgH6iBoE6Xr4gSPtco6ZO+l03WBshQbe1bPqhkcx1IPdtMug61pWp4YZ7eHEfBm7hAoej0fIEa9r0nGUduhSQdo16Sdx1GKd+zGacc662YZtG4Yoc7sd5+nzPx4n3tJ4BoYFoGhfs-bRch26mb-Fmpb5smAdh0h-o5+W4GFpWIfW8W1eYDXkZV7XAb+xmdFPFL0FhbT4RyPBTZWza1u2rddpFr2jv+qz3yNxXaeVs3jru6W-ICoLrPiULLA9lag-E2WaCggBmTs5BCzsVDCBI8zj-zckCqDC46gIlvT6Ws7vMPVBiOA87Cywa7C4vS+GmF4SgZ20FdvB4XOogdH0qAKhJaWfeEhHmFDqdjcjohp9n6W1f8dSw+KugAPyJeAiwZpzp2pbg0PgqGGGOAdF31DWx-HasZNze56147sxHt3gAnhhT+jcMKUBPrRJCVNQLgL-EhM+UBzrrFHu7JaG8Z5fzFjLAIJBn7fjmozZqzApqn3Pv0YeyDZhT3QfPSSvtH7wJoUnEyJ4T7+BXoGa+R8Eb30fpQXBEFE5hzXjzNBW9v4eTkH-MesxgHfwGuxfwDC5Eu3-hPWRmC9x8K-AI6gx82IljASBJ2zBppKMweQ-+vwM7CAXtJfMSkQS+n3FuA+R87ozRfnNKCkJ-zSzzFI+EViAnW2jv9K8+iOJGMHAtcOF0TaMLDiZe+i4qiXWsT-Eo1NA4JKnEk0IKSahEXSbkG8TsOQojoJUqp1Sam1LqdU8p9A6lwBSGgAA4tUf47A0COnYJ0tpTS6mNPqSM0ZNSkQonYJiE0woACqFI8AVApPBGJAApCAnwJ4X39lfPRjooI2hnlWUIYQwh3W2UnSqXwfitR2cEOqxYGotBspfPI+RmpyHWZs-oE03inEdHcU4vEHjEMdM7acRyYAmXSbYsQFB+IwkdMAMABz7CakPmEAAOtib6qKjnCgxWc0IOgcmvCudVEhCD+ivLwAVOA9VrktF+EIc6m4mFrQ+QDauKd-wQs+FCta7oWX9AIPCz4wAkVgAoF8kQE9FGkL6E8IuM5IXQuZSYVlYrEXIsOP8wFJwEUSuRfqmVcr4HnWoCCvMYKYmiXxewCA0L9lbkOUKPcZjMiwpYZSi5YcPacNDCMLlW53hPLgMC04YabJgn-LcpOWLsT5x9f0fhr8XEYTkHaoUDroX9UpVtNxkUAZoqrDmwVea4HNALfKqlfQ9FDGVXGv1S0M3FvtY68t7qq2+wiTWxB4bjE9zbdmjtwlfk1urea-oxCJ1rQmXAKZNoAisngpyAAEhAQe1It1SklMOEQOALiYhFIaYAkppQkRgGAUi87cQwCvQQJQyJxgQBEH0HAiBFnfDiC+i4iI+gVC6BUREKRFndARJyactwKgQCyKoGJoHPjzBaIfLNpbR2ZCIAcBg7EGAgHwzh4sDBHjgsQ+BqAihD58uNNCsjyHByZOnHR24rcqOqsFcE4yYH6MMeHEx7jtwc6hGowKsdwSrHMd9KoJVjHJMABZhPsbExQiTAnfQ5yVY0u9D6n2IhfW+j9X7gA-ogH+gDQHERtLnCgKAAB9EkiyZh2ZJJ0RpUGZ6wfbgGGJVmCA2baY524bTOihDQySMtY7sPsWizF2LcX4sJaI4l5L0WQApdi4R9L8W0tpay7h9L+HYuFeS8VmLmWEu5biJVqr1Xsu1aS6VmLtXKuNbyw1+rxYWs1dS1VtrSXEvNbix15L5XsvsWG31-rJWJuTam7N+bRGSMxOnMFkQlGVX8uhb5-zgWoCrcHJyRjq3WMbZoxxlTRBttBd26tig8glWHb46toTbHNvncsZd6z12qhBc6BQaTB2URHc6Ap17Z3lMfau3tm7f324PaB09zoABWRTb2IfSKsVDgLP29uw7k-DhAiORAADZUfg8w+Jz7fnvtOdu3AJHBPgciAAOxk9ExTi7WOYciAoMTxnROAAcbPoWU65zjunzP+d+BW50AAnML97GOqc7fF7DgXUvUAy7WwGBX6PAnK5p79nncBZca5nKt+Q62RMi85196HqvjfyETIDwn0uLcnet4r-XYvaew8t2brX8gXunfZ2IIgou7fY9947gHjSmfyFByHm3kPI-c7uxpl38eUdg9D+H231P7fR7u-jzPRP5Ck5z8npXPujd3YZ6Xt3nR5Cs8r174AmPU8O7u3zhvmuLdC9b3r9vBvC+14UJL3v5um-y8HxzlPBeo9j-kOryfWvVA69n2HiPC+08KFN6v47VulNz+r53ovahndx6J6oD3x+t-55V+f1Q92D+dFUMHz3Q+O87672oWPj3G8RBVBE9P8T9vcz8x938A9jts8k829v9H9ICS8r9ADVAK84Cv8R9F9cdjdVB68UC+838W8MCwDh8a8cD-se8CCp8gCB8SD7959ECKC1AJ9qC18Z96C89GDDdmDVAV82DnsN9ODt8mC6dVB99ltnsj80dSCECeC6cc5L8ADCCRAc5b8ZCGDT8f9z8c4X8GAgA" rel="nofollow noreferrer">https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmY...</a>
Show HN: Conway's Game of Life in TypeScript's type system
TypeScript playground: <a href="https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmYcCiBHArgQwDYDOAPABoA0cAmgHxwC8AUHHABREAq1LAlPbe3FAxgAOwAmBOCTgB+OAEY4ALjgAmXkNESmrDl151+gkMPGTKshcrW85MKFlQqAZvgLAA3AwZIUcAMLAeHj0cADkIGFwAD7hAHRhPsioAEIQOFBioYHBANoAugXevqgAavgAlmIAkuKgRACCUFDGphJwWCIA1iIQAO4iBbSMzGggAMZ4WGLARF3AiBDOcE1QFGOT07Pzi8urFAAGACQA3iJYALYARsBQAL4H1LSaZnDHJxUizrdwAHKtWkk52utweOjkvx0KhEwAAbrdvAB6RFwAAiEGABBEYXgAAscOI8KgYQBzHAwCrwuDQOAzcYVC74ODjHDuAgMZFwaowMKSAm9RCfEkICBwfGE1AwXHEuE-FnuCgEUXVMIXDoEIVwAC0ilZcBwcHcIgpMJCsPwjjgnwIwhwYiSfgAylgrvIiP8Xu1gTc1gEAa8AAwFUJDeg6eyWz2STC4QjuigB2ixGNueNwb23Wjg7XyKF-f3tXJxYv+BOFMJEkQkqVhfI6Zhyfy5Cuiau42v16zO11poslsvUYrJFZiMRuj0mQFwAAKEAIBHKU0xFFn88XjkkUfTlx9FH8BckQfyIfywx0KbjvwoGagz0nrwjwGzOrzE7akibLarNbrzAbcD7OJSzgANy0rNtawPGc5wXC1MU7f9AOA0Dm3An8EOsHVmE5MAoFlY0rTqEBqSweAljgK4IE6bQ-2sBpR3HFcYPXZcAOLICByHPxqgIAAZSlZjSDIxEdGByVQLchMyChpC3G8KAsOSd0zMNmFk+92n8CA8FqGYQAAeWcIgpJEsThEHf9FI0yQACV+l00BDOM9JMlE8SLL-OQTLc4RckoQoSGPLcIkSWj-0fDCXDcJ9aKiwgYusVx4q41AtM6GB2AcWZVigglEAKPcoKPE8z2YHKt1yT5vhaXiKHYqqfhs39LF4qCItoxsqONTLHCIGyKCQzjYoCLqMqyvq9w8lRPzQ9s6wdVKgjwHzBJc0zxKgkyZKgrSdKIpzvLM4BqAUqC7L6ByDKMw73NKkb0p62Zck7Hj+PhZzhJWihuzdEgTrgH6iBoE6Xr4gSPtco6ZO+l03WBshQbe1bPqhkcx1IPdtMug61pWp4YZ7eHEfBm7hAoej0fIEa9r0nGUduhSQdo16Sdx1GKd+zGacc662YZtG4Yoc7sd5+nzPx4n3tJ4BoYFoGhfs-bRch26mb-Fmpb5smAdh0h-o5+W4GFpWIfW8W1eYDXkZV7XAb+xmdFPFL0FhbT4RyPBTZWza1u2rddpFr2jv+qz3yNxXaeVs3jru6W-ICoLrPiULLA9lag-E2WaCggBmTs5BCzsVDCBI8zj-zckCqDC46gIlvT6Ws7vMPVBiOA87Cywa7C4vS+GmF4SgZ20FdvB4XOogdH0qAKhJaWfeEhHmFDqdjcjohp9n6W1f8dSw+KugAPyJeAiwZpzp2pbg0PgqGGGOAdF31DWx-HasZNze56147sxHt3gAnhhT+jcMKUBPrRJCVNQLgL-EhM+UBzrrFHu7JaG8Z5fzFjLAIJBn7fjmozZqzApqn3Pv0YeyDZhT3QfPSSvtH7wJoUnEyJ4T7+BXoGa+R8Eb30fpQXBEFE5hzXjzNBW9v4eTkH-MesxgHfwGuxfwDC5Eu3-hPWRmC9x8K-AI6gx82IljASBJ2zBppKMweQ-+vwM7CAXtJfMSkQS+n3FuA+R87ozRfnNKCkJ-zSzzFI+EViAnW2jv9K8+iOJGMHAtcOF0TaMLDiZe+i4qiXWsT-Eo1NA4JKnEk0IKSahEXSbkG8TsOQojoJUqp1Sam1LqdU8p9A6lwBSGgAA4tUf47A0COnYJ0tpTS6mNPqSM0ZNSkQonYJiE0woACqFI8AVApPBGJAApCAnwJ4X39lfPRjooI2hnlWUIYQwh3W2UnSqXwfitR2cEOqxYGotBspfPI+RmpyHWZs-oE03inEdHcU4vEHjEMdM7acRyYAmXSbYsQFB+IwkdMAMABz7CakPmEAAOtib6qKjnCgxWc0IOgcmvCudVEhCD+ivLwAVOA9VrktF+EIc6m4mFrQ+QDauKd-wQs+FCta7oWX9AIPCz4wAkVgAoF8kQE9FGkL6E8IuM5IXQuZSYVlYrEXIsOP8wFJwEUSuRfqmVcr4HnWoCCvMYKYmiXxewCA0L9lbkOUKPcZjMiwpYZSi5YcPacNDCMLlW53hPLgMC04YabJgn-LcpOWLsT5x9f0fhr8XEYTkHaoUDroX9UpVtNxkUAZoqrDmwVea4HNALfKqlfQ9FDGVXGv1S0M3FvtY68t7qq2+wiTWxB4bjE9zbdmjtwlfk1urea-oxCJ1rQmXAKZNoAisngpyAAEhAQe1It1SklMOEQOALiYhFIaYAkppQkRgGAUi87cQwCvQQJQyJxgQBEH0HAiBFnfDiC+i4iI+gVC6BUREKRFndARJyactwKgQCyKoGJoHPjzBaIfLNpbR2ZCIAcBg7EGAgHwzh4sDBHjgsQ+BqAihD58uNNCsjyHByZOnHR24rcqOqsFcE4yYH6MMeHEx7jtwc6hGowKsdwSrHMd9KoJVjHJMABZhPsbExQiTAnfQ5yVY0u9D6n2IhfW+j9X7gA-ogH+gDQHERtLnCgKAAB9EkiyZh2ZJJ0RpUGZ6wfbgGGJVmCA2baY524bTOihDQySMtY7sPsWizF2LcX4sJaI4l5L0WQApdi4R9L8W0tpay7h9L+HYuFeS8VmLmWEu5biJVqr1Xsu1aS6VmLtXKuNbyw1+rxYWs1dS1VtrSXEvNbix15L5XsvsWG31-rJWJuTam7N+bRGSMxOnMFkQlGVX8uhb5-zgWoCrcHJyRjq3WMbZoxxlTRBttBd26tig8glWHb46toTbHNvncsZd6z12qhBc6BQaTB2URHc6Ap17Z3lMfau3tm7f324PaB09zoABWRTb2IfSKsVDgLP29uw7k-DhAiORAADZUfg8w+Jz7fnvtOdu3AJHBPgciAAOxk9ExTi7WOYciAoMTxnROAAcbPoWU65zjunzP+d+BW50AAnML97GOqc7fF7DgXUvUAy7WwGBX6PAnK5p79nncBZca5nKt+Q62RMi85196HqvjfyETIDwn0uLcnet4r-XYvaew8t2brX8gXunfZ2IIgou7fY9947gHjSmfyFByHm3kPI-c7uxpl38eUdg9D+H231P7fR7u-jzPRP5Ck5z8npXPujd3YZ6Xt3nR5Cs8r174AmPU8O7u3zhvmuLdC9b3r9vBvC+14UJL3v5um-y8HxzlPBeo9j-kOryfWvVA69n2HiPC+08KFN6v47VulNz+r53ovahndx6J6oD3x+t-55V+f1Q92D+dFUMHz3Q+O87672oWPj3G8RBVBE9P8T9vcz8x938A9jts8k829v9H9ICS8r9ADVAK84Cv8R9F9cdjdVB68UC+838W8MCwDh8a8cD-se8CCp8gCB8SD7959ECKC1AJ9qC18Z96C89GDDdmDVAV82DnsN9ODt8mC6dVB99ltnsj80dSCECeC6cc5L8ADCCRAc5b8ZCGDT8f9z8c4X8GAgA" rel="nofollow noreferrer">https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmY...</a>
Show HN: Conway's Game of Life in TypeScript's type system
TypeScript playground: <a href="https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmYcCiBHArgQwDYDOAPABoA0cAmgHxwC8AUHHABREAq1LAlPbe3FAxgAOwAmBOCTgB+OAEY4ALjgAmXkNESmrDl151+gkMPGTKshcrW85MKFlQqAZvgLAA3AwZIUcAMLAeHj0cADkIGFwAD7hAHRhPsioAEIQOFBioYHBANoAugXevqgAavgAlmIAkuKgRACCUFDGphJwWCIA1iIQAO4iBbSMzGggAMZ4WGLARF3AiBDOcE1QFGOT07Pzi8urFAAGACQA3iJYALYARsBQAL4H1LSaZnDHJxUizrdwAHKtWkk52utweOjkvx0KhEwAAbrdvAB6RFwAAiEGABBEYXgAAscOI8KgYQBzHAwCrwuDQOAzcYVC74ODjHDuAgMZFwaowMKSAm9RCfEkICBwfGE1AwXHEuE-FnuCgEUXVMIXDoEIVwAC0ilZcBwcHcIgpMJCsPwjjgnwIwhwYiSfgAylgrvIiP8Xu1gTc1gEAa8AAwFUJDeg6eyWz2STC4QjuigB2ixGNueNwb23Wjg7XyKF-f3tXJxYv+BOFMJEkQkqVhfI6Zhyfy5Cuiau42v16zO11poslsvUYrJFZiMRuj0mQFwAAKEAIBHKU0xFFn88XjkkUfTlx9FH8BckQfyIfywx0KbjvwoGagz0nrwjwGzOrzE7akibLarNbrzAbcD7OJSzgANy0rNtawPGc5wXC1MU7f9AOA0Dm3An8EOsHVmE5MAoFlY0rTqEBqSweAljgK4IE6bQ-2sBpR3HFcYPXZcAOLICByHPxqgIAAZSlZjSDIxEdGByVQLchMyChpC3G8KAsOSd0zMNmFk+92n8CA8FqGYQAAeWcIgpJEsThEHf9FI0yQACV+l00BDOM9JMlE8SLL-OQTLc4RckoQoSGPLcIkSWj-0fDCXDcJ9aKiwgYusVx4q41AtM6GB2AcWZVigglEAKPcoKPE8z2YHKt1yT5vhaXiKHYqqfhs39LF4qCItoxsqONTLHCIGyKCQzjYoCLqMqyvq9w8lRPzQ9s6wdVKgjwHzBJc0zxKgkyZKgrSdKIpzvLM4BqAUqC7L6ByDKMw73NKkb0p62Zck7Hj+PhZzhJWihuzdEgTrgH6iBoE6Xr4gSPtco6ZO+l03WBshQbe1bPqhkcx1IPdtMug61pWp4YZ7eHEfBm7hAoej0fIEa9r0nGUduhSQdo16Sdx1GKd+zGacc662YZtG4Yoc7sd5+nzPx4n3tJ4BoYFoGhfs-bRch26mb-Fmpb5smAdh0h-o5+W4GFpWIfW8W1eYDXkZV7XAb+xmdFPFL0FhbT4RyPBTZWza1u2rddpFr2jv+qz3yNxXaeVs3jru6W-ICoLrPiULLA9lag-E2WaCggBmTs5BCzsVDCBI8zj-zckCqDC46gIlvT6Ws7vMPVBiOA87Cywa7C4vS+GmF4SgZ20FdvB4XOogdH0qAKhJaWfeEhHmFDqdjcjohp9n6W1f8dSw+KugAPyJeAiwZpzp2pbg0PgqGGGOAdF31DWx-HasZNze56147sxHt3gAnhhT+jcMKUBPrRJCVNQLgL-EhM+UBzrrFHu7JaG8Z5fzFjLAIJBn7fjmozZqzApqn3Pv0YeyDZhT3QfPSSvtH7wJoUnEyJ4T7+BXoGa+R8Eb30fpQXBEFE5hzXjzNBW9v4eTkH-MesxgHfwGuxfwDC5Eu3-hPWRmC9x8K-AI6gx82IljASBJ2zBppKMweQ-+vwM7CAXtJfMSkQS+n3FuA+R87ozRfnNKCkJ-zSzzFI+EViAnW2jv9K8+iOJGMHAtcOF0TaMLDiZe+i4qiXWsT-Eo1NA4JKnEk0IKSahEXSbkG8TsOQojoJUqp1Sam1LqdU8p9A6lwBSGgAA4tUf47A0COnYJ0tpTS6mNPqSM0ZNSkQonYJiE0woACqFI8AVApPBGJAApCAnwJ4X39lfPRjooI2hnlWUIYQwh3W2UnSqXwfitR2cEOqxYGotBspfPI+RmpyHWZs-oE03inEdHcU4vEHjEMdM7acRyYAmXSbYsQFB+IwkdMAMABz7CakPmEAAOtib6qKjnCgxWc0IOgcmvCudVEhCD+ivLwAVOA9VrktF+EIc6m4mFrQ+QDauKd-wQs+FCta7oWX9AIPCz4wAkVgAoF8kQE9FGkL6E8IuM5IXQuZSYVlYrEXIsOP8wFJwEUSuRfqmVcr4HnWoCCvMYKYmiXxewCA0L9lbkOUKPcZjMiwpYZSi5YcPacNDCMLlW53hPLgMC04YabJgn-LcpOWLsT5x9f0fhr8XEYTkHaoUDroX9UpVtNxkUAZoqrDmwVea4HNALfKqlfQ9FDGVXGv1S0M3FvtY68t7qq2+wiTWxB4bjE9zbdmjtwlfk1urea-oxCJ1rQmXAKZNoAisngpyAAEhAQe1It1SklMOEQOALiYhFIaYAkppQkRgGAUi87cQwCvQQJQyJxgQBEH0HAiBFnfDiC+i4iI+gVC6BUREKRFndARJyactwKgQCyKoGJoHPjzBaIfLNpbR2ZCIAcBg7EGAgHwzh4sDBHjgsQ+BqAihD58uNNCsjyHByZOnHR24rcqOqsFcE4yYH6MMeHEx7jtwc6hGowKsdwSrHMd9KoJVjHJMABZhPsbExQiTAnfQ5yVY0u9D6n2IhfW+j9X7gA-ogH+gDQHERtLnCgKAAB9EkiyZh2ZJJ0RpUGZ6wfbgGGJVmCA2baY524bTOihDQySMtY7sPsWizF2LcX4sJaI4l5L0WQApdi4R9L8W0tpay7h9L+HYuFeS8VmLmWEu5biJVqr1Xsu1aS6VmLtXKuNbyw1+rxYWs1dS1VtrSXEvNbix15L5XsvsWG31-rJWJuTam7N+bRGSMxOnMFkQlGVX8uhb5-zgWoCrcHJyRjq3WMbZoxxlTRBttBd26tig8glWHb46toTbHNvncsZd6z12qhBc6BQaTB2URHc6Ap17Z3lMfau3tm7f324PaB09zoABWRTb2IfSKsVDgLP29uw7k-DhAiORAADZUfg8w+Jz7fnvtOdu3AJHBPgciAAOxk9ExTi7WOYciAoMTxnROAAcbPoWU65zjunzP+d+BW50AAnML97GOqc7fF7DgXUvUAy7WwGBX6PAnK5p79nncBZca5nKt+Q62RMi85196HqvjfyETIDwn0uLcnet4r-XYvaew8t2brX8gXunfZ2IIgou7fY9947gHjSmfyFByHm3kPI-c7uxpl38eUdg9D+H231P7fR7u-jzPRP5Ck5z8npXPujd3YZ6Xt3nR5Cs8r174AmPU8O7u3zhvmuLdC9b3r9vBvC+14UJL3v5um-y8HxzlPBeo9j-kOryfWvVA69n2HiPC+08KFN6v47VulNz+r53ovahndx6J6oD3x+t-55V+f1Q92D+dFUMHz3Q+O87672oWPj3G8RBVBE9P8T9vcz8x938A9jts8k829v9H9ICS8r9ADVAK84Cv8R9F9cdjdVB68UC+838W8MCwDh8a8cD-se8CCp8gCB8SD7959ECKC1AJ9qC18Z96C89GDDdmDVAV82DnsN9ODt8mC6dVB99ltnsj80dSCECeC6cc5L8ADCCRAc5b8ZCGDT8f9z8c4X8GAgA" rel="nofollow noreferrer">https://www.typescriptlang.org/play?#code/KYDwDg9gTgLgBDAnmY...</a>