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[Other] Show HN: I built a tool that helps predict HN front page success Hey HN community,<p>I built a tool that helps optimize your post for hitting the first page of Show HN.<p>How it works: I used a Hugging Face dataset of all Hacker News posts from the past 3 years and trained a model that predicts how successful your post might be. There&#x27;s still a lot of randomness on HN, so nothing is guaranteed, but the tool helps optimize your post for higher odds.<p>A couple of interesting findings:<p>- GitHub repo links work x3 better than regular domains - Open-source tools have a steady virality rate (13.9% - one of the highest) - &quot;I built&quot; outperforms &quot;We built&quot; - Using parentheses and mentioning technologies (Lua, Postgres, Rust, etc.) helps a ton.<p>You can try the tool at wannalaunch.com or read the blog posts for more insights from the analysis. The model is also available as open source if you want to retrain it or look under the hood.<p>Happy to hear the feedback!

[Other] Show HN: I built a RISC-V emulator that runs DOOM Demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=f5uygzEmdLw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=f5uygzEmdLw</a><p>Hi HN,<p>I built a RISC-V emulator that implements the RV32IM instruction set and a minimal syscall interface to run DOOM. A few weeks ago, I got my first output with a simple hello world assembly program.<p>Since then I have been working tirelessly to get DOOM to run.<p>I needed to figure out how to run C programs first, and came across newlib, which allows the underlying environment to implement the syscall stubs one by one until the programs run.<p>I have also added ELF loading, but currently only a single `PT_LOAD` segment is supported.<p>To port DOOM, I used doomgeneric, which was quite convenient to get working once the required stubs were in place.<p>DOOM renders to a fixed area in memory (0x705FDD = VRAM_START):<p><pre><code> 0x7FFFFF +-------------------------------------+ | | | QUEUE_SIZE (32 bytes) | | | 0x7FFFDF +-------------------------------------+ &lt;-- QUEUE_START 0x7FFFDE | QUEUE_READ_IDX | 0x7FFFDD | QUEUE_WRITE_IDX | +-------------------------------------+ | | | | | VRAM (1,024,000 bytes) | | | | | 0x705FDD +-------------------------------------+ &lt;-- STACK_START | Stack | | | | | v | | | | ^ | | | | | Program data + Heap | | | 0x000000 +-------------------------------------+ </code></pre> I made a small linker script so that the entry point of a C program is at _start and virtual address is always 0. That kept the ELF loader code simple.<p>Inputs are written to the queue by rvcore which are then intercepted by DOOM running inside it.

[Other] Show HN: Apple's SHARP running in the browser via ONNX runtime web Hi HN, author here. SHARP is Apple&#x27;s recent single-image 3D Gaussian splatting model (<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2512.10685" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2512.10685</a>). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to ONNX and ran it via onnxruntime-web with the WebGPU EP.<p>What works: drop in an image, get a .ply you can download or preview live, all on your machine — your image never leaves the tab. The model is large (~2.4 GB sidecar) so first load is slow on a cold cache, but inference itself is a few seconds on a recent Mac.<p>Caveats: SHARP&#x27;s released weights are research-use only (Apple&#x27;s model license, not the code&#x27;s). I host the exported ONNX on R2 so thedemo &quot;just works&quot;, but you can also export your own from the upstream Apple repo and upload locally.<p>Happy to talk about it in the comments :)

[Other] Kimi K2.6 just beat Claude, GPT-5.5, and Gemini in a coding challenge

[DevOps] Show HN: I'm running parallel Pi agents on a local sandbox I&#x27;ve been running Pi using SmolVM to build SmolVM!<p>SmolVM provides an abstraction over microVMs to easily create sandboxes for coding agents, OpenClaw, or just to build a custom harness.<p>To use it, install using: curl -sSL <a href="https:&#x2F;&#x2F;celesto.ai&#x2F;install.sh" rel="nofollow">https:&#x2F;&#x2F;celesto.ai&#x2F;install.sh</a> | bash<p>and then run: smolvm pi start

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[Other] Show HN: State of the Art of Coding Models, According to Hacker News Commenters Hello HN,<p>I was away from my computer for two weeks, and after coming back and reading the latest discussions on HN about coding assistants (models, harnesses), I felt very out of the loop. My normal process would have been to keep reading and figure out the latest and greatest from people&#x27;s comments, but I wanted to try and automate this process.<p>Basically the goal is to get a quick overview over which coding models are popular on HN. A next iteration could also scan for harnesses that people use, or info on self-hosting or hardware setups.<p>I wrote a short intro on the page about the pipeline that collects and analyzes the data, but feel free to ask for more details or check the Google Sheet for more info.<p><a href="https:&#x2F;&#x2F;hnup.date&#x2F;hn-sota" rel="nofollow">https:&#x2F;&#x2F;hnup.date&#x2F;hn-sota</a>

[Other] VS Code inserting 'Co-Authored-by Copilot' into commits regardless of usage

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[Other] Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks Hi HN,<p>I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio.<p>The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun.<p>What MLJAR Studio does:<p>- Sets up a local Python environment automatically, runs on Mac, Windows, and Linux<p>- Installs missing packages during the conversation<p>- Built-in AutoML for tabular data (classification, regression, multiclass)<p>- Works with standard Python libraries (pandas, matplotlib, etc.)<p>- Works with any data file: CSV, Excel, Stata, Parquet ...<p>- Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase.<p>For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on.<p>I built this because I wanted something between Jupyter Notebook (flexible but manual) and AI tools that generate code but don’t preserve the workflow. Most tools I tried either hide too much or don’t give reproducible results and are cloud based<p>Demos:<p>- 60-second demo: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;BjxpZYRiY4c" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;BjxpZYRiY4c</a><p>- Full 3-minute analysis: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;1DHMMxaNJxI" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;1DHMMxaNJxI</a><p>Pricing is $199 one-time, with a 7-day trial.<p>Curious if this is useful for others doing real data work, or if I’m solving my own problem here.<p>Happy to answer questions.

[Other] Show HN: Browser-based light pollution simulator using real photometric data Hi HN — author here. iesna.eu is a browser-based ecosystem for working with photometric data: parsing standard luminaire files (LDT&#x2F;EULUMDAT, IES LM-63, Oxytech, ATLA-S001), running design calculations against EN 13201 &#x2F; ANSI&#x2F;IES RP-8 &#x2F; CJJ 45 &#x2F; IES-IDA MLO, and (the part I most want to show off here) rendering real urban scenes in Bevy with the photometric data driving actual streetlight behavior, including sky-glow contribution. The Skyglow Analysis demo loads a real LDT file into a Bevy scene (Khronos Bistro test asset). The luminaire&#x27;s intensity distribution drives the streetlight rendering directly — no fudging — and the sky-glow grade updates live as you adjust the uplight percentage. Swap to a full-cutoff fixture and the sky goes from F (Severe) back to A (Excellent). You can see the difference on the buildings as well as in the sky. Stack: Rust core (eulumdat-rs and friends, ~20 crates handling photometric formats), Bevy for the 3D rendering, WASM for browser deployment. No backend; everything runs client-side. About a thousand lines of new code on top of the existing photometric library to make the Bevy integration work. Things I&#x27;d love feedback on:<p>The atmospheric scattering model is currently single-scattering Rayleigh+Mie. Is that defensible for the use case, or should I move toward multi-scattering? The Bistro test scene works well visually but isn&#x27;t a controlled environment. Anyone know of a public urban geometry asset that&#x27;s more typical of real road-lighting evaluation? The CJJ 45 implementation (China&#x27;s national road lighting standard) is the only one I&#x27;ve had to reverse-engineer from translated PDFs. If anyone has primary-source experience with it, I&#x27;d value a sanity check.<p>Open-source on GitHub (eulumdat-rs and the related crates). Crates.io: eulumdat

[Other] Show HN: Filling PDF forms with AI using client-side tool calling Hey HN!<p>I built SimplePDF Copilot: an AI assistant that can interact with the PDF editor. It fills fields, answers questions, focuses on a specific field, adds fields, deletes pages, and so on.<p>It&#x27;s built on top of SimplePDF that I started 7 years ago, pioneering privacy-respecting client-side pdf editing, now used monthly by 200k+ people.<p>As for the privacy model: the PDF itself never leaves the browser. Parsing, rendering, and field detection all run client-side.<p>The text the model needs (and your messages) goes to whatever LLM you point at. By default that&#x27;s our demo proxy (DeepSeek V4 Flash, rate-capped), but you can BYOK and point it at any cloud provider, or go fully local (I&#x27;ve been testing with LM Studio).<p>Unlike the existing &quot;Chat with PDF&quot; tools that only retrieve the text&#x2F;OCR layer, Copilot can act on the PDF: filling fields, adding fields (detected client-side using CommonForms by Joe Barrow [1], jbarrow on HN with some post-processing heuristics I added on top), focusing on fields, deleting pages, and so on.<p>I built this because SimplePDF is mostly used by healthcare customers where document privacy is paramount, and I wanted an AI experience that didn&#x27;t require shipping PII to a third party. Stack is pretty standard:<p>- Tanstack Start<p>- AI SDK from Vercel<p>- Tailwind (I personally prefer CSS modules, I&#x27;m old-school but the goal since I open source it, I figured that Tailwind would be a better fit)<p>The more interesting part is the client-side tool calling: events are passed back and forth via iframe postMessage.<p>If you&#x27;re not familiar with &quot;tool calling&quot; and &quot;client-side tool calling&quot;, a quick primer:<p>Tool calling is what LLMs use to take actions. When Claude runs grep or ls, or hits an MCP server, those are tool calls.<p>Client-side tool calling means the intent to call a tool comes from the LLM, but the execution happens in the browser.<p>That matters for: speed, you can&#x27;t go faster than client-to-client operations and also gives you the ability to limit the data you expose to the LLM. For the demo I do feed the content of the document to the LLM, but that connection could be severed as simply as removing the tool that exposes the content data.<p>The demo is fully open source, available on Github [2] and the demo is the same as the link of this post [3]<p>What&#x27;s not open source is SimplePDF itself (loaded as the iframe).<p>I could talk on and on about this, let me know if you have any questions, anything goes!<p>[1] <a href="https:&#x2F;&#x2F;github.com&#x2F;jbarrow&#x2F;commonforms" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jbarrow&#x2F;commonforms</a><p>[2] <a href="https:&#x2F;&#x2F;github.com&#x2F;SimplePDF&#x2F;simplepdf-embed&#x2F;tree&#x2F;main&#x2F;copilot" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;SimplePDF&#x2F;simplepdf-embed&#x2F;tree&#x2F;main&#x2F;copil...</a><p>[3] <a href="https:&#x2F;&#x2F;copilot.simplepdf.com&#x2F;?share=a7d00ad073c75a75d493228e6ff7b11eb3f2d945b6175913e87898ec96ca8076&amp;form=w9&amp;lang=en" rel="nofollow">https:&#x2F;&#x2F;copilot.simplepdf.com&#x2F;?share=a7d00ad073c75a75d493228...</a>

[Other] Show HN: Large Scale Article Extract of Newspapers 1730s-1960s Hello HN, over the past 7 months I&#x27;ve spent nearly 3,000 hours on building SNEWPAPERS, the first historical newpaper archive with full-text extractions, nearly perfect OCR, a vast categorization taxonomy and of course with semantic and agentic search capabilities.<p>Problem: I wanted to search through newspaper archives, but when I tried every service only lets you search for keywords and dates, and gives you back raw images of the papers, and too many of them with no context. A sea of noise.<p>Solution: I taught machines how to read the newspapers and so far I&#x27;ve extracted the content from &gt; 600k pages (about 5TB) from the Chronicling America collection. Problems I had to deal with were an infinite variety of layouts, font sizes, image scan qualities, resolutions, aspect ratios, navigating around the images on the page. I also had to figure out how to get OCR to be nearly perfect so people wouldn&#x27;t hate reading the extracts. I stitched together a multi-model pipeline (layout tech, ocr tech, llm, vllm) with heuristics to go from layout -&gt; segmentation -&gt; classification. I put it all in OpenSearch &#x2F; Postgres and made it semantically searchable and also put an agentic search tool on top that knows how to use the API really well and helps you write queries to find what you&#x27;re looking for. Happy to discuss AWS architecture and scaling as well, that was tough!<p>If you have five minutes and you just want to jump in and have your own personalized experience, what I would suggest is:<p>Before searching for anything, go to the Sleuth page Ask it about anything from 1736 to 1963, maybe 1 or 2 follow up questions Then go to the search page so you can see the queries it wrote for you (bottom left &quot;saved queries&quot;) and uncover more info on whatever it is you&#x27;re interested in<p>If you think it&#x27;s cool and you want to learn more, then there&#x27;s about 10 minutes of video guides on the various capabilities in &quot;Guide&quot; on the nav bar<p>Some other people have also taken a crack at this, notably:<p><a href="https:&#x2F;&#x2F;dell-research-harvard.github.io&#x2F;resources&#x2F;americanstories" rel="nofollow">https:&#x2F;&#x2F;dell-research-harvard.github.io&#x2F;resources&#x2F;americanst...</a> (very good attempt) <a href="https:&#x2F;&#x2F;labs.loc.gov&#x2F;work&#x2F;experiments&#x2F;newspaper-navigator&#x2F;" rel="nofollow">https:&#x2F;&#x2F;labs.loc.gov&#x2F;work&#x2F;experiments&#x2F;newspaper-navigator&#x2F;</a> (focused on images)

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