The best Hacker News stories from Show from the past day
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Show HN: Rarbg on IPFS
Show HN: An accessible, beautiful Raspberry Pi Pico pinout
I wanted to create an accessible alternative to the official Pico pinout image, offering more complete pin details, light/dark modes, screen-readable text, and at least some capacity to focus on the peripherals you’re interested in. It’s a sister site to the now ancient Raspberry Pi pinout (<a href="https://pinout.xyz/" rel="nofollow">https://pinout.xyz/</a>) which started out life hosted on a Raspberry Pi.
Show HN: An accessible, beautiful Raspberry Pi Pico pinout
I wanted to create an accessible alternative to the official Pico pinout image, offering more complete pin details, light/dark modes, screen-readable text, and at least some capacity to focus on the peripherals you’re interested in. It’s a sister site to the now ancient Raspberry Pi pinout (<a href="https://pinout.xyz/" rel="nofollow">https://pinout.xyz/</a>) which started out life hosted on a Raspberry Pi.
Show HN: An accessible, beautiful Raspberry Pi Pico pinout
I wanted to create an accessible alternative to the official Pico pinout image, offering more complete pin details, light/dark modes, screen-readable text, and at least some capacity to focus on the peripherals you’re interested in. It’s a sister site to the now ancient Raspberry Pi pinout (<a href="https://pinout.xyz/" rel="nofollow">https://pinout.xyz/</a>) which started out life hosted on a Raspberry Pi.
Show HN: Pot is a cross-platform translation software
Introducing my cross-platform translation software that makes it easier than ever to translate text on the fly. Our software is designed to be user-friendly and intuitive, with a sleek and modern interface that works seamlessly across all major platforms.<p>One of the standout features of our software is its ability to work with multiple translation APIs simultaneously (such as OpenAI,Google and so on), giving you access to a wider range of translations and ensuring that you always get the most accurate and relevant results.<p>Whether you're a student, or a business professional, our software is the perfect tool to help you communicate more effectively and bridge the language gap. With our software, you can translate text in real-time, without ever having to leave your current application or website.<p>So why wait? Download our cross-platform translation software today and experience the smoothest translation experience!
Show HN: TodoBot is an AI coach that helps you write a better todo list
I thought I'd be more likely to do the things on my todo list if GPT-4 was watching me (fail to) do them, so I built this. Let me know what you think!
Show HN: Local implementation of text-to-3D using Shap-E
Show HN: Local implementation of text-to-3D using Shap-E
Show HN: Local implementation of text-to-3D using Shap-E
Show HN: Verify LLM Generated Code with a Spreadsheet
Hey HN! Been a minute. We launched Mito here last year (<a href="https://news.ycombinator.com/item?id=32723766" rel="nofollow">https://news.ycombinator.com/item?id=32723766</a>).<p>Mito is a spreadsheet that generates Python code as you edit it. We've spent the past three years trying to lower the startup cost to use Python for data work. In doing so, we’ve been thrust into the middle of many Python transition processes at larger enterprises, and we’ve seen up-close how non-technical folks interact with generated code.<p>The Mito AI chatbot lives inside of the Mito spreadsheet (<a href="https://www.trymito.io/">https://www.trymito.io/</a>>. The obvious benefit of this is that you can use the chatbot to transform your data and write a repeatable Pythons script. The less obvious (but equally important) benefit is that by connecting a spreadsheet and chatbot, Mito helps you understand the impact of your edits and verify LLM generated code. Every time you use the chatbot, Mito highlights the changed data in the spreadsheet. You can see a quick demo here (<a href="https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view">https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view</a>).<p>Three main insights shaped our approach to LLM code generation:<p># Consumers of generated code don't know enough Python to verify and correct the code<p>Mito users span the range of Python experience. For new programmers, generating code using LLMs is an easy step one. Ensuring the generated code is correct is the forgotten step two.<p>In practice, LLMs often generate incorrect code, or code with unexpected side effects. A user will prompt an LLM to calculate a total_revenue column from price and quantity columns. The LLM correctly calculates total_revenue = price * quantity but then mistakenly deletes price and quantity.<p>New programmers find it almost impossible to verify generated code by reading it alone. They need tooling designed for their skillsets.<p># Not everyone knows how to use a chat interface for transformations<p>We were surprised to learn that many Mito users a) had no experience with ChatGPT, and b) didn’t understand the chat interface at all! Mito AI presents users a few example prompts and an input field. A surprising number of users thought the example prompts were all they could use Mito AI for.<p>AI chatbots are new. Us builders might be using them for natural language interactions, but users are still learning how to use them in new contexts. This stands in stark contrast to spreadsheets, where pretty much ever business user has experience. Shout out 40 years of Excel dominance!<p># The more context a prompt has about the user’s data + edits, the better the LLM results<p>For the LLM to generate code that can execute correctly, the prompt should include the names of the dataframes, the column headers, (some) dataframe values, and a few previous edits as examples. Duh.<p>But there’s no reason users should be responsible for writing this prompt. No one loves writing long chats, and in practice Mito AI users expect to be able to write ~12 words. Spreadsheets are well-suited to building the rest of the prompt for you - they have all of your data context, and know your recent edits.<p>With these three insights, it became very clear to us what role a spreadsheet could play in LLM based code-gen: a spreadsheet is the prompt builder, and a spreadsheet is the code verifier.<p>Mito AI builds an effective prompt by supplementing your input with the context of your data and recent edits.<p>Mito AI then helps you to verify the LLM generated code by highlighting the added, modified, and removed data within the chat interface - and within the spreadsheet. This way, you can ensure your LLM generated code is correct.<p>Give it a spin. Let us know what you think of the recon and how we can make it more helpful!<p>Also, if you like what we’re doing, we’re hiring – come help us build! (<a href="https://www.ycombinator.com/companies/mito/jobs" rel="nofollow">https://www.ycombinator.com/companies/mito/jobs</a>)
Show HN: Verify LLM Generated Code with a Spreadsheet
Hey HN! Been a minute. We launched Mito here last year (<a href="https://news.ycombinator.com/item?id=32723766" rel="nofollow">https://news.ycombinator.com/item?id=32723766</a>).<p>Mito is a spreadsheet that generates Python code as you edit it. We've spent the past three years trying to lower the startup cost to use Python for data work. In doing so, we’ve been thrust into the middle of many Python transition processes at larger enterprises, and we’ve seen up-close how non-technical folks interact with generated code.<p>The Mito AI chatbot lives inside of the Mito spreadsheet (<a href="https://www.trymito.io/">https://www.trymito.io/</a>>. The obvious benefit of this is that you can use the chatbot to transform your data and write a repeatable Pythons script. The less obvious (but equally important) benefit is that by connecting a spreadsheet and chatbot, Mito helps you understand the impact of your edits and verify LLM generated code. Every time you use the chatbot, Mito highlights the changed data in the spreadsheet. You can see a quick demo here (<a href="https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view">https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view</a>).<p>Three main insights shaped our approach to LLM code generation:<p># Consumers of generated code don't know enough Python to verify and correct the code<p>Mito users span the range of Python experience. For new programmers, generating code using LLMs is an easy step one. Ensuring the generated code is correct is the forgotten step two.<p>In practice, LLMs often generate incorrect code, or code with unexpected side effects. A user will prompt an LLM to calculate a total_revenue column from price and quantity columns. The LLM correctly calculates total_revenue = price * quantity but then mistakenly deletes price and quantity.<p>New programmers find it almost impossible to verify generated code by reading it alone. They need tooling designed for their skillsets.<p># Not everyone knows how to use a chat interface for transformations<p>We were surprised to learn that many Mito users a) had no experience with ChatGPT, and b) didn’t understand the chat interface at all! Mito AI presents users a few example prompts and an input field. A surprising number of users thought the example prompts were all they could use Mito AI for.<p>AI chatbots are new. Us builders might be using them for natural language interactions, but users are still learning how to use them in new contexts. This stands in stark contrast to spreadsheets, where pretty much ever business user has experience. Shout out 40 years of Excel dominance!<p># The more context a prompt has about the user’s data + edits, the better the LLM results<p>For the LLM to generate code that can execute correctly, the prompt should include the names of the dataframes, the column headers, (some) dataframe values, and a few previous edits as examples. Duh.<p>But there’s no reason users should be responsible for writing this prompt. No one loves writing long chats, and in practice Mito AI users expect to be able to write ~12 words. Spreadsheets are well-suited to building the rest of the prompt for you - they have all of your data context, and know your recent edits.<p>With these three insights, it became very clear to us what role a spreadsheet could play in LLM based code-gen: a spreadsheet is the prompt builder, and a spreadsheet is the code verifier.<p>Mito AI builds an effective prompt by supplementing your input with the context of your data and recent edits.<p>Mito AI then helps you to verify the LLM generated code by highlighting the added, modified, and removed data within the chat interface - and within the spreadsheet. This way, you can ensure your LLM generated code is correct.<p>Give it a spin. Let us know what you think of the recon and how we can make it more helpful!<p>Also, if you like what we’re doing, we’re hiring – come help us build! (<a href="https://www.ycombinator.com/companies/mito/jobs" rel="nofollow">https://www.ycombinator.com/companies/mito/jobs</a>)
Show HN: Verify LLM Generated Code with a Spreadsheet
Hey HN! Been a minute. We launched Mito here last year (<a href="https://news.ycombinator.com/item?id=32723766" rel="nofollow">https://news.ycombinator.com/item?id=32723766</a>).<p>Mito is a spreadsheet that generates Python code as you edit it. We've spent the past three years trying to lower the startup cost to use Python for data work. In doing so, we’ve been thrust into the middle of many Python transition processes at larger enterprises, and we’ve seen up-close how non-technical folks interact with generated code.<p>The Mito AI chatbot lives inside of the Mito spreadsheet (<a href="https://www.trymito.io/">https://www.trymito.io/</a>>. The obvious benefit of this is that you can use the chatbot to transform your data and write a repeatable Pythons script. The less obvious (but equally important) benefit is that by connecting a spreadsheet and chatbot, Mito helps you understand the impact of your edits and verify LLM generated code. Every time you use the chatbot, Mito highlights the changed data in the spreadsheet. You can see a quick demo here (<a href="https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view">https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view</a>).<p>Three main insights shaped our approach to LLM code generation:<p># Consumers of generated code don't know enough Python to verify and correct the code<p>Mito users span the range of Python experience. For new programmers, generating code using LLMs is an easy step one. Ensuring the generated code is correct is the forgotten step two.<p>In practice, LLMs often generate incorrect code, or code with unexpected side effects. A user will prompt an LLM to calculate a total_revenue column from price and quantity columns. The LLM correctly calculates total_revenue = price * quantity but then mistakenly deletes price and quantity.<p>New programmers find it almost impossible to verify generated code by reading it alone. They need tooling designed for their skillsets.<p># Not everyone knows how to use a chat interface for transformations<p>We were surprised to learn that many Mito users a) had no experience with ChatGPT, and b) didn’t understand the chat interface at all! Mito AI presents users a few example prompts and an input field. A surprising number of users thought the example prompts were all they could use Mito AI for.<p>AI chatbots are new. Us builders might be using them for natural language interactions, but users are still learning how to use them in new contexts. This stands in stark contrast to spreadsheets, where pretty much ever business user has experience. Shout out 40 years of Excel dominance!<p># The more context a prompt has about the user’s data + edits, the better the LLM results<p>For the LLM to generate code that can execute correctly, the prompt should include the names of the dataframes, the column headers, (some) dataframe values, and a few previous edits as examples. Duh.<p>But there’s no reason users should be responsible for writing this prompt. No one loves writing long chats, and in practice Mito AI users expect to be able to write ~12 words. Spreadsheets are well-suited to building the rest of the prompt for you - they have all of your data context, and know your recent edits.<p>With these three insights, it became very clear to us what role a spreadsheet could play in LLM based code-gen: a spreadsheet is the prompt builder, and a spreadsheet is the code verifier.<p>Mito AI builds an effective prompt by supplementing your input with the context of your data and recent edits.<p>Mito AI then helps you to verify the LLM generated code by highlighting the added, modified, and removed data within the chat interface - and within the spreadsheet. This way, you can ensure your LLM generated code is correct.<p>Give it a spin. Let us know what you think of the recon and how we can make it more helpful!<p>Also, if you like what we’re doing, we’re hiring – come help us build! (<a href="https://www.ycombinator.com/companies/mito/jobs" rel="nofollow">https://www.ycombinator.com/companies/mito/jobs</a>)
Show HN: Verify LLM Generated Code with a Spreadsheet
Hey HN! Been a minute. We launched Mito here last year (<a href="https://news.ycombinator.com/item?id=32723766" rel="nofollow">https://news.ycombinator.com/item?id=32723766</a>).<p>Mito is a spreadsheet that generates Python code as you edit it. We've spent the past three years trying to lower the startup cost to use Python for data work. In doing so, we’ve been thrust into the middle of many Python transition processes at larger enterprises, and we’ve seen up-close how non-technical folks interact with generated code.<p>The Mito AI chatbot lives inside of the Mito spreadsheet (<a href="https://www.trymito.io/">https://www.trymito.io/</a>>. The obvious benefit of this is that you can use the chatbot to transform your data and write a repeatable Pythons script. The less obvious (but equally important) benefit is that by connecting a spreadsheet and chatbot, Mito helps you understand the impact of your edits and verify LLM generated code. Every time you use the chatbot, Mito highlights the changed data in the spreadsheet. You can see a quick demo here (<a href="https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view">https://www.tella.tv/video/clibtwssv00000fl65oky13nu/view</a>).<p>Three main insights shaped our approach to LLM code generation:<p># Consumers of generated code don't know enough Python to verify and correct the code<p>Mito users span the range of Python experience. For new programmers, generating code using LLMs is an easy step one. Ensuring the generated code is correct is the forgotten step two.<p>In practice, LLMs often generate incorrect code, or code with unexpected side effects. A user will prompt an LLM to calculate a total_revenue column from price and quantity columns. The LLM correctly calculates total_revenue = price * quantity but then mistakenly deletes price and quantity.<p>New programmers find it almost impossible to verify generated code by reading it alone. They need tooling designed for their skillsets.<p># Not everyone knows how to use a chat interface for transformations<p>We were surprised to learn that many Mito users a) had no experience with ChatGPT, and b) didn’t understand the chat interface at all! Mito AI presents users a few example prompts and an input field. A surprising number of users thought the example prompts were all they could use Mito AI for.<p>AI chatbots are new. Us builders might be using them for natural language interactions, but users are still learning how to use them in new contexts. This stands in stark contrast to spreadsheets, where pretty much ever business user has experience. Shout out 40 years of Excel dominance!<p># The more context a prompt has about the user’s data + edits, the better the LLM results<p>For the LLM to generate code that can execute correctly, the prompt should include the names of the dataframes, the column headers, (some) dataframe values, and a few previous edits as examples. Duh.<p>But there’s no reason users should be responsible for writing this prompt. No one loves writing long chats, and in practice Mito AI users expect to be able to write ~12 words. Spreadsheets are well-suited to building the rest of the prompt for you - they have all of your data context, and know your recent edits.<p>With these three insights, it became very clear to us what role a spreadsheet could play in LLM based code-gen: a spreadsheet is the prompt builder, and a spreadsheet is the code verifier.<p>Mito AI builds an effective prompt by supplementing your input with the context of your data and recent edits.<p>Mito AI then helps you to verify the LLM generated code by highlighting the added, modified, and removed data within the chat interface - and within the spreadsheet. This way, you can ensure your LLM generated code is correct.<p>Give it a spin. Let us know what you think of the recon and how we can make it more helpful!<p>Also, if you like what we’re doing, we’re hiring – come help us build! (<a href="https://www.ycombinator.com/companies/mito/jobs" rel="nofollow">https://www.ycombinator.com/companies/mito/jobs</a>)
Show HN: Automating daily reports, because fuck it
Show HN: Automating daily reports, because fuck it
Show HN: Automating daily reports, because fuck it
Show HN: Mercury – Convert Jupyter notebooks to web apps
Author here. Mercury is the simplest way to serve your notebooks as web apps.
The simplicity of the framework is very important to us. Mercury has some useful features to make sharing easier:<p>- you can show or hide your code,<p>- your users can easily export executed notebook to PDF/HTML,<p>- there is built-in authentication,<p>- you can produce files in the notebook and make them downloadable,<p>- you can share multiple notebooks.<p>We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file. Below clickable links:<p>The GitHub repository <a href="https://github.com/mljar/mercury">https://github.com/mljar/mercury</a><p>Documentation <a href="https://RunMercury.com/docs/" rel="nofollow">https://RunMercury.com/docs/</a><p>Mercury Cloud <a href="https://cloud.runmercury.com" rel="nofollow">https://cloud.runmercury.com</a>
Show HN: Mercury – Convert Jupyter notebooks to web apps
Author here. Mercury is the simplest way to serve your notebooks as web apps.
The simplicity of the framework is very important to us. Mercury has some useful features to make sharing easier:<p>- you can show or hide your code,<p>- your users can easily export executed notebook to PDF/HTML,<p>- there is built-in authentication,<p>- you can produce files in the notebook and make them downloadable,<p>- you can share multiple notebooks.<p>We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file. Below clickable links:<p>The GitHub repository <a href="https://github.com/mljar/mercury">https://github.com/mljar/mercury</a><p>Documentation <a href="https://RunMercury.com/docs/" rel="nofollow">https://RunMercury.com/docs/</a><p>Mercury Cloud <a href="https://cloud.runmercury.com" rel="nofollow">https://cloud.runmercury.com</a>
Show HN: HN Resume to Jobs – AI Powered Job Matching Tailored to Your Resume
Hey HN! I'm excited to show off this side project I've been working on. This project matches your resume with the best matching jobs from the monthly HN Who's Hiring post. It works by creating a vector embedding of your resume using OpenAI's embedding API, and then ranking the jobs using a vector similarity score. (You can toggle between max inner product, cosine, and euclidean in the "Advanced Options")<p>I was laid off in August and it took a whole 6 months for me to find my new job. Fortunately, I found my new role on January's HN Who's Hiring post. So I hope this will prove useful to any job seekers out there. I know it's a tough time right now, but you will get through it!<p>Thanks HN! I would greatly appreciate any and all feedback!
Show HN: HN Resume to Jobs – AI Powered Job Matching Tailored to Your Resume
Hey HN! I'm excited to show off this side project I've been working on. This project matches your resume with the best matching jobs from the monthly HN Who's Hiring post. It works by creating a vector embedding of your resume using OpenAI's embedding API, and then ranking the jobs using a vector similarity score. (You can toggle between max inner product, cosine, and euclidean in the "Advanced Options")<p>I was laid off in August and it took a whole 6 months for me to find my new job. Fortunately, I found my new role on January's HN Who's Hiring post. So I hope this will prove useful to any job seekers out there. I know it's a tough time right now, but you will get through it!<p>Thanks HN! I would greatly appreciate any and all feedback!