The best Hacker News stories from Show from the past day
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Show HN: Resurrecting Infocom's Unix Z-Machine with Cosmopolitan
I recently brought Infocom's original UNIX z-machine source code back to life on modern systems. The modified source code, instructions on usage, a build of the z-machine, and examples of embedded game executables are available.<p>There is also a detailed write-up about the state of the original source code, the porting process, and the invaluable role Justine Tunney's Cosmpolitan project played in bringing the Zork trilogy (and more) to Windows/Mac/Linux/bsd for arm/x86 machines over the course of a lazy Sunday.
Show HN: Resurrecting Infocom's Unix Z-Machine with Cosmopolitan
I recently brought Infocom's original UNIX z-machine source code back to life on modern systems. The modified source code, instructions on usage, a build of the z-machine, and examples of embedded game executables are available.<p>There is also a detailed write-up about the state of the original source code, the porting process, and the invaluable role Justine Tunney's Cosmpolitan project played in bringing the Zork trilogy (and more) to Windows/Mac/Linux/bsd for arm/x86 machines over the course of a lazy Sunday.
Show HN: Resurrecting Infocom's Unix Z-Machine with Cosmopolitan
I recently brought Infocom's original UNIX z-machine source code back to life on modern systems. The modified source code, instructions on usage, a build of the z-machine, and examples of embedded game executables are available.<p>There is also a detailed write-up about the state of the original source code, the porting process, and the invaluable role Justine Tunney's Cosmpolitan project played in bringing the Zork trilogy (and more) to Windows/Mac/Linux/bsd for arm/x86 machines over the course of a lazy Sunday.
Show HN: Resurrecting Infocom's Unix Z-Machine with Cosmopolitan
I recently brought Infocom's original UNIX z-machine source code back to life on modern systems. The modified source code, instructions on usage, a build of the z-machine, and examples of embedded game executables are available.<p>There is also a detailed write-up about the state of the original source code, the porting process, and the invaluable role Justine Tunney's Cosmpolitan project played in bringing the Zork trilogy (and more) to Windows/Mac/Linux/bsd for arm/x86 machines over the course of a lazy Sunday.
Show HN: Nissan's Leaf app doesn't have a home screen widget so I made my own
Nissan's official mobile app for their LEAF electric car doesn't have a widget for quickly checking the car's battery charge status on your phone's home screen, so for a fun side project I decided to make my own using free tools like GitHub Actions, Appium, Tailscale, and Apple Shortcuts.
Show HN: I made a free tool that analyzes SEC filings and posts detailed reports
(* within a few minutes of SEC filing)<p>Currently does it for 1000+ US companies and specifically earnings related filings.
By US companies, I mean the ones that are obliged to file SEC filings.<p>This was the result of almost a year long effort and hundreds of prototypes :)<p>It currently auto-publishes for 1000 ish US companies by market cap, relies on 8-K filing as a trigger.<p>e.g. <a href="https://www.signalbloom.ai/news/NVDA" rel="nofollow">https://www.signalbloom.ai/news/NVDA</a> will take you to NVDA earnings<p>Would be grateful to get some feedback. Especially if you follow a company, check its reports out. Thank you!<p>Some examples:
<a href="https://www.signalbloom.ai/news/AAPL/apple-q1-eps-beats-despite-revenue-miss-china-woes" rel="nofollow">https://www.signalbloom.ai/news/AAPL/apple-q1-eps-beats-desp...</a><p><a href="https://www.signalbloom.ai/news/NVDA/nvidia-revenue-soars-margin-headwinds-emerge" rel="nofollow">https://www.signalbloom.ai/news/NVDA/nvidia-revenue-soars-ma...</a><p><a href="https://www.signalbloom.ai/news/JPM/jpm-beats-estimates-on-cib-strength" rel="nofollow">https://www.signalbloom.ai/news/JPM/jpm-beats-estimates-on-c...</a> (JPM earnings from Friday)<p>Hallucination note: <a href="https://www.signalbloom.ai/hallucination-benchmark" rel="nofollow">https://www.signalbloom.ai/hallucination-benchmark</a>
Show HN: I made a free tool that analyzes SEC filings and posts detailed reports
(* within a few minutes of SEC filing)<p>Currently does it for 1000+ US companies and specifically earnings related filings.
By US companies, I mean the ones that are obliged to file SEC filings.<p>This was the result of almost a year long effort and hundreds of prototypes :)<p>It currently auto-publishes for 1000 ish US companies by market cap, relies on 8-K filing as a trigger.<p>e.g. <a href="https://www.signalbloom.ai/news/NVDA" rel="nofollow">https://www.signalbloom.ai/news/NVDA</a> will take you to NVDA earnings<p>Would be grateful to get some feedback. Especially if you follow a company, check its reports out. Thank you!<p>Some examples:
<a href="https://www.signalbloom.ai/news/AAPL/apple-q1-eps-beats-despite-revenue-miss-china-woes" rel="nofollow">https://www.signalbloom.ai/news/AAPL/apple-q1-eps-beats-desp...</a><p><a href="https://www.signalbloom.ai/news/NVDA/nvidia-revenue-soars-margin-headwinds-emerge" rel="nofollow">https://www.signalbloom.ai/news/NVDA/nvidia-revenue-soars-ma...</a><p><a href="https://www.signalbloom.ai/news/JPM/jpm-beats-estimates-on-cib-strength" rel="nofollow">https://www.signalbloom.ai/news/JPM/jpm-beats-estimates-on-c...</a> (JPM earnings from Friday)<p>Hallucination note: <a href="https://www.signalbloom.ai/hallucination-benchmark" rel="nofollow">https://www.signalbloom.ai/hallucination-benchmark</a>
Show HN: Crystal, the most accurate U.S. gov't data search tool
Hi everyone! We're relaunching Crystal, which lets you search and analyze 300k+ government datasets using plain English. For example, prompting "Air quality since 2020 in NYC" will find the most relevant datasets for you.<p>We find it's way better than any search tool out there today, like data.gov. We're hoping anyone who uses public data as a resource, like researchers, consultants, journalists, etc. will find it helpful.<p>Crystal is straightforward - it's in alpha, so there's only a few queries per person, and the app itself is in its infancy. We're invested in making this better for people, and we'd love feedback + beta signups - you can provide either via <a href="https://www.askcrystal.info" rel="nofollow">https://www.askcrystal.info</a> or down below!<p>If you'd like to partner with us more closely or have other thoughts, please email us at cedric@crystal.info or ari@crystal.info
Show HN: I Made YC Rejection Simulator
Show HN: I Made YC Rejection Simulator
Show HN: Chonky – a neural approach for text semantic chunking
TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks.<p>The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences).<p>I propose a fully neural approach to semantic chunking.<p>I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti.<p>The library could be used as a text splitter module in a RAG system or for splitting transcripts for example.<p>The usage pattern that I see is the following: strip all the markup tags to produce pure text and feed this text into the model.<p>The problem is that although in theory this should improve overall RAG pipeline performance I didn’t manage to measure it properly. Other limitations: the model only supports English for now and the output text is downcased.<p>Please give it a try. I'll appreciate a feedback.<p>The Python library: <a href="https://github.com/mirth/chonky">https://github.com/mirth/chonky</a><p>The transformer model: <a href="https://huggingface.co/mirth/chonky_distilbert_base_uncased_1" rel="nofollow">https://huggingface.co/mirth/chonky_distilbert_base_uncased_...</a>
Show HN: Chonky – a neural approach for text semantic chunking
TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks.<p>The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences).<p>I propose a fully neural approach to semantic chunking.<p>I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti.<p>The library could be used as a text splitter module in a RAG system or for splitting transcripts for example.<p>The usage pattern that I see is the following: strip all the markup tags to produce pure text and feed this text into the model.<p>The problem is that although in theory this should improve overall RAG pipeline performance I didn’t manage to measure it properly. Other limitations: the model only supports English for now and the output text is downcased.<p>Please give it a try. I'll appreciate a feedback.<p>The Python library: <a href="https://github.com/mirth/chonky">https://github.com/mirth/chonky</a><p>The transformer model: <a href="https://huggingface.co/mirth/chonky_distilbert_base_uncased_1" rel="nofollow">https://huggingface.co/mirth/chonky_distilbert_base_uncased_...</a>
Show HN: Pg_CRDT – CRDTs in Postgres Using Automerge
Show HN: Python at the Speed of Rust
I’m sure many of you are familiar, but there’s a treacherous gap between finding (or building) a model that works in PyTorch, and getting that deployed into your application, especially in consumer-facing applications.<p>I’ve been very interested in solving this problem with a great developer experience. Over time, I gradually realized that the highest-impact thing to have was a way to go from existing Python code to a self-contained native binary—in other words, a Python compiler.<p>I was already pretty familiar with a successful attempt: when Apple introduced armv8 on the iPhone 5s, they quickly mandated 64-bit support for all apps. Unity—where I had been programming since I was 11—kinda got f*cked because they used Mono to run developers’ C# code, and Mono didn’t support 64-bit ARM. Unity ended up building IL2CPP, which transpiles the C# intermediate language into C++, then cross-compiles it. Till date, this is perhaps the most amazing technical feat Unity has achieved imo.<p>I set out to build something similar, but this time starting from Python. It’s a pretty difficult problem, given the dynamic nature of the language. The key unlock was the PyTorch 2.0 release, where they pioneered the use of symbolic tracing to power `torch.compile`. In a nutshell, they register a callback with the Python interpreter (using CPython’s frame evaluation API), run a function with fake inputs, and record an IR graph of everything that happened in the function.<p>Once you have an IR graph, you can lower it to C++/Rust code, operation-by-operation, by propagating type information throughout the program (see the blog post for an example). And now is the perfect time to have this infrastructure, because LLMs can do all the hard work of writing and validating the required operations in native code.<p>Anyway, I wanted to share the proof-of-concept and gather feedback. Using Function is pretty simple, just decorate a module-level function with `@compile` then use the CLI to compile it: `fxn compile module.py` .<p>TL;DR: Get Rust performance without having to learn Rust ;)
Show HN: Python at the Speed of Rust
I’m sure many of you are familiar, but there’s a treacherous gap between finding (or building) a model that works in PyTorch, and getting that deployed into your application, especially in consumer-facing applications.<p>I’ve been very interested in solving this problem with a great developer experience. Over time, I gradually realized that the highest-impact thing to have was a way to go from existing Python code to a self-contained native binary—in other words, a Python compiler.<p>I was already pretty familiar with a successful attempt: when Apple introduced armv8 on the iPhone 5s, they quickly mandated 64-bit support for all apps. Unity—where I had been programming since I was 11—kinda got f*cked because they used Mono to run developers’ C# code, and Mono didn’t support 64-bit ARM. Unity ended up building IL2CPP, which transpiles the C# intermediate language into C++, then cross-compiles it. Till date, this is perhaps the most amazing technical feat Unity has achieved imo.<p>I set out to build something similar, but this time starting from Python. It’s a pretty difficult problem, given the dynamic nature of the language. The key unlock was the PyTorch 2.0 release, where they pioneered the use of symbolic tracing to power `torch.compile`. In a nutshell, they register a callback with the Python interpreter (using CPython’s frame evaluation API), run a function with fake inputs, and record an IR graph of everything that happened in the function.<p>Once you have an IR graph, you can lower it to C++/Rust code, operation-by-operation, by propagating type information throughout the program (see the blog post for an example). And now is the perfect time to have this infrastructure, because LLMs can do all the hard work of writing and validating the required operations in native code.<p>Anyway, I wanted to share the proof-of-concept and gather feedback. Using Function is pretty simple, just decorate a module-level function with `@compile` then use the CLI to compile it: `fxn compile module.py` .<p>TL;DR: Get Rust performance without having to learn Rust ;)
Show HN: Omiword – A daily, sector-based word puzzle
Hi everybody. I occasionally make little browser-based games, and this is my latest attempt. It's not quite done, but it's quite playable (note: it does include audio):<p><a href="https://www.omiword.com/" rel="nofollow">https://www.omiword.com/</a><p>This has been my occasional tinker target for ~5 years now, starting in the early days of Covid. The objective is to drag letter tiles within certain boundaries to spell four common American-English words.<p>It hasn't got ads or anything, it's just supposed to be fun for its own sake. If people happen to like it, I might add an option for folks to make a small, one-time payment to unlock access to the archive.<p>I'm happy to hear any feedback, or about any shortcomings that you might discover.
Show HN: memEx, a personal knowledge base inspired by zettlekasten and org-mode
Show HN: memEx, a personal knowledge base inspired by zettlekasten and org-mode
Show HN: memEx, a personal knowledge base inspired by zettlekasten and org-mode
Show HN: Building better base images
This project addresses the inefficiencies of traditional Dockerfile-based container builds where each customization layer creates storage bloat through duplicate dependencies from repeated apt-get install commands, network inefficiency from redundant package downloads across different images, and slow iteration cycles requiring full rebuilds of all previous steps. Our solution enables building minimal base images from scratch using debootstrap that precisely include only required components in the initial build, while allowing creation of specialized variants (Java, Kafka, etc.) from these common foundations - resulting in significantly leaner images, faster builds, and more efficient resource utilization compared to standard Docker layer stacking approaches.