The best Hacker News stories from Show from the past day
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Show HN: Refine – A Local Alternative to Grammarly
Show HN: Ten years of running every day, visualized
Today marks ten years, 3653 consecutive days, of running at least one mile every day under the USRSA rules [1]. To celebrate, I built an interactive dashboard that turns a decade of GPX files into charts you can explore.<p>Running has truly changed my life: I've made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I'm starting to see the positive benefits as I get older.<p>The stack is pretty simple: a NextJS app, with a Postgres database to keep all my running data, and all the stats are pre-computed and cached in Redis, so I effectively only hit the database once a day when a new run is ingested. On the fronted, I toyed with the idea of using D3 or pre-existing data viz libraries, but ended up rolling my own using SVGs directly, it gave me more control on the visualizations.<p>I used the Strava bulk export to pre-populate the database, and I'm using their webhook API to do incremental updates. I have to tap into OpenWeatherMap and OpenCageDate to enrich the running data a little bit.<p>Happy to answer anything about the stack, data pipeline, or how I stayed motivated for 10 years!<p>[1] <a href="https://www.runeveryday.com" rel="nofollow">https://www.runeveryday.com</a> Run Streak Association rules: ≥ 1 mile per day
Show HN: Ten years of running every day, visualized
Today marks ten years, 3653 consecutive days, of running at least one mile every day under the USRSA rules [1]. To celebrate, I built an interactive dashboard that turns a decade of GPX files into charts you can explore.<p>Running has truly changed my life: I've made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I'm starting to see the positive benefits as I get older.<p>The stack is pretty simple: a NextJS app, with a Postgres database to keep all my running data, and all the stats are pre-computed and cached in Redis, so I effectively only hit the database once a day when a new run is ingested. On the fronted, I toyed with the idea of using D3 or pre-existing data viz libraries, but ended up rolling my own using SVGs directly, it gave me more control on the visualizations.<p>I used the Strava bulk export to pre-populate the database, and I'm using their webhook API to do incremental updates. I have to tap into OpenWeatherMap and OpenCageDate to enrich the running data a little bit.<p>Happy to answer anything about the stack, data pipeline, or how I stayed motivated for 10 years!<p>[1] <a href="https://www.runeveryday.com" rel="nofollow">https://www.runeveryday.com</a> Run Streak Association rules: ≥ 1 mile per day
Show HN: I made a JSFiddle-style playground to test and share prompts fast
I built this out of frustration as I lead the development of AI features at Yola.com.<p>Prompt testing should be simple and straightforward. All I wanted was a simple way to test prompts with variables and jinja2 templates across different models, ideally somthing I could open during a call, run few tests, and share results with my team. But every tool I tried hit me with a clunky UI, required login and API keys, or forced a lengthy setup process.<p>And that's not all.<p>Then came the pricing. The last quote I got for one of the tools on the market was $6,000/year for a team of 16 people in a use-it-or-loose-it way. For a tool we use maybe 2–3 times per sprint. That’s just ridiculous!<p>IMO, it should be something more like JSFiddle. A simple prompt playground that does not require you to signup, does not require API keys, and let's experiment instantly, i.e. you just enter a browser URL and start working. Like JSFiddle has. And mainly, something that costs me nothing if I'm or my team is not using it.<p>Eventually I gave up looking for solution and decided to build it by myself.<p>Here it is: <a href="https://langfa.st" rel="nofollow">https://langfa.st</a><p>Help me find what's wrong or missing or does not work from you perspctive.<p>P.S. I did not put any limits or restrictions yet, so test it wisely. Don't make me broke, please.
Show HN: ArchGW – An intelligent edge and service proxy for agents
Hey HN!<p>This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again.<p>A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague input so agents don’t make mistakes. You’re routing prompts to the right expert agent based on context or task type. You’re writing integration code to quickly and safely add support for new LLMs. And every time a new framework hits the market or is updated, you’re validating or re-implementing that same logic—again and again.<p>Putting all the low-level plumbing code in a framework gets messy to manage, harder to update and scale. Low-level work isn't business logic. That’s why we built archgw - an intelligent proxy server that handles prompts during ingress and egress and offers several related capabilities from a single software service. It lives outside your app runtime, so you can keep your business logic clean and focus on what matters. Think of it like a service mesh, but for AI agents.<p>Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized NLP models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that rule-based, single-purpose tools that handle the work around resiliency, processing and routing prompts should move into a dedicated infrastructure layer for agents, but built on the battle-tested foundational of Envoy Proxy.<p>The intelligence in archgw comes from our fast Task-specific LLMs [3] that can handle things like agent routing and hand off, guardrails and preference-based intelligent LLM calling. Here are some additional details about the open source project. archgw is written in rust, and the request path has three main parts:<p>* Listener subsystem which handles downstream (ingress) and upstream (egress) request processing.
* Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard hooks and identifies where to forward the conversation to via its prompt_target primitive.
* Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models<p>We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4]
Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7].<p>[1] <a href="https://github.com/katanemo/archgw">https://github.com/katanemo/archgw</a>
[2] <a href="https://www.envoyproxy.io/" rel="nofollow">https://www.envoyproxy.io/</a>
[3] <a href="https://huggingface.co/collections/katanemo/arch-function-66" rel="nofollow">https://huggingface.co/collections/katanemo/arch-function-66</a>...
[4] <a href="https://discord.com/channels/1292630766827737088/12926307682" rel="nofollow">https://discord.com/channels/1292630766827737088/12926307682</a>...
[5] <a href="https://www.youtube.com/watch?v=I4Lbhr-NNXk" rel="nofollow">https://www.youtube.com/watch?v=I4Lbhr-NNXk</a>
[6] <a href="https://docs.archgw.com/" rel="nofollow">https://docs.archgw.com/</a>
[7] <a href="https://huggingface.co/katanemo" rel="nofollow">https://huggingface.co/katanemo</a>
Show HN: ArchGW – An intelligent edge and service proxy for agents
Hey HN!<p>This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again.<p>A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague input so agents don’t make mistakes. You’re routing prompts to the right expert agent based on context or task type. You’re writing integration code to quickly and safely add support for new LLMs. And every time a new framework hits the market or is updated, you’re validating or re-implementing that same logic—again and again.<p>Putting all the low-level plumbing code in a framework gets messy to manage, harder to update and scale. Low-level work isn't business logic. That’s why we built archgw - an intelligent proxy server that handles prompts during ingress and egress and offers several related capabilities from a single software service. It lives outside your app runtime, so you can keep your business logic clean and focus on what matters. Think of it like a service mesh, but for AI agents.<p>Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized NLP models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that rule-based, single-purpose tools that handle the work around resiliency, processing and routing prompts should move into a dedicated infrastructure layer for agents, but built on the battle-tested foundational of Envoy Proxy.<p>The intelligence in archgw comes from our fast Task-specific LLMs [3] that can handle things like agent routing and hand off, guardrails and preference-based intelligent LLM calling. Here are some additional details about the open source project. archgw is written in rust, and the request path has three main parts:<p>* Listener subsystem which handles downstream (ingress) and upstream (egress) request processing.
* Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard hooks and identifies where to forward the conversation to via its prompt_target primitive.
* Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models<p>We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4]
Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7].<p>[1] <a href="https://github.com/katanemo/archgw">https://github.com/katanemo/archgw</a>
[2] <a href="https://www.envoyproxy.io/" rel="nofollow">https://www.envoyproxy.io/</a>
[3] <a href="https://huggingface.co/collections/katanemo/arch-function-66" rel="nofollow">https://huggingface.co/collections/katanemo/arch-function-66</a>...
[4] <a href="https://discord.com/channels/1292630766827737088/12926307682" rel="nofollow">https://discord.com/channels/1292630766827737088/12926307682</a>...
[5] <a href="https://www.youtube.com/watch?v=I4Lbhr-NNXk" rel="nofollow">https://www.youtube.com/watch?v=I4Lbhr-NNXk</a>
[6] <a href="https://docs.archgw.com/" rel="nofollow">https://docs.archgw.com/</a>
[7] <a href="https://huggingface.co/katanemo" rel="nofollow">https://huggingface.co/katanemo</a>
Show HN: ArchGW – An intelligent edge and service proxy for agents
Hey HN!<p>This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again.<p>A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague input so agents don’t make mistakes. You’re routing prompts to the right expert agent based on context or task type. You’re writing integration code to quickly and safely add support for new LLMs. And every time a new framework hits the market or is updated, you’re validating or re-implementing that same logic—again and again.<p>Putting all the low-level plumbing code in a framework gets messy to manage, harder to update and scale. Low-level work isn't business logic. That’s why we built archgw - an intelligent proxy server that handles prompts during ingress and egress and offers several related capabilities from a single software service. It lives outside your app runtime, so you can keep your business logic clean and focus on what matters. Think of it like a service mesh, but for AI agents.<p>Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized NLP models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that rule-based, single-purpose tools that handle the work around resiliency, processing and routing prompts should move into a dedicated infrastructure layer for agents, but built on the battle-tested foundational of Envoy Proxy.<p>The intelligence in archgw comes from our fast Task-specific LLMs [3] that can handle things like agent routing and hand off, guardrails and preference-based intelligent LLM calling. Here are some additional details about the open source project. archgw is written in rust, and the request path has three main parts:<p>* Listener subsystem which handles downstream (ingress) and upstream (egress) request processing.
* Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard hooks and identifies where to forward the conversation to via its prompt_target primitive.
* Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models<p>We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4]
Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7].<p>[1] <a href="https://github.com/katanemo/archgw">https://github.com/katanemo/archgw</a>
[2] <a href="https://www.envoyproxy.io/" rel="nofollow">https://www.envoyproxy.io/</a>
[3] <a href="https://huggingface.co/collections/katanemo/arch-function-66" rel="nofollow">https://huggingface.co/collections/katanemo/arch-function-66</a>...
[4] <a href="https://discord.com/channels/1292630766827737088/12926307682" rel="nofollow">https://discord.com/channels/1292630766827737088/12926307682</a>...
[5] <a href="https://www.youtube.com/watch?v=I4Lbhr-NNXk" rel="nofollow">https://www.youtube.com/watch?v=I4Lbhr-NNXk</a>
[6] <a href="https://docs.archgw.com/" rel="nofollow">https://docs.archgw.com/</a>
[7] <a href="https://huggingface.co/katanemo" rel="nofollow">https://huggingface.co/katanemo</a>
Show HN: ArchGW – An intelligent edge and service proxy for agents
Hey HN!<p>This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again.<p>A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague input so agents don’t make mistakes. You’re routing prompts to the right expert agent based on context or task type. You’re writing integration code to quickly and safely add support for new LLMs. And every time a new framework hits the market or is updated, you’re validating or re-implementing that same logic—again and again.<p>Putting all the low-level plumbing code in a framework gets messy to manage, harder to update and scale. Low-level work isn't business logic. That’s why we built archgw - an intelligent proxy server that handles prompts during ingress and egress and offers several related capabilities from a single software service. It lives outside your app runtime, so you can keep your business logic clean and focus on what matters. Think of it like a service mesh, but for AI agents.<p>Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized NLP models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that rule-based, single-purpose tools that handle the work around resiliency, processing and routing prompts should move into a dedicated infrastructure layer for agents, but built on the battle-tested foundational of Envoy Proxy.<p>The intelligence in archgw comes from our fast Task-specific LLMs [3] that can handle things like agent routing and hand off, guardrails and preference-based intelligent LLM calling. Here are some additional details about the open source project. archgw is written in rust, and the request path has three main parts:<p>* Listener subsystem which handles downstream (ingress) and upstream (egress) request processing.
* Prompt handler subsystem. This is where archgw makes decisions on the safety of the incoming request via its prompt_guard hooks and identifies where to forward the conversation to via its prompt_target primitive.
* Model serving subsystem is the interface that hosts all the lightweight LLMs engineered in archgw and offers a framework for things like hallucination detection of our these models<p>We loved building this open source project, and our belief is that this infra primitive would help developers build faster, safer and more personalized agents without all the manual prompt engineering and systems integration work needed to get there. We hope to invite other developers to use and improve Arch. Please give it a shot and leave feedback here, or at our discord channel [4]
Also here is a quick demo of the project in action [5]. You can check out our public docs here at [6]. Our models are also available here [7].<p>[1] <a href="https://github.com/katanemo/archgw">https://github.com/katanemo/archgw</a>
[2] <a href="https://www.envoyproxy.io/" rel="nofollow">https://www.envoyproxy.io/</a>
[3] <a href="https://huggingface.co/collections/katanemo/arch-function-66" rel="nofollow">https://huggingface.co/collections/katanemo/arch-function-66</a>...
[4] <a href="https://discord.com/channels/1292630766827737088/12926307682" rel="nofollow">https://discord.com/channels/1292630766827737088/12926307682</a>...
[5] <a href="https://www.youtube.com/watch?v=I4Lbhr-NNXk" rel="nofollow">https://www.youtube.com/watch?v=I4Lbhr-NNXk</a>
[6] <a href="https://docs.archgw.com/" rel="nofollow">https://docs.archgw.com/</a>
[7] <a href="https://huggingface.co/katanemo" rel="nofollow">https://huggingface.co/katanemo</a>
Show HN: I built an LLM chat app because we shouldn't need 10 AI subscriptions
I'm lost between ChatGPT vs Claude vs Gemini... which subscriptions to take? With Cursor and all these specific AI tools, I just wanted one simple chat app where I can use any model and pay only when I use it.<p>Couldn't find one, so I built one.<p>Pay only for what you use. Your prompts and docs, knowledge bases work with every model - no more copy-pasting between apps.<p>Started as a personal project, but thought someone else might benefit from this too.<p><a href="https://prismharmony.com/chat" rel="nofollow">https://prismharmony.com/chat</a><p>What do you think?
Show HN: I built an LLM chat app because we shouldn't need 10 AI subscriptions
I'm lost between ChatGPT vs Claude vs Gemini... which subscriptions to take? With Cursor and all these specific AI tools, I just wanted one simple chat app where I can use any model and pay only when I use it.<p>Couldn't find one, so I built one.<p>Pay only for what you use. Your prompts and docs, knowledge bases work with every model - no more copy-pasting between apps.<p>Started as a personal project, but thought someone else might benefit from this too.<p><a href="https://prismharmony.com/chat" rel="nofollow">https://prismharmony.com/chat</a><p>What do you think?
Show HN: Learn LLMs LeetCode Style
Show HN: Learn LLMs LeetCode Style
Show HN: A Raycast-compatible launcher for Linux
Hey HN!<p>I'm a huge fan of Raycast, but as a Linux user, I was always disappointed it wasn't available on my main OS. This summer, I decided to just build it myself. This project has the goal of being interoperable with Raycast itself, including a majority of the extensions.<p>It's built with Tauri and Rust on the backend, with a Svelte frontend. The biggest challenge was getting it to run existing Raycast extensions, which required building a custom React renderer as well as making a custom API.<p>I also wrote a quick post, which I hope to expand on in the future, about this project. You can find it here: <a href="https://byteatatime.dev/posts/recreating-raycast" rel="nofollow">https://byteatatime.dev/posts/recreating-raycast</a><p>The project is still very rough, but I'm sharing it now to get any feedback you may have!
Show HN: A Raycast-compatible launcher for Linux
Hey HN!<p>I'm a huge fan of Raycast, but as a Linux user, I was always disappointed it wasn't available on my main OS. This summer, I decided to just build it myself. This project has the goal of being interoperable with Raycast itself, including a majority of the extensions.<p>It's built with Tauri and Rust on the backend, with a Svelte frontend. The biggest challenge was getting it to run existing Raycast extensions, which required building a custom React renderer as well as making a custom API.<p>I also wrote a quick post, which I hope to expand on in the future, about this project. You can find it here: <a href="https://byteatatime.dev/posts/recreating-raycast" rel="nofollow">https://byteatatime.dev/posts/recreating-raycast</a><p>The project is still very rough, but I'm sharing it now to get any feedback you may have!
Show HN: A Raycast-compatible launcher for Linux
Hey HN!<p>I'm a huge fan of Raycast, but as a Linux user, I was always disappointed it wasn't available on my main OS. This summer, I decided to just build it myself. This project has the goal of being interoperable with Raycast itself, including a majority of the extensions.<p>It's built with Tauri and Rust on the backend, with a Svelte frontend. The biggest challenge was getting it to run existing Raycast extensions, which required building a custom React renderer as well as making a custom API.<p>I also wrote a quick post, which I hope to expand on in the future, about this project. You can find it here: <a href="https://byteatatime.dev/posts/recreating-raycast" rel="nofollow">https://byteatatime.dev/posts/recreating-raycast</a><p>The project is still very rough, but I'm sharing it now to get any feedback you may have!
Show HN: RULER – Easily apply RL to any agent
Hey HN, Kyle here, one of the co-founders of OpenPipe.<p>Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement.<p>One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate.<p>RULER is a drop-in reward function that works across different tasks without any of that complexity.<p>It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).<p>We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! <a href="https://openpipe.ai/blog/ruler">https://openpipe.ai/blog/ruler</a><p>Repo: <a href="https://github.com/OpenPipe/ART">https://github.com/OpenPipe/ART</a>
Show HN: RULER – Easily apply RL to any agent
Hey HN, Kyle here, one of the co-founders of OpenPipe.<p>Reinforcement learning is one of the best techniques for making agents more reliable, and has been widely adopted by frontier labs. However, adoption in the outside community has been slow because it's so hard to implement.<p>One of the biggest challenges when adapting RL to a new task is the need for a task-specific "reward function" (way of measuring success). This is often difficult to define, and requires either high-quality labeled data and/or significant domain expertise to generate.<p>RULER is a drop-in reward function that works across different tasks without any of that complexity.<p>It works by showing N trajectories to an LLM judge and asking it to rank them relative to each other. This sidesteps the calibration issues that plague most LLM-as-judge approaches. Combined with GRPO (which only cares about relative scores within groups), it just works (surprisingly well!).<p>We have a full writeup on the blog, including results on 4 production tasks. On all 4 tasks, small Qwen 2.5 models trained with RULER+GRPO beat the best prompted frontier model, despite being significantly smaller and cheaper to run. Surprisingly, they even beat models trained with hand-crafted reward functions on 3/4 tasks! <a href="https://openpipe.ai/blog/ruler">https://openpipe.ai/blog/ruler</a><p>Repo: <a href="https://github.com/OpenPipe/ART">https://github.com/OpenPipe/ART</a>
Show HN: DesignArena – crowdsourced benchmark for AI-generated UI/UX
I’ve been using AI to generate some repetitive frontend (guilty), and while most outputs felt vibe-coded, some results were surprisingly good. So I cleaned it up and made a ranking game out of it with friends, and you can check it out here: <a href="https://www.designarena.ai/vote" rel="nofollow">https://www.designarena.ai/vote</a><p>/vote: Your prompt will be answered by four random, anonymous models. You pick the one you prefer and crown the winner, tournament-style.<p>/leaderboard: See the current winning models, as dictated by voter preferences.<p>/play: Iterate quickly by seeing four models respond to the same input and pressing space to regenerate the results you don’t lock-in.<p>We were especially impressed with the quality of DeepSeek and Grok, and variance between categories (To judge by the results so far, OpenAI is very good for game dev, but seems to suck everywhere else).<p>We’ve learned a lot, and are curious to hear your comments and questions. Excited to make this better!
Show HN: DesignArena – crowdsourced benchmark for AI-generated UI/UX
I’ve been using AI to generate some repetitive frontend (guilty), and while most outputs felt vibe-coded, some results were surprisingly good. So I cleaned it up and made a ranking game out of it with friends, and you can check it out here: <a href="https://www.designarena.ai/vote" rel="nofollow">https://www.designarena.ai/vote</a><p>/vote: Your prompt will be answered by four random, anonymous models. You pick the one you prefer and crown the winner, tournament-style.<p>/leaderboard: See the current winning models, as dictated by voter preferences.<p>/play: Iterate quickly by seeing four models respond to the same input and pressing space to regenerate the results you don’t lock-in.<p>We were especially impressed with the quality of DeepSeek and Grok, and variance between categories (To judge by the results so far, OpenAI is very good for game dev, but seems to suck everywhere else).<p>We’ve learned a lot, and are curious to hear your comments and questions. Excited to make this better!
Show HN: BinaryRPC – Lightweight WebSocket-based RPC framework in modern C++
Hi HN,<p>I’m a recent CS graduate.
During the past few months I wrote BinaryRPC, an open-source RPC framework in modern C++20 focused on low-latency, binary WebSocket messaging.<p>Why I built it
* Wanted first-class session support, pluggable QoS levels and a simple middleware chain (global, specific, multi handler) without extra JSON/XML parsing.
* Easy developer experience<p>A quick feature list
* Binary WebSocket frames – minimal overhead
* Built-in session layer (login / reconnect / heartbeat)
* QoS1 / QoS2 with automatic ACK & retry
* Plugin system – rooms, msgpack, etc. can be added in one line
* Thread-safe core: RAII + folly<p>Still early (solo project), so any feedback on design, concurrency model or missing must-have features would help a lot.<p>Thanks for reading!<p>also see "Chat Server in 5 Minutes with BinaryRPC": <a href="https://medium.com/@efecanerdem0907/building-a-chat-server-in-5-minutes-with-binaryrpc-qos2-session-management-and-room-plugin-ccb66d722466" rel="nofollow">https://medium.com/@efecanerdem0907/building-a-chat-server-i...</a>