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Show HN: Clamshell- an experimental Python based shell

About a month ago I started a suprisingly-not-that tricky project to build an experimental python repl that I could use instead of bash as a daily shell in windows, mac or linux. It"s more hack-for-exploration than a production ready shell, but here it is! Hope somebody finds it anywhere near as interesting to check out as I found making it.<p>Disclaimer: I know about xonsh and love it (if you haven't heard of it, google it). This project is more pure python and less python and bash interacting - see the readme for more details.

Show HN: Clamshell- an experimental Python based shell

About a month ago I started a suprisingly-not-that tricky project to build an experimental python repl that I could use instead of bash as a daily shell in windows, mac or linux. It"s more hack-for-exploration than a production ready shell, but here it is! Hope somebody finds it anywhere near as interesting to check out as I found making it.<p>Disclaimer: I know about xonsh and love it (if you haven't heard of it, google it). This project is more pure python and less python and bash interacting - see the readme for more details.

Show HN: ML paper podcast generator using GPT and Tortoise-TTS

I built a pipeline that turns tweets about ML papers into a podcast.<p>Code's up here. Happy hacking. <a href="https://github.com/yacineMTB/scribepod">https://github.com/yacineMTB/scribepod</a>

Show HN: Knotend – a keyboard-driven flowchart editor

Hey all, I made knotend [0] because I wanted a flowchart editor that was keyboard-driven and super fast. I was tired of dragging boxes around on a canvas. You may have seen knotend around here before when I wrote an initial blog post [1] about why I wanted a new kind of flowchart editor. Thank you to everyone who has given me feedback here on HN!<p>Since that blog post I’ve been working hard to get to a v1 which I’m showing now. You can use the free version without having to sign up for an account. I would love your feedback!<p>What makes knotend different is two main things: 1) The nodes are constrained to a grid which enables a keyboard-centric experience for selection and navigation, and 2) there’s autolayout so each time you add a node, the graph automatically lays itself out and places each node in a cell.<p>In the future I’ll be working on supporting more complex editing actions, linking graphs together, collaboration, and more.<p>Please drop your feedback below, reach out on twitter [2], or email scott@knotend.com.<p>[0] <a href="https://www.knotend.com" rel="nofollow">https://www.knotend.com</a><p>[1] <a href="https://www.scottantipa.com/why-knotend" rel="nofollow">https://www.scottantipa.com/why-knotend</a><p>[2] <a href="https://twitter.com/ScottyAntipa" rel="nofollow">https://twitter.com/ScottyAntipa</a>

Show HN: PlantUML based collaborative UML editor is now open source

Show HN: PlantUML based collaborative UML editor is now open source

Show HN: PlantUML based collaborative UML editor is now open source

Show HN: Interact with the terminal in plain English using GPT-3

Show HN: Interact with the terminal in plain English using GPT-3

Show HN: Chrome extension to close Zoom/Notion tabs after launching desktop app

Hi HN! I use the desktop versions of Zoom, Notion, and Asana, so at the end of the day, I have a ton of Chrome tabs left over from these services launching their apps. I threw together a little extension to clean these tabs up.<p>Do folks tend to use the browser versions of these apps? Or are there other sites that this extension should support?

Show HN: Chrome extension to close Zoom/Notion tabs after launching desktop app

Hi HN! I use the desktop versions of Zoom, Notion, and Asana, so at the end of the day, I have a ton of Chrome tabs left over from these services launching their apps. I threw together a little extension to clean these tabs up.<p>Do folks tend to use the browser versions of these apps? Or are there other sites that this extension should support?

Show HN: Syncing data to your customer’s Google Sheets

Hey HN! Charles here from Prequel (<a href="https://prequel.co">https://prequel.co</a>). We just launched the ability to sync data from your own app/db/data warehouse to any of your customer’s Google Sheets, CSV, or Excel – and I wanted to share a bit more about how we built the Google Sheets integration. If you’re curious, see here for a quick GIF demo of our Google Sheets destination: <a href="https://storage.googleapis.com/hn_asset/Prequel_GoogleSheetsDemo.webp" rel="nofollow">https://storage.googleapis.com/hn_asset/Prequel_GoogleSheets...</a>.<p>Quick background on us: we make it easy to integrate with and sync data to data warehouses. Problem is, there are plenty of folks who want access to their data, but don’t have or don’t know how to use a data warehouse. For example, FP&A teams, customer success teams, etc.<p>To get around that, we added some non-db destinations to Prequel: Google Sheets, CSV, and Excel. We had to rework some core assumptions in order to get Google Sheets to work.<p>By default, Prequel does incremental syncs, meaning we only write net new or updated data to the destination. To avoid duplicate rows, we typically perform those writes as upserts – this is pretty trivial in most SQL dialects. But since Google Sheets is not actually a db, it doesn’t have a concept of upserts, and we had to get creative.<p>We had two options: either force all Google Sheets syncs to be “full refreshes” every time (eg grab all the data and brute-force write it to the sheet). The downside is, this can get expensive quickly for our customers, especially when data gets refreshed at higher frequencies (eg every 15 minutes).<p>The other, and better, option was to figure out how to perform upserts in Sheets. To do so, we read the data from the sheet we’re about to write to into memory. We store it in a large map by primary key. We reconcile it with the data we’re about to write. We then dump the contents of the map back to the sheet. In order to make the user experience smoother, we also sort the rows by timestamp before writing it back. This guarantees that we don’t accidentally shuffle rows with every transfer, which might leave users feeling confused.<p>“Wait, you keep all the data in memory… so how do you avoid blowing up your pods?”. Great question! Luckily, Google Sheets has pretty stringent cell / row size limits. This allows us to restrict the amount of data that can be written to these destinations (we throw a nice error if someone tries to sync too much data), and thereby also guarantees that we don’t OOM our poor pods.<p>Another interesting problem we had to solve was auth: how do we let users give us access to their sheets in a way that both feels intuitive and upholds strong security guarantees? It seemed like the cleanest user experience was to ask the spreadsheet owner to share access with a new user – much like they would with any real human user. To make this possible without creating a superuser that would have access to _all_ the sheets, we had to programmatically generate a different user for each of our customers. We do this via the GCP IAM API, creating a new service account every time. We then auth into the sheet through this service account.<p>One last fun UX challenge to think through was how to prevent users from editing the “golden” data we just sync’d. It might not be immediately clear to them that this data is meant as a source of truth record, rather than a playground. To get around this, we create protected ranges and prevent them from editing the sheets we write to. Sheets even adds a little padlock icon to the relevant sheets, which helps convey the “don’t mess with this”.<p>If you want to take it for a spin, you can sign up on our site or reach us at hello (at) prequel.co. Happy to answer any other questions about the design!

Show HN: Syncing data to your customer’s Google Sheets

Hey HN! Charles here from Prequel (<a href="https://prequel.co">https://prequel.co</a>). We just launched the ability to sync data from your own app/db/data warehouse to any of your customer’s Google Sheets, CSV, or Excel – and I wanted to share a bit more about how we built the Google Sheets integration. If you’re curious, see here for a quick GIF demo of our Google Sheets destination: <a href="https://storage.googleapis.com/hn_asset/Prequel_GoogleSheetsDemo.webp" rel="nofollow">https://storage.googleapis.com/hn_asset/Prequel_GoogleSheets...</a>.<p>Quick background on us: we make it easy to integrate with and sync data to data warehouses. Problem is, there are plenty of folks who want access to their data, but don’t have or don’t know how to use a data warehouse. For example, FP&A teams, customer success teams, etc.<p>To get around that, we added some non-db destinations to Prequel: Google Sheets, CSV, and Excel. We had to rework some core assumptions in order to get Google Sheets to work.<p>By default, Prequel does incremental syncs, meaning we only write net new or updated data to the destination. To avoid duplicate rows, we typically perform those writes as upserts – this is pretty trivial in most SQL dialects. But since Google Sheets is not actually a db, it doesn’t have a concept of upserts, and we had to get creative.<p>We had two options: either force all Google Sheets syncs to be “full refreshes” every time (eg grab all the data and brute-force write it to the sheet). The downside is, this can get expensive quickly for our customers, especially when data gets refreshed at higher frequencies (eg every 15 minutes).<p>The other, and better, option was to figure out how to perform upserts in Sheets. To do so, we read the data from the sheet we’re about to write to into memory. We store it in a large map by primary key. We reconcile it with the data we’re about to write. We then dump the contents of the map back to the sheet. In order to make the user experience smoother, we also sort the rows by timestamp before writing it back. This guarantees that we don’t accidentally shuffle rows with every transfer, which might leave users feeling confused.<p>“Wait, you keep all the data in memory… so how do you avoid blowing up your pods?”. Great question! Luckily, Google Sheets has pretty stringent cell / row size limits. This allows us to restrict the amount of data that can be written to these destinations (we throw a nice error if someone tries to sync too much data), and thereby also guarantees that we don’t OOM our poor pods.<p>Another interesting problem we had to solve was auth: how do we let users give us access to their sheets in a way that both feels intuitive and upholds strong security guarantees? It seemed like the cleanest user experience was to ask the spreadsheet owner to share access with a new user – much like they would with any real human user. To make this possible without creating a superuser that would have access to _all_ the sheets, we had to programmatically generate a different user for each of our customers. We do this via the GCP IAM API, creating a new service account every time. We then auth into the sheet through this service account.<p>One last fun UX challenge to think through was how to prevent users from editing the “golden” data we just sync’d. It might not be immediately clear to them that this data is meant as a source of truth record, rather than a playground. To get around this, we create protected ranges and prevent them from editing the sheets we write to. Sheets even adds a little padlock icon to the relevant sheets, which helps convey the “don’t mess with this”.<p>If you want to take it for a spin, you can sign up on our site or reach us at hello (at) prequel.co. Happy to answer any other questions about the design!

Show HN: Syncing data to your customer’s Google Sheets

Hey HN! Charles here from Prequel (<a href="https://prequel.co">https://prequel.co</a>). We just launched the ability to sync data from your own app/db/data warehouse to any of your customer’s Google Sheets, CSV, or Excel – and I wanted to share a bit more about how we built the Google Sheets integration. If you’re curious, see here for a quick GIF demo of our Google Sheets destination: <a href="https://storage.googleapis.com/hn_asset/Prequel_GoogleSheetsDemo.webp" rel="nofollow">https://storage.googleapis.com/hn_asset/Prequel_GoogleSheets...</a>.<p>Quick background on us: we make it easy to integrate with and sync data to data warehouses. Problem is, there are plenty of folks who want access to their data, but don’t have or don’t know how to use a data warehouse. For example, FP&A teams, customer success teams, etc.<p>To get around that, we added some non-db destinations to Prequel: Google Sheets, CSV, and Excel. We had to rework some core assumptions in order to get Google Sheets to work.<p>By default, Prequel does incremental syncs, meaning we only write net new or updated data to the destination. To avoid duplicate rows, we typically perform those writes as upserts – this is pretty trivial in most SQL dialects. But since Google Sheets is not actually a db, it doesn’t have a concept of upserts, and we had to get creative.<p>We had two options: either force all Google Sheets syncs to be “full refreshes” every time (eg grab all the data and brute-force write it to the sheet). The downside is, this can get expensive quickly for our customers, especially when data gets refreshed at higher frequencies (eg every 15 minutes).<p>The other, and better, option was to figure out how to perform upserts in Sheets. To do so, we read the data from the sheet we’re about to write to into memory. We store it in a large map by primary key. We reconcile it with the data we’re about to write. We then dump the contents of the map back to the sheet. In order to make the user experience smoother, we also sort the rows by timestamp before writing it back. This guarantees that we don’t accidentally shuffle rows with every transfer, which might leave users feeling confused.<p>“Wait, you keep all the data in memory… so how do you avoid blowing up your pods?”. Great question! Luckily, Google Sheets has pretty stringent cell / row size limits. This allows us to restrict the amount of data that can be written to these destinations (we throw a nice error if someone tries to sync too much data), and thereby also guarantees that we don’t OOM our poor pods.<p>Another interesting problem we had to solve was auth: how do we let users give us access to their sheets in a way that both feels intuitive and upholds strong security guarantees? It seemed like the cleanest user experience was to ask the spreadsheet owner to share access with a new user – much like they would with any real human user. To make this possible without creating a superuser that would have access to _all_ the sheets, we had to programmatically generate a different user for each of our customers. We do this via the GCP IAM API, creating a new service account every time. We then auth into the sheet through this service account.<p>One last fun UX challenge to think through was how to prevent users from editing the “golden” data we just sync’d. It might not be immediately clear to them that this data is meant as a source of truth record, rather than a playground. To get around this, we create protected ranges and prevent them from editing the sheets we write to. Sheets even adds a little padlock icon to the relevant sheets, which helps convey the “don’t mess with this”.<p>If you want to take it for a spin, you can sign up on our site or reach us at hello (at) prequel.co. Happy to answer any other questions about the design!

Show HN: Syncing data to your customer’s Google Sheets

Hey HN! Charles here from Prequel (<a href="https://prequel.co">https://prequel.co</a>). We just launched the ability to sync data from your own app/db/data warehouse to any of your customer’s Google Sheets, CSV, or Excel – and I wanted to share a bit more about how we built the Google Sheets integration. If you’re curious, see here for a quick GIF demo of our Google Sheets destination: <a href="https://storage.googleapis.com/hn_asset/Prequel_GoogleSheetsDemo.webp" rel="nofollow">https://storage.googleapis.com/hn_asset/Prequel_GoogleSheets...</a>.<p>Quick background on us: we make it easy to integrate with and sync data to data warehouses. Problem is, there are plenty of folks who want access to their data, but don’t have or don’t know how to use a data warehouse. For example, FP&A teams, customer success teams, etc.<p>To get around that, we added some non-db destinations to Prequel: Google Sheets, CSV, and Excel. We had to rework some core assumptions in order to get Google Sheets to work.<p>By default, Prequel does incremental syncs, meaning we only write net new or updated data to the destination. To avoid duplicate rows, we typically perform those writes as upserts – this is pretty trivial in most SQL dialects. But since Google Sheets is not actually a db, it doesn’t have a concept of upserts, and we had to get creative.<p>We had two options: either force all Google Sheets syncs to be “full refreshes” every time (eg grab all the data and brute-force write it to the sheet). The downside is, this can get expensive quickly for our customers, especially when data gets refreshed at higher frequencies (eg every 15 minutes).<p>The other, and better, option was to figure out how to perform upserts in Sheets. To do so, we read the data from the sheet we’re about to write to into memory. We store it in a large map by primary key. We reconcile it with the data we’re about to write. We then dump the contents of the map back to the sheet. In order to make the user experience smoother, we also sort the rows by timestamp before writing it back. This guarantees that we don’t accidentally shuffle rows with every transfer, which might leave users feeling confused.<p>“Wait, you keep all the data in memory… so how do you avoid blowing up your pods?”. Great question! Luckily, Google Sheets has pretty stringent cell / row size limits. This allows us to restrict the amount of data that can be written to these destinations (we throw a nice error if someone tries to sync too much data), and thereby also guarantees that we don’t OOM our poor pods.<p>Another interesting problem we had to solve was auth: how do we let users give us access to their sheets in a way that both feels intuitive and upholds strong security guarantees? It seemed like the cleanest user experience was to ask the spreadsheet owner to share access with a new user – much like they would with any real human user. To make this possible without creating a superuser that would have access to _all_ the sheets, we had to programmatically generate a different user for each of our customers. We do this via the GCP IAM API, creating a new service account every time. We then auth into the sheet through this service account.<p>One last fun UX challenge to think through was how to prevent users from editing the “golden” data we just sync’d. It might not be immediately clear to them that this data is meant as a source of truth record, rather than a playground. To get around this, we create protected ranges and prevent them from editing the sheets we write to. Sheets even adds a little padlock icon to the relevant sheets, which helps convey the “don’t mess with this”.<p>If you want to take it for a spin, you can sign up on our site or reach us at hello (at) prequel.co. Happy to answer any other questions about the design!

Show HN: Search inside 15,000 pitchdeck slides

Show HN: Search inside 15,000 pitchdeck slides

Show HN: Search inside 15,000 pitchdeck slides

Show HN: Search inside 15,000 pitchdeck slides

Show HN: YouTube Summaries Using GPT

Hi, I'm Alex. I created Eightify to take my mind off things during a weekend, but I was surprised that my friends were genuinely interested in it. I kept going, and now it's been nine weeks since I started.<p>I got the idea to summarize videos when my friend sent me a lengthy video again. This happens to me often; the video title is so enticing, and then it turns out to be nothing. I had been working with GPT for 6 months by the time, so everything looked like a nail to me.<p>It's a Chrome extension, and I'm offering 5 free tries for videos under an hour. After that, you have to buy a package. I'm not making money yet, but it pays for GPT, which can be pricey for long texts. And some of Lex Fridman's podcasts are incredibly long.<p>I'm one of those overly optimistic people when it comes to GPT. So many people tell me, "Oh, it doesn't solve this problem yet; let's wait for GPT-4". The real issue is that their prompts are usually inadequate, and it takes you anywhere from two days to two weeks to make it work. Testing and debugging, preferably with automated tests. I believe you can solve many problems with GPT-3 already.<p>I would love to answer any questions you have about the product and GPT in general. I've invested at least 500 hours into prompt engineering. And I enjoy watching other people's prompts too!