The best Hacker News stories from Show from the past day
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Show HN: Apollo Lunar Module landing simulation
I have always had deep fascination for Apollo spacecraft, especially the Lunar Module (LM). The technical ingenuity of its design, reliability during the missions, and the quirky form make it my favorite machine of all time. I have often dreamed about what the Apollo astronauts (particularly Armstrong and Aldrin) experienced, as they guided the vehicle towards the unknown lunar surface.<p>This is my attempt to simulate the last 3 stages of the descent sequence: Braking (P63), Approach (P64) and Final Landing (P66). The last one requires manual attitude and thrust control to achieve a soft touchdown. I've tried to maintain balance between historical accuracy with simplification, so that an average person like me can manage to land safely with a bit of training.<p>I hope fellow space enthusiasts will enjoy learning its nuances, and experience the challenge & thrill of achieving a soft lunar landing. Note that this requires a keyboard, and is not mobile friendly yet.
Show HN: 1080p is 920px tall – 1k real browser viewports
Show HN: EterDB, a Postgres fork that makes it easy to recover from incidents
Hey everyone, a few months ago, after recovering from a Clade-generated bug, I was thinking to myself “wouldn’t it be nice if prod DB writes were easy to roll back”. I’ve put something together: it’s a two-container deploy plus a CLI tool. It’s far from being production ready, so please be gentle. All feedback welcome!<p>The nitty gritty: <a href="https://eterdb.com/tech" rel="nofollow">https://eterdb.com/tech</a><p>GitHub: <a href="https://github.com/eterdb/eterdb" rel="nofollow">https://github.com/eterdb/eterdb</a>
Show HN: Is It Greg?
My friend Greg often posts incredible things he just made, just for fun.<p>Often, I don't realize that it's something HE made and then I see the URL, AND IT'S MY FRIEND, GREG!<p>A few things he made that I think are amazing:<p>- ikea complexity index: <a href="https://ikea.greg.technology/" rel="nofollow">https://ikea.greg.technology/</a><p>- Boing: <a href="https://boing.greg.technology/" rel="nofollow">https://boing.greg.technology/</a><p>I want to make sure I don't miss anything from him, so I created this Chrome extension that flags things posted by Greg, or linking to his website or a subdomain.<p>I hope other people will install the extension and never miss anything Greg made.
Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost
Hey HN, Toby from Nari Labs here.<p>We've been working on making OSS speech models super-fast. Last year, we built Dia, the first OSS text-to-speech model capable of doing natural dialogue. Since then, so many more great speech models have been released to the public.<p>But the market is still dominated by closed source models. We think that's an inference problem. Existing systems such as vLLM / SGLang are not well suited for multimodal inference. To prove this, we built an inference engine specialized for Qwen3-TTS and open-sourced it (<a href="https://github.com/nari-labs/nari-qwen3-tts" rel="nofollow">https://github.com/nari-labs/nari-qwen3-tts</a>). Running at sub-50 ms latency at 10 RPS, this showed open models can be run much faster and cheaper.<p>Since then, we've been working hard to bring cheap, fast, and high quality serving to all. And we've even beat closed models at their game!<p>Measured on the highly cited Coval (YC S24) voice AI benchmarks, our Qwen3-TTS endpoint not just is #2 in latency, but #1 in accuracy (WER) compared to 11Labs, Cartesia etc. while being the cheapest endpoint. Our Qwen3-ASR endpoint has the lowest latency and #2 accuracy, just 0.1% away from #1. It is the second cheapest model on the list.<p>It took a lot of clever inference engineering to make these models quick, perform well while keeping costs low. Interestingly, Alibaba's official endpoints seem to perform worse in terms of accuracy and latency compared to ours. But nonetheless, much love to the Qwen team for OSS-ing these amazing speech models.<p>We want to continue to push prices down to make speech technology a commodity - so that every app can have great TTS and STT without worrying about unit costs. We're also working on other parts of audio such as diarization - as well as video and world model inference. More to come!
Show HN: Kinesis – Control your Mac with the Meta Neural Band
a couple hours ago i had astra take on a 10 month old repo of mine <a href="https://github.com/callbacked/neural-band-poc/" rel="nofollow">https://github.com/callbacked/neural-band-poc/</a> that involved finding a way to use the meta neural band independently from the glasses to read the sEMG data off of it. i really think it is a cool piece of technology that largely gets overlooked by the glasses, and always thought it was a shame it couldn't be used outside of its main application.<p>after making quick work of that, i wanted to use it to control my mac using gesture controls and that's how kinesis was born. it has been very cool being able to swipe from desktops using my fingers, swiping up to open up mission control or controlling a virtual knob to adjust my volume.<p>i definitely havent scratched the surface with the neural band but it has been a really fun endeavor thus far! i really hope others can find creative uses for their neural bands too :)<p>note: i really hope this works on other bands because the sample size has just been me so far.
Show HN: Pelican-bicycle alternatives
In November and December 2025, inspired by Simon Willison’s pelican-riding-a-bicycle benchmark, I had some then-current LLMs create SVGs from thirty similar prompts, such as “Generate an SVG of an octopus operating a pipe organ.” Simon mentioned that experiment on his blog [1].<p>Nine months have passed and much stronger models have been released, so I tried the experiment again today. The linked site shows the results.<p>Running ten of the prompts through six models at OpenRouter cost about twenty dollars, so I stopped there for now.<p>[1] <a href="https://simonwillison.net/2025/Nov/25/" rel="nofollow">https://simonwillison.net/2025/Nov/25/</a>
Show HN: Neobrutalism.dev – Just added Base UI support and added new color theme
Show HN: I built my own knowledge graph from the code AI writes
Show HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XP
Show HN: What If Donut.c but with Any ASCII Art
Show HN: Most Penalized HN Stories
This page shows which stories have the heaviest penalty applied to their Hacker News rank -- stories that rank lower than they should given their raw HN ranking formula.
Show HN: Analyst Index – analysts who make money telling you good stock calls
Hi HN!<p>What if analyst ratings could be relied on to help you find the next Sandisk, before it takes off?<p>On Analyst Index, analysts publish price targets with a maturity date. Every call gets scored against what the stock actually did, so an analyst builds a record on accuracy instead of on follower count.<p>I started this because after riding the Tesla wave in 2019/2020, I messed up epically with some high risk bets that didn’t work out, including one pick from an analyst I didn’t know was not much good. I needed something like this to help me figure out a) which analysts are worth listening to, and b) which stocks to buy based on their ratings.<p>On what already exists: TipRanks has scored analysts since 2009 and Benzinga sells accuracy scores through an API, so this isn’t entirely new. The difference here is that analyst incentives are aligned with that of investors through a subscription. TipRanks and others scrape published sell side calls and sell access to investors without analysts seeing a dollar of investor revenue. So without aligned incentives, the quality of their work is going to be lower.<p>(Yes, I scraped sell side calls to bootstrap Analyst Index with some data - better than launching with a ghost town).<p>On methodology: Right now scoring is naive. I compare the call against the stock's movement over the stated horizon and score the difference. I thought it better to ship simpler something sooner rather than later. If scoring design is your area, I'd like to talk.<p>What's live: 44 price targets from 25 analysts across 3 tickers, browsable without an account.
What isn't: no analyst signup yet, no payments, and no paywall enforcement — anyone can currently subscribe to anyone and see everything.<p>Business model (planned): analysts set their own subscription price, readers subscribe directly, I take 10%.<p>Open problems I don't have answers to:<p>- Cold start on supply. The analysts I want are good and undiscovered, which means they have no audience by definition, so the score has to do the work an audience normally does. If I can't drive demand-side traffic, no amount of scoring sophistication saves this.<p>- Whether price targets carry information at all. Plenty of people think they're noise. My guess is that aligning incentives will produce a signal, but I can't prove that just yet.<p>Regulatory: I operate as a publisher, not an adviser. Content is impersonal and general circulation, with no personalized recommendations and no performance promises. Nothing on the site is investment advice.<p>Link: <a href="https://www.analystidx.com/" rel="nofollow">https://www.analystidx.com/</a><p>The feedback I want most is who you would like to see on Analyst Index (analysts) and what it would take for you to become a customer (as an investor). I know most people out of the gate are going to have objections to using Analyst Index for their own investing, but it would be helpful to learn what those objections are to evaluate whether they can be overcome.<p>Any other feedback is welcome too!
Show HN: Everything a web page can learn about you, in plain English
Show HN: See Sounds on Your Webcam
Show HN: Liniora – Ever thought about replacing your project manager?
Hi HN, the title could be a little bit aggressive sorry for that... Basically i am Computer engineer graduated recently but i was and still working remotely for approx. 3 years with the same foreign company in EU. and i noticed that we are always somehow lose the context of a ticket -- even though we are using the popular project management systems -- so i decided why not build something that can fix our context loosing problems and with some improvements. here i created Liniora. an AI powered project management system. basically the core idea is to link the tools that we use to gather information about tickets in one place. i integrated Google meet for the meetings transcripts so you do your meeting it automatically got registered and indexed. You discuss about a bug in Slack thread so you mention @Liniora app and it will handle the bug context and create a ticket for you. We have the version control also connected so you create a ticket with ability of creating a separate branch with one-click for both Gitlab and Github. you have as well the Pull request controlled in our place inside the app. lets suppose you have a ticket and you created a branch called 'USER-325-create-login-form' then you merged to staging or production ? it will be automatically flagged with staging/production badge so you know what tickets are done. there are a lot of interesting features i wanted to describe but i will just let you guys discover and i will be very happy about your feedback. Thanks
Show HN: Extension to filter LLM written articles
Firefox Extension/Userscript and API to get Pangram scores for all articles on the hackernews frontpage. The extension allows you to hide articles with a high score.<p>This is about detecting posts written by LLMs, not posts about AI.<p>Feel free to use the API to build your own tooling/readers.<p>Big thanks to <a href="https://news.ycombinator.com/user?id=salahadawi">https://news.ycombinator.com/user?id=salahadawi</a> for providing the data :).
Show HN: I built a hand-modeled 3D Windows 98 portfolio with Three.js
Show HN: Clawfight.ai MCP-driven agentic game play
How should agents interact with other agents? What happens when they rap or fight against each other with the pressure of human spectators? Clawfight.ai is an experiment to explore this space.<p>This is my first post about it. The journey began on a beefed up machine I purchased with a decent GPU (5090). I installed claude code and set to dangerously skip permissions, enabled /rc and became completely submerged in the all-hours modern AI builder workflow.<p>I stood up openclaw to see if I could have an agentic org drive this project. I spent a good amount of time on “openclaw ops”, babysitting 3 agents and trying to make them the best versions of themselves. They still do stupid things that cost me tokens.<p>For the game render, I started out using Unreal Engine and allowing agents to remote control their players, but the quality just wasn’t there. I recently moved to doing near-real time video renders of the match and will bring UE back in for multi-agent games.<p>Everything is AI generated. I’m fascinated by the future of realtime video generation and building out native MCP infrastructure.<p>You can play directly from model provider apps (claude connector or openai plugin) and has fallback support for more basic HTTP clients. But the architecture and gameplay is MCP first.<p>Tell your agent/app “go read clawfight.ai/agents.md and play”
Show HN: Graphify C# – Compiler-accurate Find Usages for coding agents