The best Hacker News stories from Show from the past day
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Show HN: Simulator for a custom 8-bit discreet logic computer
5 years ago, I made a derivative of SAP-1 (mainly inspired by Ben Eater) on breadboard with few improvement and called it MSAP-1 (<a href="https://github.com/mehrantsi/MSAP-1" rel="nofollow">https://github.com/mehrantsi/MSAP-1</a>) I made my own very primitive Assembly language and a simple Arduino programmer (<a href="https://github.com/mehrantsi/8-bit_CPU_Programmer" rel="nofollow">https://github.com/mehrantsi/8-bit_CPU_Programmer</a>) where I could load my programs onto MSAP-1 and even Debug them (<a href="https://github.com/mehrantsi/8-bit_CPU_Debugger" rel="nofollow">https://github.com/mehrantsi/8-bit_CPU_Debugger</a>).<p>After that I started worked on the second version of it (<a href="https://github.com/mehrantsi/MSAP-2" rel="nofollow">https://github.com/mehrantsi/MSAP-2</a>) by adding Stack, Interrupts and etc. with the goal of having a very primitive OS running on the second version, but changes in life meant I didn't have time to work on it as much, but I was progressing.<p>Since 2 years ago, I had this little test of mine to see which one can understand MSAP-1, My own Assembly, the programs I wrote and ultimately if they would understand MSAP-2. none of the models could really understand the whole stack and initial models were quite bad at understanding hardware and electronics in general. Until Fable 5...<p>Same prompt to Fable and it's the first model that understood the full stack and my intentions for the second version that is in progress. I asked it to create a simulator which it did (not one shotted ofc) and I managed to finalize the design using that. Then together with Fable I created an OS using my own Assembly (<a href="https://github.com/mehrantsi/MOS-1" rel="nofollow">https://github.com/mehrantsi/MOS-1</a>) and loaded it there and it all works!<p>It's all quite fascinating how much better at electronics Fable is compared to the predecessors!<p>Simulator for MSAP-1: <a href="https://msap1.mehran.dk" rel="nofollow">https://msap1.mehran.dk</a><p>Simulator for MSAP-1: <a href="https://msap2.mehran.dk" rel="nofollow">https://msap2.mehran.dk</a>
Show HN: Rejourney – Open-source revenue leak prediction for web and mobile apps
Hello there (General Kenobi's favorite way to start the conversation),<p>I’m Rashid, a sophomore at UT Austin. I built Rejourney.co (<a href="https://rejourney.co/" rel="nofollow">https://rejourney.co/</a>) to predict issues with your apps and websites before they happen, based on real user session recordings.<p>Here is a silly video (with cats) on how it works: <a href="https://www.youtube.com/watch?v=Z95MDxBXMjk" rel="nofollow">https://www.youtube.com/watch?v=Z95MDxBXMjk</a><p>It’s open source, and the post link is the github repo, but here it is again: <a href="https://github.com/rejourneyco/rejourney" rel="nofollow">https://github.com/rejourneyco/rejourney</a><p>I originally built this because I had a campus freebie finder app that grew quickly, and I had a lot of users dm me on instagram about issues with the app’s onboarding and UX confusion. I initially lost about 340 users out of my 5,000ish users because of these issues, and I had to recover some by nudging them with notifications. It was a big pain, and I felt bad that I lost this many users to small and easy fixes. So I built Rejourney to predict that before it happens. Here is how it works:<p>First, the SDK is installed on Web JS, Swift, or React Native apps. You then help the SDK a little with a few lines of tracking important events -- such as a subscription bought, a signup completed, etc -- before you ship the app. We called these “critical conversion events”.<p>From here, Rejourney records the user session along with the meta data you set up, and relates it to the sequence of the user journey, each touch/scroll/pan interaction, and rage taps. If deemed an issue, it bundles in API response times and codes, ANRS, and crash traces into the context.<p>A heuristic then bundles all the user recordings into similarity cohorts for processing, and finds similar user journeys and outcomes in relation to the critical conversion actions that matter to you. If a trend is found that is possibly worrying, it admits the user recordings into segmentation and processing by an LLM on our back (in this case Gemini for cost and speed, but it has been tested on GPT 5.5 if you decide to self-host and set this up on your side).<p>If the LLM views similarities in the touch sequence frame by frame, it can determine whether the cohort is likely to present a negative outlook on the critical conversion event that matters to you. Based on the replays and all the surrounding context, it outputs a .MD file with the context and the fix that would patch it (which you can copy into your coding agent). Optionally, you can attach your github repo so the .MD file includes a code fix with the detected issue.<p>Furthermore, this occurs at the scale of thousands of user recordings daily. We have seen how this works on a medium-scale, as Rejourney has been tested with about 2.5 million user recordings from people shipping the SDK. One of our users even emailed us reporting a 30% increase in onboarding after 2 weeks of fixing non-stop issues found.<p>We have made it soooo cost effective to run with different strategies, that our first 3 paid users made us break even on costs…and this means more compute space for cool things later :D<p>Other considerations and criteria: Privacy was also very very important as we have to consider GDPR, after the retention period (usually 7-days) we quantize all the user recordings, anonymize all the fingerprints and aggregate them into a general dashboard (similar to Firebase’s general analytics dashboard).<p>I’d love to hear your feedback, critics, and requests in the comments! I’m all ears (or eyes since I’m reading).
Show HN: Get alerts for good seats at 70mm IMAX showings of The Odyssey
Show HN: Q3Edit – Edit and play Quake 3 maps in the browser
I've been building a level editor for Quake 3 that runs entirely in the browser: Radiant-style layout, brush and patch editing, CSG, terrain sculpting, and entity editing. It opens and saves .map file and you can play the maps you build directly in the browser using a webassembly build of ioquake3.
Show HN: IKEA Complexity Index
Show HN: QBasic Gorillas (Repeeled)
I've found the most engaging way to practice techniques for AI-assisted development and test models is to build fun side projects in vanilla JS.<p>I spent many hours playing (and studying and editing) QBasic Gorillas, and this is a vanilla JS implementation using Fable and Opus.<p>Play 1-on-1 hotseat or against the computer. A bit of extra camera snazz as well.
Show HN: Be the ChatBOT
I made this experimental art project/game that's an LLM chat assistant, but where <i>you're</i> the AI.<p>I wanted people to get a visceral sense of what it's like to answer the kinds of things that people prompt their chatbots day in and day out.<p>If you're interested, I wrote up some more info on how I made it, including how the "user" prompts are generated with an eye for realism: <a href="https://bethechatbot.com/about" rel="nofollow">https://bethechatbot.com/about</a><p>Hope you enjoy it! I'd love to hear people's takeaways.
Show HN: A zoomable timeline of 4M Wikipedia events
I'm building a journal app in Kotlin Multiplatform and for this purpose I have created a zoomable timeline interface.<p>This is a side-project where I reuse the timeline interface to display 4 million events imported from Wikipedia / Wikidata, scored using PageRank. There is more information on the about page.<p>If you're interested in the stack: I use Kotlin Multiplatform extensively, with Compose Multiplatform for the UI, communicates with the backend using Kotlinx-RPC and behind the hood a simple Postgres database on a Hetzner machine.
Show HN: A zoomable timeline of 4M Wikipedia events
I'm building a journal app in Kotlin Multiplatform and for this purpose I have created a zoomable timeline interface.<p>This is a side-project where I reuse the timeline interface to display 4 million events imported from Wikipedia / Wikidata, scored using PageRank. There is more information on the about page.<p>If you're interested in the stack: I use Kotlin Multiplatform extensively, with Compose Multiplatform for the UI, communicates with the backend using Kotlinx-RPC and behind the hood a simple Postgres database on a Hetzner machine.
Show HN: Mojibake – A low-level Unicode library written in C
I've written Mojibake because I don't like the other Unicode libraries for Unicode support.<p>It consists of only two amalgamation files: mojibake.h and mojibake.c. I've added all the most important Unicode algorithms, such as normalization, case conversion, segmentation, bidirectional text, collation, confusable, and others.<p>I regularly test it in these OSes: Linux, macOS, FreeBSD, OpenBSD, NetBSD, and Windows 11.<p>You can find a WASM demo on that site of all the public API functions and the documentation. If you want to participate, feel free to do it. Any kind of help is welcome. Check the CONTRIBUTING.md and API.md files in the GitHub repository for instructions on how to do it.
Show HN: Mojibake – A low-level Unicode library written in C
I've written Mojibake because I don't like the other Unicode libraries for Unicode support.<p>It consists of only two amalgamation files: mojibake.h and mojibake.c. I've added all the most important Unicode algorithms, such as normalization, case conversion, segmentation, bidirectional text, collation, confusable, and others.<p>I regularly test it in these OSes: Linux, macOS, FreeBSD, OpenBSD, NetBSD, and Windows 11.<p>You can find a WASM demo on that site of all the public API functions and the documentation. If you want to participate, feel free to do it. Any kind of help is welcome. Check the CONTRIBUTING.md and API.md files in the GitHub repository for instructions on how to do it.
Workspaces – Explore the workspaces of modern creators
Want to see what items show up in these setups time and time again?<p>I compiled all of the data from 500+ interviews into one easy to read report: <a href="https://workspaces.xyz/gear-report" rel="nofollow">https://workspaces.xyz/gear-report</a>
Show HN: Watch bots interact with an SSH honeypot in real time
Show HN: Watch bots interact with an SSH honeypot in real time
Show HN: E-- – A language you dial between English and Python
Show HN: Low-latency local LLM runner via OpenJDK Panama FFM (Java 22)
I wanted to run AI from inside the JVM. I started out with the standard REST sidecar, ripped that out to use Project Panama (Foreign Function & Memory API) in the new JDK versions to interface directly with llama.cpp. I still wasn't happy with how that functioned, so I built libargus.cc to get a clean ABI to expose a structured API up in the JVM landscape. It still uses Project Panama to interface directly with llama.cpp, whisper.cpp, and ggml compute graphs.<p>I have zero-allocation on the hot paths, memory segments for prompts and tokens are allocated once inside confined Arenas. Raw pointers pass straight through down to the low C level. This avoids primitive array cloning and heap churn.<p>I mapped out the native structures from llama.cpp and whisper.cpp while matching the compiler's padding to maintain safe memory access.<p>I bundle pre-compiled native binaries in the jar for easy deployment.<p>This execution engine provides the foundation I need for work I'm doing on a spatio-temporal memory layer (L-TABB) to replace RAGs. I'd love to get technical feedback to polish any issues while I continue working on the next layer.
Deep-dives from anyone hacking on Project Panama or low-latency systems in modern JDK would be very appreciated!<p>I'm much better with code than prose, so I'll let the code do most of the talking.<p>Happy Hacking!
/David<p>Code: <a href="https://libargus.cc" rel="nofollow">https://libargus.cc</a>
Project Landing Page: <a href="https://projectargus.cc" rel="nofollow">https://projectargus.cc</a>
Show HN: Clx – Compile Lua to Native Executables Through C++20
Hi HN,<p>clx is an ahead-of-time compiler for standard Lua that generates C++20 and produces standalone native executables through GCC, Clang or MSVC.<p>The project started as an experiment to see whether modern C++ could be used as a portable compiler backend instead of LLVM or direct machine code generation. The generated code is then compiled and optimized by the host toolchain.<p>The latest release replaces the previous NaN-tagged value representation with a new shadow-types implementation, adds full int64 support, improves native arithmetic code generation and adds ARM64 macOS coroutine support.<p>Performance is typically much faster than the Lua interpreter and can outperform LuaJIT on some computation-heavy workloads while remaining fully ahead-of-time compiled.<p>The repository also contains graphical examples written in Lua, including a Pong game and a Mandelbrot explorer using a Sokol binary module (using the clx C++ API)<p>I'd be very interested in feedback on clx :)
Show HN: Clx – Compile Lua to Native Executables Through C++20
Hi HN,<p>clx is an ahead-of-time compiler for standard Lua that generates C++20 and produces standalone native executables through GCC, Clang or MSVC.<p>The project started as an experiment to see whether modern C++ could be used as a portable compiler backend instead of LLVM or direct machine code generation. The generated code is then compiled and optimized by the host toolchain.<p>The latest release replaces the previous NaN-tagged value representation with a new shadow-types implementation, adds full int64 support, improves native arithmetic code generation and adds ARM64 macOS coroutine support.<p>Performance is typically much faster than the Lua interpreter and can outperform LuaJIT on some computation-heavy workloads while remaining fully ahead-of-time compiled.<p>The repository also contains graphical examples written in Lua, including a Pong game and a Mandelbrot explorer using a Sokol binary module (using the clx C++ API)<p>I'd be very interested in feedback on clx :)
Show HN: Clx – Compile Lua to Native Executables Through C++20
Hi HN,<p>clx is an ahead-of-time compiler for standard Lua that generates C++20 and produces standalone native executables through GCC, Clang or MSVC.<p>The project started as an experiment to see whether modern C++ could be used as a portable compiler backend instead of LLVM or direct machine code generation. The generated code is then compiled and optimized by the host toolchain.<p>The latest release replaces the previous NaN-tagged value representation with a new shadow-types implementation, adds full int64 support, improves native arithmetic code generation and adds ARM64 macOS coroutine support.<p>Performance is typically much faster than the Lua interpreter and can outperform LuaJIT on some computation-heavy workloads while remaining fully ahead-of-time compiled.<p>The repository also contains graphical examples written in Lua, including a Pong game and a Mandelbrot explorer using a Sokol binary module (using the clx C++ API)<p>I'd be very interested in feedback on clx :)
Show HN: Leaves – A text-UI disk usage treemap visualizer
GUI disk analyzers are great for figuring out what's filling up your laptop/desktop drive.<p>On containers or remote servers, the options are limited to purely text based
utilities (e.g. du) or list-centric TUIs (e.g. ncdu) which are usually limited
to viewing one directory at a time.<p>I created <i>leaves</i> to fill that gap.<p>Inspired by classic utilities like WinDirStat and KDirStat, it uses a
2-dimensional treemap^1 visualization to show the entire directory hierarchy
with proportionally sized rectangles.<p>It's performant enough to handle millions of files, thanks to Rust and
multi-threading. However, block characters aren't as suited as pixels for
resolving a large number of items. Leaves can show file-type summaries per
directory or partition the top-level directories by extension, allowing you to
see not only where space is being used, but also how.<p>For instance, I can see the largest chunk of my home directory is taken up by
uv caches for python and old Linux ISOs that I could easily re-download if
needed. Or in a particular container, +600MB is used by standard Rust
documentation and tutorials, and that it is the only location with HTML/JS files,
when only the libraries and build tools are needed (note to self: remember to
use the <i>minimal</i> profile next time).<p>^1: <a href="https://github.com/shundhammer/qdirstat/blob/master/doc/Treemap.md" rel="nofollow">https://github.com/shundhammer/qdirstat/blob/master/doc/Tree...</a>