The best Hacker News stories from Show from the past day
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Show HN: Fructose – LLM calls as strongly typed functions
Hi HN! Erik here from Banana (formerly the serverless GPU platform), excited to show you what we’ve been working on next:<p>Fructose<p>Fructose is a python package to call LLMs as strongly typed functions. It uses function type signatures to guide the generation and guarantee a correctly typed output, in whatever basic/complex python datatype requested.<p>By guaranteeing output structure, we believe this will enable more complex applications to be built, interweaving code with LLMs with code. For now, we’ve shipped Fructose as a client-only library simply calling gpt-4 (by default) with json mode, pretty simple and not unlike other packages such as marvin and instructor, but we’re also working on our own lightweight formatting model that we’ll host and/or distribute to the client, to help reduce token burn and increase accuracy.<p>We figure, no time like the present to show y’all what we’re working on! Questions, compliments, and roasts welcomed.
Show HN: dockerc – Docker image to static executable "compiler"
Show HN: dockerc – Docker image to static executable "compiler"
Show HN: dockerc – Docker image to static executable "compiler"
Show HN: dockerc – Docker image to static executable "compiler"
Show HN: Niquests – a simple HTTP library, a drop-in replacement for Requests
Show HN: Workout Tracker PWA
Show HN: Workout Tracker PWA
Show HN: NeedleDrop – Guess the movie from a song
Backstory: I'm a product designer who's mostly worked for startups and now big tech, and I haven't really touched html/css for nearly a decade. I've worked closely with engineers my entire career but never really rolled the sleeves up and dived into a scripting language. I'd seen some engineers playing around with CodeGPT over a year ago when it launched–we huddled around a screen and tried to decide how quickly our jobs would be replaced by this new technology. At the time, we weren’t in any real danger, but I caught a glimpse of how well it understood prompts and stubbed out large amounts of code.<p>For the past four or five years, I've played a hacky trivia game with family and friends where I play a song, and they have to guess the movie that features the song; Guess the Needle Drop. After many passionate debates and over-the-top celebrations fueled by my generation’s nostalgia for popular classic songs and films, people often told me that I needed to “build an app for this.”<p>I started doodling in Figma before quickly starting to build the website in Node, and then read somewhere that it's a better approach to learn vanilla javascript before trying to benefit from frameworks like React, etc. So I started again with a static vanilla website and, piece by piece, built out each chunk of functionality I’d envisioned. My mind was consistently blown at how helpful ChatGPT was–far beyond my lofty expectations, even with all the AI hype. It was like having a 24/7 personal tutor for free. I rarely had to google console errors hoping that a Stack Overflow discussion catered to my exact scenario. With enough information, ChatGPT always knew what was wrong and explained in terms I could understand.<p>The workflow went like this: I would describe the desired user experience, parse the code GPT suggested, copy it to my editor, and paste back any errors I came across along the way. The errors were abundant at the beginning, but I got better over time at anticipating issues. Perhaps my biggest takeaway was that I had to learn how to converse with ChatGPT: sometimes I would spend 10 minutes crafting a prompt, forcing me to fully understand and articulate my own line of thinking about what I was trying to achieve .<p>Using ChatGPT to make a static local website is fairly trivial, but the deployment and automation stage is where I fully realised the scope of what I could achieve. As a product designer, I’ve had the luxury of listening to engineers discuss solutions without personally having to sweat the execution. Working solo I couldn’t stay in the periphery anymore. I kinda knew AWS was a whole thing. That git was non-negotiable. That having a staging server is sensible and that APIs could do a lot of the heavy lifting for me. I would sanity-check with ChatGPT whether I understood these tools correctly and whether it was appropriate to use them for what I was building. A few of the things that initially intimidated me but I ended up working out:<p>- GitHub Actions workflows<p>- AWS hosting and CloudFront<p>- Route 53 DNS hosting<p>- SSL certificates<p>- Implementing fuzzy search<p>- LocalStorage and JSON manipulation<p>- Even some basic python to scrub data<p>It’s a fairly basic game, and for anyone sneaking a look with the inspector, it’s a dog’s div soup breakfast served with a side of spaghetti logic. But it still goes to show how much AI seems like a learning steroid.
Show HN: NeedleDrop – Guess the movie from a song
Backstory: I'm a product designer who's mostly worked for startups and now big tech, and I haven't really touched html/css for nearly a decade. I've worked closely with engineers my entire career but never really rolled the sleeves up and dived into a scripting language. I'd seen some engineers playing around with CodeGPT over a year ago when it launched–we huddled around a screen and tried to decide how quickly our jobs would be replaced by this new technology. At the time, we weren’t in any real danger, but I caught a glimpse of how well it understood prompts and stubbed out large amounts of code.<p>For the past four or five years, I've played a hacky trivia game with family and friends where I play a song, and they have to guess the movie that features the song; Guess the Needle Drop. After many passionate debates and over-the-top celebrations fueled by my generation’s nostalgia for popular classic songs and films, people often told me that I needed to “build an app for this.”<p>I started doodling in Figma before quickly starting to build the website in Node, and then read somewhere that it's a better approach to learn vanilla javascript before trying to benefit from frameworks like React, etc. So I started again with a static vanilla website and, piece by piece, built out each chunk of functionality I’d envisioned. My mind was consistently blown at how helpful ChatGPT was–far beyond my lofty expectations, even with all the AI hype. It was like having a 24/7 personal tutor for free. I rarely had to google console errors hoping that a Stack Overflow discussion catered to my exact scenario. With enough information, ChatGPT always knew what was wrong and explained in terms I could understand.<p>The workflow went like this: I would describe the desired user experience, parse the code GPT suggested, copy it to my editor, and paste back any errors I came across along the way. The errors were abundant at the beginning, but I got better over time at anticipating issues. Perhaps my biggest takeaway was that I had to learn how to converse with ChatGPT: sometimes I would spend 10 minutes crafting a prompt, forcing me to fully understand and articulate my own line of thinking about what I was trying to achieve .<p>Using ChatGPT to make a static local website is fairly trivial, but the deployment and automation stage is where I fully realised the scope of what I could achieve. As a product designer, I’ve had the luxury of listening to engineers discuss solutions without personally having to sweat the execution. Working solo I couldn’t stay in the periphery anymore. I kinda knew AWS was a whole thing. That git was non-negotiable. That having a staging server is sensible and that APIs could do a lot of the heavy lifting for me. I would sanity-check with ChatGPT whether I understood these tools correctly and whether it was appropriate to use them for what I was building. A few of the things that initially intimidated me but I ended up working out:<p>- GitHub Actions workflows<p>- AWS hosting and CloudFront<p>- Route 53 DNS hosting<p>- SSL certificates<p>- Implementing fuzzy search<p>- LocalStorage and JSON manipulation<p>- Even some basic python to scrub data<p>It’s a fairly basic game, and for anyone sneaking a look with the inspector, it’s a dog’s div soup breakfast served with a side of spaghetti logic. But it still goes to show how much AI seems like a learning steroid.
Show HN: Free comments section for personal sites
I've been working on creating threaded blog posts using RSS feeds. Something similar to Twitter but kind of worse and better in its own way.<p>I had an idea that if users are allowed to embed the threads to their posts, it acts like a simple comment section.<p>Try it out, no accounts required.
Show HN: Workflow orchestrator in Golang
A brief overview:
1. Workflows steps share a running context, with access to data they need require.
2. Steps in the workflow (builders) are chained together based on a topologically sorted built from the predefined input & output.
3. No servers spin up (like Conductor/Cadence) - the orchestrator is low level and meant for simplifying business logic.
4. Before/After listeners for each step.<p>Would love to hear your thoughts and feedback!
Show HN: Workflow orchestrator in Golang
A brief overview:
1. Workflows steps share a running context, with access to data they need require.
2. Steps in the workflow (builders) are chained together based on a topologically sorted built from the predefined input & output.
3. No servers spin up (like Conductor/Cadence) - the orchestrator is low level and meant for simplifying business logic.
4. Before/After listeners for each step.<p>Would love to hear your thoughts and feedback!
Show HN: Astro App
I really like Stellarium and SkySafari but I felt like these are primarily geared towards exploring the sky but not so much "here are the long list of things I want to see, when can I see them tonight and where". There's also not really a great option I've found that combines sky object planning + location weather details while still being free so I built this. The UI's heavily inspired by NINAs sky atlas + Robinhood.<p>Right now you can:<p>View the altitude chart of objects and 3D view<p>Create lists of objects of interest<p>View the annual max/min daily altitude of an object to find the best time of year to view<p>See live clouds from GOES satellite view + weekly night-centric forecast
Show HN: Astro App
I really like Stellarium and SkySafari but I felt like these are primarily geared towards exploring the sky but not so much "here are the long list of things I want to see, when can I see them tonight and where". There's also not really a great option I've found that combines sky object planning + location weather details while still being free so I built this. The UI's heavily inspired by NINAs sky atlas + Robinhood.<p>Right now you can:<p>View the altitude chart of objects and 3D view<p>Create lists of objects of interest<p>View the annual max/min daily altitude of an object to find the best time of year to view<p>See live clouds from GOES satellite view + weekly night-centric forecast
Show HN: Astro App
I really like Stellarium and SkySafari but I felt like these are primarily geared towards exploring the sky but not so much "here are the long list of things I want to see, when can I see them tonight and where". There's also not really a great option I've found that combines sky object planning + location weather details while still being free so I built this. The UI's heavily inspired by NINAs sky atlas + Robinhood.<p>Right now you can:<p>View the altitude chart of objects and 3D view<p>Create lists of objects of interest<p>View the annual max/min daily altitude of an object to find the best time of year to view<p>See live clouds from GOES satellite view + weekly night-centric forecast
Show HN: 3 years and 1M users later, I just open-sourced my "Internet OS"
Show HN: 3 years and 1M users later, I just open-sourced my "Internet OS"
Show HN: 3 years and 1M users later, I just open-sourced my "Internet OS"