The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Tech jobs on the command line
Hi HN! I'm Nico and lately I've been struggling looking for jobs. I've gotten specially frustrated with having to spend hours reading through listings just to find good matches. So I built something to scratch my own itch:<p>Command Jobs, a terminal-based job finder and application tracker designed specifically for me, and maybe also software engineers<p>The app scrapes web listings, saves them, processes them with GPT[1], then based on your resume and job preferences, it gives you the best matches<p>This is my first open source project, and also my first public project using LLMs<p>I'm really excited to share this with the HN community and would love to hear your feedback, suggestions, and any features you'd like to see in the future. Please check it out and let me know what you think!<p><a href="https://github.com/nicobrenner/commandjobs">https://github.com/nicobrenner/commandjobs</a><p>I'm currently working on adding more sources for job listings, periodic scraping via cronjobs, and alerts for new matches<p>Looking forward to your questions, feedback and requests. Thank you<p>[1]: Bring Your Own (OpenAPI) Key
Show HN: Tech jobs on the command line
Hi HN! I'm Nico and lately I've been struggling looking for jobs. I've gotten specially frustrated with having to spend hours reading through listings just to find good matches. So I built something to scratch my own itch:<p>Command Jobs, a terminal-based job finder and application tracker designed specifically for me, and maybe also software engineers<p>The app scrapes web listings, saves them, processes them with GPT[1], then based on your resume and job preferences, it gives you the best matches<p>This is my first open source project, and also my first public project using LLMs<p>I'm really excited to share this with the HN community and would love to hear your feedback, suggestions, and any features you'd like to see in the future. Please check it out and let me know what you think!<p><a href="https://github.com/nicobrenner/commandjobs">https://github.com/nicobrenner/commandjobs</a><p>I'm currently working on adding more sources for job listings, periodic scraping via cronjobs, and alerts for new matches<p>Looking forward to your questions, feedback and requests. Thank you<p>[1]: Bring Your Own (OpenAPI) Key
Show HN: Elodin – A better framework for physics simulation
Hey HN! We are the co-founders of Elodin (<a href="https://elodin.systems/">https://elodin.systems/</a>), a code-first physics simulation framework. We just open-sourced our core libraries on Github: <a href="https://github.com/elodin-sys/elodin">https://github.com/elodin-sys/elodin</a><p>Have you ever wondered what happens when a satellite is first placed into orbit? It turns out that often, they tumble out of control, end over end, in a miraculous fight against stability. Check out our demo here of a control system attempting to rectify this: <a href="https://app.elodin.systems/sandbox/hn/cube-sat">https://app.elodin.systems/sandbox/hn/cube-sat</a><p>The spacecraft does not appreciate tumbling: deploying solar panels and antennas might not work, and doing anything worthwhile is out of the picture. So what are you to do about this? You'll equip your satellite with reaction wheels (or magnetorquers), but now you have a second problem. What commands do you send to the reaction wheels?<p>We created Elodin to help solve problems like this. These problems are firmly in the realm of GNC (guidance, navigation, and control) engineers; they are traditionally solved with MatLab / Simulink, a bunch of Python scripts, and/or a tool like Gazebo or Basilisk. While building the flight software for a deep-space mission, I tried all of these tools and didn’t like them. In particular, I found that running Monte Carlo simulations in the cloud was painful.<p>So we set out to build a better framework for physics simulation. You might call it "Tensorflow for Physics." The name fits for two reasons: we use XLA to accelerate your math, and we've built an extensible framework for creating new physics engines. Most physics engines are fixed-function. For example, something like MuJoCo (<a href="https://mujoco.readthedocs.io/en/stable/overview.html" rel="nofollow">https://mujoco.readthedocs.io/en/stable/overview.html</a>) is great for traditional robotics, but GMAT (<a href="https://gmat.atlassian.net/wiki/spaces/GW/overview" rel="nofollow">https://gmat.atlassian.net/wiki/spaces/GW/overview</a>) is far better for orbital analysis. No single physics engine can solve all problems, and it’s hard to integrate multiple engines. Our framework allows you to easily compose different physics algorithms.<p>Space is hard enough—let’s not have software make it harder than it needs to be! We are still early in building our stack, so we’d be grateful to hear any feedback that you have.
Show HN: Elodin – A better framework for physics simulation
Hey HN! We are the co-founders of Elodin (<a href="https://elodin.systems/">https://elodin.systems/</a>), a code-first physics simulation framework. We just open-sourced our core libraries on Github: <a href="https://github.com/elodin-sys/elodin">https://github.com/elodin-sys/elodin</a><p>Have you ever wondered what happens when a satellite is first placed into orbit? It turns out that often, they tumble out of control, end over end, in a miraculous fight against stability. Check out our demo here of a control system attempting to rectify this: <a href="https://app.elodin.systems/sandbox/hn/cube-sat">https://app.elodin.systems/sandbox/hn/cube-sat</a><p>The spacecraft does not appreciate tumbling: deploying solar panels and antennas might not work, and doing anything worthwhile is out of the picture. So what are you to do about this? You'll equip your satellite with reaction wheels (or magnetorquers), but now you have a second problem. What commands do you send to the reaction wheels?<p>We created Elodin to help solve problems like this. These problems are firmly in the realm of GNC (guidance, navigation, and control) engineers; they are traditionally solved with MatLab / Simulink, a bunch of Python scripts, and/or a tool like Gazebo or Basilisk. While building the flight software for a deep-space mission, I tried all of these tools and didn’t like them. In particular, I found that running Monte Carlo simulations in the cloud was painful.<p>So we set out to build a better framework for physics simulation. You might call it "Tensorflow for Physics." The name fits for two reasons: we use XLA to accelerate your math, and we've built an extensible framework for creating new physics engines. Most physics engines are fixed-function. For example, something like MuJoCo (<a href="https://mujoco.readthedocs.io/en/stable/overview.html" rel="nofollow">https://mujoco.readthedocs.io/en/stable/overview.html</a>) is great for traditional robotics, but GMAT (<a href="https://gmat.atlassian.net/wiki/spaces/GW/overview" rel="nofollow">https://gmat.atlassian.net/wiki/spaces/GW/overview</a>) is far better for orbital analysis. No single physics engine can solve all problems, and it’s hard to integrate multiple engines. Our framework allows you to easily compose different physics algorithms.<p>Space is hard enough—let’s not have software make it harder than it needs to be! We are still early in building our stack, so we’d be grateful to hear any feedback that you have.
Show HN: Elodin – A better framework for physics simulation
Hey HN! We are the co-founders of Elodin (<a href="https://elodin.systems/">https://elodin.systems/</a>), a code-first physics simulation framework. We just open-sourced our core libraries on Github: <a href="https://github.com/elodin-sys/elodin">https://github.com/elodin-sys/elodin</a><p>Have you ever wondered what happens when a satellite is first placed into orbit? It turns out that often, they tumble out of control, end over end, in a miraculous fight against stability. Check out our demo here of a control system attempting to rectify this: <a href="https://app.elodin.systems/sandbox/hn/cube-sat">https://app.elodin.systems/sandbox/hn/cube-sat</a><p>The spacecraft does not appreciate tumbling: deploying solar panels and antennas might not work, and doing anything worthwhile is out of the picture. So what are you to do about this? You'll equip your satellite with reaction wheels (or magnetorquers), but now you have a second problem. What commands do you send to the reaction wheels?<p>We created Elodin to help solve problems like this. These problems are firmly in the realm of GNC (guidance, navigation, and control) engineers; they are traditionally solved with MatLab / Simulink, a bunch of Python scripts, and/or a tool like Gazebo or Basilisk. While building the flight software for a deep-space mission, I tried all of these tools and didn’t like them. In particular, I found that running Monte Carlo simulations in the cloud was painful.<p>So we set out to build a better framework for physics simulation. You might call it "Tensorflow for Physics." The name fits for two reasons: we use XLA to accelerate your math, and we've built an extensible framework for creating new physics engines. Most physics engines are fixed-function. For example, something like MuJoCo (<a href="https://mujoco.readthedocs.io/en/stable/overview.html" rel="nofollow">https://mujoco.readthedocs.io/en/stable/overview.html</a>) is great for traditional robotics, but GMAT (<a href="https://gmat.atlassian.net/wiki/spaces/GW/overview" rel="nofollow">https://gmat.atlassian.net/wiki/spaces/GW/overview</a>) is far better for orbital analysis. No single physics engine can solve all problems, and it’s hard to integrate multiple engines. Our framework allows you to easily compose different physics algorithms.<p>Space is hard enough—let’s not have software make it harder than it needs to be! We are still early in building our stack, so we’d be grateful to hear any feedback that you have.
Show HN: Piping logs, visualizing in a web app – just suffix "| npx logscreen"
Show HN: Piping logs, visualizing in a web app – just suffix "| npx logscreen"
Show HN: Piping logs, visualizing in a web app – just suffix "| npx logscreen"
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: 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: 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.