The best Hacker News stories from Show from the past day
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Show HN: Lapdev, a new open-source remote dev environment management software
Show HN: Leaping – Debug Python tests instantly with an LLM debugger
Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging.<p>We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - <a href="https://news.ycombinator.com/item?id=39528087">https://news.ycombinator.com/item?id=39528087</a><p>As we were building the root-cause phase of our automated debugger, we realized that we developed something that resembled an omniscient debugger. Like an omniscient debugger, it also keeps track of variable assignments over time but, you can interact with it at a higher level than a debugger using natural language. We ended up sticking it in a pytest plugin and have been using it ourselves for development over the past few weeks.<p>Using this pytest plugin, you’re able to reason at a much higher level than conventional debuggers and can ask questions like:<p>- Why did function x get hit?<p>- Why was variable y set to this value?<p>- What changes can I make to this code to make this test pass?<p>Here’s a brief demo of this in action: <a href="https://www.loom.com/share/94ebe34097a343c39876d7109f2a1428" rel="nofollow">https://www.loom.com/share/94ebe34097a343c39876d7109f2a1428</a><p>To achieve this, we first instrument the test using sys.settrace (or, on versions of python >3.12, the far better sys.monitoring!) to keep a history of all the functions that were called, along with the calling line numbers. We then re-run the test and use AST parsing to find all the variable assignments and keep track of those changes over time. We also use AST parsing to obtain the source code for these functions. We then neatly format and pass all this context to GPT.<p>We’d love it if you checked the pytest plugin out and we welcome all feedback :) . If you want to chat bugs, our emails are also always open - kanav@leaping.io
Show HN: Leaping – Debug Python tests instantly with an LLM debugger
Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging.<p>We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - <a href="https://news.ycombinator.com/item?id=39528087">https://news.ycombinator.com/item?id=39528087</a><p>As we were building the root-cause phase of our automated debugger, we realized that we developed something that resembled an omniscient debugger. Like an omniscient debugger, it also keeps track of variable assignments over time but, you can interact with it at a higher level than a debugger using natural language. We ended up sticking it in a pytest plugin and have been using it ourselves for development over the past few weeks.<p>Using this pytest plugin, you’re able to reason at a much higher level than conventional debuggers and can ask questions like:<p>- Why did function x get hit?<p>- Why was variable y set to this value?<p>- What changes can I make to this code to make this test pass?<p>Here’s a brief demo of this in action: <a href="https://www.loom.com/share/94ebe34097a343c39876d7109f2a1428" rel="nofollow">https://www.loom.com/share/94ebe34097a343c39876d7109f2a1428</a><p>To achieve this, we first instrument the test using sys.settrace (or, on versions of python >3.12, the far better sys.monitoring!) to keep a history of all the functions that were called, along with the calling line numbers. We then re-run the test and use AST parsing to find all the variable assignments and keep track of those changes over time. We also use AST parsing to obtain the source code for these functions. We then neatly format and pass all this context to GPT.<p>We’d love it if you checked the pytest plugin out and we welcome all feedback :) . If you want to chat bugs, our emails are also always open - kanav@leaping.io
Show HN: Free Plain-Text Bookmarking
Show HN: Free Plain-Text Bookmarking
Show HN: magick.css – Minimalist CSS for Wizards
Show HN: magick.css – Minimalist CSS for Wizards
Mapping almost every law, regulation and case in Australia
Hey HN,<p>After months of hard work, I am excited to share the first ever semantic map of Australian law.<p>My map represents the first attempt to map Australian laws, cases and regulations across the Commonwealth, States and Territories semantically, that is, by their underlying meaning.<p>Each point on the map is a unique document in the Open Australian Legal Corpus, the largest open database of Australian law (which, full disclosure, I created). The closer any two points are on the map, the more similar they are in underlying meaning.<p>As I cover in my article, there’s a lot you can learn by mapping Australian law. Some of the most interesting insights to come out of this initiative are that:<p>⦁ Migration, family and substantive criminal law are the most isolated branches of case law on the map;<p>⦁ Migration, family and substantive criminal law are the most distant branches of case law from legislation on the map;<p>⦁ Development law is the closest branch of case law to legislation on the map;<p>⦁ Case law is more of a continuum than a rigidly defined structure and the borders between branches of case law can often be quite porous; and<p>⦁ The map does not reveal any noticeable distinctions between Australian state and federal law, whether it be in style, principles of interpretation or general jurisprudence.<p>If you’re interested in learning more about what the map has to teach us about Australian law or if you’d like to find out how you can create semantic maps of your own, check out the full article on my blog, which provides a detailed analysis of my map and also covers the finer details of how I built it, with code examples offered along the way.
Show HN: Nebula – A network agnostic DHT crawler
Show HN: Ragas – Open-source library for evaluating RAG pipelines
Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: <a href="https://docs.ragas.io/en/stable/">https://docs.ragas.io/en/stable/</a>, docs: <a href="https://docs.ragas.io/">https://docs.ragas.io/</a>.<p>Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year.<p><i>Problems Ragas can solve</i><p>- How do you choose the best components for your RAG, such as the retriever, reranker, and LLM?<p>- How do you formulate a test dataset without spending tons of money and time?<p>We believe there needs to be an open-source standard for evaluating and testing LLM applications, and our vision is to build it for the community. We are tackling this challenge by evolving the ideas from the traditional ML lifecycle for LLM applications.<p><i>ML Testing Evolved for LLM Applications</i><p>We built Ragas on the principles of metrics-driven development and aim to develop and innovate techniques inspired by state-of-the-art research to solve the problems in evaluating and testing LLM applications.<p>We don't believe that the problem of evaluating and testing applications can be solved by building a fancy tracing tool; rather, we want to solve the problem from a layer under the stack. For this, we are introducing methods like automated synthetic test data curation, metrics, and feedback utilisation, which are inspired by lessons learned from deploying stochastic models in our careers as ML engineers.<p>While currently focused on RAG pipelines, our goal is to extend Ragas for testing a wide array of compound systems, including those based on RAGs, agentic workflows, and various transformations.<p>Try out Ragas here <a href="https://colab.research.google.com/github/shahules786/openai-cookbook/blob/ragas/examples/evaluation/ragas/openai-ragas-eval-cookbook.ipynb" rel="nofollow">https://colab.research.google.com/github/shahules786/openai-...</a> in Google Colab. Read our docs - <a href="https://docs.ragas.io/">https://docs.ragas.io/</a> to know more<p>We would love to hear feedback from the HN community :)
Show HN: Ragas – Open-source library for evaluating RAG pipelines
Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: <a href="https://docs.ragas.io/en/stable/">https://docs.ragas.io/en/stable/</a>, docs: <a href="https://docs.ragas.io/">https://docs.ragas.io/</a>.<p>Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year.<p><i>Problems Ragas can solve</i><p>- How do you choose the best components for your RAG, such as the retriever, reranker, and LLM?<p>- How do you formulate a test dataset without spending tons of money and time?<p>We believe there needs to be an open-source standard for evaluating and testing LLM applications, and our vision is to build it for the community. We are tackling this challenge by evolving the ideas from the traditional ML lifecycle for LLM applications.<p><i>ML Testing Evolved for LLM Applications</i><p>We built Ragas on the principles of metrics-driven development and aim to develop and innovate techniques inspired by state-of-the-art research to solve the problems in evaluating and testing LLM applications.<p>We don't believe that the problem of evaluating and testing applications can be solved by building a fancy tracing tool; rather, we want to solve the problem from a layer under the stack. For this, we are introducing methods like automated synthetic test data curation, metrics, and feedback utilisation, which are inspired by lessons learned from deploying stochastic models in our careers as ML engineers.<p>While currently focused on RAG pipelines, our goal is to extend Ragas for testing a wide array of compound systems, including those based on RAGs, agentic workflows, and various transformations.<p>Try out Ragas here <a href="https://colab.research.google.com/github/shahules786/openai-cookbook/blob/ragas/examples/evaluation/ragas/openai-ragas-eval-cookbook.ipynb" rel="nofollow">https://colab.research.google.com/github/shahules786/openai-...</a> in Google Colab. Read our docs - <a href="https://docs.ragas.io/">https://docs.ragas.io/</a> to know more<p>We would love to hear feedback from the HN community :)
Show HN: An AI-Powered WordPress Site Builder That We Are Open-Sourcing Today
Show HN: An AI-Powered WordPress Site Builder That We Are Open-Sourcing Today
Show HN: An AI-Powered WordPress Site Builder That We Are Open-Sourcing Today
Show HN: Dropflow, a CSS layout engine for node or <canvas>
For the last 5 years I've been working on a layout engine that targets CSS2 and some more modern properties.<p>Live demo: <a href="https://chearon.github.io/dropflow/" rel="nofollow">https://chearon.github.io/dropflow/</a><p>It matches browsers in all cases I can find where they agree, and it's fast. It supports `position`, `inline-block`, `z-index`, and complex properties like `float` and `vertical-align`. It doesn't support high-level layout like flexbox or grid yet, but you can get intrinsics to easily divide space yourself and paint multiple layout trees. It has a great text layout implementation, and supporting non-Latin scripts is a top priority.<p>I've wanted this to exist as far back as 2013, and the desire kept coming up: for a way to get detailed intrinsics, for high quality rich text layout to canvas and SVG, and for server-side rich text. We currently use it in CellEngine for our new canvas-based spreadsheet library to layout text in hundreds of thousands of cells, and will be using it soon to render PDFs with thousands of pages in a few seconds.
Show HN: Dropflow, a CSS layout engine for node or <canvas>
For the last 5 years I've been working on a layout engine that targets CSS2 and some more modern properties.<p>Live demo: <a href="https://chearon.github.io/dropflow/" rel="nofollow">https://chearon.github.io/dropflow/</a><p>It matches browsers in all cases I can find where they agree, and it's fast. It supports `position`, `inline-block`, `z-index`, and complex properties like `float` and `vertical-align`. It doesn't support high-level layout like flexbox or grid yet, but you can get intrinsics to easily divide space yourself and paint multiple layout trees. It has a great text layout implementation, and supporting non-Latin scripts is a top priority.<p>I've wanted this to exist as far back as 2013, and the desire kept coming up: for a way to get detailed intrinsics, for high quality rich text layout to canvas and SVG, and for server-side rich text. We currently use it in CellEngine for our new canvas-based spreadsheet library to layout text in hundreds of thousands of cells, and will be using it soon to render PDFs with thousands of pages in a few seconds.
Show HN: Dropflow, a CSS layout engine for node or <canvas>
For the last 5 years I've been working on a layout engine that targets CSS2 and some more modern properties.<p>Live demo: <a href="https://chearon.github.io/dropflow/" rel="nofollow">https://chearon.github.io/dropflow/</a><p>It matches browsers in all cases I can find where they agree, and it's fast. It supports `position`, `inline-block`, `z-index`, and complex properties like `float` and `vertical-align`. It doesn't support high-level layout like flexbox or grid yet, but you can get intrinsics to easily divide space yourself and paint multiple layout trees. It has a great text layout implementation, and supporting non-Latin scripts is a top priority.<p>I've wanted this to exist as far back as 2013, and the desire kept coming up: for a way to get detailed intrinsics, for high quality rich text layout to canvas and SVG, and for server-side rich text. We currently use it in CellEngine for our new canvas-based spreadsheet library to layout text in hundreds of thousands of cells, and will be using it soon to render PDFs with thousands of pages in a few seconds.
Show HN: Memories – FOSS Google Photos alternative built for high performance
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin).<p>Website: <a href="https://memories.gallery/" rel="nofollow">https://memories.gallery/</a><p>GitHub: <a href="https://github.com/pulsejet/memories">https://github.com/pulsejet/memories</a><p>Demo Server: <a href="https://demo.memories.gallery/apps/memories/" rel="nofollow">https://demo.memories.gallery/apps/memories/</a>
(demo runs in San Francisco on a free-tier cloud vm)<p>Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time!<p>Some features to highlight:<p>* A timeline similar to Google Photos where you can skip to any time in history instantly.<p>* AI-based tagging that runs locally on your server, identifying and tagging people and objects.<p>* Albums and external sharing.<p>* Metadata editing support<p>* A world map of your photos, supported both on mobile and the web<p>* Did I mention it's extremely fast?<p>Would love to hear feedback from the HN community! :)
Show HN: Memories – FOSS Google Photos alternative built for high performance
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin).<p>Website: <a href="https://memories.gallery/" rel="nofollow">https://memories.gallery/</a><p>GitHub: <a href="https://github.com/pulsejet/memories">https://github.com/pulsejet/memories</a><p>Demo Server: <a href="https://demo.memories.gallery/apps/memories/" rel="nofollow">https://demo.memories.gallery/apps/memories/</a>
(demo runs in San Francisco on a free-tier cloud vm)<p>Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time!<p>Some features to highlight:<p>* A timeline similar to Google Photos where you can skip to any time in history instantly.<p>* AI-based tagging that runs locally on your server, identifying and tagging people and objects.<p>* Albums and external sharing.<p>* Metadata editing support<p>* A world map of your photos, supported both on mobile and the web<p>* Did I mention it's extremely fast?<p>Would love to hear feedback from the HN community! :)
Show HN: Memories – FOSS Google Photos alternative built for high performance
Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin).<p>Website: <a href="https://memories.gallery/" rel="nofollow">https://memories.gallery/</a><p>GitHub: <a href="https://github.com/pulsejet/memories">https://github.com/pulsejet/memories</a><p>Demo Server: <a href="https://demo.memories.gallery/apps/memories/" rel="nofollow">https://demo.memories.gallery/apps/memories/</a>
(demo runs in San Francisco on a free-tier cloud vm)<p>Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time!<p>Some features to highlight:<p>* A timeline similar to Google Photos where you can skip to any time in history instantly.<p>* AI-based tagging that runs locally on your server, identifying and tagging people and objects.<p>* Albums and external sharing.<p>* Metadata editing support<p>* A world map of your photos, supported both on mobile and the web<p>* Did I mention it's extremely fast?<p>Would love to hear feedback from the HN community! :)