The best Hacker News stories from Show from the past day
Latest posts:
Show HN: We built a terminal-only Bluesky / AT Proto client written in Fortran
Yes, that Fortran.
Show HN: I made an email app inspired by Arc browser
Email is one of those tools we check daily but its underlying experience didn’t evolve much. I use Gmail, as probably most of you reading this.<p>The Arc browser brought joy and taste to browsing the web. Cursor created a new UX with agents ready to work for you in a handy right panel.<p>I use these three tools every day. Since Arc was acquired by Atlassian, I’ve been wondering: what if I built a new interface that applied Arc’s UX to email rather than browser tabs, while making AI agents easily available to help manage emails, events, and files?<p>I built a frontend PoC to showcase the idea.<p>Try it: <a href="https://demo.define.app" rel="nofollow">https://demo.define.app</a><p>I’m not sure about it though... Is it worth continuing to explore this idea?
Show HN: Sonar – A tiny CLI to see and kill whatever's running on localhost
Show HN: Browser grand strategy game for hundreds of players on huge maps
Hi HN,<p>I've been building a browser-based multiplayer strategy game called Borderhold.<p>Matches run on large maps designed for hundreds of players. Players expand territory, attack neighbors, and adapt as borders shift across the map. You can put buildings down, build ships, and launch nukes.<p>The main thing I wanted to explore was scale: most strategy games are small matches, modest maps, or modest player counts, but here maps are large and game works well with hundreds of players.<p>Matches are relatively short so you can jump in and see a full game play out.<p>Curious what people think.<p><a href="https://borderhold.io/play" rel="nofollow">https://borderhold.io/play</a><p>Gameplay:<p><a href="https://youtu.be/nrJTZEP-Cw8" rel="nofollow">https://youtu.be/nrJTZEP-Cw8</a><p>Discord:<p><a href="https://discord.gg/xVDNt2G5" rel="nofollow">https://discord.gg/xVDNt2G5</a>
Show HN: Playing LongTurn FreeCiv with Friends
Show HN: Playing LongTurn FreeCiv with Friends
Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training
I replicated David Ng's RYS method (<a href="https://dnhkng.github.io/posts/rys/" rel="nofollow">https://dnhkng.github.io/posts/rys/</a>) on consumer AMD GPUs
(RX 7900 XT + RX 6950 XT) and found something I didn't expect.<p>Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that
act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning
pipeline twice. No weights change. No training. The model just thinks longer.<p>The results on standard benchmarks (lm-evaluation-harness, n=50):<p>Devstral-24B, layers 12-14 duplicated once:
- BBH Logical Deduction: 0.22 → 0.76
- GSM8K (strict): 0.48 → 0.64
- MBPP (code gen): 0.72 → 0.78
- Nothing degraded<p>Qwen2.5-Coder-32B, layers 7-9 duplicated once:
- Reasoning probe: 76% → 94%<p>The weird part: different duplication patterns create different cognitive "modes" from the same
weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling
(13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing.<p>The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts.
Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B).<p>Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo.
The whole thing — sweep, discovery, validation — took one evening.<p>Happy to answer questions.
Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training
I replicated David Ng's RYS method (<a href="https://dnhkng.github.io/posts/rys/" rel="nofollow">https://dnhkng.github.io/posts/rys/</a>) on consumer AMD GPUs
(RX 7900 XT + RX 6950 XT) and found something I didn't expect.<p>Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that
act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning
pipeline twice. No weights change. No training. The model just thinks longer.<p>The results on standard benchmarks (lm-evaluation-harness, n=50):<p>Devstral-24B, layers 12-14 duplicated once:
- BBH Logical Deduction: 0.22 → 0.76
- GSM8K (strict): 0.48 → 0.64
- MBPP (code gen): 0.72 → 0.78
- Nothing degraded<p>Qwen2.5-Coder-32B, layers 7-9 duplicated once:
- Reasoning probe: 76% → 94%<p>The weird part: different duplication patterns create different cognitive "modes" from the same
weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling
(13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing.<p>The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts.
Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B).<p>Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo.
The whole thing — sweep, discovery, validation — took one evening.<p>Happy to answer questions.
Show HN: Three new Kitten TTS models – smallest less than 25MB
Kitten TTS (<a href="https://github.com/KittenML/KittenTTS" rel="nofollow">https://github.com/KittenML/KittenTTS</a>) is an open-source series of tiny and expressive text-to-speech models for on-device applications. We had a thread last year here: <a href="https://news.ycombinator.com/item?id=44807868">https://news.ycombinator.com/item?id=44807868</a>.<p>Today we're releasing three new models with 80M, 40M and 14M parameters.<p>The largest model (80M) has the highest quality. The 14M variant reaches new SOTA in expressivity among similar sized models, despite being <25MB in size. This release is a major upgrade from the previous one and supports English text-to-speech applications in eight voices: four male and four female.<p>Here's a short demo: <a href="https://www.youtube.com/watch?v=ge3u5qblqZA" rel="nofollow">https://www.youtube.com/watch?v=ge3u5qblqZA</a>.<p>Most models are quantized to int8 + fp16, and they use ONNX for runtime. Our models are designed to run anywhere eg. raspberry pi, low-end smartphones, wearables, browsers etc. No GPU required! This release aims to bridge the gap between on-device and cloud models for tts applications. Multi-lingual model release is coming soon.<p>On-device AI is bottlenecked by one thing: a lack of tiny models that actually perform. Our goal is to open-source more models to run production-ready voice agents and apps entirely on-device.<p>We would love your feedback!
Show HN: Three new Kitten TTS models – smallest less than 25MB
Kitten TTS (<a href="https://github.com/KittenML/KittenTTS" rel="nofollow">https://github.com/KittenML/KittenTTS</a>) is an open-source series of tiny and expressive text-to-speech models for on-device applications. We had a thread last year here: <a href="https://news.ycombinator.com/item?id=44807868">https://news.ycombinator.com/item?id=44807868</a>.<p>Today we're releasing three new models with 80M, 40M and 14M parameters.<p>The largest model (80M) has the highest quality. The 14M variant reaches new SOTA in expressivity among similar sized models, despite being <25MB in size. This release is a major upgrade from the previous one and supports English text-to-speech applications in eight voices: four male and four female.<p>Here's a short demo: <a href="https://www.youtube.com/watch?v=ge3u5qblqZA" rel="nofollow">https://www.youtube.com/watch?v=ge3u5qblqZA</a>.<p>Most models are quantized to int8 + fp16, and they use ONNX for runtime. Our models are designed to run anywhere eg. raspberry pi, low-end smartphones, wearables, browsers etc. No GPU required! This release aims to bridge the gap between on-device and cloud models for tts applications. Multi-lingual model release is coming soon.<p>On-device AI is bottlenecked by one thing: a lack of tiny models that actually perform. Our goal is to open-source more models to run production-ready voice agents and apps entirely on-device.<p>We would love your feedback!
Show HN: Tmux-IDE, OSS agent-first terminal IDE
Hey HN,<p>Small OSS project that i created for myself and want to share with the community. It's a declarative, scriptable, terminal-based IDE focussed on agentic engineering.<p>That's a lot of jargon, but essentially its a multi-agent IDE that you start in your terminal.<p>Why is that relevant? Thanks to tmux and SSH, it means that you have a really simple and efficient way to create your own always-on coding setup.<p>Boot into your IDE through ssh, give a prompt to claude and close off your machine. In tmux-ide claude will keep working.<p>The tool is intentionally really lightweight, because I think the power should come from the harnesses that you are working with.<p>I'm hoping to share this with the community and get feedback and suggestions to shape this project! I think that "remote work" is directionally correct, because we can now have extremely long-running coding tasks. But I also think we should be able to control and orchstrate that experience according to what we need.<p>The project is 100% open-source, and i hope to shape it together with others who like to work in this way too!<p>Github: <a href="https://github.com/wavyrai/tmux-ide" rel="nofollow">https://github.com/wavyrai/tmux-ide</a>
Docs: <a href="https://tmux.thijsverreck.com/docs" rel="nofollow">https://tmux.thijsverreck.com/docs</a>
Show HN: Tmux-IDE, OSS agent-first terminal IDE
Hey HN,<p>Small OSS project that i created for myself and want to share with the community. It's a declarative, scriptable, terminal-based IDE focussed on agentic engineering.<p>That's a lot of jargon, but essentially its a multi-agent IDE that you start in your terminal.<p>Why is that relevant? Thanks to tmux and SSH, it means that you have a really simple and efficient way to create your own always-on coding setup.<p>Boot into your IDE through ssh, give a prompt to claude and close off your machine. In tmux-ide claude will keep working.<p>The tool is intentionally really lightweight, because I think the power should come from the harnesses that you are working with.<p>I'm hoping to share this with the community and get feedback and suggestions to shape this project! I think that "remote work" is directionally correct, because we can now have extremely long-running coding tasks. But I also think we should be able to control and orchstrate that experience according to what we need.<p>The project is 100% open-source, and i hope to shape it together with others who like to work in this way too!<p>Github: <a href="https://github.com/wavyrai/tmux-ide" rel="nofollow">https://github.com/wavyrai/tmux-ide</a>
Docs: <a href="https://tmux.thijsverreck.com/docs" rel="nofollow">https://tmux.thijsverreck.com/docs</a>
Show HN: Tmux-IDE, OSS agent-first terminal IDE
Hey HN,<p>Small OSS project that i created for myself and want to share with the community. It's a declarative, scriptable, terminal-based IDE focussed on agentic engineering.<p>That's a lot of jargon, but essentially its a multi-agent IDE that you start in your terminal.<p>Why is that relevant? Thanks to tmux and SSH, it means that you have a really simple and efficient way to create your own always-on coding setup.<p>Boot into your IDE through ssh, give a prompt to claude and close off your machine. In tmux-ide claude will keep working.<p>The tool is intentionally really lightweight, because I think the power should come from the harnesses that you are working with.<p>I'm hoping to share this with the community and get feedback and suggestions to shape this project! I think that "remote work" is directionally correct, because we can now have extremely long-running coding tasks. But I also think we should be able to control and orchstrate that experience according to what we need.<p>The project is 100% open-source, and i hope to shape it together with others who like to work in this way too!<p>Github: <a href="https://github.com/wavyrai/tmux-ide" rel="nofollow">https://github.com/wavyrai/tmux-ide</a>
Docs: <a href="https://tmux.thijsverreck.com/docs" rel="nofollow">https://tmux.thijsverreck.com/docs</a>
Show HN: Pgit – A Git-like CLI backed by PostgreSQL
Show HN: Pgit – A Git-like CLI backed by PostgreSQL
Show HN: Pgit – A Git-like CLI backed by PostgreSQL
Show HN: I built 48 lightweight SVG backgrounds you can copy/paste
Show HN: I built 48 lightweight SVG backgrounds you can copy/paste
Show HN: I built 48 lightweight SVG backgrounds you can copy/paste
Show HN: Sub-millisecond VM sandboxes using CoW memory forking
I wanted to see how fast an isolated code sandbox could start if I never had to boot a fresh VM.<p>So instead of launching a new microVM per execution, I boot Firecracker once with Python and numpy already loaded, then snapshot the full VM state. Every execution after that creates a new KVM VM backed by a `MAP_PRIVATE` mapping of the snapshot memory, so Linux gives me copy-on-write pages automatically.<p>That means each sandbox starts from an already-running Python process inside a real VM, runs the code, and exits.<p>These are real KVM VMs, not containers: separate guest kernel, separate guest memory, separate page tables. When a VM writes to memory, it gets a private copy of that page.<p>The hard part was not CoW itself. The hard part was resuming the snapshotted VM correctly.<p>Rust, Apache 2.0.