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Show HN: Gosax – A high-performance SAX XML parser for Go

I've just released gosax, a new Go library for high-performance SAX (Simple API for XML) parsing. It's designed for efficient, memory-conscious XML processing, drawing inspiration from quick-xml and pkg/json. <a href="https://github.com/orisano/gosax">https://github.com/orisano/gosax</a> Key features:<p>- Read-only SAX parsing - Highly efficient parsing using techniques inspired by quick-xml and pkg/json - SWAR (SIMD Within A Register) optimizations for fast text processing<p>gosax is particularly useful for processing large XML files or streams without loading the entire document into memory. It's well-suited for data feeds, large configuration files, or any scenario where XML parsing speed is crucial. I'd appreciate any feedback, especially from those working with large-scale XML processing in Go. What are your current pain points with XML parsing? How could gosax potentially help your projects?

Show HN: A Modern Palletization App

When searching on the internet for these type of apps, I didn't find many that were open-source AND easy to use. A lot of them had complicated interfaces, although they had loads of features as well.<p>So what I had in mind when making Stack Solver was an app with a modern interface that has the most essential features. It is also well integrated with Microsoft Excel and renders a 3D customizable drawing.<p>Stack Solver is programmed in C# using the WPF framework to ensure it is fast and light. The interface is built using WPF UI, a library that allows it to keep up with modern trends (specifically the Fluent design).<p>It is a work in progress with tons of new features planned and it is my first "serious" project so I would appreciate any feedback :)

Show HN: A Modern Palletization App

When searching on the internet for these type of apps, I didn't find many that were open-source AND easy to use. A lot of them had complicated interfaces, although they had loads of features as well.<p>So what I had in mind when making Stack Solver was an app with a modern interface that has the most essential features. It is also well integrated with Microsoft Excel and renders a 3D customizable drawing.<p>Stack Solver is programmed in C# using the WPF framework to ensure it is fast and light. The interface is built using WPF UI, a library that allows it to keep up with modern trends (specifically the Fluent design).<p>It is a work in progress with tons of new features planned and it is my first "serious" project so I would appreciate any feedback :)

Show HN: A Modern Palletization App

When searching on the internet for these type of apps, I didn't find many that were open-source AND easy to use. A lot of them had complicated interfaces, although they had loads of features as well.<p>So what I had in mind when making Stack Solver was an app with a modern interface that has the most essential features. It is also well integrated with Microsoft Excel and renders a 3D customizable drawing.<p>Stack Solver is programmed in C# using the WPF framework to ensure it is fast and light. The interface is built using WPF UI, a library that allows it to keep up with modern trends (specifically the Fluent design).<p>It is a work in progress with tons of new features planned and it is my first "serious" project so I would appreciate any feedback :)

Show HN: A Modern Palletization App

When searching on the internet for these type of apps, I didn't find many that were open-source AND easy to use. A lot of them had complicated interfaces, although they had loads of features as well.<p>So what I had in mind when making Stack Solver was an app with a modern interface that has the most essential features. It is also well integrated with Microsoft Excel and renders a 3D customizable drawing.<p>Stack Solver is programmed in C# using the WPF framework to ensure it is fast and light. The interface is built using WPF UI, a library that allows it to keep up with modern trends (specifically the Fluent design).<p>It is a work in progress with tons of new features planned and it is my first "serious" project so I would appreciate any feedback :)

Show HN: Dorkly – Open source feature flags

Dorkly is a free open source Feature Flag backend for LaunchDarkly SDKs. It uses simple yaml files stored in GitHub as the source of truth.<p>Full disclosure: made by a former LaunchDarkly employee + current fan.

Show HN: Dorkly – Open source feature flags

Dorkly is a free open source Feature Flag backend for LaunchDarkly SDKs. It uses simple yaml files stored in GitHub as the source of truth.<p>Full disclosure: made by a former LaunchDarkly employee + current fan.

Show HN: Voice bots with 500ms response times

Last year when GPT-4 was released I started making lots of little voice + LLM experiments. Voice interfaces are fun; there are several interesting new problem spaces to explore.<p>I'm convinced that voice is going to be a bigger and bigger part of how we all interact with generative AI. But one thing that's hard, today, is building voice bots that respond as quickly as humans do in conversation. A 500ms voice-to-voice response time is just <i>barely</i> possible with today's AI models.<p>You can get down to 500ms if you: host transcription, LLM inference, and voice generation all together in one place; are careful about how you route and pipeline all the data; and the gods of both wifi and vram caching smile on you.<p>Here's a demo of a 500ms-capable voice bot, plus a container you can deploy to run it yourself on an A10/A100/H100 if you want to:<p><a href="https://fastvoiceagent.cerebrium.ai/">https://fastvoiceagent.cerebrium.ai/</a><p>We've been collecting lots of metrics. Here are typical numbers (in milliseconds) for all the easily measurable parts of the voice-to-voice response cycle.<p><pre><code> macOS mic input 40 opus encoding 30 network stack and transit 10 packet handling 2 jitter buffer 40 opus decoding 30 transcription and endpointing 200 llm ttfb 100 sentence aggregation 100 tts ttfb 80 opus encoding 30 packet handling 2 network stack and transit 10 jitter buffer 40 opus decoding 30 macOS speaker output 15 ---------------------------------- total ms 759 </code></pre> Everything in AI is changing all the time. LLMs with native audio input and output capabilities will likely make it easier to build fast-responding voice bots soon. But for the moment, I think this is the fastest possible approach/tech stack.

Show HN: Voice bots with 500ms response times

Last year when GPT-4 was released I started making lots of little voice + LLM experiments. Voice interfaces are fun; there are several interesting new problem spaces to explore.<p>I'm convinced that voice is going to be a bigger and bigger part of how we all interact with generative AI. But one thing that's hard, today, is building voice bots that respond as quickly as humans do in conversation. A 500ms voice-to-voice response time is just <i>barely</i> possible with today's AI models.<p>You can get down to 500ms if you: host transcription, LLM inference, and voice generation all together in one place; are careful about how you route and pipeline all the data; and the gods of both wifi and vram caching smile on you.<p>Here's a demo of a 500ms-capable voice bot, plus a container you can deploy to run it yourself on an A10/A100/H100 if you want to:<p><a href="https://fastvoiceagent.cerebrium.ai/">https://fastvoiceagent.cerebrium.ai/</a><p>We've been collecting lots of metrics. Here are typical numbers (in milliseconds) for all the easily measurable parts of the voice-to-voice response cycle.<p><pre><code> macOS mic input 40 opus encoding 30 network stack and transit 10 packet handling 2 jitter buffer 40 opus decoding 30 transcription and endpointing 200 llm ttfb 100 sentence aggregation 100 tts ttfb 80 opus encoding 30 packet handling 2 network stack and transit 10 jitter buffer 40 opus decoding 30 macOS speaker output 15 ---------------------------------- total ms 759 </code></pre> Everything in AI is changing all the time. LLMs with native audio input and output capabilities will likely make it easier to build fast-responding voice bots soon. But for the moment, I think this is the fastest possible approach/tech stack.

Show HN: Voice bots with 500ms response times

Last year when GPT-4 was released I started making lots of little voice + LLM experiments. Voice interfaces are fun; there are several interesting new problem spaces to explore.<p>I'm convinced that voice is going to be a bigger and bigger part of how we all interact with generative AI. But one thing that's hard, today, is building voice bots that respond as quickly as humans do in conversation. A 500ms voice-to-voice response time is just <i>barely</i> possible with today's AI models.<p>You can get down to 500ms if you: host transcription, LLM inference, and voice generation all together in one place; are careful about how you route and pipeline all the data; and the gods of both wifi and vram caching smile on you.<p>Here's a demo of a 500ms-capable voice bot, plus a container you can deploy to run it yourself on an A10/A100/H100 if you want to:<p><a href="https://fastvoiceagent.cerebrium.ai/">https://fastvoiceagent.cerebrium.ai/</a><p>We've been collecting lots of metrics. Here are typical numbers (in milliseconds) for all the easily measurable parts of the voice-to-voice response cycle.<p><pre><code> macOS mic input 40 opus encoding 30 network stack and transit 10 packet handling 2 jitter buffer 40 opus decoding 30 transcription and endpointing 200 llm ttfb 100 sentence aggregation 100 tts ttfb 80 opus encoding 30 packet handling 2 network stack and transit 10 jitter buffer 40 opus decoding 30 macOS speaker output 15 ---------------------------------- total ms 759 </code></pre> Everything in AI is changing all the time. LLMs with native audio input and output capabilities will likely make it easier to build fast-responding voice bots soon. But for the moment, I think this is the fastest possible approach/tech stack.

Show HN: Voice bots with 500ms response times

Last year when GPT-4 was released I started making lots of little voice + LLM experiments. Voice interfaces are fun; there are several interesting new problem spaces to explore.<p>I'm convinced that voice is going to be a bigger and bigger part of how we all interact with generative AI. But one thing that's hard, today, is building voice bots that respond as quickly as humans do in conversation. A 500ms voice-to-voice response time is just <i>barely</i> possible with today's AI models.<p>You can get down to 500ms if you: host transcription, LLM inference, and voice generation all together in one place; are careful about how you route and pipeline all the data; and the gods of both wifi and vram caching smile on you.<p>Here's a demo of a 500ms-capable voice bot, plus a container you can deploy to run it yourself on an A10/A100/H100 if you want to:<p><a href="https://fastvoiceagent.cerebrium.ai/">https://fastvoiceagent.cerebrium.ai/</a><p>We've been collecting lots of metrics. Here are typical numbers (in milliseconds) for all the easily measurable parts of the voice-to-voice response cycle.<p><pre><code> macOS mic input 40 opus encoding 30 network stack and transit 10 packet handling 2 jitter buffer 40 opus decoding 30 transcription and endpointing 200 llm ttfb 100 sentence aggregation 100 tts ttfb 80 opus encoding 30 packet handling 2 network stack and transit 10 jitter buffer 40 opus decoding 30 macOS speaker output 15 ---------------------------------- total ms 759 </code></pre> Everything in AI is changing all the time. LLMs with native audio input and output capabilities will likely make it easier to build fast-responding voice bots soon. But for the moment, I think this is the fastest possible approach/tech stack.

Show HN: I built an indie, browser-based MMORPG

I've been working on an MMORPG that is now in alpha as a solo developer.<p>Here are the major open source technologies that I use:<p>Blender - 3D modeling software for creating the overall environment and every game object. I've gotten a lot of CC and Public Domain assets from <a href="https://poly.pizza" rel="nofollow">https://poly.pizza</a><p>GLTF - I export assets from blender to the GLTF asset format<p>JSON - I write a JSON config for every game object that describes things like its name, its interactions, its collisions, etc.<p>Node.js exporter - I iterate over the environment and every asset to create a scene hierarchy. I use gltf-transform for processing all GLTF files, compressing them, removing redundancies, etc.<p>Node.js server - Uses express and socket.io to process game state updates. It keeps track of every client's game state and issues delta's at each game tick (currently 600ms). The client can send interactions with different objects. The server validates those and updates the game state accordingly.<p>HTML/CSS/JavaScript/Three.js client - I use regular web technologies for the UI elements and three.js for the 3D rending on the browser. The client is responsible for rending the world state and providing the client with different interactions. All code is written in JavaScript which means less context switching. Performance seems to be good enough, and I figure I can always optimize the server code in C++ if necessary.<p>I am currently running two cheap shared instances but based on my testing, they can likely support about 200 users each. This is a low-poly browser based game so it should be compatible across many devices. The data a user needs to download to play, including all 3d assets, is approximately 2 MB, even though there are hundreds of assets.<p>Overall, it's been a fun project. Web development and open source software have progressed to the point that this is no longer an incredibly difficult feat. I feel like development is going pretty well and in a year or so there will be plenty of good content to play.

Show HN: I built an indie, browser-based MMORPG

I've been working on an MMORPG that is now in alpha as a solo developer.<p>Here are the major open source technologies that I use:<p>Blender - 3D modeling software for creating the overall environment and every game object. I've gotten a lot of CC and Public Domain assets from <a href="https://poly.pizza" rel="nofollow">https://poly.pizza</a><p>GLTF - I export assets from blender to the GLTF asset format<p>JSON - I write a JSON config for every game object that describes things like its name, its interactions, its collisions, etc.<p>Node.js exporter - I iterate over the environment and every asset to create a scene hierarchy. I use gltf-transform for processing all GLTF files, compressing them, removing redundancies, etc.<p>Node.js server - Uses express and socket.io to process game state updates. It keeps track of every client's game state and issues delta's at each game tick (currently 600ms). The client can send interactions with different objects. The server validates those and updates the game state accordingly.<p>HTML/CSS/JavaScript/Three.js client - I use regular web technologies for the UI elements and three.js for the 3D rending on the browser. The client is responsible for rending the world state and providing the client with different interactions. All code is written in JavaScript which means less context switching. Performance seems to be good enough, and I figure I can always optimize the server code in C++ if necessary.<p>I am currently running two cheap shared instances but based on my testing, they can likely support about 200 users each. This is a low-poly browser based game so it should be compatible across many devices. The data a user needs to download to play, including all 3d assets, is approximately 2 MB, even though there are hundreds of assets.<p>Overall, it's been a fun project. Web development and open source software have progressed to the point that this is no longer an incredibly difficult feat. I feel like development is going pretty well and in a year or so there will be plenty of good content to play.

Show HN: Rubbrband – A hosted ComfyUI alternative for image generation

Hey HN! My friends and I built a new platform for generating images. The app is easy to use for people who find ComfyUI hard to use, or just simply don’t have a GPU to use it on.<p>For those not familiar, ComfyUI is a great tool for using open-source models like Stable Diffusion. It’s primarily great because it’s a node-based tool, which means you can chain together models, upscalers, prompting nodes, etc… which let you create images in the exact aesthetic you want. There’s also a vibrant dev community behind ComfyUI, which means that there are a ton of nodes and customizability.<p>We’re users of Comfy, but there are some major problems we’ve had with it. First is that it runs primarily on your own hardware, so if you don’t have beefy GPUs it’s not possible to use on your machine. Second, we found that the interface is rather clunkly. Lastly, the ecosystem is very fragmented. All extensions and workflows are scattered around Github/Reddit/Discord, which means new tools are hard to find, and also often times incompatible with your local installation of Comfy, which is super frustrating.<p>We built Rubbrband as own take on an image-generation tool, taking the customizability of ComfyUI, with the ease-of-use of something like Midjourney.<p>Here are the key features:<p>- Fully hosted as a website - Use any Stable-Diffusion checkpoint or LORA from CivitAI - Unlimited image storage - Over 20 nodes, including SD, ControlNet, Masking nodes, GPT-4V, etc… - Color Palettes control, Image References(IP-adapter), etc… - A Playground page, for using workflows in a much simpler interface - A Community page, for sharing workflows with others<p>Would love to get your thoughts! You can use the app here: <a href="https://rubbrband.com">https://rubbrband.com</a><p>We’re looking to also create an API so that you can create nodes on our platform as well! If you’re interested in getting early-access, please let me know!

Show HN: Rubbrband – A hosted ComfyUI alternative for image generation

Hey HN! My friends and I built a new platform for generating images. The app is easy to use for people who find ComfyUI hard to use, or just simply don’t have a GPU to use it on.<p>For those not familiar, ComfyUI is a great tool for using open-source models like Stable Diffusion. It’s primarily great because it’s a node-based tool, which means you can chain together models, upscalers, prompting nodes, etc… which let you create images in the exact aesthetic you want. There’s also a vibrant dev community behind ComfyUI, which means that there are a ton of nodes and customizability.<p>We’re users of Comfy, but there are some major problems we’ve had with it. First is that it runs primarily on your own hardware, so if you don’t have beefy GPUs it’s not possible to use on your machine. Second, we found that the interface is rather clunkly. Lastly, the ecosystem is very fragmented. All extensions and workflows are scattered around Github/Reddit/Discord, which means new tools are hard to find, and also often times incompatible with your local installation of Comfy, which is super frustrating.<p>We built Rubbrband as own take on an image-generation tool, taking the customizability of ComfyUI, with the ease-of-use of something like Midjourney.<p>Here are the key features:<p>- Fully hosted as a website - Use any Stable-Diffusion checkpoint or LORA from CivitAI - Unlimited image storage - Over 20 nodes, including SD, ControlNet, Masking nodes, GPT-4V, etc… - Color Palettes control, Image References(IP-adapter), etc… - A Playground page, for using workflows in a much simpler interface - A Community page, for sharing workflows with others<p>Would love to get your thoughts! You can use the app here: <a href="https://rubbrband.com">https://rubbrband.com</a><p>We’re looking to also create an API so that you can create nodes on our platform as well! If you’re interested in getting early-access, please let me know!

Show HN: The Tomb of Nefertari [QV 66] Guided Virtual Tour

I 3d scanned the Tomb of Nefertari and am building this guided virtual tour, trying to bring in photogrammetry of artifacts that I've made at various museums. It crashes sometimes still on mobile devices.<p>I wasn't able to take photogrammetry photos of the artifacts from the tomb in the Museo Egizio in Turin because they were traveling while I was there (and now the museum is closed to install a new roof anyhow), so I tried to include comparanda from other museums where I had scanned artifacts.<p>I tested the same dataset with 3d Gaussian Splatting but that had lower resolution results for great complexity in frontend code and reduced performance on older devices. [3DGS tour: <a href="https://mused.com/en/tours/860/learn-about-3d-gaussian-splatting" rel="nofollow">https://mused.com/en/tours/860/learn-about-3d-gaussian-splat...</a>]<p>Moving forward, if anyone's working on the same idea, I didn't find a good path to monetization through web-based 3d content, so I'll take the high resolution photogrammetry of spaces into Unreal and switch to desktop and headset builds.

Show HN: The Tomb of Nefertari [QV 66] Guided Virtual Tour

I 3d scanned the Tomb of Nefertari and am building this guided virtual tour, trying to bring in photogrammetry of artifacts that I've made at various museums. It crashes sometimes still on mobile devices.<p>I wasn't able to take photogrammetry photos of the artifacts from the tomb in the Museo Egizio in Turin because they were traveling while I was there (and now the museum is closed to install a new roof anyhow), so I tried to include comparanda from other museums where I had scanned artifacts.<p>I tested the same dataset with 3d Gaussian Splatting but that had lower resolution results for great complexity in frontend code and reduced performance on older devices. [3DGS tour: <a href="https://mused.com/en/tours/860/learn-about-3d-gaussian-splatting" rel="nofollow">https://mused.com/en/tours/860/learn-about-3d-gaussian-splat...</a>]<p>Moving forward, if anyone's working on the same idea, I didn't find a good path to monetization through web-based 3d content, so I'll take the high resolution photogrammetry of spaces into Unreal and switch to desktop and headset builds.

Show HN: SmokeScanner – Using cigarette price arbitrage to find free flights

Show HN: Find AI – Perplexity Meets LinkedIn

As a founder, finding early customers is always a challenge. I'd come up with specific guesses for people to talk to - such "VCs that used to be startup founders" or "Former lawyers who are now CTOs." Running those types of searches typically involves opening dozens of LinkedIn profiles in tabs, and looking at them one-by-one. And, it turns out that going through LinkedIn profiles one-by-one is a daily job for many people.<p>I started building Find AI to make it easier to search for people. I initially started just having GPT review people's LinkedIn profiles and websites, but it cost thousands of dollars per search (!). The product we're launching today can now run the same searches in seconds for pennies.<p>Find AI is Perplexity-style search over LinkedIn-type data. Ask vague questions, and the AI will go find and analyze people to get you matches.<p>The results are really impressive - here are some questions I've used:<p>- Find potential future founders by looking for tech company PMs who previously started a company<p>- Find potential chief science officers by looking for PhDs with industry experience who now work at a startup but have never founded a company before<p>- Find other founders who have a dog and might want my vet app product<p>The database currently consists of tech companies and people, but we're working to scale up to more people. The data is all first-party and retrieved from public sources.<p>Our first customers have been VCs, who are using Find AI to keep track of new AI companies. We just launched email alerts on searches, so you can get updates as new companies match your criteria.<p>Try it out and let me know what you think.

Show HN: Find AI – Perplexity Meets LinkedIn

As a founder, finding early customers is always a challenge. I'd come up with specific guesses for people to talk to - such "VCs that used to be startup founders" or "Former lawyers who are now CTOs." Running those types of searches typically involves opening dozens of LinkedIn profiles in tabs, and looking at them one-by-one. And, it turns out that going through LinkedIn profiles one-by-one is a daily job for many people.<p>I started building Find AI to make it easier to search for people. I initially started just having GPT review people's LinkedIn profiles and websites, but it cost thousands of dollars per search (!). The product we're launching today can now run the same searches in seconds for pennies.<p>Find AI is Perplexity-style search over LinkedIn-type data. Ask vague questions, and the AI will go find and analyze people to get you matches.<p>The results are really impressive - here are some questions I've used:<p>- Find potential future founders by looking for tech company PMs who previously started a company<p>- Find potential chief science officers by looking for PhDs with industry experience who now work at a startup but have never founded a company before<p>- Find other founders who have a dog and might want my vet app product<p>The database currently consists of tech companies and people, but we're working to scale up to more people. The data is all first-party and retrieved from public sources.<p>Our first customers have been VCs, who are using Find AI to keep track of new AI companies. We just launched email alerts on searches, so you can get updates as new companies match your criteria.<p>Try it out and let me know what you think.