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[Other] Show HN: FluidAudio – Swift Speaker Diarization on CoreML We needed a speaker diarization solution that could run every few seconds alongside transcription on iOS and macOS. But native Swift support was either limited or locked behind paid licenses. Since diarization is a common need in speech-to-text workflows, we decided to open source our work and give back to the community.<p>We initially tried sherpa-onnx, which works, but running both diarization and transcription models slowed down older devices. CPU-only inference just isn’t ideal for near real-time workloads, so we wanted the option to offload segmentation and speaker embedding to the GPU or ANE. Supporting M1 Macs in particular meant pushing more of the workload to the ANE.<p>Instead of shoehorning the ONNX model into CoreML with C++, we converted the original PyTorch models directly to CoreML. This approach required some monkey-patching in the PyTorch and pyannote code, but the initial benchmarks look promising.<p>We’d love feedback! We&#x27;re currently working on adding VAD and integrating Parakeet for transcription, but still wrestling with CoreML model conversion.

[Other] 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&#x27;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:&#x2F;&#x2F;github.com&#x2F;katanemo&#x2F;archgw">https:&#x2F;&#x2F;github.com&#x2F;katanemo&#x2F;archgw</a> [2] <a href="https:&#x2F;&#x2F;www.envoyproxy.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.envoyproxy.io&#x2F;</a> [3] <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;katanemo&#x2F;arch-function-66" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;katanemo&#x2F;arch-function-66</a>... [4] <a href="https:&#x2F;&#x2F;discord.com&#x2F;channels&#x2F;1292630766827737088&#x2F;12926307682" rel="nofollow">https:&#x2F;&#x2F;discord.com&#x2F;channels&#x2F;1292630766827737088&#x2F;12926307682</a>... [5] <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=I4Lbhr-NNXk" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=I4Lbhr-NNXk</a> [6] <a href="https:&#x2F;&#x2F;docs.archgw.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;docs.archgw.com&#x2F;</a> [7] <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;katanemo" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;katanemo</a>

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[Other] 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&#x27;s not all.<p>Then came the pricing. The last quote I got for one of the tools on the market was $6,000&#x2F;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&#x27;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&#x27;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:&#x2F;&#x2F;langfa.st" rel="nofollow">https:&#x2F;&#x2F;langfa.st</a><p>Help me find what&#x27;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&#x27;t make me broke, please.

[Other] 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&#x2F;XML parsing. * Easy developer experience<p>A quick feature list * Binary WebSocket frames – minimal overhead * Built-in session layer (login &#x2F; reconnect &#x2F; heartbeat) * QoS1 &#x2F; QoS2 with automatic ACK &amp; 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 &quot;Chat Server in 5 Minutes with BinaryRPC&quot;: <a href="https:&#x2F;&#x2F;medium.com&#x2F;@efecanerdem0907&#x2F;building-a-chat-server-in-5-minutes-with-binaryrpc-qos2-session-management-and-room-plugin-ccb66d722466" rel="nofollow">https:&#x2F;&#x2F;medium.com&#x2F;@efecanerdem0907&#x2F;building-a-chat-server-i...</a>

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