The best Hacker News stories from Show from the past day
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Show HN: My local climbing gym from photogrammetry
Commented on yesterday's Ask HN: What are you working on? (August 2026), thought I might as well submit a Show HN.<p>If you see climbing world cup/championship, there is this 3D modelling thing [1]. I want that for my weekly bouldering.<p>I scanned my local climbing gym into 3D mesh using iphone. Built a simple editor to trim, merge, move/rotate meshes. You can interact with the mesh, view routes, view climb, etc. So this is unlike many gaussian splatting projects where the main use is for viewing. I built a pipeline in updating climbing walls. Climbing routes are manually annotated. Climbing videos are registered against the 3D mesh. Based on one input video, body positions resolved into 3D/4D space. you can view the body landmarks from different angle.<p>COLMAP, OpenMVS, fastapi, svelte, database is just local json files. A read only build is exported so I can host on github page<p>If you climb, or know someone who climbs, would love to hear your feedback. Do you usually record your climbs? If I make this a service, what will make you use it?<p>[1] <a href="https://www.youtube.com/shorts/8zdUOaCr6DY" rel="nofollow">https://www.youtube.com/shorts/8zdUOaCr6DY</a>
Show HN: Mcptoon – Token-efficient MCP CLI client
Show HN: Write.md – A free, open-source, themeable Markdown editor for macOS
Show HN: Git-knife – Edit commit messages, authors, and dates like a spreadsheet
Show HN: Scroll through all 43252003274489856000 Rubik's Cube states
Show HN: iPhone app takes simultaneous images from 2 lenses, fuses into 1 photo
Show HN: A replayable A2A jury for tracing how agents influence decisions
Show HN: I made alchemical-cosmological PCB badges
Hey guys,<p>A couple weeks ago, I was in Shenzhen, China for a hackathon and on the first day, we all used the one-day PCB fabrication to make PCB badges to wear during the event. I decided to go a little crazy and make some cool 3D alchemical-cosmological badges.<p>It's 3 PCB's stacked on-top of each other that's connected using testpoints on each board. The innerboard has a 10 cent CH32V003 MCU that powers the WS2812B neopixels on the outer rings using the testpoints and shines through using exposed soldermask.<p>If you have any questions about how the design works, feel free to let me know!
Show HN: A tiny LLM running at 21,000 tok/s on a $250 FPGA (Live Demo)
Show HN: A tiny LLM running at 21,000 tok/s on a $250 FPGA (Live Demo)
Show HN: Ante, a coding agent in a single binary that runs offline
Show HN: Ante, a coding agent in a single binary that runs offline
Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
Hey HN,<p>Henry from Cactus here!<p>We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2.<p>The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges 300-700 on sub-$200 phones such as the Samsung A-Series.<p>On the tool call and mobile device use benchmarks, Needle 2 trades wins with closest small models like LFM2.5 230M and Apple Foundation Model, at 5x to 70x smaller, both at f16 vs Needle 2 at 2bit. Needle is based on Simple Attention Networks from our paper (<a href="https://arxiv.org/abs/2607.18363" rel="nofollow">https://arxiv.org/abs/2607.18363</a>).<p>Edge AI has lately meant Macs and PCs, but that is just 1.5 billion of over 21 billion connected IoT devices in the world today, and in emerging markets most phones ship under $200, no NPU, cheap GPUs. These include budget phones, Raspberry Pis, microcontrollers, wearables, small robots like Reachy Mini, and connected home devices.<p>A conventional transformer of Needle's width and depth spends 164 MFLOPs per token, and even one squeezed down to Needle's parameter count spends 87, Needle spends 70. Even on a high-end phone, an always-on assistant lives inside a power budget; every MFLOP is milliwatt-hours, and Needle spends 7x to 85x fewer of them per token than the smallest performant LLMs. More about the architecture in the link.<p>When we structure intelligence for consumer devices as functions with typed parameters, the only hard part is mapping a messy sentence onto them; which function, with which values. Our research found that when framed that way, the problem needs no world knowledge and no open-ended prose, which is why 45M parameters suffice.<p>Needle 2 expands to structured extraction where the schema can be passed in-place of tools and the model returns structured output. You can use Needle as a text-classification model with an enum field, as a summarization model by providing a schema that extracts key fields, everything but free-range decode.<p>Every product has its own tool vocabulary and fine-tuning needle helps it achieve frontier-level performance on custom tasks, so using the python package (<a href="https://github.com/cactus-compute/needle" rel="nofollow">https://github.com/cactus-compute/needle</a>), Needle can be fine-tuned Needle on a Mac/PC in minutes to a few hours, with automated data-generation pipeline, just pass a couple samples.<p>Nonetheless, every response carries a learned confidence score based our Cactus Hybrid technique. If above your threshold, act, below it, escalate to the cloud or bigger model. Combining Needle 2 with a private DeepSeek-v4-Flash deployment works particularly well for enterprise-level tasks at barely any cost, we can help with this setup.<p>We have put a lot of thoughts into Needle 2 but might still be missing quite a lot, please use the playground in the provided link to test Needle and share your thoughts, always appreciated!
Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
Hey HN,<p>Henry from Cactus here!<p>We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2.<p>The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges 300-700 on sub-$200 phones such as the Samsung A-Series.<p>On the tool call and mobile device use benchmarks, Needle 2 trades wins with closest small models like LFM2.5 230M and Apple Foundation Model, at 5x to 70x smaller, both at f16 vs Needle 2 at 2bit. Needle is based on Simple Attention Networks from our paper (<a href="https://arxiv.org/abs/2607.18363" rel="nofollow">https://arxiv.org/abs/2607.18363</a>).<p>Edge AI has lately meant Macs and PCs, but that is just 1.5 billion of over 21 billion connected IoT devices in the world today, and in emerging markets most phones ship under $200, no NPU, cheap GPUs. These include budget phones, Raspberry Pis, microcontrollers, wearables, small robots like Reachy Mini, and connected home devices.<p>A conventional transformer of Needle's width and depth spends 164 MFLOPs per token, and even one squeezed down to Needle's parameter count spends 87, Needle spends 70. Even on a high-end phone, an always-on assistant lives inside a power budget; every MFLOP is milliwatt-hours, and Needle spends 7x to 85x fewer of them per token than the smallest performant LLMs. More about the architecture in the link.<p>When we structure intelligence for consumer devices as functions with typed parameters, the only hard part is mapping a messy sentence onto them; which function, with which values. Our research found that when framed that way, the problem needs no world knowledge and no open-ended prose, which is why 45M parameters suffice.<p>Needle 2 expands to structured extraction where the schema can be passed in-place of tools and the model returns structured output. You can use Needle as a text-classification model with an enum field, as a summarization model by providing a schema that extracts key fields, everything but free-range decode.<p>Every product has its own tool vocabulary and fine-tuning needle helps it achieve frontier-level performance on custom tasks, so using the python package (<a href="https://github.com/cactus-compute/needle" rel="nofollow">https://github.com/cactus-compute/needle</a>), Needle can be fine-tuned Needle on a Mac/PC in minutes to a few hours, with automated data-generation pipeline, just pass a couple samples.<p>Nonetheless, every response carries a learned confidence score based our Cactus Hybrid technique. If above your threshold, act, below it, escalate to the cloud or bigger model. Combining Needle 2 with a private DeepSeek-v4-Flash deployment works particularly well for enterprise-level tasks at barely any cost, we can help with this setup.<p>We have put a lot of thoughts into Needle 2 but might still be missing quite a lot, please use the playground in the provided link to test Needle and share your thoughts, always appreciated!
Show HN: Voice driven murder mystery, Interview AI suspects with your voice
Hey HN!<p>I'm excited to show off this really fun project I put together. I originally built this project 2-3 years ago, AI was already booming at the time, however voice AI agents were still very early. I loved my proof of concept at the time, but wasn't quite happy with it.<p>I recently had the desire to check out the tech again, and know many of you will be interested.<p>Interviews are speech to speech with OpenAI's gpt-realtime-2.1 over WebRTC. This model is... expensive, and because of that, I have to add some amount of restrictions, conversations are tied to a authenticated Clerk user id. I have also added a 30 minute timer because well, I really don't want to go broke while I sleep tonight.<p>Each suspect has a tool they call when you make a direct accusation. It captures who you accused and a faithful list of the evidence you actually stated.<p>A separate gpt-5-mini judge then decides which of the case's required evidence facts you genuinely presented. Paraphrasing counts, vague suspicion and fishing don't.<p>The rest is Next.js, MongoDB, and Clerk.<p>Let me know whether the suspects hold up under a real interrogation.
Show HN: Voice driven murder mystery, Interview AI suspects with your voice
Hey HN!<p>I'm excited to show off this really fun project I put together. I originally built this project 2-3 years ago, AI was already booming at the time, however voice AI agents were still very early. I loved my proof of concept at the time, but wasn't quite happy with it.<p>I recently had the desire to check out the tech again, and know many of you will be interested.<p>Interviews are speech to speech with OpenAI's gpt-realtime-2.1 over WebRTC. This model is... expensive, and because of that, I have to add some amount of restrictions, conversations are tied to a authenticated Clerk user id. I have also added a 30 minute timer because well, I really don't want to go broke while I sleep tonight.<p>Each suspect has a tool they call when you make a direct accusation. It captures who you accused and a faithful list of the evidence you actually stated.<p>A separate gpt-5-mini judge then decides which of the case's required evidence facts you genuinely presented. Paraphrasing counts, vague suspicion and fishing don't.<p>The rest is Next.js, MongoDB, and Clerk.<p>Let me know whether the suspects hold up under a real interrogation.
Show HN: 35k+ paper psychedelic library that knows LSD from Lumpy Skin Disease
Show HN: Vibez – Open-Source Rust Based Digital Audio Workstation (DAW)
Show HN: Today's cities on a globe of Earth's tectonic past and future
Show HN: Today's cities on a globe of Earth's tectonic past and future