The best Hacker News stories from Show from the past day
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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: 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>
Show HN: I've built a words game based on binary search
Show HN: For 10 World Cups, my model's 2 favorites had the champion every time
Show HN: One More Letter
Show HN: Make senders work to get into your inbox
Hi HN :) really excited to share this with you.<p>The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.<p>There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it's customized slop with the appearance of effort with none of the cost. It is painful that the open internet / open channels have been abused like this.<p>Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).<p>How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you've emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.<p>Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive and put back into your inbox.<p>if you want to see what this looks like from a sender's point of view, send me an email here: doerpfelix15@gmail.com<p>The service only ever reads metadata, never message content. And since nothing is ever deleted, you can't lose a message. There is a legitimate risk / downside: if you sign up to a new service, these emails also land in the archive. Since we do not process the content, a first-time sender who can't solve the challenge (say an automated activation email) will sit in your archive until you spot it.<p>Happy to answer anything! :)
Show HN: Make senders work to get into your inbox
Hi HN :) really excited to share this with you.<p>The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.<p>There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it's customized slop with the appearance of effort with none of the cost. It is painful that the open internet / open channels have been abused like this.<p>Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).<p>How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you've emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.<p>Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive and put back into your inbox.<p>if you want to see what this looks like from a sender's point of view, send me an email here: doerpfelix15@gmail.com<p>The service only ever reads metadata, never message content. And since nothing is ever deleted, you can't lose a message. There is a legitimate risk / downside: if you sign up to a new service, these emails also land in the archive. Since we do not process the content, a first-time sender who can't solve the challenge (say an automated activation email) will sit in your archive until you spot it.<p>Happy to answer anything! :)
Show HN: misa77 - a codec that decodes 2x faster than LZ4 (at better ratios)
I've spent the last few months working on this codec.<p>It has the following characteristics:<p><pre><code> - SOTA decompression throughput in its ratio class
- Decent ratios (comparable to LZ4 at high effort levels)
- Slow compression
</code></pre>
Most of the gains can be attributed to reducing branches and making decompression very friendly to out-of-order cores, by using a smart format.<p>Results on the tarred Silesia corpus on Intel x86-64 follow:<p><pre><code> codec decode ratio encode
misa77 -0 5219 MB/s 42.64% 54.5 MB/s
misa77 -1 4274 MB/s 39.65% 51.2 MB/s
lz4 2505 MB/s 47.59% 371 MB/s
lz4hc -12 2531 MB/s 36.45% 7.31 MB/s</code></pre>
Show HN: misa77 - a codec that decodes 2x faster than LZ4 (at better ratios)
I've spent the last few months working on this codec.<p>It has the following characteristics:<p><pre><code> - SOTA decompression throughput in its ratio class
- Decent ratios (comparable to LZ4 at high effort levels)
- Slow compression
</code></pre>
Most of the gains can be attributed to reducing branches and making decompression very friendly to out-of-order cores, by using a smart format.<p>Results on the tarred Silesia corpus on Intel x86-64 follow:<p><pre><code> codec decode ratio encode
misa77 -0 5219 MB/s 42.64% 54.5 MB/s
misa77 -1 4274 MB/s 39.65% 51.2 MB/s
lz4 2505 MB/s 47.59% 371 MB/s
lz4hc -12 2531 MB/s 36.45% 7.31 MB/s</code></pre>
Show HN: Firefox in WebAssembly
This is the entire Firefox browser rendering to a <canvas> element. Gecko, all UI components, and the Spidermonkey JS engine are all compiled and running in WebAssembly.<p>Here are a few things you might find interesting:<p>- This is fully end to end encrypted! We use the WISP protocol for TCP-over-websockets.<p>- There is a novel WASM->JS JIT for experimental site speedup<p>- This port cost over 25k in opus/fable tokens for debugging and JIT research<p>This was just a fun experiment to push the boundaries of WebAssembly. For a more usable "browser in browser" experience, we also built <a href="https://github.com/HeyPuter/browser.js" rel="nofollow">https://github.com/HeyPuter/browser.js</a> that eats a bit less RAM.
Show HN: Firefox in WebAssembly
This is the entire Firefox browser rendering to a <canvas> element. Gecko, all UI components, and the Spidermonkey JS engine are all compiled and running in WebAssembly.<p>Here are a few things you might find interesting:<p>- This is fully end to end encrypted! We use the WISP protocol for TCP-over-websockets.<p>- There is a novel WASM->JS JIT for experimental site speedup<p>- This port cost over 25k in opus/fable tokens for debugging and JIT research<p>This was just a fun experiment to push the boundaries of WebAssembly. For a more usable "browser in browser" experience, we also built <a href="https://github.com/HeyPuter/browser.js" rel="nofollow">https://github.com/HeyPuter/browser.js</a> that eats a bit less RAM.
Show HN: I implemented a neural network in SQL
Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now that this feature existed, what demos could we make that would prove this data model works?<p>With down time on a warm beach during a heatwave, cool salty water giving me fresh ideas, I had an idea: what if we used Coiled's Geospatial benchmark discussion as a comprehensive overview of geo and climate queries. Are all of these common operations secretly relational, just with the wrong data model? Using Claude Code on the beach, I can confirm that this seemed to be the case: Claude and I publish a benchmark that illustrated how every common operation in geo and climate sciences (at the 100 TB range) were actually secretly relational operations: <a href="https://github.com/xqlsystems/xarray-sql/blob/main/docs/geospatial.md" rel="nofollow">https://github.com/xqlsystems/xarray-sql/blob/main/docs/geos...</a>.<p>Most surprisingly of all, from these examples was that a core operation, regridding, was just a sparse matrix-vector product. Claude had pointed out to me that in this data model, matmul was just a `SUM(val * val) ... JOIN .. GROUP BY`. This has a direct parallel to einsum notation, but can be expressed in (arguably) elegant SQL syntax! This capability seemed to be greater than the sum of it's parts.<p>Back in the cool water of the Ionian, I thought about the implications of this more deeply. I reflected that, all of the Coiled benchmarks did, deep down, was _post process_ simulations that happen in numerical/array code. Why couldn't these physics calculations be push down into the database also, if we could so matmul in SQL? Then it hit me: maybe they could, if in addition to linear algebra, if SQL could do calculus! <a href="https://bsky.app/profile/al.merose.com/post/3mpbods7wts2y" rel="nofollow">https://bsky.app/profile/al.merose.com/post/3mpbods7wts2y</a><p>Later on, I implemented autograd on top of DataFusion's visitor pattern based on JAX's implementation. In my simplified array model, it turns out that we only care about partial differentiation on the diagonal of the Jacobian, meaning that `grad()`, `jvp` and `vjp` are just row-wise operations! I then implemented a common physics calculation from the coiled benchmark that required gradients. From here, I realized if I can autograd in the database, why can't I create a neural network?<p>As I came back home, I created some slides, and presented this work to DataFusion's inaugural showcase: <a href="https://www.youtube.com/watch?t=1511&v=5o-4hL8vGPw&feature=youtu.be" rel="nofollow">https://www.youtube.com/watch?t=1511&v=5o-4hL8vGPw&feature=y...</a> I realized in this synthesis that SQL is not necessarily a toy language for writing neural networks, but in fact, may be highly desirable in the future due to the fundamental principles of relational databases: the logical layer should be independent from the physical layer. If that property holds, and a neural network is a series of relations, could we create a SOTA distributed system for training more easily? For example, if we had one global logical plan of dataflow, could we better distribute work on 1000+ GPUs?<p>Several scientists and engineers and I are working together to explore this weird world of relational arrays at <a href="https://xql.systems" rel="nofollow">https://xql.systems</a> (discord link at the bottom if you want to get involved).
Show HN: I implemented a neural network in SQL
Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now that this feature existed, what demos could we make that would prove this data model works?<p>With down time on a warm beach during a heatwave, cool salty water giving me fresh ideas, I had an idea: what if we used Coiled's Geospatial benchmark discussion as a comprehensive overview of geo and climate queries. Are all of these common operations secretly relational, just with the wrong data model? Using Claude Code on the beach, I can confirm that this seemed to be the case: Claude and I publish a benchmark that illustrated how every common operation in geo and climate sciences (at the 100 TB range) were actually secretly relational operations: <a href="https://github.com/xqlsystems/xarray-sql/blob/main/docs/geospatial.md" rel="nofollow">https://github.com/xqlsystems/xarray-sql/blob/main/docs/geos...</a>.<p>Most surprisingly of all, from these examples was that a core operation, regridding, was just a sparse matrix-vector product. Claude had pointed out to me that in this data model, matmul was just a `SUM(val * val) ... JOIN .. GROUP BY`. This has a direct parallel to einsum notation, but can be expressed in (arguably) elegant SQL syntax! This capability seemed to be greater than the sum of it's parts.<p>Back in the cool water of the Ionian, I thought about the implications of this more deeply. I reflected that, all of the Coiled benchmarks did, deep down, was _post process_ simulations that happen in numerical/array code. Why couldn't these physics calculations be push down into the database also, if we could so matmul in SQL? Then it hit me: maybe they could, if in addition to linear algebra, if SQL could do calculus! <a href="https://bsky.app/profile/al.merose.com/post/3mpbods7wts2y" rel="nofollow">https://bsky.app/profile/al.merose.com/post/3mpbods7wts2y</a><p>Later on, I implemented autograd on top of DataFusion's visitor pattern based on JAX's implementation. In my simplified array model, it turns out that we only care about partial differentiation on the diagonal of the Jacobian, meaning that `grad()`, `jvp` and `vjp` are just row-wise operations! I then implemented a common physics calculation from the coiled benchmark that required gradients. From here, I realized if I can autograd in the database, why can't I create a neural network?<p>As I came back home, I created some slides, and presented this work to DataFusion's inaugural showcase: <a href="https://www.youtube.com/watch?t=1511&v=5o-4hL8vGPw&feature=youtu.be" rel="nofollow">https://www.youtube.com/watch?t=1511&v=5o-4hL8vGPw&feature=y...</a> I realized in this synthesis that SQL is not necessarily a toy language for writing neural networks, but in fact, may be highly desirable in the future due to the fundamental principles of relational databases: the logical layer should be independent from the physical layer. If that property holds, and a neural network is a series of relations, could we create a SOTA distributed system for training more easily? For example, if we had one global logical plan of dataflow, could we better distribute work on 1000+ GPUs?<p>Several scientists and engineers and I are working together to explore this weird world of relational arrays at <a href="https://xql.systems" rel="nofollow">https://xql.systems</a> (discord link at the bottom if you want to get involved).
Show HN: I RL-trained an agent that trains models with RL (for ~$1.3k)
Show HN: I RL-trained an agent that trains models with RL (for ~$1.3k)
Show HN: Jacquard, a programming language for AI-written, human-reviewed code
I'm fascinated by the generative AI wave rolling over us, and wondered if AI could create a language that it might prefer using over the ones created by and for humans.<p>To create the design, I had AI analyze the ASTs of several mainstream languages plus a few of the conceptually groundbreaking but esoteric ones (listed in the README) and then create a new structure and new syntax. It was named after the Jacquard machine (<a href="https://en.wikipedia.org/wiki/Jacquard_machine" rel="nofollow">https://en.wikipedia.org/wiki/Jacquard_machine</a>), a precursor to Babbage's Analytical Engine (and punch cards).<p>The result reused a lot of existing ideas but combined them in what I found to be an interesting way. External/world effects are visible in function signatures, and the runtime requires explicit permission to touch the filesystem, network, etc. Effect interactions can be recorded and replayed to see what happens under different conditions or code. And since code is given a content-addressed semantic identity internally, renames and formatting changes don't require recompile or retesting.<p>Another piece that fell out of this was a testing framework called Warp, which combines replay, results caching, handler substitution, and a few other tools that I frankly wish I had when writing Python. There are a few examples available in the demos directory.<p>There's more to do, but it's installable and usable. I'm hoping people will have their agents digest the docs/SKILL.md file and maybe write a few programs or see where it might fit in their projects. It should be particularly useful in agent systems. If an agent says something is painful or you as a human find the code tough to understand, I'd like to hear about it so I can address it.<p>More detail here:<p>Repository: <a href="https://github.com/jbwinters/jacquard-lang" rel="nofollow">https://github.com/jbwinters/jacquard-lang</a><p>Further intro/human-oriented write-up here: <a href="https://research.friendmachine.co/jacquard/" rel="nofollow">https://research.friendmachine.co/jacquard/</a>
Show HN: Beautiful Type Erasure with C++26 Reflection
Try it on Compiler Explorer: <a href="https://godbolt.org/z/91dj5jeGW" rel="nofollow">https://godbolt.org/z/91dj5jeGW</a><p>Check out the source code: <a href="https://github.com/RyanJK5/rjk-duck" rel="nofollow">https://github.com/RyanJK5/rjk-duck</a>
Show HN: Beautiful Type Erasure with C++26 Reflection
Try it on Compiler Explorer: <a href="https://godbolt.org/z/91dj5jeGW" rel="nofollow">https://godbolt.org/z/91dj5jeGW</a><p>Check out the source code: <a href="https://github.com/RyanJK5/rjk-duck" rel="nofollow">https://github.com/RyanJK5/rjk-duck</a>