Show HN: Postiz – open-source social media scheduling tool https://ift.tt/nSKJi6D

Show HN: Postiz – open-source social media scheduling tool https://postiz.com/ November 20, 2024 at 08:07PM

Show HN: Weave - actually measure engineering productivity https://ift.tt/oTFhlpa

Show HN: Weave - actually measure engineering productivity Hey HN, We’re building Weave: an ML-powered tool to measure engineering output, that actually understands engineering output! Why? Here’s the thing: almost every eng leader already measures output - either openly or behind closed doors. But they rely on metrics like lines of code (correlation with effort: ~0.3), number of PRs, or story points (slightly better at ~0.35). These metrics are, frankly, terrible proxies for productivity. We’ve developed a custom model that analyzes code and its impact directly, with a far better 0.94 correlation. The result? A standardized engineering output metric that doesn’t reward vanity. Even better, you can benchmark your team’s output against peers while keeping everything private. Although this one metric is much better than anything else out there, of course it still doesn't tell the whole story. In the future, we’ll build more metrics that go deeper into things like code quality and technical leadership. And we'll build actionable suggestions on top of all of it to help teams improve and track progress. After testing with several startups, the feedback has been fantastic, so we’re opening it up today. Connect your GitHub and see what Weave can tell you: https://ift.tt/cnELadm . I’ll be around all day to chat, answer questions, or take a beating. Fire away! https://ift.tt/cnELadm November 20, 2024 at 11:13PM

Show HN: DDoS Photon Cannon – A Toy DDoS https://ift.tt/tyKeIua

Show HN: DDoS Photon Cannon – A Toy DDoS Blog Post: https://christopherchmielewski.xyz/blog/2024-11-18-homemade-... https://ift.tt/pzNlDFZ November 20, 2024 at 09:51AM

Show HN: Browser-based website builder powered by LLMs https://ift.tt/3sq9NHt

Show HN: Browser-based website builder powered by LLMs I wanted to share what I've been working on - it's a AI site builder that runs in the browser powered by WebGPU and OnnxRuntime-Web. I have got the following all working to varying degrees: - text to code generation - image to code generation - microphone to text to code generation If you are on Mac for instance, it will interface directly with your GPU to power the LLM interface. It only requires downloading the models, and then everything after that is offline. It's not even close to as powerful as Claude or ChatGPT, but I like the idea of having the LLM run directly on your machine. I just did this for fun, but I am looking for a new role if anyone's hiring - https://ift.tt/f69oe2K ! More technical insight: - I also got the Typescript / React app to compile itself in the browser via a service worker https://ift.tt/YW5DNj7 but took it offline due to some oddities with service workers. - A lot of the new speech models are a lot better than anything built into your phone / computer. I wonder when more computers will have them built in. - I added a CSP to the iframe only because I was worried about spamming sites since I update the iframe anytime a new token comes in. So if you have an image on the page it will get reloaded every time the iframe is updated. Otherwise there would be no reason for it. https://ift.tt/H6mZ2PJ November 20, 2024 at 01:54AM

Show HN: Serverless code execution, but for AI agents https://ift.tt/tUjfzPi

Show HN: Serverless code execution, but for AI agents https://sandboxed.ai November 20, 2024 at 05:06AM

Show HN: archgw: open-source, intelligent proxy for AI agents, built on Envoy https://ift.tt/U0sTV67

Show HN: archgw: open-source, intelligent proxy for AI agents, built on Envoy Hi HN! This is Adil, Salman, Co and Shuguang and we're excited to introduce archgw [1], an open source intelligent proxy for agents built on Envoy [2]. Arch moves the critical but crufty work around safety, observability, and routing of prompts outside business logic. Arch is a uniquely intelligent infrastructure primitive, engineered with purpose-built fast LLMs [3] for tasks like intent detection over multi-turn, parameter identification and extraction, triggering single/multiple function calls, and offers convenience features to auto dispatch LLM calls for summarization based on data from your APIs via system prompts configured in archgw. Today, the approach to build a smart production-ready agent is weaving together a large set of mono-functional opinionated libraries, adding extra layers like LLM-based preprocessing to determine things like relevance and safety of the user's prompt (e.g. applying governance and guardrails). Once past that stage, developers must extract relevant information from the user prompt to determine intent, extract parameters as necessary, package relevant tools calls to an LLM to trigger a backend API to execute particular domain-specific task. etc. After all that is done then only are developers ready to trigger an LLM call for summarization and must manage upstream error handling and retry logic themselves. Not to mention, if they want to experiment with multiple LLMs or move between LLM versions, they have to write crufty undifferentiated code. This entire experience is slow, error prone, cumbersome, and not specifically unique. Prior to building archgw, the team spent time building Envoy [2] at Lyft, API Gateway at AWS, specialized search and intent models at Microsoft Research and worked on safety at Meta. archgw was born out of the belief that several rules based mono-functional tools should be converged into a multi-functional infrastructure primitive designed for prompts and agents. We built archgw on the highly popular, battle-tested open source proxy Envoy and re-imagined it for prompts and agents. For this we had to build blazing fast LLMs [3] that can handle crufty, ahead-in-the-request-path type of work in handling and processing prompts that are sent to an agent, so that developers can focus on what matters most: building fast personalized agents without the unnecessary prompt engineering and systems integration work needed to get there. Here are some additional details about the open source project. arghw 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 primitive 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 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]. [1] https://ift.tt/JqozSv9 [2] https://ift.tt/Wo0VJPR [3] https://ift.tt/4TInQR8... [4] https://ift.tt/SZFC9Xx... [5] https://www.youtube.com/watch?v=I4Lbhr-NNXk [6] https://ift.tt/DjRJ1T2 [7] https://ift.tt/yKTrX8V https://ift.tt/JqozSv9 November 20, 2024 at 12:56AM

Show HN: Venmo Unofficial API https://ift.tt/vzVNaqc

Show HN: Venmo Unofficial API https://ift.tt/bwDZlFJ November 19, 2024 at 04:34AM