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Show HN: Clace – Application Server with support for scaling down to zero

I have been building the open source project <a href="https://github.com/claceio/clace">https://github.com/claceio/clace</a>. Clace is an application server that builds and deploys containers, allowing it to manage webapps in any language/framework.<p>Compared to application servers like Nginx Unit, Clace has the advantage of being able to work with any application, without requiring any dependency or packaging changes. Clace provides a blue-green staged deployment model for apps. Not just code changes, even configuration changes are staged and can be verified before being made live.<p>Clace is not a PaaS solution, it does not support deploying databases and other auxiliary services. It does share the fact that it manages containers with PaaS solutions. Clace is different in that it builds its own reverse proxy, instead of depending on Traefik/Nginx. This allows Clace to implement features like shutting down idle apps and adding app level OAuth authentication. Clace runs natively on Windows/OSX in addition to Linux. Clace works with Docker/Podman/Orbstack.<p>Clace allows you to run hundreds of apps on a single machine. Since app containers are shut down when not in use, there is no CPU/memory resource usage when the apps are idle. It provides a Google Cloud Run type interface on your own hardware.<p><a href="https://clace.io/" rel="nofollow">https://clace.io/</a> has a demo video and docs. Do let me know any feedback.

Show HN: How much is 13B euros?

Hi,<p>I made this page to contextualize 13 billion euros (or 14): an amount due to Ireland in an EU Apple tax case and all over the airwaves here this week. I use some pretty silly back-of-the-envelope type calculations (the same ones also repeated a lot in Ireland this week!).<p>These calculations aren't especially interesting, but at least they are present: you can see them, and you can change them. If you do - change the 13 billion to 14 billion for example, related numbers will flash with updates.<p>It's an example using calculang[1]: a language for calculations, and an example that focuses on a close connection between numbers that we read or share and formulas/workings behind those numbers.<p>I plan to do a separate Show HN about calculang perhaps when I have more docs and newer playground and gallery together, but Showing this page in case it's interesting, & happy if there is feedback!<p>Declan<p>[0] <a href="https://HowMuchIs13BillionEuros.com" rel="nofollow">https://HowMuchIs13BillionEuros.com</a> repo: <a href="https://github.com/declann/HowMuchIs13BillionEuros.com">https://github.com/declann/HowMuchIs13BillionEuros.com</a><p>[1] <a href="https://calculang.dev" rel="nofollow">https://calculang.dev</a>

Show HN: How much is 13B euros?

Hi,<p>I made this page to contextualize 13 billion euros (or 14): an amount due to Ireland in an EU Apple tax case and all over the airwaves here this week. I use some pretty silly back-of-the-envelope type calculations (the same ones also repeated a lot in Ireland this week!).<p>These calculations aren't especially interesting, but at least they are present: you can see them, and you can change them. If you do - change the 13 billion to 14 billion for example, related numbers will flash with updates.<p>It's an example using calculang[1]: a language for calculations, and an example that focuses on a close connection between numbers that we read or share and formulas/workings behind those numbers.<p>I plan to do a separate Show HN about calculang perhaps when I have more docs and newer playground and gallery together, but Showing this page in case it's interesting, & happy if there is feedback!<p>Declan<p>[0] <a href="https://HowMuchIs13BillionEuros.com" rel="nofollow">https://HowMuchIs13BillionEuros.com</a> repo: <a href="https://github.com/declann/HowMuchIs13BillionEuros.com">https://github.com/declann/HowMuchIs13BillionEuros.com</a><p>[1] <a href="https://calculang.dev" rel="nofollow">https://calculang.dev</a>

Show HN: Tune LLaMa3.1 on Google Cloud TPUs

Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs).<p>The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs.<p>But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla integration with pytorch is clunky, missing libraries (bitsandbytes didn't work), and cryptic HuggingFace errors.<p>We then took a different route and translated Llama 3.1 from PyTorch to JAX. Now, it’s running smoothly on TPUs! We still have challenges ahead, there is no good LoRA library in JAX, but this feels like the right path forward.<p>Here's a demo (<a href="https://dub.sh/felafax-demo" rel="nofollow">https://dub.sh/felafax-demo</a>) of our managed solution.<p>Would love your thoughts on our repo and vision as we keep chugging along!

Show HN: Tune LLaMa3.1 on Google Cloud TPUs

Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs).<p>The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs.<p>But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla integration with pytorch is clunky, missing libraries (bitsandbytes didn't work), and cryptic HuggingFace errors.<p>We then took a different route and translated Llama 3.1 from PyTorch to JAX. Now, it’s running smoothly on TPUs! We still have challenges ahead, there is no good LoRA library in JAX, but this feels like the right path forward.<p>Here's a demo (<a href="https://dub.sh/felafax-demo" rel="nofollow">https://dub.sh/felafax-demo</a>) of our managed solution.<p>Would love your thoughts on our repo and vision as we keep chugging along!

Show HN: Tune LLaMa3.1 on Google Cloud TPUs

Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs).<p>The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs.<p>But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla integration with pytorch is clunky, missing libraries (bitsandbytes didn't work), and cryptic HuggingFace errors.<p>We then took a different route and translated Llama 3.1 from PyTorch to JAX. Now, it’s running smoothly on TPUs! We still have challenges ahead, there is no good LoRA library in JAX, but this feels like the right path forward.<p>Here's a demo (<a href="https://dub.sh/felafax-demo" rel="nofollow">https://dub.sh/felafax-demo</a>) of our managed solution.<p>Would love your thoughts on our repo and vision as we keep chugging along!

Show HN: Tune LLaMa3.1 on Google Cloud TPUs

Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs).<p>The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs.<p>But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla integration with pytorch is clunky, missing libraries (bitsandbytes didn't work), and cryptic HuggingFace errors.<p>We then took a different route and translated Llama 3.1 from PyTorch to JAX. Now, it’s running smoothly on TPUs! We still have challenges ahead, there is no good LoRA library in JAX, but this feels like the right path forward.<p>Here's a demo (<a href="https://dub.sh/felafax-demo" rel="nofollow">https://dub.sh/felafax-demo</a>) of our managed solution.<p>Would love your thoughts on our repo and vision as we keep chugging along!

Show HN: GitOps Template for Kubernetes

Hello HN, we’re Philip Louis from Glasskube (<a href="https://github.com/glasskube/glasskube">https://github.com/glasskube/glasskube</a>). We are working on a package manager for Kubernetes to simplify the packaging of complex applications with multiple dependencies, ensuring they are installed and kept up-to-date across multiple Kubernetes clusters.<p>Nowadays, it is best practice to use Git as a revision control system for your Kubernetes configurations. Update automation workflows like Renovate or Dependabot can create pull requests for new versions of Docker images and Helm charts, but ensuring these new package versions work is still a manual task. By using the central (or a private) Glasskube repository (<a href="https://github.com/glasskube/packages">https://github.com/glasskube/packages</a>) together with our Renovate integration (<a href="https://docs.renovatebot.com/modules/manager/glasskube/" rel="nofollow">https://docs.renovatebot.com/modules/manager/glasskube/</a>), you can ensure that new package versions will run through our Minikube-based CI workflows before they get published—similar to how the Homebrew core tap works. We’ve just introduced readiness checks for manifest-based deployments and utilize the flux-helm-controller to wait for a Helm release to succeed.<p>Dependencies are resolved by our package controller. These dependencies can either be cluster-scoped (installed in the recommended namespace, e.g., operators wird CRDs) or namespace-scoped components of a package (e.g., a database or Redis cache). In such cases, we will prefix resources with the dependent package name to ensure multiple packages can use the same dependencies without naming conflicts (we use Kustomize on a virtual filesystem for this).<p>Glasskube packages can currently be Helm charts (from an OCI or Helm repository) or manifests, which are mostly built using Kustomize’s overlay approach.<p>Since neither the overlay approach (using Kustomize) nor Helm’s limited templating functionality will help us and other Kubernetes users scale to more complex packages, we are considering creating a more programmatic approach to package creation, similar to Timoni. Currently, KCL is our frontrunner (<a href="https://github.com/glasskube/glasskube/discussions/1018">https://github.com/glasskube/glasskube/discussions/1018</a>), as it already integrates well with the Kubernetes ecosystem.<p>We would appreciate if you give our GitOps template a try. It also works work existing Kubernetes clusters if just want to use GitOps for some applications. Just make sure that the argocd and glasskube-system namespaces are not yet in use. See: <a href="https://github.com/glasskube/gitops-template/">https://github.com/glasskube/gitops-template/</a>

Show HN: GitOps Template for Kubernetes

Hello HN, we’re Philip Louis from Glasskube (<a href="https://github.com/glasskube/glasskube">https://github.com/glasskube/glasskube</a>). We are working on a package manager for Kubernetes to simplify the packaging of complex applications with multiple dependencies, ensuring they are installed and kept up-to-date across multiple Kubernetes clusters.<p>Nowadays, it is best practice to use Git as a revision control system for your Kubernetes configurations. Update automation workflows like Renovate or Dependabot can create pull requests for new versions of Docker images and Helm charts, but ensuring these new package versions work is still a manual task. By using the central (or a private) Glasskube repository (<a href="https://github.com/glasskube/packages">https://github.com/glasskube/packages</a>) together with our Renovate integration (<a href="https://docs.renovatebot.com/modules/manager/glasskube/" rel="nofollow">https://docs.renovatebot.com/modules/manager/glasskube/</a>), you can ensure that new package versions will run through our Minikube-based CI workflows before they get published—similar to how the Homebrew core tap works. We’ve just introduced readiness checks for manifest-based deployments and utilize the flux-helm-controller to wait for a Helm release to succeed.<p>Dependencies are resolved by our package controller. These dependencies can either be cluster-scoped (installed in the recommended namespace, e.g., operators wird CRDs) or namespace-scoped components of a package (e.g., a database or Redis cache). In such cases, we will prefix resources with the dependent package name to ensure multiple packages can use the same dependencies without naming conflicts (we use Kustomize on a virtual filesystem for this).<p>Glasskube packages can currently be Helm charts (from an OCI or Helm repository) or manifests, which are mostly built using Kustomize’s overlay approach.<p>Since neither the overlay approach (using Kustomize) nor Helm’s limited templating functionality will help us and other Kubernetes users scale to more complex packages, we are considering creating a more programmatic approach to package creation, similar to Timoni. Currently, KCL is our frontrunner (<a href="https://github.com/glasskube/glasskube/discussions/1018">https://github.com/glasskube/glasskube/discussions/1018</a>), as it already integrates well with the Kubernetes ecosystem.<p>We would appreciate if you give our GitOps template a try. It also works work existing Kubernetes clusters if just want to use GitOps for some applications. Just make sure that the argocd and glasskube-system namespaces are not yet in use. See: <a href="https://github.com/glasskube/gitops-template/">https://github.com/glasskube/gitops-template/</a>

Show HN: GitOps Template for Kubernetes

Hello HN, we’re Philip Louis from Glasskube (<a href="https://github.com/glasskube/glasskube">https://github.com/glasskube/glasskube</a>). We are working on a package manager for Kubernetes to simplify the packaging of complex applications with multiple dependencies, ensuring they are installed and kept up-to-date across multiple Kubernetes clusters.<p>Nowadays, it is best practice to use Git as a revision control system for your Kubernetes configurations. Update automation workflows like Renovate or Dependabot can create pull requests for new versions of Docker images and Helm charts, but ensuring these new package versions work is still a manual task. By using the central (or a private) Glasskube repository (<a href="https://github.com/glasskube/packages">https://github.com/glasskube/packages</a>) together with our Renovate integration (<a href="https://docs.renovatebot.com/modules/manager/glasskube/" rel="nofollow">https://docs.renovatebot.com/modules/manager/glasskube/</a>), you can ensure that new package versions will run through our Minikube-based CI workflows before they get published—similar to how the Homebrew core tap works. We’ve just introduced readiness checks for manifest-based deployments and utilize the flux-helm-controller to wait for a Helm release to succeed.<p>Dependencies are resolved by our package controller. These dependencies can either be cluster-scoped (installed in the recommended namespace, e.g., operators wird CRDs) or namespace-scoped components of a package (e.g., a database or Redis cache). In such cases, we will prefix resources with the dependent package name to ensure multiple packages can use the same dependencies without naming conflicts (we use Kustomize on a virtual filesystem for this).<p>Glasskube packages can currently be Helm charts (from an OCI or Helm repository) or manifests, which are mostly built using Kustomize’s overlay approach.<p>Since neither the overlay approach (using Kustomize) nor Helm’s limited templating functionality will help us and other Kubernetes users scale to more complex packages, we are considering creating a more programmatic approach to package creation, similar to Timoni. Currently, KCL is our frontrunner (<a href="https://github.com/glasskube/glasskube/discussions/1018">https://github.com/glasskube/glasskube/discussions/1018</a>), as it already integrates well with the Kubernetes ecosystem.<p>We would appreciate if you give our GitOps template a try. It also works work existing Kubernetes clusters if just want to use GitOps for some applications. Just make sure that the argocd and glasskube-system namespaces are not yet in use. See: <a href="https://github.com/glasskube/gitops-template/">https://github.com/glasskube/gitops-template/</a>

Show HN: GitOps Template for Kubernetes

Hello HN, we’re Philip Louis from Glasskube (<a href="https://github.com/glasskube/glasskube">https://github.com/glasskube/glasskube</a>). We are working on a package manager for Kubernetes to simplify the packaging of complex applications with multiple dependencies, ensuring they are installed and kept up-to-date across multiple Kubernetes clusters.<p>Nowadays, it is best practice to use Git as a revision control system for your Kubernetes configurations. Update automation workflows like Renovate or Dependabot can create pull requests for new versions of Docker images and Helm charts, but ensuring these new package versions work is still a manual task. By using the central (or a private) Glasskube repository (<a href="https://github.com/glasskube/packages">https://github.com/glasskube/packages</a>) together with our Renovate integration (<a href="https://docs.renovatebot.com/modules/manager/glasskube/" rel="nofollow">https://docs.renovatebot.com/modules/manager/glasskube/</a>), you can ensure that new package versions will run through our Minikube-based CI workflows before they get published—similar to how the Homebrew core tap works. We’ve just introduced readiness checks for manifest-based deployments and utilize the flux-helm-controller to wait for a Helm release to succeed.<p>Dependencies are resolved by our package controller. These dependencies can either be cluster-scoped (installed in the recommended namespace, e.g., operators wird CRDs) or namespace-scoped components of a package (e.g., a database or Redis cache). In such cases, we will prefix resources with the dependent package name to ensure multiple packages can use the same dependencies without naming conflicts (we use Kustomize on a virtual filesystem for this).<p>Glasskube packages can currently be Helm charts (from an OCI or Helm repository) or manifests, which are mostly built using Kustomize’s overlay approach.<p>Since neither the overlay approach (using Kustomize) nor Helm’s limited templating functionality will help us and other Kubernetes users scale to more complex packages, we are considering creating a more programmatic approach to package creation, similar to Timoni. Currently, KCL is our frontrunner (<a href="https://github.com/glasskube/glasskube/discussions/1018">https://github.com/glasskube/glasskube/discussions/1018</a>), as it already integrates well with the Kubernetes ecosystem.<p>We would appreciate if you give our GitOps template a try. It also works work existing Kubernetes clusters if just want to use GitOps for some applications. Just make sure that the argocd and glasskube-system namespaces are not yet in use. See: <a href="https://github.com/glasskube/gitops-template/">https://github.com/glasskube/gitops-template/</a>

Show HN: DBOS transact – Ultra-lightweight durable execution in Python

Hi HN - DBOS CEO here with the co-founders of DBOS, Peter (KraftyOne) and Qian (qianli_cs). The company started as a research project of Stanford and MIT, and Peter and Qian were advised by Mike Stonebreaker, the creator of Postgres, and Matei Zaharia, the creator of Spark. They believe so strongly in reliable, serverless compute that they started a company (with Mike) to bring it to the world!<p>Today we want to share our brand new Python library providing ultra-lightweight durable execution.<p><a href="https://github.com/dbos-inc/dbos-transact-py">https://github.com/dbos-inc/dbos-transact-py</a><p>Durable execution means your program is resilient to any failure. If it is ever interrupted or crashes, all your workflows will automatically resume from the last completed step. If you want to see durable execution in action, check out this demo app:<p><a href="https://demo-widget-store.cloud.dbos.dev/" rel="nofollow">https://demo-widget-store.cloud.dbos.dev/</a><p>Or if you’re like me and want to skip straight to the Python decorators in action, here’s the demo app’s backend – an online store with reliability and correctness in just 200 LOC:<p><a href="https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/widget-store/widget_store/main.py">https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/...</a><p>Don't want to keep reading and just try it out:<p><a href="https://console.dbos.dev/launch" rel="nofollow">https://console.dbos.dev/launch</a><p>No matter how many times you try to crash it, it always resumes from exactly where it left off! And yes, that button really does crash the app.<p>Under the hood, this works by storing your program's execution state (which workflows are currently executing and which steps they've completed) in a Postgres database. So all you need to use it is a Postgres database to connect to—there's no need for a "workflow server." This approach is also incredibly fast, for example 25x faster than AWS Step Functions.<p>Some more cool features include:<p>* Scheduled jobs—run your workflows exactly-once per time interval, no more need for cron.<p>* Exactly-once event processing—use workflows to process incoming events (for example, from a Kafka topic) exactly-once. No more need for complex code to avoid repeated processing<p>* Observability—all workflows automatically emit OpenTelemetry traces.<p>Docs: <a href="https://docs.dbos.dev/" rel="nofollow">https://docs.dbos.dev/</a><p>Examples: <a href="https://docs.dbos.dev/examples" rel="nofollow">https://docs.dbos.dev/examples</a><p>We also have a webinar on Thursday where we will walk through the new library, you can sign up here: <a href="https://www.dbos.dev/webcast/dbos-transact-python" rel="nofollow">https://www.dbos.dev/webcast/dbos-transact-python</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions you may have.

Show HN: DBOS transact – Ultra-lightweight durable execution in Python

Hi HN - DBOS CEO here with the co-founders of DBOS, Peter (KraftyOne) and Qian (qianli_cs). The company started as a research project of Stanford and MIT, and Peter and Qian were advised by Mike Stonebreaker, the creator of Postgres, and Matei Zaharia, the creator of Spark. They believe so strongly in reliable, serverless compute that they started a company (with Mike) to bring it to the world!<p>Today we want to share our brand new Python library providing ultra-lightweight durable execution.<p><a href="https://github.com/dbos-inc/dbos-transact-py">https://github.com/dbos-inc/dbos-transact-py</a><p>Durable execution means your program is resilient to any failure. If it is ever interrupted or crashes, all your workflows will automatically resume from the last completed step. If you want to see durable execution in action, check out this demo app:<p><a href="https://demo-widget-store.cloud.dbos.dev/" rel="nofollow">https://demo-widget-store.cloud.dbos.dev/</a><p>Or if you’re like me and want to skip straight to the Python decorators in action, here’s the demo app’s backend – an online store with reliability and correctness in just 200 LOC:<p><a href="https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/widget-store/widget_store/main.py">https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/...</a><p>Don't want to keep reading and just try it out:<p><a href="https://console.dbos.dev/launch" rel="nofollow">https://console.dbos.dev/launch</a><p>No matter how many times you try to crash it, it always resumes from exactly where it left off! And yes, that button really does crash the app.<p>Under the hood, this works by storing your program's execution state (which workflows are currently executing and which steps they've completed) in a Postgres database. So all you need to use it is a Postgres database to connect to—there's no need for a "workflow server." This approach is also incredibly fast, for example 25x faster than AWS Step Functions.<p>Some more cool features include:<p>* Scheduled jobs—run your workflows exactly-once per time interval, no more need for cron.<p>* Exactly-once event processing—use workflows to process incoming events (for example, from a Kafka topic) exactly-once. No more need for complex code to avoid repeated processing<p>* Observability—all workflows automatically emit OpenTelemetry traces.<p>Docs: <a href="https://docs.dbos.dev/" rel="nofollow">https://docs.dbos.dev/</a><p>Examples: <a href="https://docs.dbos.dev/examples" rel="nofollow">https://docs.dbos.dev/examples</a><p>We also have a webinar on Thursday where we will walk through the new library, you can sign up here: <a href="https://www.dbos.dev/webcast/dbos-transact-python" rel="nofollow">https://www.dbos.dev/webcast/dbos-transact-python</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions you may have.

Show HN: DBOS transact – Ultra-lightweight durable execution in Python

Hi HN - DBOS CEO here with the co-founders of DBOS, Peter (KraftyOne) and Qian (qianli_cs). The company started as a research project of Stanford and MIT, and Peter and Qian were advised by Mike Stonebreaker, the creator of Postgres, and Matei Zaharia, the creator of Spark. They believe so strongly in reliable, serverless compute that they started a company (with Mike) to bring it to the world!<p>Today we want to share our brand new Python library providing ultra-lightweight durable execution.<p><a href="https://github.com/dbos-inc/dbos-transact-py">https://github.com/dbos-inc/dbos-transact-py</a><p>Durable execution means your program is resilient to any failure. If it is ever interrupted or crashes, all your workflows will automatically resume from the last completed step. If you want to see durable execution in action, check out this demo app:<p><a href="https://demo-widget-store.cloud.dbos.dev/" rel="nofollow">https://demo-widget-store.cloud.dbos.dev/</a><p>Or if you’re like me and want to skip straight to the Python decorators in action, here’s the demo app’s backend – an online store with reliability and correctness in just 200 LOC:<p><a href="https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/widget-store/widget_store/main.py">https://github.com/dbos-inc/dbos-demo-apps/blob/main/python/...</a><p>Don't want to keep reading and just try it out:<p><a href="https://console.dbos.dev/launch" rel="nofollow">https://console.dbos.dev/launch</a><p>No matter how many times you try to crash it, it always resumes from exactly where it left off! And yes, that button really does crash the app.<p>Under the hood, this works by storing your program's execution state (which workflows are currently executing and which steps they've completed) in a Postgres database. So all you need to use it is a Postgres database to connect to—there's no need for a "workflow server." This approach is also incredibly fast, for example 25x faster than AWS Step Functions.<p>Some more cool features include:<p>* Scheduled jobs—run your workflows exactly-once per time interval, no more need for cron.<p>* Exactly-once event processing—use workflows to process incoming events (for example, from a Kafka topic) exactly-once. No more need for complex code to avoid repeated processing<p>* Observability—all workflows automatically emit OpenTelemetry traces.<p>Docs: <a href="https://docs.dbos.dev/" rel="nofollow">https://docs.dbos.dev/</a><p>Examples: <a href="https://docs.dbos.dev/examples" rel="nofollow">https://docs.dbos.dev/examples</a><p>We also have a webinar on Thursday where we will walk through the new library, you can sign up here: <a href="https://www.dbos.dev/webcast/dbos-transact-python" rel="nofollow">https://www.dbos.dev/webcast/dbos-transact-python</a><p>We'd love to hear what you think! We’ll be in the comments for the rest of the day to answer any questions you may have.

Show HN: Vomitorium – all of your project in 1 text file

Neat little nodejs/cli tool for putting all of your project's files into 1. I built it for LLM assistance with small-ish projects.

Show HN: Free tool to find RSS feeds, even if not linked on the page

I developed a small tool to find RSS feeds for websites. You can try it out here: <a href="https://lighthouseapp.io/tools/feed-finder" rel="nofollow">https://lighthouseapp.io/tools/feed-finder</a><p>In >90% of cases the standard way of checking meta tags is enough to find the feeds. But my goal for this tool is that it finds feeds regardless if they're linked somewhere or not. That if this feed finder doesn't find a feed, no feed exists.<p>It's a big goal and admittedly not there yet, but it does a few things that are a step in that direction.<p>* Checks meta tags of parent pages (sometimes the article itself doesn't have the meta tag, but the main blog page does)<p>* Checks common suffixes like /rss, /index.xml and many others (sometimes the feed exists but isn't linked)<p>* Checks the sitemap<p>* Checks all links on the page<p>* Checks 3rd party feeds (OpenRSS for now, when I find more such repositories I'll add them too)<p>There are a couple of additional ideas I have, like checking search engines and crawling the entire domain (highly inefficient, but possible).<p>Would love if you could try it, and even more if you post sites where it doesn't work.

Show HN: Free tool to find RSS feeds, even if not linked on the page

I developed a small tool to find RSS feeds for websites. You can try it out here: <a href="https://lighthouseapp.io/tools/feed-finder" rel="nofollow">https://lighthouseapp.io/tools/feed-finder</a><p>In >90% of cases the standard way of checking meta tags is enough to find the feeds. But my goal for this tool is that it finds feeds regardless if they're linked somewhere or not. That if this feed finder doesn't find a feed, no feed exists.<p>It's a big goal and admittedly not there yet, but it does a few things that are a step in that direction.<p>* Checks meta tags of parent pages (sometimes the article itself doesn't have the meta tag, but the main blog page does)<p>* Checks common suffixes like /rss, /index.xml and many others (sometimes the feed exists but isn't linked)<p>* Checks the sitemap<p>* Checks all links on the page<p>* Checks 3rd party feeds (OpenRSS for now, when I find more such repositories I'll add them too)<p>There are a couple of additional ideas I have, like checking search engines and crawling the entire domain (highly inefficient, but possible).<p>Would love if you could try it, and even more if you post sites where it doesn't work.

Show HN: Free tool to find RSS feeds, even if not linked on the page

I developed a small tool to find RSS feeds for websites. You can try it out here: <a href="https://lighthouseapp.io/tools/feed-finder" rel="nofollow">https://lighthouseapp.io/tools/feed-finder</a><p>In >90% of cases the standard way of checking meta tags is enough to find the feeds. But my goal for this tool is that it finds feeds regardless if they're linked somewhere or not. That if this feed finder doesn't find a feed, no feed exists.<p>It's a big goal and admittedly not there yet, but it does a few things that are a step in that direction.<p>* Checks meta tags of parent pages (sometimes the article itself doesn't have the meta tag, but the main blog page does)<p>* Checks common suffixes like /rss, /index.xml and many others (sometimes the feed exists but isn't linked)<p>* Checks the sitemap<p>* Checks all links on the page<p>* Checks 3rd party feeds (OpenRSS for now, when I find more such repositories I'll add them too)<p>There are a couple of additional ideas I have, like checking search engines and crawling the entire domain (highly inefficient, but possible).<p>Would love if you could try it, and even more if you post sites where it doesn't work.

Show HN: Visual DB – Web front end for your database

If you have a cloud-hosted database, read on: Visual DB was designed for you.<p>Visual DB is the fastest way to create data entry forms for your database: Starting with an Excel spreadsheet, you can import your data into the database and create a great-looking form in under 10 minutes. Watch this video: <a href="https://youtu.be/6rVD5rmrjN8" rel="nofollow">https://youtu.be/6rVD5rmrjN8</a><p>Visual DB is a comprehensive SaaS frontend for your database. In addition to data entry forms, Visual DB also has a spreadsheet-like interface for inserting and updating data in your database. You can also build interactive reports using Visual DB. Finally, although not intended as a replacement for your database's admin tool, Visual DB can browse schema, create tables, set up relationships, and import and export data.<p>Visual DB began as a drag-and-drop form builder for databases. Forms created with Visual DB are practically indistinguishable from those hand-coded using React. You can add client-side validation, set available values (displayed in dropdowns), define default values, and even add logic to dynamically hide or disable fields—all without writing a single line of code! With Visual DB Forms, you’ll never have to write another CRUD app again.<p>If you have been using Excel to manage data and running into its limits because the volume of data has grown, Visual DB Sheets may be of interest to you. With its spreadsheet-like interface, Visual DB Sheets allows users to interact with data as they would in Excel, while securely storing that data in a robust relational database. Spreadsheet-database hybrids have been around for a while now, but we believe we have one of the best implementations, with features such as advanced grouping, support for foreign keys and lookup tables, query parameters, full-text as-you-type filtering, and so on.<p>The newest feature of Visual DB is interactive reporting. Traditional reporting tools offer limited interactivity. For example, while most reporting tools support time series charts, they do not allow users to zoom or pan along the time axis. In contrast, Visual DB supports this capability thanks to its innovative approach: it downloads the dataset to the client and processes and visualizes data directly in the browser. This allows it to handle user interactions without a server round trip. Visual DB has excellent support for query parameters, which allows you to bring only the subset of data that's of interest (up to 100K rows), to the client.<p>Visual DB supports PostgreSQL (including Neon), MySQL, SQL Server and Oracle. Give it a whirl, and we look forward to getting your feedback: <a href="https://visualdb.com" rel="nofollow">https://visualdb.com</a>

Show HN: Visual DB – Web front end for your database

If you have a cloud-hosted database, read on: Visual DB was designed for you.<p>Visual DB is the fastest way to create data entry forms for your database: Starting with an Excel spreadsheet, you can import your data into the database and create a great-looking form in under 10 minutes. Watch this video: <a href="https://youtu.be/6rVD5rmrjN8" rel="nofollow">https://youtu.be/6rVD5rmrjN8</a><p>Visual DB is a comprehensive SaaS frontend for your database. In addition to data entry forms, Visual DB also has a spreadsheet-like interface for inserting and updating data in your database. You can also build interactive reports using Visual DB. Finally, although not intended as a replacement for your database's admin tool, Visual DB can browse schema, create tables, set up relationships, and import and export data.<p>Visual DB began as a drag-and-drop form builder for databases. Forms created with Visual DB are practically indistinguishable from those hand-coded using React. You can add client-side validation, set available values (displayed in dropdowns), define default values, and even add logic to dynamically hide or disable fields—all without writing a single line of code! With Visual DB Forms, you’ll never have to write another CRUD app again.<p>If you have been using Excel to manage data and running into its limits because the volume of data has grown, Visual DB Sheets may be of interest to you. With its spreadsheet-like interface, Visual DB Sheets allows users to interact with data as they would in Excel, while securely storing that data in a robust relational database. Spreadsheet-database hybrids have been around for a while now, but we believe we have one of the best implementations, with features such as advanced grouping, support for foreign keys and lookup tables, query parameters, full-text as-you-type filtering, and so on.<p>The newest feature of Visual DB is interactive reporting. Traditional reporting tools offer limited interactivity. For example, while most reporting tools support time series charts, they do not allow users to zoom or pan along the time axis. In contrast, Visual DB supports this capability thanks to its innovative approach: it downloads the dataset to the client and processes and visualizes data directly in the browser. This allows it to handle user interactions without a server round trip. Visual DB has excellent support for query parameters, which allows you to bring only the subset of data that's of interest (up to 100K rows), to the client.<p>Visual DB supports PostgreSQL (including Neon), MySQL, SQL Server and Oracle. Give it a whirl, and we look forward to getting your feedback: <a href="https://visualdb.com" rel="nofollow">https://visualdb.com</a>