Show HN: LymeScribe – one computer on your network transcribes for the rest https://ift.tt/TvrWpyE

Show HN: LymeScribe – one computer on your network transcribes for the rest Transcribed with LymeScribe just before posting this: "I realize it's a little bit late in the day to be posting on Hacker News. However, I've been looking at this frog for a good two weeks now, and F it, I'm just going to ship it. I've posted on Hacker News now like three or four times with little to no results. So if a tree falls in the woods..." (I censored the F word, but it transcribed just fine.) TL;DR: LymeScribe is local speech to text for Mac and Windows: dictate into any app, or drop in a recording and get a transcript with speaker labels, and nothing goes to a server you don't own. On a machine that can handle it, it all runs right there. On anything that can't, or anywhere you'd rather keep transcription in one place, one computer does the work for the rest of the network. Offices care about the second one, because the audio and the transcripts never leave hardware they control. This whole thing started as a command line program I built to transcribe and label speakers in audio files sitting on my computer, mostly voice notes using Whisper on my gaming PC. A few friends started asking me to run their meeting recordings through it, which I was happy to do until it got frequent enough to be a chore. So I built them an app that they could drag a file onto themselves, with my gaming PC doing the actual work and sending the results back to their clipboard. That drag and drop piece is still in the app. Since then I added real time dictation, which uses the same local models. When I started thinking about actually selling this thing, I found the market was more crowded than I expected, which was humbling for about a day. What kept me going is that none of the others let one machine do the work for everyone else... that and there's just something sort of cool about owning and using my own transcription software. So, here we are. An older machine can handle its own dictation on the small English models. I found that out when I tried the app on a ten year old iMac expecting it to fail, and deleted the code I'd written to keep machines like it out. Speaker labels are the part that still wants real hardware, and that's where pointing that old machine at a stronger one on the network earns its keep. One license covers the house. It's local by design, and you can test that yourself: unplug the Ethernet mid dictation and it keeps going. Works on a plane. The free client never talks to me at all. Unlocking hosting means one activation call, and after that the license is verified locally against a signed document, so an activated install keeps working with no internet and would keep working if my licensing server went away. AI assisted summaries and translation are experimental. They ship turned off, and you can turn them on if you dare. They only talk to an endpoint you choose, your own key or a local model. The client is free on Mac and Windows. Hosting is a one time $39 unlock with a 7 day trial that starts the first time you host a transcription. It's a perpetual license: you own this major version, point updates are free, and if there's ever a next major version it's a discounted upgrade rather than a new purchase. I'm tired of renting everything, aren't you? Two things worth knowing before you download. The Windows installer is signed, but the certificate is new enough that Chrome and SmartScreen may still warn you about it. And the paid unlock is US only for now, while I work out the international side. Feedback welcome, especially from anyone who has fought text injection on Windows. There's also a heavier server for offices, administered through a web app that handles keys, transcription history, logs, etc. Frog reference: "There's a saying: if you have to eat a frog, it's best you don't spend a whole lot of time looking at it. And if you have to eat two, don't start with the small one." Randy Pausch - Time Management - https://www.youtube.com/watch?v=oTugjssqOT0 https://ift.tt/TaktmH4 August 13, 2026 at 11:48PM

Show HN: OpenCode Senses, An insanely fast and highly accurate vision plugin https://ift.tt/w4KDAJQ

Show HN: OpenCode Senses, An insanely fast and highly accurate vision plugin The vision plugin for OpenCode that truly understands images. Inspect, read, and reason about any screenshot or picture with deeper understanding than any other plugin — fully local, private, and free. Normally, it takes 300ms to analyse one image on my laptop, which is pretty fast for a local vision model. I use moondream2 as my vision model, you can set your custom model like moondream3.1 if you have a good GPU (for comparison I have currently have an RTX 3050). It works cross-platform. Just follow the README. If you like my work, you leave me a tip as an act for supporting open source!! Looking for some feedback! Thank you. NPM: https://ift.tt/4IqAhgn https://ift.tt/UJiDBM2 August 13, 2026 at 11:43PM

Show HN: Programmable timer web app (for gym workouts or stretching sessions) https://ift.tt/QwO6dkg

Show HN: Programmable timer web app (for gym workouts or stretching sessions) Over the last couple of months, I’ve been building a timer web app for myself that I use for workout and stretching sessions. My main use-case is gym routines that consist of repeatable sequences, e.g. where you are holding certain positions for a set time (rinse and repeat). The app counts down the program, beeps, and reads the activities out loud. Two things (I suppose) are special about it: - The timers are “programmable”, so you can freely express your own routines and procedures in a declarative notation. - The app is all static (no backend): the entire program is encoded in the URL and can be bookmarked or shared/transferred via QR-code. You can check it out at https://ift.tt/naEmYB7 , optionally with a demo program pre-loaded: https://ift.tt/HpwdZfz . Source code is at https://ift.tt/03I2UfR . I’ve also written up a small behind-the-scenes on my blog: https://ift.tt/THupEMG https://ift.tt/naEmYB7 August 12, 2026 at 11:57PM

Show HN: Mooneva Cycle – Offline period tracker with no servers and no accounts https://ift.tt/9iOVSP1

Show HN: Mooneva Cycle – Offline period tracker with no servers and no accounts Data stays encrypted on your device (PBKDF2/AES-GCM), no internet permission, nothing to breach or subpoena. GPL-3.0, TypeScript/React + Capacitor. Android & iOS. https://ift.tt/j69GpHl August 13, 2026 at 12:40AM

Show HN: StarryKit – MCP for editable slides, posters and all visual designs https://ift.tt/KNFSfq6

Show HN: StarryKit – MCP for editable slides, posters and all visual designs https://ift.tt/eD3tCH0 August 12, 2026 at 11:34PM

Show HN: Pulp – Zero-allocation C11 telemetry engine (28M logs/SEC to disk) https://ift.tt/AIsJt5a

Show HN: Pulp – Zero-allocation C11 telemetry engine (28M logs/SEC to disk) https://ift.tt/EX0DKTp August 12, 2026 at 12:15AM

Show HN: HyperSAE – Hyperbolic Sparse Autoencoders for LLM Interpretability https://ift.tt/3hgqInu

Show HN: HyperSAE – Hyperbolic Sparse Autoencoders for LLM Interpretability https://ift.tt/5uVS0XQ August 11, 2026 at 11:49PM

Show HN: Graph2agent; Mermaid diagrams, explained for agents https://ift.tt/xywP7eQ

Show HN: Graph2agent; Mermaid diagrams, explained for agents During the implementation of a huge high performance service. In order to keep context small (mainly for humans) I kept the specs into mermaid diagrams. When communicating with humans; diagrams were easy to follow and to remember. But when I asked the agent to implement what's in the diagram, most of the times it failed. So I came into conclusion that agents are good into writing mermaid diagrams but they are not good into reading them. I built graph2agent in order to deterministically (without inference :) ) convert mermaid diagrams into digestible rich text for agents. examples: https://ift.tt/qDyvQHM... This gave us 50% error reduction for any class of diagrams and 80% error reduction for sequence diagrams specifically. Also Input tokens increased on avg by 8% (which is expected) but Reasoning tokens dropped by almost 50%. You can use it either with MCP so agents can call it with any mermaid diagram, and also can put it in pre-commit jobs and run it on every PR so all diagrams are agent ready! I hope you like it! Let me know your thoughts! https://graph2agent.github.io/ August 11, 2026 at 02:59AM

Show HN: My local climbing gym from photogrammetry https://ift.tt/FbERNxp

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. If you see climbing world cup/championship, there is this 3D modelling thing [1]. I want that for my weekly bouldering. 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. COLMAP, OpenMVS, fastapi, svelte, database is just local json files. A read only build is exported so I can host on github page 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? [1] https://www.youtube.com/shorts/8zdUOaCr6DY https://kmcheung12.github.io/climb-preview/tour/ae43c6e5 August 11, 2026 at 03:09AM

Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots https://ift.tt/wS5y2TL

Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots Hey HN, Henry from Cactus here! 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. 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. 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 ( https://ift.tt/Z7O9KUj ). 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. 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. 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. 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. 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 ( https://ift.tt/xGTEdZD ), 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. 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. 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! https://ift.tt/c4tQqnD August 10, 2026 at 10:52PM

Show HN: 100% native Swift harness (NOT Electron) https://ift.tt/EXfWiPV

Show HN: 100% native Swift harness (NOT Electron) hi everybody, I’ve been working on this harness that is all native Swift for macOS. It’s fully featured with every feature i could find in cline, codex, and claude code. There is all swift command line too It’s taking me a long, long time, a few months to build this, and it’s fully open source Apache 2 licensed. Hoping people fork this and make many branches I’m looking for people who want to work on something that has much lower memory usage and much more potential to be just more efficient. Every other single harness has is written in using electron, so this harness is really unique in this way. It’s connected to trusted router so that it has end-to-end encryption confidential mode as well as access to hundreds of models, any model in the world. It works great with Deepseek. it’s great for swift development ios development. and for people who love Linux, I also made a compatibility layer that lets you compile SwiftUI into native Linux so that you either can new or QT actually so that you can just compile all the code directly and make it run on Linux https://ift.tt/5w8n1OG August 10, 2026 at 06:56PM

Show HN: Run your GitHub Actions locally or self-hosted in isolated microvms https://ift.tt/d4i6syT

Show HN: Run your GitHub Actions locally or self-hosted in isolated microvms Hey HN. I've been frustrated with Github Actions' reliability, and inability to run workflows locally. Preloop is a Rust reimplementation of the GitHub Actions, both the runner(tracking the latest version) and the control plane that runs in hardware-isolated microvms on MacOS/Linux/Windows(we use smolvm project that uses the libkrun vmm).The microvms starts in <400ms from a packed artifact, and cow-clones for each job are instant. The control plane speaks the exact official runner protocol, so the unmodified actions/runner binary can register, poll, execute, and report against it. Your existing workflow files run unchanged, but on your own hardware with zero GitHub-hosted minutes. Locally, you can also run uncomitted changes so you dont have to create a commit to run ci. If you are working solo or in a small team and have fast local hardware, you can use something like DHH's gh-signoff extension and merge directly so long as certain checks pass. We also support features such as pause-on-failure where upon failure, the vm is paused, opens up a shell, and allows you to fix the errors, then you can retry without rerunning all the jobs that worked(new step/job runs in a new fresh microvm to verify the changes) or you can attach directly to a running vm and see what steps each jobs are doing in real-time. Preloop also implements the Debug Adapter Protocol so you or your agent can drive your CI locally(with the built-in client) or you can use a DAP client like Vscode. You can also submit your changes to the server for CI to run and then after it can open a draft PR for you, with the updated checks, so we dont only rely on Webhooks from Github. I've invested heavily in ensuring conformance with the official runner protocol so I do different kinds of mitm proxying to make sure not only the request/response bodies match but also down to the job/step level conclusions/logs/annotations etc. Effectively, the official runner should work with our control plane. Act, Forgejo are all great, but they dont faithfully implement the official runner protocol so in practice some workflows dont quite work out of the box, and they also run the jobs in docker containers. Forgejo has the closest compatibility and is probably still a good option if you are fully migrating off github. With Act, there's a few mostly server-side features it doesnt implement(concurrency, some matrix features, oidc etc). forgejo has the closest compatibility, and is a good option if you are fully migrating off Github. Agent-ci is also a great option that uses the official runner image and implements the official protocol, but it is designed to run locally, runs in containers, and doesnt support some features we do(concurrency, newer V2 cache, Github token etc). Preloop is designed to also be run as a peristent server so we implement more submit updates to the checks api. Lot more stuff coming, but i figured this is a good point to share with the community incase it might be interesting to some folks. Feel free to try it out. Website: https://preloop.dev Github: https://github.com/preloopdev/preloop https://preloop.dev August 10, 2026 at 01:25AM

Show HN: Inertia – Not your Grandfathers animation editor https://ift.tt/0PbBOYa

Show HN: Inertia – Not your Grandfathers animation editor https://ift.tt/K37HyMS August 9, 2026 at 03:57AM

Show HN: A Discord bot running in a 4MiB microVM https://ift.tt/rx8diA3

Show HN: A Discord bot running in a 4MiB microVM https://ift.tt/GquysbB August 8, 2026 at 11:04PM

Show HN: Algebruh - Cross-check arithmetic claims with Z3, cvc5, and Lean https://ift.tt/ytLl4vO

Show HN: Algebruh - Cross-check arithmetic claims with Z3, cvc5, and Lean https://ift.tt/fcNwrIH August 8, 2026 at 11:34PM

Show HN: Check if any of the $656M in unclaimed royalties at The MLC is yours https://ift.tt/qYFMHKa

Show HN: Check if any of the $656M in unclaimed royalties at The MLC is yours https://pub.doub.ly/ August 7, 2026 at 11:05PM

Show HN: Zaivern Code – a Rust cockpit for parallel AI coding agents https://ift.tt/gC24wh3

Show HN: Zaivern Code – a Rust cockpit for parallel AI coding agents https://ift.tt/JdxwrWs August 8, 2026 at 12:53AM

Show HN: Mousecrack – Teaching an LSTM to move a mouse like a human https://ift.tt/t1VPjba

Show HN: Mousecrack – Teaching an LSTM to move a mouse like a human https://ift.tt/0Tfzg8v August 7, 2026 at 10:23PM