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Show HN: Open-source Auth0 alternative Ory Kratos v0.13 released – nearing v1.0

Show HN: Open-source Auth0 alternative Ory Kratos v0.13 released – nearing v1.0

Show HN: Database for analyzing US companies, visualize using Apache SuperSet

My main motivation was that I wanted to be able to drill down and filter across all the available stocks, look at the data for myself, and narrow down on the stocks I am interested based on my own sets of criteria, and make data-driven analysis for my personal investment strategies.<p>I used PostgreSQL as the backend database for ELT data pipelines, and used Citus Data cstore_fdw for columnar compression for the final dataset. All financial data is coming from SEC Edgar, <a href="https://www.sec.gov/developer" rel="nofollow">https://www.sec.gov/developer</a>. I used Python for downloading most of the data.<p>I also run the data load development locally on my home Ubuntu server that I built 5 years ago. I bought 4TB of M2 disks for best database performance, with PRIME B360M-A motherboard and Intel Chip Coffee Lake S.<p>I built the website simply using WordPress, and I run Apache Superset using gunicorn via Apache Webserver reverse proxy.<p>The registration form I had to build myself with PHP and some JavaScript, because it needed to automatically create a SuperSet user upon registration. Otherwise, I would need to input everyone manually. I used Python again for the data integration.<p>Please don't use the database directly as an investment tool, as its in Beta, and the data still needs to undergo heavy data quality checks, please confirm all the numbers yourself, as I provide a link for every company to the SEC filings.

Show HN: Database for analyzing US companies, visualize using Apache SuperSet

My main motivation was that I wanted to be able to drill down and filter across all the available stocks, look at the data for myself, and narrow down on the stocks I am interested based on my own sets of criteria, and make data-driven analysis for my personal investment strategies.<p>I used PostgreSQL as the backend database for ELT data pipelines, and used Citus Data cstore_fdw for columnar compression for the final dataset. All financial data is coming from SEC Edgar, <a href="https://www.sec.gov/developer" rel="nofollow">https://www.sec.gov/developer</a>. I used Python for downloading most of the data.<p>I also run the data load development locally on my home Ubuntu server that I built 5 years ago. I bought 4TB of M2 disks for best database performance, with PRIME B360M-A motherboard and Intel Chip Coffee Lake S.<p>I built the website simply using WordPress, and I run Apache Superset using gunicorn via Apache Webserver reverse proxy.<p>The registration form I had to build myself with PHP and some JavaScript, because it needed to automatically create a SuperSet user upon registration. Otherwise, I would need to input everyone manually. I used Python again for the data integration.<p>Please don't use the database directly as an investment tool, as its in Beta, and the data still needs to undergo heavy data quality checks, please confirm all the numbers yourself, as I provide a link for every company to the SEC filings.

Show HN: AI Playground by Vercel Labs

Hey, Jared Palmer (creator of this playground) here. Really excited to ship this. I’ve been building this over the past few weeks to compare LLMs from different providers like OpenAI, Anthropic, Cohere, etc. At Vercel, I manage our Frameworks division (including Next.js, Svelte, and Turbo) and wanted to also dogfood some of the latest features in a slightly larger application. This playground takes a lot of inspiration from <a href="https://nat.dev" rel="nofollow">https://nat.dev</a> and is built on Tailwind, ui.shadcn.com, and some upcoming Vercel products we’re announcing soon. We’re going to continue adding models to compare and add other frameworks to generate code snippets from.

Show HN: AI Playground by Vercel Labs

Hey, Jared Palmer (creator of this playground) here. Really excited to ship this. I’ve been building this over the past few weeks to compare LLMs from different providers like OpenAI, Anthropic, Cohere, etc. At Vercel, I manage our Frameworks division (including Next.js, Svelte, and Turbo) and wanted to also dogfood some of the latest features in a slightly larger application. This playground takes a lot of inspiration from <a href="https://nat.dev" rel="nofollow">https://nat.dev</a> and is built on Tailwind, ui.shadcn.com, and some upcoming Vercel products we’re announcing soon. We’re going to continue adding models to compare and add other frameworks to generate code snippets from.

Show HN: IronBoy, a highly accurate GameBoy emulator written in Rust, runs WASM

Hey HN! Been working on this emulator on and off for the last couple years, and thought now would be a good time to try and show it off here.<p>As far as unique features... I think this is the only online GameBoy emulator with support for save files? So there's your reason to exist, I guess.<p>The WASM port was something I had in the back of my mind but I just kept putting it off because it seemed like it would've been a pain in the ass to implement, but all in all I think it took less than a week so that was a nice surprise!

Show HN: IronBoy, a highly accurate GameBoy emulator written in Rust, runs WASM

Hey HN! Been working on this emulator on and off for the last couple years, and thought now would be a good time to try and show it off here.<p>As far as unique features... I think this is the only online GameBoy emulator with support for save files? So there's your reason to exist, I guess.<p>The WASM port was something I had in the back of my mind but I just kept putting it off because it seemed like it would've been a pain in the ass to implement, but all in all I think it took less than a week so that was a nice surprise!

Show HN: ThinkGPT: a library to prompt GPT to think, memorize and self-refine

Show HN: ThinkGPT: a library to prompt GPT to think, memorize and self-refine

Show HN: The Fuck Cards – make a card for a friend or enemy

The Fuck Card started years ago as a physical card I printed for some friends.<p>Now it's this simple web app you can use to express yourself.<p>Quickly make a card, copy the link and send it to someone who deserves it.<p>It's fun and there's lots you can say with it.<p>I built it built in 100% handcrafted in Vanilla HTML, CSS and JS. No frameworks, no BS.<p>Fonts are provided by Google Fonts. Image generation thanks to html2canvas. No-tracking analytics by Matomo.<p>It started as a weekend project to train my JS skills. I'd love to know your thoughts.

Show HN: Trivai.app – AI powered trivia questions, with references

Hi HN!<p>As many of you, I've had a lot of fun playing around with LLMs the past few months and I wanted to show you what I've built.<p>I made a trivia website using GPT3 a while back just to have something to play with. My initial interest was to see if I could get structured responses to build a UI around, and if I could get the LLM to refer back to what piece of text it used to create questions and answers with.<p>The initial results were pretty good, but not good enough. Fast forward a few months, GPT 3.5 was released to the public and I was released from my work.<p>I decided to pick this up again and have been making some changes.<p>- I've generated almost 3000 questions.<p>- I initially only let signed up users generate questions due to cost concerns. Since 3.5 is much cheaper, I've opened up the question generation for everybody.<p>- I built a prompt comparison tool so I could tweak the prompt to get better responses.<p>- I've added some more data to the questions. This includes a more free form explanation to the correct answer (separate from the references), and categories for all questions.<p>- I've been working on a question improvement process. This means I collect issues, generate new variations of the question and let people vote on them. Voting and question improvement is currently only available to signed in users.<p>I have many more ideas I'd like to explore, but I would appreciate your feedback and would be happy to answer any questions about the site or its development.<p>You can access the references by pressing the explanation text when you've answered a question correctly.

Show HN: Trivai.app – AI powered trivia questions, with references

Hi HN!<p>As many of you, I've had a lot of fun playing around with LLMs the past few months and I wanted to show you what I've built.<p>I made a trivia website using GPT3 a while back just to have something to play with. My initial interest was to see if I could get structured responses to build a UI around, and if I could get the LLM to refer back to what piece of text it used to create questions and answers with.<p>The initial results were pretty good, but not good enough. Fast forward a few months, GPT 3.5 was released to the public and I was released from my work.<p>I decided to pick this up again and have been making some changes.<p>- I've generated almost 3000 questions.<p>- I initially only let signed up users generate questions due to cost concerns. Since 3.5 is much cheaper, I've opened up the question generation for everybody.<p>- I built a prompt comparison tool so I could tweak the prompt to get better responses.<p>- I've added some more data to the questions. This includes a more free form explanation to the correct answer (separate from the references), and categories for all questions.<p>- I've been working on a question improvement process. This means I collect issues, generate new variations of the question and let people vote on them. Voting and question improvement is currently only available to signed in users.<p>I have many more ideas I'd like to explore, but I would appreciate your feedback and would be happy to answer any questions about the site or its development.<p>You can access the references by pressing the explanation text when you've answered a question correctly.

Show HN: MindPop – Great Lectures Set to Ambient

Show HN: MindPop – Great Lectures Set to Ambient

Show HN: MindPop – Great Lectures Set to Ambient

Show HN: Magic Happens – let ChatGPT manage your Kubernetes cluster

I built this kubernetes operator as a proof of concept this weekend.. It only has a single required item in the spec, a freeform description field. The operator will use chatgpt to generate a spec, then immediately apply it to the cluster. It makes some attempt to correct errors if there's a problem with the syntax. It will leave additional comments, questions or instructions in the status field of the object. I built this in a weekend and it's still quite unrefined. It's in no way production ready, please don't use it for anything real, but it works better than you would think, considering how simple it is. If you're going to use it, run it on a local cluster like 'kind'.<p>Some descriptions to try:<p>* install a redis namespace with a redis cluster and a service in it * create an argocd application in the argocd namespace to install velero. * write a python script that lists all ec2 instances in us-east-1, and run it as a k8s job with the aws credentials already saved in the default namespace..<p>a somewhat longer description that also worked: given the following spec: --- kind: MagicHappens apiVersion: gptmagic.io/v1 metadata: name: foo spec: description: this is a freeform description field that will be sent to chatgpt to generate kubernetes resources dryRun: false --- Can you create more magic happens resources, each of which describes an argocd application that needs to be created to install a helm chart for one of the standard cluster addons that need to be installed on a cluster for it to be production ready. The description should be be freeform text like the following: "Create an argocd application in the argocd namespace to install istio from the helm chart with all the defaults" or "Create an argocd application in the argocd namespace to install prometheus and grafana, with an ingress enabled for grafana". Be very thorough and included as many apps that might be needed for a prod ready cluster using industry standard CNCF projects if possible.<p>(this produces a list of additional resources for the operator, which the operator then goes on to create argocd applications for -- it also left comments with instructions on one of the resources for how configure it to work with your cloud provider<p>something to note is that since you can run arbitrary containers with arbitrary commands, and chatgpt can write arbitrary code, you don't have to limit yourself to k8s stuff.. if you've got saas credentials on the cluster, you can just tell it to run a python script as a job to do whatever you want.<p>Since most people are cowards, there's a dryRun field that defaults to true, so it only attaches the spec to the object.<p>It is <i>scary</i> how well this works.

Show HN: Magic Happens – let ChatGPT manage your Kubernetes cluster

I built this kubernetes operator as a proof of concept this weekend.. It only has a single required item in the spec, a freeform description field. The operator will use chatgpt to generate a spec, then immediately apply it to the cluster. It makes some attempt to correct errors if there's a problem with the syntax. It will leave additional comments, questions or instructions in the status field of the object. I built this in a weekend and it's still quite unrefined. It's in no way production ready, please don't use it for anything real, but it works better than you would think, considering how simple it is. If you're going to use it, run it on a local cluster like 'kind'.<p>Some descriptions to try:<p>* install a redis namespace with a redis cluster and a service in it * create an argocd application in the argocd namespace to install velero. * write a python script that lists all ec2 instances in us-east-1, and run it as a k8s job with the aws credentials already saved in the default namespace..<p>a somewhat longer description that also worked: given the following spec: --- kind: MagicHappens apiVersion: gptmagic.io/v1 metadata: name: foo spec: description: this is a freeform description field that will be sent to chatgpt to generate kubernetes resources dryRun: false --- Can you create more magic happens resources, each of which describes an argocd application that needs to be created to install a helm chart for one of the standard cluster addons that need to be installed on a cluster for it to be production ready. The description should be be freeform text like the following: "Create an argocd application in the argocd namespace to install istio from the helm chart with all the defaults" or "Create an argocd application in the argocd namespace to install prometheus and grafana, with an ingress enabled for grafana". Be very thorough and included as many apps that might be needed for a prod ready cluster using industry standard CNCF projects if possible.<p>(this produces a list of additional resources for the operator, which the operator then goes on to create argocd applications for -- it also left comments with instructions on one of the resources for how configure it to work with your cloud provider<p>something to note is that since you can run arbitrary containers with arbitrary commands, and chatgpt can write arbitrary code, you don't have to limit yourself to k8s stuff.. if you've got saas credentials on the cluster, you can just tell it to run a python script as a job to do whatever you want.<p>Since most people are cowards, there's a dryRun field that defaults to true, so it only attaches the spec to the object.<p>It is <i>scary</i> how well this works.

Show HN: Magic Happens – let ChatGPT manage your Kubernetes cluster

I built this kubernetes operator as a proof of concept this weekend.. It only has a single required item in the spec, a freeform description field. The operator will use chatgpt to generate a spec, then immediately apply it to the cluster. It makes some attempt to correct errors if there's a problem with the syntax. It will leave additional comments, questions or instructions in the status field of the object. I built this in a weekend and it's still quite unrefined. It's in no way production ready, please don't use it for anything real, but it works better than you would think, considering how simple it is. If you're going to use it, run it on a local cluster like 'kind'.<p>Some descriptions to try:<p>* install a redis namespace with a redis cluster and a service in it * create an argocd application in the argocd namespace to install velero. * write a python script that lists all ec2 instances in us-east-1, and run it as a k8s job with the aws credentials already saved in the default namespace..<p>a somewhat longer description that also worked: given the following spec: --- kind: MagicHappens apiVersion: gptmagic.io/v1 metadata: name: foo spec: description: this is a freeform description field that will be sent to chatgpt to generate kubernetes resources dryRun: false --- Can you create more magic happens resources, each of which describes an argocd application that needs to be created to install a helm chart for one of the standard cluster addons that need to be installed on a cluster for it to be production ready. The description should be be freeform text like the following: "Create an argocd application in the argocd namespace to install istio from the helm chart with all the defaults" or "Create an argocd application in the argocd namespace to install prometheus and grafana, with an ingress enabled for grafana". Be very thorough and included as many apps that might be needed for a prod ready cluster using industry standard CNCF projects if possible.<p>(this produces a list of additional resources for the operator, which the operator then goes on to create argocd applications for -- it also left comments with instructions on one of the resources for how configure it to work with your cloud provider<p>something to note is that since you can run arbitrary containers with arbitrary commands, and chatgpt can write arbitrary code, you don't have to limit yourself to k8s stuff.. if you've got saas credentials on the cluster, you can just tell it to run a python script as a job to do whatever you want.<p>Since most people are cowards, there's a dryRun field that defaults to true, so it only attaches the spec to the object.<p>It is <i>scary</i> how well this works.

Show HN: Let your body be the gamepad using a webcam

Hi HN,<p>I made a keyboard emulator that tracks your body pose and emits the key presses accordingly.<p>My main motivation was to make my kids move more on rainy days. I already made a couple of mini-games using the body pose [1-3], and those were definitely fun to make and play! However, once the kids learned the tricks, they got bored. I could only produce that much content myself, and soon realized that I lack time for churning out games.<p>Finally, I decided to tap into an endless pool of PC games, preferrably simple and less addictive ones like the Gameboy and DOS games.<p>This project has been also inspired by [4], which has been featured on HN recently.<p>[1]: <a href="https://github.com/mristin/pop-that-balloon-desktop">https://github.com/mristin/pop-that-balloon-desktop</a><p>[2]: <a href="https://github.com/mristin/cactusss-desktop">https://github.com/mristin/cactusss-desktop</a><p>[3]: <a href="https://github.com/mristin/ski-leu-desktop/">https://github.com/mristin/ski-leu-desktop/</a><p>[4]: <a href="https://github.com/everythingishacked/Semaphore">https://github.com/everythingishacked/Semaphore</a><p>Edit: clarified title; layout of references