The best Hacker News stories from Show from the past day
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Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch
Hi everyone,<p>I started working on nanoeuler after the ban of anthropic's fable because my ambition and dream is to work in the AI field in anthropic. The two interesting reasons that led me to create nanoeuler were (1) interfacing with llm does not mean understanding how they are composed and (2), working on llm with a very low-level layer to understand the correlation between parameters and data and growth of the model and how the GPU works and how some layers can be optimized.<p>So I started working on it with a research aspect by making nanoeuler grow more and more but doing one step after another starting from Shakespeare.txt and understanding what a text generation model understands at 23 million parameters. For example, nanoeuler at that number had understood that Name: started a line and wrote that line with sense.<p>I wrote everything in CUDA because I wanted to not use any intermediary between the model in training and inference and what it had to do. Then the use of SFT and much more, even if in small ways, were really useful to understand the various step to make an llm like a chatbot.Any feedback, help, or suggestions are absolutely welcome!
Show HN: Bash4LLM+ – A lightweight, dependency-free Bash wrapper for LLM APIs
Bash4LLM is a single-file Bash wrapper for interacting with LLMs from the terminal. I created it because I wanted something simple that worked without installing Python, Node, or any other runtime.<p>It uses only Bash, curl, and jq. You can send prompts, start a small chat, process files line by line, stream output, and save session metadata in JSON format.<p>I tried to make it safe and predictable: no use of the system /tmp, no use of eval. Groq is supported by default, and other providers can be added with dedicated Bash scripts in the extras/providers/ folder.<p>Example:<p><pre><code> echo "explains the command: ls -l" | ./bash4llm</code></pre>
Show HN: DRM-Free Books
After several years of mandatory DRM lockdowns from most commercial book sources, now authors have a choice when it comes to DRM for their books. Pick authors and books that are DRM-free, or download DRM-free classics that are out of copyright.<p><a href="https://frequal.com/Perspectives/DrmFreeAuthors.html" rel="nofollow">https://frequal.com/Perspectives/DrmFreeAuthors.html</a>
Show HN: Zanagrams
Show HN: Decomp Academy – Learn to decompile GameCube games into matching C
Over the past few months I've been heavily involved in the decompilation community. I've been hands-on decompiling a beloved game from my childhood (Star Fox Adventures). I started this journey with zero prior decomp experience—and to make things worse I had never really touched C nor assembly either.<p>Learning how to decompile was challenging. It's difficult to find any good learning resources for it and any open-source projects for this are inactive and/or contain little actual learning material.<p>So I put together Decomp Academy! Decomp Academy is an interactive way to learn how to decompile PowerPC assembly back into C. The site runs a live Metrowerks CodeWarrior GC/2.0 compiler, converts your C into assembly, and then checks how close your assembly matches the target. If even 1 instruction or bit is off, that's a fail. This is the gold standard for video game decompilation and this is much stricter than a normal decompile.<p>As of writing there are 250+ lessons on the site and the lessons start at the very basics so anyone with a little programming experience should be able to jump straight in, even if you're not a C expert. Some lessons also have real functions taken from live open source decomp projects (Star Fox Adventures, Mario Party 4, Pikmin, Metroid Prime). The idea being you learn everything you need to know to be able to jump in and contribute to a real decompilation project when done.<p>The site is completely free, open source and you have access to all lessons without having to sign up. All lessons are stored in markdown in the repo (src/curriculum), it's trivial to add or modify lessons. The site is very new and the lessons are rapidly changing every day with a whole C++ section on the way. The site has already been well received by the decomp community and I'm happy to share it with HN. I'm very keen on others to contribute to this project and I hope this becomes the best resource on the internet for learning the art of decompilation. Please let me know what you think!<p>Source: <a href="https://github.com/JackPriceBurns/decomp-academy-fe" rel="nofollow">https://github.com/JackPriceBurns/decomp-academy-fe</a>
Show HN: DBOSify – Drop-in Temporal replacement built on Postgres
Show HN: DBOSify – Drop-in Temporal replacement built on Postgres
Show HN: Hacker News on a train station-style flip board
Although the page itself is more just fun to have made and look at (I like the flip sound), the fun part is how I made it to verify the (and I hate to say it) vibe host service I've been working on. The recent flip board back and forth's on Twitter (X) are what inspired me.<p>The idea here is that people (like me or you) can create something neat like this, and others can remix it, change it and publish their own version. This is that all in action and it worked great. I wrote a blog about it (the blog is dogfooding, it's just an app hosted on quickish that uses the built in db lib).<p>For the HN version of this flip board I use their firebase api via the built in quickish server functions that make use of the fact that the front-end can get realtime updates (now that you mention firebase) from cloud function db updates. Of course that's over-kill but I wanted to show something fun. You can remix and host your own version for free, just need a google oauth login that's it.<p>OG flip board I built (Portland Based - Current Weather): <a href="https://popflame.quickish.space/flipboard-preview" rel="nofollow">https://popflame.quickish.space/flipboard-preview</a><p>Blog post that dives a tiny bit deeper: <a href="https://popflame.quickish.space/blog/hacker-news-on-a-split-flap-board/" rel="nofollow">https://popflame.quickish.space/blog/hacker-news-on-a-split-...</a>
Show HN: Hacker News on a train station-style flip board
Although the page itself is more just fun to have made and look at (I like the flip sound), the fun part is how I made it to verify the (and I hate to say it) vibe host service I've been working on. The recent flip board back and forth's on Twitter (X) are what inspired me.<p>The idea here is that people (like me or you) can create something neat like this, and others can remix it, change it and publish their own version. This is that all in action and it worked great. I wrote a blog about it (the blog is dogfooding, it's just an app hosted on quickish that uses the built in db lib).<p>For the HN version of this flip board I use their firebase api via the built in quickish server functions that make use of the fact that the front-end can get realtime updates (now that you mention firebase) from cloud function db updates. Of course that's over-kill but I wanted to show something fun. You can remix and host your own version for free, just need a google oauth login that's it.<p>OG flip board I built (Portland Based - Current Weather): <a href="https://popflame.quickish.space/flipboard-preview" rel="nofollow">https://popflame.quickish.space/flipboard-preview</a><p>Blog post that dives a tiny bit deeper: <a href="https://popflame.quickish.space/blog/hacker-news-on-a-split-flap-board/" rel="nofollow">https://popflame.quickish.space/blog/hacker-news-on-a-split-...</a>
Show HN: Adrafinil – keep a lid-closed Mac awake only while agents work
A month ago there was a wave of posts and tweets about engineers walking around cafes and parks with their MacBooks propped half-open, as fully closing the lid forces sleep that stops their AI agents. Some people made snarky comments about using tmux or Amphetamine, and some defended their choice with “but I only need it sometimes, and forgetting to disable Amphetamine and finding my laptop discharged in my bag is worse.”<p>This is a solution to this problem. Unlike caffeinate, it will prevent your MacBook from sleeping even with the lid closed, with no external power or display, using pmset disablesleep 1. Unlike other sleep-preventing apps, Adrafinil only activates when there’s an agent actively doing something. It detects agent activity through hooks it installs into Claude Code, Codex, and others. To reassure you it’s working, the app shows the active status in the menu bar, and it plays a chime when you close the lid.<p>Once the agent is done, Adrafinil detects it and lets the laptop go to sleep by setting pmset disablesleep back to 0. It will also let it sleep in case of overheating. And if you want to manually toggle it, you can install an optional MCP and tell your agent to keep the MacBook awake for a specific time.<p>It has four binaries, one of which is a root helper exposing a single setSleepBlocked call. All the logic and policy live in the unprivileged parts. They’re all notarized, and the app is fully open source (MIT).
Show HN: Adrafinil – keep a lid-closed Mac awake only while agents work
A month ago there was a wave of posts and tweets about engineers walking around cafes and parks with their MacBooks propped half-open, as fully closing the lid forces sleep that stops their AI agents. Some people made snarky comments about using tmux or Amphetamine, and some defended their choice with “but I only need it sometimes, and forgetting to disable Amphetamine and finding my laptop discharged in my bag is worse.”<p>This is a solution to this problem. Unlike caffeinate, it will prevent your MacBook from sleeping even with the lid closed, with no external power or display, using pmset disablesleep 1. Unlike other sleep-preventing apps, Adrafinil only activates when there’s an agent actively doing something. It detects agent activity through hooks it installs into Claude Code, Codex, and others. To reassure you it’s working, the app shows the active status in the menu bar, and it plays a chime when you close the lid.<p>Once the agent is done, Adrafinil detects it and lets the laptop go to sleep by setting pmset disablesleep back to 0. It will also let it sleep in case of overheating. And if you want to manually toggle it, you can install an optional MCP and tell your agent to keep the MacBook awake for a specific time.<p>It has four binaries, one of which is a root helper exposing a single setSleepBlocked call. All the logic and policy live in the unprivileged parts. They’re all notarized, and the app is fully open source (MIT).
Show HN: StartupsBR – A map of Brazilian startups
I couldn't find a simple way to explore the Brazilian startup ecosystem geographically, as I can in other places like the Bay Area or London, so I built one.<p>The map currently includes hundreds of startups from Sao Paulo and their job opportunities.<p>The most interesting thing I've learned so far is how clustered startup activity is in a handful of areas.<p>I'd love to hear your thoughts.<p>The EN version: <a href="https://www.startupsbr.com/sao-paulo" rel="nofollow">https://www.startupsbr.com/sao-paulo</a>
Show HN: WebBase-III – dBASE III rebuilt in the browser with its own interpreter
Show HN: WebBase-III – dBASE III rebuilt in the browser with its own interpreter
Show HN: WebBase-III – dBASE III rebuilt in the browser with its own interpreter
Show HN: Overfitted a 900KB Transformer to Compress a 100MB CSV into 7MB
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files.<p>Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.<p>On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).<p>It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).<p>Checkout the repo - <a href="https://github.com/samyak112/pym-particles" rel="nofollow">https://github.com/samyak112/pym-particles</a>
Show HN: Overfitted a 900KB Transformer to Compress a 100MB CSV into 7MB
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files.<p>Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.<p>On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).<p>It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).<p>Checkout the repo - <a href="https://github.com/samyak112/pym-particles" rel="nofollow">https://github.com/samyak112/pym-particles</a>
Show HN: Smart model routing directly in Claude, Codex and Cursor
We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here's a quick demo of running it locally: <a href="https://www.youtube.com/watch?v=isKhAyivtfM" rel="nofollow">https://www.youtube.com/watch?v=isKhAyivtfM</a>.<p>At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for <i>everything</i> but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.<p>The Weave Router acts as an Anthropic/OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster/cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 & GPT 5.5 (& Fable whenever it's back)) when necessary.<p>How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.<p>Here's an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.<p>We've been using this internally for the last month or so. We've saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.<p>The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.<p>I'll be here to answer any questions you may have!
Show HN: Smart model routing directly in Claude, Codex and Cursor
We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here's a quick demo of running it locally: <a href="https://www.youtube.com/watch?v=isKhAyivtfM" rel="nofollow">https://www.youtube.com/watch?v=isKhAyivtfM</a>.<p>At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for <i>everything</i> but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.<p>The Weave Router acts as an Anthropic/OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster/cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 & GPT 5.5 (& Fable whenever it's back)) when necessary.<p>How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.<p>Here's an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.<p>We've been using this internally for the last month or so. We've saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.<p>The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.<p>I'll be here to answer any questions you may have!
Show HN: Smart model routing directly in Claude, Codex and Cursor
We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here's a quick demo of running it locally: <a href="https://www.youtube.com/watch?v=isKhAyivtfM" rel="nofollow">https://www.youtube.com/watch?v=isKhAyivtfM</a>.<p>At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for <i>everything</i> but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.<p>The Weave Router acts as an Anthropic/OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster/cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 & GPT 5.5 (& Fable whenever it's back)) when necessary.<p>How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.<p>Here's an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.<p>We've been using this internally for the last month or so. We've saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.<p>The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.<p>I'll be here to answer any questions you may have!