The best Hacker News stories from Show from the past day
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Show HN: I made a drag and drop CSS grid generator
Show HN: I made a drag and drop CSS grid generator
Show HN: I made a drag and drop CSS grid generator
Show HN: Resurrecting a dead Dune RTS game
Show HN: Resurrecting a dead Dune RTS game
Show HN: Resurrecting a dead Dune RTS game
Show HN: Resurrecting a dead Dune RTS game
Show HN: 30ms latency screen sharing in Rust
Show HN: 30ms latency screen sharing in Rust
Show HN: 30ms latency screen sharing in Rust
Show HN: Click counter using iPhone volume buttons
Show HN: Smelt — an open source test runner for chip developers
Hey everyone, James from Silogy here.<p>We’re excited to open-source our test runner, Smelt. Smelt is a simple and extensible test runner optimized for chip development workflows. Smelt enables developers to:<p>* Programmatically define numerous test variants<p>* Execute these tests in parallel<p>* Easily analyze test results<p>As chip designs get more complex, the state space that needs to be explored in design verification is exploding. In chip development, it's common to run thousands of tests, each with multiple hyperparameters that result in even more variation. Smelt offers a straightforward approach to generating test variants and extracting valuable insights from your test runs. Smelt integrates seamlessly with most popular simulators and other chip design tools.<p>Key features:<p>* Procedural test generation: Programmatically generate tests with python<p>* Automatic rerun on failure: Describe the computation required re-run failing tests<p>* Analysis APIs: All of the data needed to track and reproduce tests<p>* Extensible: Define your tests with a simple python interface<p>Yves (<a href="https://github.com/silogy-io/yves">https://github.com/silogy-io/yves</a>) is a suite of directed performance tests that we brought up with smelt – check it out if you’d like to see smelt in action.<p>Repo: <a href="https://github.com/silogy-io/smelt">https://github.com/silogy-io/smelt</a><p>We built Smelt to streamline the testing process for chip developers. We're eager to hear your feedback and see how it performs in your projects!
Show HN: Smelt — an open source test runner for chip developers
Hey everyone, James from Silogy here.<p>We’re excited to open-source our test runner, Smelt. Smelt is a simple and extensible test runner optimized for chip development workflows. Smelt enables developers to:<p>* Programmatically define numerous test variants<p>* Execute these tests in parallel<p>* Easily analyze test results<p>As chip designs get more complex, the state space that needs to be explored in design verification is exploding. In chip development, it's common to run thousands of tests, each with multiple hyperparameters that result in even more variation. Smelt offers a straightforward approach to generating test variants and extracting valuable insights from your test runs. Smelt integrates seamlessly with most popular simulators and other chip design tools.<p>Key features:<p>* Procedural test generation: Programmatically generate tests with python<p>* Automatic rerun on failure: Describe the computation required re-run failing tests<p>* Analysis APIs: All of the data needed to track and reproduce tests<p>* Extensible: Define your tests with a simple python interface<p>Yves (<a href="https://github.com/silogy-io/yves">https://github.com/silogy-io/yves</a>) is a suite of directed performance tests that we brought up with smelt – check it out if you’d like to see smelt in action.<p>Repo: <a href="https://github.com/silogy-io/smelt">https://github.com/silogy-io/smelt</a><p>We built Smelt to streamline the testing process for chip developers. We're eager to hear your feedback and see how it performs in your projects!
Show HN: Smelt — an open source test runner for chip developers
Hey everyone, James from Silogy here.<p>We’re excited to open-source our test runner, Smelt. Smelt is a simple and extensible test runner optimized for chip development workflows. Smelt enables developers to:<p>* Programmatically define numerous test variants<p>* Execute these tests in parallel<p>* Easily analyze test results<p>As chip designs get more complex, the state space that needs to be explored in design verification is exploding. In chip development, it's common to run thousands of tests, each with multiple hyperparameters that result in even more variation. Smelt offers a straightforward approach to generating test variants and extracting valuable insights from your test runs. Smelt integrates seamlessly with most popular simulators and other chip design tools.<p>Key features:<p>* Procedural test generation: Programmatically generate tests with python<p>* Automatic rerun on failure: Describe the computation required re-run failing tests<p>* Analysis APIs: All of the data needed to track and reproduce tests<p>* Extensible: Define your tests with a simple python interface<p>Yves (<a href="https://github.com/silogy-io/yves">https://github.com/silogy-io/yves</a>) is a suite of directed performance tests that we brought up with smelt – check it out if you’d like to see smelt in action.<p>Repo: <a href="https://github.com/silogy-io/smelt">https://github.com/silogy-io/smelt</a><p>We built Smelt to streamline the testing process for chip developers. We're eager to hear your feedback and see how it performs in your projects!
Show HN: Open-source CLI coding framework using Claude
Show HN: Open-source CLI coding framework using Claude
Show HN: Open-source CLI coding framework using Claude
Show HN: Open-source CLI coding framework using Claude
Show HN: Dropbase AI – A Prompt-Based Python Web App Builder
Hey HN,<p>Dropbase is an AI-based Python web app builder.<p>To build this, we had to make significant changes from our original launch: <a href="https://news.ycombinator.com/item?id=38534920">https://news.ycombinator.com/item?id=38534920</a>. Now, any web app can be entirely defined using just two files: `properties.json` for the UI and `main.py` for the backend logic, which makes it significantly easier for GPT to work with.<p>In the latest version, developers can use natural language prompts to build apps. But instead of generating a black-box app or promising an AI software engineer we just generate simple Python code that is easily interpreted by our internal web framework. This allows developers to:<p>(1) See and understand the generated app code. We regenerate the `main.py` file and highlight changes in a diff viewer, allowing developers to understand what exactly was changed.<p>(2) Edit the app code: Developers can correct any errors, occasional hallucinations, or edit code to handle specific use cases. Once they like the code, they can commit changes and immediately preview the app.<p>Incidentally, if you’ve tried Anthropic’s Artifacts to create “apps”, our experience will feel familiar. Dropbase AI is like Claude Artifacts, but for fully functional apps: you can connect to your database, make external API calls, and deploy to servers.<p>Our goal is to create a universal, prompt-based app builder that’s highly customizable. Code should always be accessible and developers should be in control. We believe most apps will be built or prototyped this way, and we're taking the first steps towards that goal.<p>A fun fact is that model improvements were critical here: we could not achieve the consistent results we needed with any LLM prior to GPT-4o and Claude 3.5 Sonnet. In the future, we’ll allow users to modify the code to call their local GPT/LLM deployment via Ollama, rather than relying on OpenAI or Anthropic calls.<p>If you’re building admin panels, database editors, back-office tools, billing/customer dashboards, and internal dev tools that can fetch data and trigger actions across any database, internal/external service or API, please give Dropbase a shot!<p>We're excited to get your thoughts and questions!<p>Demos:<p>- Here’s a demo video: <a href="https://youtu.be/RaxHOjhy3hY" rel="nofollow">https://youtu.be/RaxHOjhy3hY</a><p>- We also introduced Charts (beta) in this version based on suggestions from cjohnson318 in our previous HN post: <a href="https://youtu.be/YWtdD7THTxE" rel="nofollow">https://youtu.be/YWtdD7THTxE</a><p>Useful links:<p>- Repo here: <a href="https://github.com/DropbaseHQ/dropbase">https://github.com/DropbaseHQ/dropbase</a>. To setup locally, follow the quickstart guide in our docs<p>- Docs: <a href="https://docs.dropbase.io">https://docs.dropbase.io</a><p>- Homepage: <a href="https://dropbase.io">https://dropbase.io</a>
Show HN: Dropbase AI – A Prompt-Based Python Web App Builder
Hey HN,<p>Dropbase is an AI-based Python web app builder.<p>To build this, we had to make significant changes from our original launch: <a href="https://news.ycombinator.com/item?id=38534920">https://news.ycombinator.com/item?id=38534920</a>. Now, any web app can be entirely defined using just two files: `properties.json` for the UI and `main.py` for the backend logic, which makes it significantly easier for GPT to work with.<p>In the latest version, developers can use natural language prompts to build apps. But instead of generating a black-box app or promising an AI software engineer we just generate simple Python code that is easily interpreted by our internal web framework. This allows developers to:<p>(1) See and understand the generated app code. We regenerate the `main.py` file and highlight changes in a diff viewer, allowing developers to understand what exactly was changed.<p>(2) Edit the app code: Developers can correct any errors, occasional hallucinations, or edit code to handle specific use cases. Once they like the code, they can commit changes and immediately preview the app.<p>Incidentally, if you’ve tried Anthropic’s Artifacts to create “apps”, our experience will feel familiar. Dropbase AI is like Claude Artifacts, but for fully functional apps: you can connect to your database, make external API calls, and deploy to servers.<p>Our goal is to create a universal, prompt-based app builder that’s highly customizable. Code should always be accessible and developers should be in control. We believe most apps will be built or prototyped this way, and we're taking the first steps towards that goal.<p>A fun fact is that model improvements were critical here: we could not achieve the consistent results we needed with any LLM prior to GPT-4o and Claude 3.5 Sonnet. In the future, we’ll allow users to modify the code to call their local GPT/LLM deployment via Ollama, rather than relying on OpenAI or Anthropic calls.<p>If you’re building admin panels, database editors, back-office tools, billing/customer dashboards, and internal dev tools that can fetch data and trigger actions across any database, internal/external service or API, please give Dropbase a shot!<p>We're excited to get your thoughts and questions!<p>Demos:<p>- Here’s a demo video: <a href="https://youtu.be/RaxHOjhy3hY" rel="nofollow">https://youtu.be/RaxHOjhy3hY</a><p>- We also introduced Charts (beta) in this version based on suggestions from cjohnson318 in our previous HN post: <a href="https://youtu.be/YWtdD7THTxE" rel="nofollow">https://youtu.be/YWtdD7THTxE</a><p>Useful links:<p>- Repo here: <a href="https://github.com/DropbaseHQ/dropbase">https://github.com/DropbaseHQ/dropbase</a>. To setup locally, follow the quickstart guide in our docs<p>- Docs: <a href="https://docs.dropbase.io">https://docs.dropbase.io</a><p>- Homepage: <a href="https://dropbase.io">https://dropbase.io</a>