Show HN: EnvCloak: lightweight, env file encryption – ready for CICD pipelines https://ift.tt/sTFM1w4

Show HN: EnvCloak: lightweight, env file encryption – ready for CICD pipelines When someone here told me to focus on something more useful than reinventing the wheel. So. EnvCloak, a lightweight and simple tool for securely managing sensitive environment files. The design focuses on simplicity - just a few intuitive commands using the Click Python library. I assume seamless integration with CI/CD workflows. The aim is to provide a streamlined solution without the need for clunky tools. If you're tired of complex configurations or bloated alternatives, this might be worth a look! I would appreciate any feedback, feature ideas or input on how to improve this solution. I'd love to hear your thoughts! Regards! https://ift.tt/qDB6p3a November 23, 2024 at 11:29PM

Show HN: ChessGPT https://ift.tt/jz9TRrV

Show HN: ChessGPT I made this quite a while back - but there seems to have been some interest in playing chess with ChatGPT again https://ift.tt/vrYOEFM You can paste you API key in, it all runs locally so should be pretty safe. November 23, 2024 at 04:56AM

Show HN: AI bot that automatically processes unstructured documents https://ift.tt/wm0iRIa

Show HN: AI bot that automatically processes unstructured documents Hi HN! We’re excited to share what we’ve been working on—a bot that automates the tedious task of processing unstructured documents from emails and entering them into ERPs. After many iterations, we’ve achieved 99.8% accuracy in extracting and mapping data from invoices, POs, and other documents. One surprising takeaway from this journey: building the AI was only 10% of the challenge! The real work came from handling edge cases, integrating seamlessly with various ERPs, and creating a reliable pipeline for real-world documents with messy formats. We’d love your feedback, thoughts, or questions about how we built this, the challenges we faced, or anything else. Let us know what you think! Thanks for checking it out! https://ift.tt/5xSU3ds November 23, 2024 at 06:50AM

Show HN: Open-Source Pull Request AI Reviewer https://ift.tt/ZOMI0Ub

Show HN: Open-Source Pull Request AI Reviewer Hey HN, Over the last year, I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building Presubmit.ai, an open-source AI reviewer that runs as a Github Action right when you open a Pull Request. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I save 80hours (~10 working days) per year. Unlike similar SaaS solutions, the goal is not to replace the human reviewer but to highlight obvious mistakes early, spot security vulnerabilities and give more context about the change. I like to think of it as a “pre-reviewer”. Some of its features are: * Line-by-line comments * PR summarization * Title generation on request * Responds to review comments It supports all major LLMs, but I’ve found Anthropic's Claude works best for this use case. Please give it a try and share your feedback! https://ift.tt/zEDw2fl https://ift.tt/zEDw2fl November 22, 2024 at 08:28PM

Show HN: Pull Request Reviewed by LLM https://ift.tt/RpWiKwX

Show HN: Pull Request Reviewed by LLM This year I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building an LLM-based prereviewer that should take a first pass before the actual human reviewer. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I save 80hours (~10 working days) per year. Linked above is an example of a PR where I'm testing the AI reviewer and it showcase how it can detect bugs, suggest best practices about token validity, generate summary and title, and even chat with me in review comments. The AI reviewer is a simple Github action that runs everytime I open or synchronize a pull request and you can see the source code at https://ift.tt/zEDw2fl . https://ift.tt/bnXKP85 November 22, 2024 at 11:29PM

Show HN: VS Code extensions that display CGM blood glucose levels in status bar https://ift.tt/lrmxKn7

Show HN: VS Code extensions that display CGM blood glucose levels in status bar As a Type 1 diabetic, I need to continuously monitor my blood glucose levels. I’ve implemented a couple of Visual Studio Code extensions that retrieve the latest blood glucose readings from your CGM and display them in your VS Code status bar. One VS code extension uses the Nightscout CGM to retrieve the blood glucose readings. It requires users to run the Nightscout application on a hosted server. A nice benefit of Nightscout application is that it works with all the major CGM devices. However, a slight drawback of this option is that it requires a hosted third party software (Nightscout) for proper functionality. I’ve also implemented a Visual Studio code extension for those (like myself) that use the Freestyle Libre CGM. This version connects directly to LibreLinkUp to retrieve the latest blood glucose readings and display them in your VS code status bar. This removes the dependency for the intermediary Nightscout application. If you are or know any software engineers living with diabetes, these tools might be helpful with diabetes management. These are tools I built for myself that help me manage my diabetes. They are completely free and open source. I am not selling anything. Users of the tools can use them without any restrictions or connection to me. I am genuinely trying to help others in the community that are software engineers and might find this helpful. If you try out any of these extensions, I’d love to hear back from you. Any feedback on improvements are very welcome and appreciated. - https://ift.tt/asUNE72... - https://ift.tt/xXcgqaz... November 22, 2024 at 12:19AM

Show HN: My Remote Teaching Station (Mk IV) https://ift.tt/DkgcRQ9

Show HN: My Remote Teaching Station (Mk IV) The remote teaching station has been evolving for the last four years. The Mk IV is the most advanced and most attractive version so far. https://ift.tt/vTZYLRU November 21, 2024 at 10:07PM

Show HN: Self-Host Next.js in Production https://ift.tt/qIK6hrJ

Show HN: Self-Host Next.js in Production https://ift.tt/yoYnRpC November 21, 2024 at 03:37AM

Show HN: Autotab – Programmable AI browser for turning web tasks into APIs https://ift.tt/w9BF7yR

Show HN: Autotab – Programmable AI browser for turning web tasks into APIs Hey HN, we're Alexi and Jonas the co-founders of Autotab ( https://autotab.com ). Autotab is a chrome-based browser you can teach to do complex tasks, with a simple API for running them from your app or backend. Here is a walkthrough of how it works: https://youtu.be/63co74JHy1k , and you can try it for free at https://autotab.com by downloading the app. Why a dedicated editor? The number one blocker we've found in building more flexible, agentic automations is performance quality BY FAR ( https://ift.tt/9bzdnoX... ). For all the talk of cost, latency, and safety, the fact is most people are still just struggling to get agents to work. The keys to solving reliability are better models, yes, but also intent specification. Even humans don't zero-shot these tasks from a prompt. They need to be shown how to perform them, and then refined with question-asking + feedback over time. It is also quite difficult to formulate complete requirements on the spot from memory. The editor makes it easy to build the specification up as you step through your workflow, while generating successful task trajectories for the model. This is the only way we've been able to get the reliability we need for production use cases. But why build a browser? Autotab started as a Chrome extension (with a Show HN post! https://ift.tt/p6EmFJA ). As we iterated with users, we realized that we needed to focus on creating the control surface for intent specification, and that being stuck in a chrome sidepanel wasn't going to work. We also knew that we needed a level of control for the model that we couldn't get without owning the browser. In Autotab, the browser becomes a canvas on which the user and the model are taking turns showing and explaining the task. Key features: 1. Self-healing automations that don't break when sites change 2. Dedicated authoring tool that builds memory for the model while defining steps for the automation 3. Control flows and deep configurability to keep automations on track, even when navigating complex reasoning tasks 4. Works with any website (no site-specific APIs needed) 5. Runs securely in the cloud or locally 6. Simple REST API + client libraries for Python, Node We'd love to get any early feedback from the HN community, ideas for where you'd like the product to go, or experiences in this space. We will be in the comments for the next few hours to respond! November 21, 2024 at 01:52AM

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

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

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

Show HN: DDoS Photon Cannon – A Toy DDoS Blog Post: https://christopherchmielewski.xyz/blog/2024-11-18-homemade-... https://ift.tt/EUZG37D November 20, 2024 at 09:51AM

Show HN: Browser-based website builder powered by LLMs https://ift.tt/dfJ3QzM

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/6apzsqR ! More technical insight: - I also got the Typescript / React app to compile itself in the browser via a service worker https://ift.tt/1cgJ9mt 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/gQZ62Xf November 20, 2024 at 01:54AM

Show HN: Serverless code execution, but for AI agents https://ift.tt/U8H35nm

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/GwO0Tlx

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/8uDVWLg [2] https://ift.tt/ZF9YTDf [3] https://ift.tt/EP4J3Cv... [4] https://ift.tt/DAt4fXM... [5] https://www.youtube.com/watch?v=I4Lbhr-NNXk [6] https://ift.tt/QvAJDPy [7] https://ift.tt/o2ewFMC https://ift.tt/8uDVWLg November 20, 2024 at 12:56AM

Show HN: Tailwind Box Shadow Generator https://ift.tt/J4fcHnu

Show HN: Tailwind Box Shadow Generator https://ift.tt/BV2oLry November 19, 2024 at 02:57AM

Show HN: Nosia – Privacy-Focused AI to Run Models on Your Own Data and Device https://ift.tt/1pdKlfh

Show HN: Nosia – Privacy-Focused AI to Run Models on Your Own Data and Device What happens when you wait months after the official release of ChatGPT, with all the media buzz, before you actually try it for the first time? What happens when your first question to ChatGPT is about its carbon footprint, including Scope 3 emissions, and whether OpenAI complies with the Paris Agreement? What happens when, back in 2013, you almost left tech to become a beekeeper, but then returned to the field driven by passion and a vision for doing things differently? What happens when you believe in extending the life of terminals and servers, recycling, and reusing hardware, instead of succumbing to programmed obsolescence or deleting old emails? What happens when you believe in the power of the French and European tech ecosystem to provide digital solutions that respect GDPR and uphold core values? What happens when you stand for data sovereignty, empowering organizations to protect their data and act independently? What happens when you champion open-source and collective intelligence as key drivers of innovation? What happens when you work with code, systems, networks, and cybersecurity, and approach your work like a craftsman, building something meaningful? Introducing Nosia – a platform that allows you to run an AI model directly on your own data and device, from small models (SLM) to large models (LLM). It's designed to be easy to install and use, empowering you to take control of your AI needs while respecting privacy, sustainability, and autonomy. https://ift.tt/ZToVFsg November 19, 2024 at 04:44AM

Show HN: CSV Table – Proper GUI for View and Edit CSV, JSON https://ift.tt/1fqxDWK

Show HN: CSV Table – Proper GUI for View and Edit CSV, JSON https://csvtable.com November 18, 2024 at 10:04PM

Show HN: Understanding the Bloch Sphere https://ift.tt/dpYhFzr

Show HN: Understanding the Bloch Sphere https://ift.tt/M1u8BkK November 18, 2024 at 01:48AM

Show HN: I made Picle (a.k.a. Catchphrase x Wordle x AI) https://ift.tt/jSbY56R

Show HN: I made Picle (a.k.a. Catchphrase x Wordle x AI) Love to hear what you think! Thank you! https://picle.fi/ November 17, 2024 at 08:38PM

Show HN: Knight's Graph – game based on the Knight's tour problem https://ift.tt/iaWhQgZ

Show HN: Knight's Graph – game based on the Knight's tour problem When I was in high school, my dad showed me how to play Knight Tour on a piece of paper. Many years passed before I decided to create the Knight's Graph app. “Knight's Graph” is an intellectual puzzle game based on the classic knight tour problem, known since the 18th century. Your task is to move the chess knight across the board so that each square is visited exactly once. Test your logical and strategic skills in an exciting game where every game is a new challenge! The app is already available for download on the App Store. Google Play will be available a little later. App Store: https://ift.tt/OLN41aI... Website: https://ift.tt/Z3sH2jp https://ift.tt/WtLfCGp November 13, 2024 at 03:23PM