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.Running has truly changed my life: I&#x27;ve made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I&#x27;m starting to see the positive benefits as I get older.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.I used the Strava bulk export to pre-populate the database, and I&#x27;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.Happy to answer anything about the stack, data pipeline, or how I stayed motivated for 10 years![1] <a href="https:&#x2F;&#x2F;www.runeveryday.com" rel="nofollow">https:&#x2F;&#x2F;www.runeveryday.com</a> Run Streak Association rules: ≥ 1 mile per day

Show HN: Ten years of running every day, visualized

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Hey HN!I&#x27;m a huge fan of Raycast, but as a Linux user, I was always disappointed it wasn&#x27;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.It&#x27;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.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:&#x2F;&#x2F;byteatatime.dev&#x2F;posts&#x2F;recreating-raycast" rel="nofollow">https:&#x2F;&#x2F;byteatatime.dev&#x2F;posts&#x2F;recreating-raycast</a>The project is still very rough, but I&#x27;m sharing it now to get any feedback you may have!

Show HN: A Raycast-compatible launcher for Linux

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I found that I am using ChatGPT more and more to get the FFmpeg command I need, but the process can be a bit tedious: copy-pasting commands, dealing with input file names and locations, making sure the prompt contains enough info about the input files.This site attempts to solve that. You just describe what you want to do, pick the input files and an LLM (currently DeepSeek) generates the FFmpeg command. You can then run it directly in your browser or use the command elsewhere.

Show HN: FFmpeg in plain English – LLM-assisted FFmpeg in the browser

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Hey HN!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.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.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.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.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:* 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 modelsWe 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].[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>

Show HN: ArchGW – An intelligent edge and service proxy for agents