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Show HN: Curious HN – Hacker News without the snark

Sometimes I get tired of seeing sarcastic, uncurious, low effort, snark on HN. But sometimes those comments do have some insight. I made this site which rewrites and filters with Claude to amplify the curiosity and attenuate the snark.

Show HN: Agents infer Lego mechanisms from sources, then animate them

Hi there :)<p>Around a month ago, I released LDraw Nova, an agentic toolset for assembling LEGO CAD models by generating LDraw source code files.<p>For more details, here's the Show HN post:<p><a href="https://news.ycombinator.com/item?id=49937916">https://news.ycombinator.com/item?id=49937916</a><p>From the comments, I got the impression that there was quite an interest in seeing not only AI-generated LEGO models, but also AI-animated LEGO ones.<p>I took a look at results from past experiments, and found it feasible to use agentic LLMs for:<p>- analyzing the LDraw source code for LEGO models - reverse-engineer! the mechanisms present by reading the descriptions and sequence for the parts there - and then, animate them via Blender!<p>This new project I'm presenting, LDraw Mecha, is the end result and proof that can be done:<p>- Read specially annotated LDraw LEGO models .mpd source files - Infer the model structure in terms of submodels - Select from those the ones that are mechanisms - Plan animations - Execute them in Blender<p>In summary, from an initial .mpd source file, get a rigged and animated version for the model as a Blender (.blend) file.<p>See the results here:<p>- Repo (v0.5.0): <a href="https://github.com/anteloc/ldraw-mecha/tree/v0.5.0" rel="nofollow">https://github.com/anteloc/ldraw-mecha/tree/v0.5.0</a> - Gallery: <a href="https://github.com/anteloc/ldraw-mecha/blob/v0.5.0/GALLERY.md" rel="nofollow">https://github.com/anteloc/ldraw-mecha/blob/v0.5.0/GALLERY.m...</a> - Process: <a href="https://github.com/anteloc/ldraw-mecha/blob/v0.5.0/PROCESS.md" rel="nofollow">https://github.com/anteloc/ldraw-mecha/blob/v0.5.0/PROCESS.m...</a><p>This project is very unusual, I'd really appreciate some feedback from the community.<p>Cheers!

Show HN: TubeHaus – an open-source YouTube client with no Google account

Show HN: CloneGuard – Scan a repo for credential-stealing tricks before it runs

Show HN: Waymark – A workflow engine that compiles jobs instead of replaying

Hey everyone! @mozgiii and I have been working on Waymark for the better part of the last year. Waymark’s an open source durable execution library with similar functionality to Temporal or DBOS.<p>I fell in love with durable execution a few years ago, after working at a company with a heavy pubsub architecture. In comparison to job orchestrators that force a certain task design or special syntax, many of these durable libs lets you write business logic in regular code: `asyncio.gather` to parallelize tasks, `for` loops, conditionals, and let the runtime figure out how to run them efficiently across a cluster of nodes. But most of the current solutions require a heavy broker, runtime hacks to try to ensure deterministic workflow logic, and feel like you have to learn a whole new ecosystem just to get started.<p>A few unique design decisions in waymark:<p>— Write jobs in normal code (right now Python but JS coming soon). Instead of running control flow as-is, which requires booting up an interpreter to replay past state, we’ll just compile your code directly. We walk your AST and map your logic to our stackless VM bytecode. We support variables, conditionals, await, sleep, helper functions, loops... basically what you’d expect in workflow logic.<p>— Since we’re compiling a language subset, we can proactively flag errors in your workflows locally instead of when they run in production.<p>— It only requires Postgres as the task broker. We’ve spent a lot of time tuning our design to play nicely with its strengths and limitations; our current benchmarks show around 1500 workflows/second but we still have some more optimization work to do here.<p>— Actions plug-and-play with FastAPI’s dependency injection syntax. So if you already have an ecosystem based on typehinting/DI, it should fit like a glove.<p>It allows for parallelization of workflows across one machine or a whole cluster. The bulk of the work per node is done by our central Rust core - which distributes workflows to properly saturate all cores. The idea is to be as batteries included as possible with the broker, webapp, and runtime.<p>What to use it for? It works great as the runner loop controlling agents, but it also works well for basically any task that you need to kick off in the background. It’s largely replaced our way of writing api code. Most frontend actions at this point just push to a simple REST endpoint that kick off a background job.<p>I spent an embarrassing amount of time making our <a href="https://waymark.sh" rel="nofollow">https://waymark.sh</a> site interactive - it’s probably the best place to learn more. We’re starting with Python support and looking for community input on what language to ship next. Would love to hear your thoughts!

Show HN: Firenook – Firebase emulators in Rust: 40× faster writes, 10× less RAM

Hi HN, we have been developing with Firebase for a long time, and the only way Firebase developers can build apps without directly costing them is using the official Firebase Emulator Suite.<p>However, the issue with this suite is, it's dependent and running on Java with a old Console UI (not updated in years), that was not built to support tons and millions of data and has pretty bad developer experience. For a long time, we were hitting Out of Memory on even high specs laptop while developing and the worst part is while this happens all the persists data keep getting lost.<p>We were honestly frustrated with this, and thought what if we built the Firebase Emulator Suite the right way, as it always should have been.<p>Introducing Firenook, a faster drop-in replacement for Firebase Emulator Suite for Firebase developers. It's 40x faster writes and needs 10x less RAM to run it. We took the right approach of first building it Rust as a single engine, so that there will be no Java dependencies and built from scratch a new Console UI using modern web stacks.<p>The result is, now we were able to run 2 millions datas loaded to Firenook with only taking around 2GB ram compared to 22GB of the official suite. Things are way faster and rebuilt Console UI gives best developer experience from logging Firebase Functions, to editing Firestore documents in JSON and many more.<p>We developed this throughly for around 2 months now and ran this internally for a month before we decided it's time to release it to public so that other devs can make use of Firenook and also contribute it to make it better.<p>Firenook is still new and I'm sure it misses things. So please try it our on your project and let us if there are any issues.<p>Thank you.

Show HN: The DNA of DOOM – How code structure survives across 44 ports

I used my custom built structural extraction engine to analyze 44 DOOM ports across history to see what functions remain, if the keyword structure of those functions remained, if comparable call networks could be seen, etc. - <a href="https://github.com/squid-protocol/gitgalaxy" rel="nofollow">https://github.com/squid-protocol/gitgalaxy</a>

Show HN: Stroq – a local audit of what your coding agent did and why

Show HN: Stroq – a local audit of what your coding agent did and why

Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)

My brother and I run a 2-person lab that trains our own generative video models from scratch. The ultimate goal is a set of creative tools that make it easier for anyone to make animation.<p>Video models today are too slow/expensive and lack sufficient controls. Creativity is an iterative process. You make, you tweak, you get better. In our opinion, they're not usable yet for storytelling.<p>When we set out to build Linum v3 (our next video model) earlier this year, the primary goal was to bring the cost envelope of inference down significantly. If our model is fast and cheap, it's the first step (of many) towards useful creative tools.<p>Typically, you see "latent diffusion models". A VAE (Variational Autoencoder) compresses the input data into a smaller "latent space" and the diffusion model learns how to generate within this subspace.<p>Over the past few months, we've been publishing our work on efficient pixel-space diffusion models. These throw away the VAE, having the diffusion model learn a latent-space optimized for its exact task.<p>This serves 3 purposes: 1) Simplify Architecture (no 2-stage model training) 2) Faster Convergence (11x faster convergence vs. our latent diffusion counterpart) 3) Cheaper Inference (4x more aggressive token reduction in attention context-windows, without significant loss in quality)<p>This past month we were pressure testing our latest architecture (Pyramid-JiT) by training a text-to-image model longer than the typical one-off experiment. Specifically, we trained it for ~$20K on 660M samples.<p>We were surprised by the quality of results, so we thought we'd release it as open weights for the community (Apache 2.0). The research itself is already public, on our website.<p>For us, it's a big stepping stone towards cheaper video models, since pretty much all video models start as image models. We like to say these models have to learn nouns before they learn verbs.

Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)

My brother and I run a 2-person lab that trains our own generative video models from scratch. The ultimate goal is a set of creative tools that make it easier for anyone to make animation.<p>Video models today are too slow/expensive and lack sufficient controls. Creativity is an iterative process. You make, you tweak, you get better. In our opinion, they're not usable yet for storytelling.<p>When we set out to build Linum v3 (our next video model) earlier this year, the primary goal was to bring the cost envelope of inference down significantly. If our model is fast and cheap, it's the first step (of many) towards useful creative tools.<p>Typically, you see "latent diffusion models". A VAE (Variational Autoencoder) compresses the input data into a smaller "latent space" and the diffusion model learns how to generate within this subspace.<p>Over the past few months, we've been publishing our work on efficient pixel-space diffusion models. These throw away the VAE, having the diffusion model learn a latent-space optimized for its exact task.<p>This serves 3 purposes: 1) Simplify Architecture (no 2-stage model training) 2) Faster Convergence (11x faster convergence vs. our latent diffusion counterpart) 3) Cheaper Inference (4x more aggressive token reduction in attention context-windows, without significant loss in quality)<p>This past month we were pressure testing our latest architecture (Pyramid-JiT) by training a text-to-image model longer than the typical one-off experiment. Specifically, we trained it for ~$20K on 660M samples.<p>We were surprised by the quality of results, so we thought we'd release it as open weights for the community (Apache 2.0). The research itself is already public, on our website.<p>For us, it's a big stepping stone towards cheaper video models, since pretty much all video models start as image models. We like to say these models have to learn nouns before they learn verbs.

Show HN: After a decade of reversing, I got co-op working in DOS Wing Commander

Show HN: After a decade of reversing, I got co-op working in DOS Wing Commander

Show HN: After a decade of reversing, I got co-op working in DOS Wing Commander

Show HN: Kairos – Open-source risk and fraud decisioning platform

Hey HN, Ayan here from Finic. We’re launching Kairos, an open source alternative to Sift, Sardine, and other real-time fraud decisioning platforms. Kairos addresses two of the biggest problems facing traditional decisioning tools: a lack of labeled data to train on and dealing with fraudsters that are constantly changing their tactics.<p>1. Traditional fraud solutions use ML models such as XGBoost to detect fraud, but these models are dependent on quality labeled data which many businesses do not have. We address this by using “System One” decision models in additional to traditional models such as XGBoost and logistic regression. This allows you to incorporate plain language questions such as “Is this a throwaway email” into your decisioning, bypassing the need for large amounts of labeled data. This also allows you to capture aspects of fraud that are more unstructured, which is something that XGBoost models struggle with.<p>2. Fraud teams are facing off with live players that are constantly changing their behavior to bypass the detection logic they have in place. You can train a detection model or define a ruleset once, but it will become obsolete in a matter of weeks as fraudsters figure out what your detection logic is. This means that you need a team regularly training models and updating rules based on new fraud patterns. Typically this takes a large data science/ML team. Kairos addresses this by introducing an MCP server, so that your agent (Claude Code, Cursor, Codex, etc.) can define a decision workflow, train models on your data, and pick the variant that performs best.<p>You can self-host Kairos with Docker or use the hosted version at <a href="https://kairos.finic.ai">https://kairos.finic.ai</a>

Show HN: Kairos – Open-source risk and fraud decisioning platform

Hey HN, Ayan here from Finic. We’re launching Kairos, an open source alternative to Sift, Sardine, and other real-time fraud decisioning platforms. Kairos addresses two of the biggest problems facing traditional decisioning tools: a lack of labeled data to train on and dealing with fraudsters that are constantly changing their tactics.<p>1. Traditional fraud solutions use ML models such as XGBoost to detect fraud, but these models are dependent on quality labeled data which many businesses do not have. We address this by using “System One” decision models in additional to traditional models such as XGBoost and logistic regression. This allows you to incorporate plain language questions such as “Is this a throwaway email” into your decisioning, bypassing the need for large amounts of labeled data. This also allows you to capture aspects of fraud that are more unstructured, which is something that XGBoost models struggle with.<p>2. Fraud teams are facing off with live players that are constantly changing their behavior to bypass the detection logic they have in place. You can train a detection model or define a ruleset once, but it will become obsolete in a matter of weeks as fraudsters figure out what your detection logic is. This means that you need a team regularly training models and updating rules based on new fraud patterns. Typically this takes a large data science/ML team. Kairos addresses this by introducing an MCP server, so that your agent (Claude Code, Cursor, Codex, etc.) can define a decision workflow, train models on your data, and pick the variant that performs best.<p>You can self-host Kairos with Docker or use the hosted version at <a href="https://kairos.finic.ai">https://kairos.finic.ai</a>

Show HN: I built a tool that prices a pre-built PC part by part

I've missed out on an awesome computer that I could use for a project because figuring out value took too long. Users paste a listing and receive a value grade, dated component estimates and a shareable report. AI reads the listing but my custom built price engine determines the grade (updated every day). Self financed and working now. Looking for feedback re: scoring, interface, and if its helpful. <a href="https://www.youtube.com/watch?v=Tpft--x3WlI" rel="nofollow">https://www.youtube.com/watch?v=Tpft--x3WlI</a>

Show HN: I built a tool that prices a pre-built PC part by part

I've missed out on an awesome computer that I could use for a project because figuring out value took too long. Users paste a listing and receive a value grade, dated component estimates and a shareable report. AI reads the listing but my custom built price engine determines the grade (updated every day). Self financed and working now. Looking for feedback re: scoring, interface, and if its helpful. <a href="https://www.youtube.com/watch?v=Tpft--x3WlI" rel="nofollow">https://www.youtube.com/watch?v=Tpft--x3WlI</a>

Show HN: I built a tool that prices a pre-built PC part by part

I've missed out on an awesome computer that I could use for a project because figuring out value took too long. Users paste a listing and receive a value grade, dated component estimates and a shareable report. AI reads the listing but my custom built price engine determines the grade (updated every day). Self financed and working now. Looking for feedback re: scoring, interface, and if its helpful. <a href="https://www.youtube.com/watch?v=Tpft--x3WlI" rel="nofollow">https://www.youtube.com/watch?v=Tpft--x3WlI</a>

Show HN: Uncracked City, detective game where you question LLM suspects that lie

To give you some context on the project. I'm a solo founder and I use AI heavily in my work. The idea came from wanting players to discover cities. Each city has its own history, so every case is set in a real city with real public places. The crime and the people are on other hand invented. The part that I care most is the playable aspect of the game, a model plays the detective and must solve the case before it goes live.<p>As of today: it's free on Android, the iOS version is still waiting to be reviewed by Apple. It's available in six popular languages, but adding more languages is just a matter of time and demand.My long term vision if it takes off: weekly cases, private parties like a team-building escape game, an MCP so a game master can build their own scenario from ChatGPT or Claude, and maybe tourism offices since players travel the world.