Show HN: Turn your kid's drawing into a hardcover storybook (using GPT vision) https://ift.tt/J9ID6bM

Show HN: Turn your kid's drawing into a hardcover storybook (using GPT vision) Here's a video demo if you're just curious to see it in action: https://ift.tt/LKkT9sq Feedback welcome. https://ift.tt/qJf4bDZ November 18, 2023 at 01:36AM

Show HN: Challenge Your AI Agents/custom GPTs – Can They Survive My Tester? https://ift.tt/dKUw7H1

Show HN: Challenge Your AI Agents/custom GPTs – Can They Survive My Tester? I've pivoted to create AI Agent Tester, a SaaS platform dedicated to testing and validating AI agents from OpenAI, Character.ai, and FlowGPT.com. The platform offers: - Hassle-Free Setup: Get your first simulation up and running in just 5 minutes. - Realistic Simulations: Rigorous user interaction tests to prep your AI agent for real-world scenarios. - Intelligent Scoring: Each response is meticulously evaluated, scoring from 0 to 100(GPT-4 smart enough to evaluate it for you). - Broad Platform Support: Initially rolling out with OpenAI, Character.ai, and FlowGPT.com compatibility. The link: https://ift.tt/4Uj2Cof Eager to hear your thoughts and discuss improvements! https://ift.tt/4Uj2Cof November 17, 2023 at 07:46PM

Show HN: Beta test Execute Program's interactive "Python for Programmers" course https://ift.tt/62KxmYu

Show HN: Beta test Execute Program's interactive "Python for Programmers" course I'm Gary Bernhardt, founder of Execute Program. Our "Python for Programmers" course is in a free open beta for the next week or so. We don't normally do open betas, but the infrastructure behind this course is new and very complex, so we want to stress test it. https://ift.tt/al71Wj5... Today, "Python for Programmers" contains 581 interactive code examples covering the core language. It's aimed at established programmers, not beginners. We don't explain basic language features like `while`, but we do show them briefly and note anything special about how they work in Python. We pay special attention to foot guns. For example, we have an entire lesson about Python's mutable default argument foot gun. This is the first of two courses, with the second coming in 2024. For this course, we drew the line at __dunder__ methods: if a topic requires a dunder method other than `__init__`, then it'll be in the follow-up course. This beta is concurrent with the tail end of our editing process, so you may see the course grow by another 17 lessons (214 code examples) during the beta. Some details about how the course works internally, and why we need a beta at all: First, all Python code in the course runs in your browser via Pyodide. (Reality continues to look more and more like my PyCon 2014 talk [1].) You'll feel a pause when the first code example runs, as your browser loads and boots CPython (around 12 MB). After that, it should be as responsive as a local app. Second, if you look at the course page, you'll see that it's structured as a DAG, similar to a "tech tree" in Civilization, Age of Empires, Stellaris, Satisfactory, etc. (Some of those games have true trees, but some of their "trees" are actually DAGs like ours.) You make progress through the course by traversing one graph edge at a time. Our courses have always been structured as graphs internally, but the raw graphs are simply unreadable due to the number of edges [2]. This year, I taught Execute Program to simplify its own course graphs by breaking them into the level subgraphs that you see on the page, so we can finally render them. It automatically turns the mess that I linked above into the clean graphs that you see in the course. The graph for this course is currently a bit dull, but it'll fill out as we finish editing the remaining lessons. I like Everyday TypeScript's graph [3] the best. Please try the course and use the "Give Feedback" entry in the menu to tell us what you think! I'll also stick around in this thread today. [1] https://ift.tt/hGqvaQr... [2] https://ift.tt/JuP81p5... [3] https://ift.tt/BEHaqiM https://ift.tt/q4rMcw5 November 17, 2023 at 02:51AM

Show HN: I made a really silly personal landing page https://ift.tt/sgy5oSA

Show HN: I made a really silly personal landing page Also, yes I am looking for a new role https://ift.tt/EZDH1Jg November 17, 2023 at 02:36AM

Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks https://ift.tt/stUj2ui

Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks https://ift.tt/0q8vYP2 November 17, 2023 at 02:13AM

Show HN: Version code, models, & datasets together in GitHub https://ift.tt/4RzKLag

Show HN: Version code, models, & datasets together in GitHub Hi HN! We just launched a GitHub integration that scales your Git repos to handle 100 terabytes of files in a single repo. XetData enables data scientists and machine learning engineers to version code, models, and datasets together. Most teams have glued together clunky workflows using S3, DVC, Git, Git LFS, and other tools and make true reproducibility difficult: https://ift.tt/U6QEl4w We instead embrace and extend Git so end-users don’t need to learn a new tool and a new set of commands. Our implementation is similar to Git LFS, where we take over the .gitattributes file, push pointers to large files in GitHub, and push the raw, large files to us. We have a few distinct features that we’re proud of that improve the user experience: - Our XetData bot comments on your pull requests to provide links to useful dataset views and model diffs. We’re working on rendering these inside GitHub itself using browser extensions. - Git LFS and similar tools only implement file-level deduplication. We created a new technique called block-based deduplication (published in CIDR’23 conference) specifically for data and ML workflows. The ML lifecycle consists of making lots of iterative changes and our technique helps save storage and time spent downloading and uploading changes. - You can mount large repos to your local machine using git-xet mount for exploratory work. Individual files that are needed are streamed in just in time behind the scenes. We open sourced our implementation of mount and it was well received here on HN: https://ift.tt/X5YkjC7 - To give more users access to your data, just add them to your GitHub repo. This is a beta product and we would love all of your feedback. You can find all instructions to try this out here: https://ift.tt/uIPyvaV While we’re in beta, our product is completely free to use. We have a Slack you can join or a GitHub issue tracker. - Slack: https://ift.tt/l2krcJt - GitHub: https://ift.tt/JnU7bzG November 16, 2023 at 11:56PM

Show HN: LiftLog – An easy to use open source gym tracking app https://ift.tt/iAUNuwe

Show HN: LiftLog – An easy to use open source gym tracking app I've been working on an open source iOS and Android app for keeping track of the weight you lift at the gym. You can select a plan from a list of included ones, or create your own. I made LiftLog after trying numerous apps and finding either that they were really cumbersome to use, or only usable with a subscription. LiftLog stores everything locally on device and loads quickly. LiftLog is entirely open source and licensed under the AGPL. https://ift.tt/ilGdQJk November 16, 2023 at 04:29AM

Show HN: AI Receptionist, Speaks 64 Languages https://ift.tt/f5SLkGb

Show HN: AI Receptionist, Speaks 64 Languages If you are looking for a receptionist for your startup, consider using Lomni. Lomni is an AI receptionist that can: * Read your website to answer questions * Upsell your product or service according to your instructions * Send texts and emails to you or the caller, for example: * Send links and confirmation texts to callers * Forward customer feedback to support@startup.com * Connect to any API or webhook, in order to: * Generate leads * Book appointments * Process payments * Integrate with any application * Read from and write to your database * Do all of the above in any language, all day, everyday. * Do all of the above over SMS messaging or a chatbot that you could embed into your website. * Coming very soon: Do all of the above over WhatsApp, Facebook Messenger, and Google’s Business Messages. * Coming very soon: generate actionable insights from conversational data using state-of-the-art machine learning. Pricing: Lomni can do all of the above starting at $89/month, approximately 9 cents per minute of calling. This is 1/10th to 1/20th of the standard pricing of calling centres. Custom Demo: Set up in 30 minutes using our user-friendly dashboard and start testing. Alternatively, email hello@lomni.ai for a free custom demo. https://lomni.ai/ November 16, 2023 at 01:59AM

Show HN: Multi-Object Tracking in Python https://ift.tt/4nwVYNW

Show HN: Multi-Object Tracking in Python Hello! I've created a small library for tracking, along with a tutorial. I plan to continue developing it. Tracking is an important topic, closely related to object detection. However, I've noticed that it doesn't receive as much attention compared to machine learning approaches. Or, the focus is on filters like the Kalman filter. This tutorial begins with single object tracking and progressively complicates the tasks, introducing various models and a hypothesis tree to solve them. https://ift.tt/4swoFOJ November 16, 2023 at 12:06AM

Show HN: Movis – A Video Editing Library in Python https://ift.tt/Nrgvcym

Show HN: Movis – A Video Editing Library in Python Originally, this library was created due to my motivation to make videos in Python. Of course, I considered other Python libraries, but I decided to create a new library for video editing because I needed the following features and also for study purposes: - The existence of composition. Especially, the ability to perform advanced video editing by inserting compositions within compositions. - The ability to add animations based on keyframes. - Effects like chroma key and drop shadow. - A caching mechanism to render the same frame quickly. - Various blending modes. These features, despite being standard in proprietary video editing software, were not present in previous Python libraries. Currently, I am using this library to automatically generate videos from raw materials. I would be grateful for any comments, thoughts, or requests. https://ift.tt/79oD0qX November 15, 2023 at 10:50PM

Show HN: Soccer video analysis from your match videos https://ift.tt/ZvjO0xy

Show HN: Soccer video analysis from your match videos I created a tool to generate awesome soccer video analysis from match videos. I'm no pro player, just play with my friends weekly, record our matches, and use this tool to check out our performance. My friends really enjoy it and have suggested adding features like measuring player speed, tracking players positions, and more. https://futvis.com/ November 15, 2023 at 02:12AM

Show HN: Interactive Demo of a Tag-Based Bookmark Manager https://ift.tt/VPyBvp9

Show HN: Interactive Demo of a Tag-Based Bookmark Manager https://ift.tt/R6ctVSI November 14, 2023 at 10:08PM

Show HN: Find hidden engineering bottlenecks with LLMs https://ift.tt/CcXq72k

Show HN: Find hidden engineering bottlenecks with LLMs https://ift.tt/tyXqZTB November 15, 2023 at 12:32AM

Show HN: Llm.f90 fast, hackable transformer implementation in Fortran https://ift.tt/VBNjLMX

Show HN: Llm.f90 fast, hackable transformer implementation in Fortran I submitted an earlier version of this a few months ago (as llama2.f90). At that time it had a lot of steps to run and was just a toy, now it's easy to run and is a competitive option for llm inference. See the motivation section for discussion and the `Performance` issue for an ongoing discussion about performance. https://ift.tt/UoqfwSl November 14, 2023 at 01:17AM

Show HN: Twogether AI – Multi-Person Photo Generation API https://ift.tt/iMdpJcj

Show HN: Twogether AI – Multi-Person Photo Generation API Hey everyone, at Magicflow (YC W23) we're helping our customers run AI image generation in production, enabling them to produce high-quality photos at scale. We are launching a scalable API today that makes it possible to create multi-person portrait photos: which means the ability to create real-looking photos of any two persons interacting with each other in some way only by providing a prompt and the person's pose. Generating this kind of photo requires a deep understanding of the AI ecosystem, a knowledge gap many companies face. In order to make the photos look real with high consistency and for a low cost, chaining of many models is required, and an excellent understanding of how to tweak with the various params of each one. We also handle the infrastructure required to generate the photos, which can be a challenge when dealt with alone, especially for companies with a small backend team (we can scale to thousands of requests per day and generate 100 photos in about 3 minutes). Our customers today use this technology for the following use cases: creating new photo albums from old-scanned albums, providing personalized content for user acquisition campaigns, enabling new kinds of experiences in physical venues, and creating humorous photos with celebrities. There is a significant tradeoff between creating a robust abstraction layer on top of Stable Diffusion capabilities and providing customers with more control over various options. The API currently allows you to manipulate the following parameters: the pose of the couple (hugging, taking a selfie, etc.), their facial expressions, the style of the photo (realistic, cartoon, painting, etc.), as well as the location, theme, and outfits (e.g., ski vacation, on the beach) We created a free demo app for you to view examples and try live: https://ift.tt/fM6tdIq (no user or payment needed). For full API access, contact me at yardenst@magicflow.ai. We can typically set you up within a day, but an onboarding session is required to ensure responsible API usage. https://ift.tt/GyTZnsv November 13, 2023 at 08:37PM

Show HN: Bitemporal, Binary JSON Database System and Event Store https://ift.tt/VxvGpyk

Show HN: Bitemporal, Binary JSON Database System and Event Store I had already posted the project a couple of years ago, and it gained some interest, but a lot of stuff has been done since then, especially regarding performance, a completely new JSON store, a REST API, various internals refactored, an improved JSONiq based query engine allowing updates, implementing set-oriented join optimizations, a now already dated web UI, a new Kotlin based CLI, a Python and TypeScript client to ease the use of Sirix... First prototypes from a precursor stem already from 2005. So, what is it all about? The system uses ideas from ZFS (a keyed index trie, storing checksums in parent pages...) and Git (a persistent index structure that shares unchanged pages between revisions) but appends new tree roots on each commit [1][2]. It is a JSON DBS. The system stores fine granular JSON nodes. Thus, there's almost no limit to the structure and size of an object. Objects can be arbitrarily nested, and updates are cheap. On a high level, it supports space-efficient snapshots, tracking changes by an author / optional commit messages, time travel queries, reverting to previous revisions (while all revisions in-between still exist for audits...), or retrieving the changes of whole (sub)trees. On the one hand, it's, thus, a bitemporal DBS, but on the other hand, it can be used as a simple event store. It stores the state after an event or a change occurs and tracks the changes. Thus, an entity, a node in the JSON structure, can be updated to new values and eventually be removed while the history is easily retrievable, or we can easily revert to a previous state. The system assigns a unique ID to each new node, which never changes and is never reused (even after the deletion of the node). Thus, the system stores the state after the change/event and the event itself (the change event). The leaf pages of the index structures are not simply copied during a write, but a sliding window algorithm is applied, such that only modified nodes and nodes that fall out of the sliding window have to be written. A predefined window length is configurable. The system avoids write-peaks, which would occur due to full snapshots and having to read a long chain of incremental changes in between. Thus, it's best suited for fast flash drives with fast random reads and sequential writes. Data is never overwritten thus, audit trails are given for free. Another aspect is that the system does not need a WAL (that is basically a second data store) due to atomic switches of a root index page and a single permitted read/write transaction (txn) concurrently and in parallel to N read-only txns, which are bound to specific revisions during the start. Reads do not involve any locks.[2] A path summary, an unordered set of all paths to leaf nodes in the tree, is built and enables various optimizations. Furthermore, a rolling hash is optionally built, whereas all ancestor node hashes are adapted during inserts. A dated Jupyter notebook with some examples can be found in [3], and overall documentation in [4]. The query engine[5] Brackit is retargetable (a couple of interfaces and rewrite rules have to be implemented for DB systems) and especially finds implicit joins and applies known algorithms from the relational DB systems world to optimize joins and aggregate functions due to set-oriented processing of the operators.[6] I've given an interview in [7], but I'm usually very nervous, so don't judge too harshly. Give it a try, and happy coding! Kind regards Johannes [1] https://sirix.io | https://ift.tt/6xv1PDt [2] https://ift.tt/E0cLBOk [3] https://ift.tt/7r0gXAp [4] https://sirix.io/docs/ [5] http://brackit.io [6] https://ift.tt/873CnSm [7] https://youtu.be/Ee-5ruydgqo?si=Ift73d49w84RJWb2 November 13, 2023 at 11:21PM

Show HN: "Interactive LinkedIn profile" for better networking/job hunting https://ift.tt/JjDT0N7

Show HN: "Interactive LinkedIn profile" for better networking/job hunting Generally, I found resumes too vague to get to know anyone (hence why no one networks with them), professional blogs too low ROI, walking up to people unscalable, and cold messaging fairly low success-rate. I wanted the 'marketing tool' of networking to get myself out there. Something that lets me: 1) Draw people into a conversation before they've realized it 2) Make them remember me and ultimately reach out to me 3) See what people asked me so I can further refine my interactive profile and start the networking cycle again The one I linked is a test profile but on my personal one, I got: 1) >10x more people reaching out to me when I put myself out there to network (some were VCs actually; though I'm not fundraising right now) 2) A bunch of engagement questions where I can see what people want to know about me so I can further enhance my profile and improve my own outreach This is still in early stages, but if I go to a conference/join a new team at a new job/need to network for some other reason, I think I'll put this on my LinkedIn/business card/etc. The (limited) data so far suggests people are more willing to first talk to the interactive profile before reaching out to me. I guess that makes sense, it's less commitment than emailing me. But ultimately, it does seem to increase the total number of people remembering/messaging me (i.e. improving the professional networking funnel as it were). I would love y'all's thoughts on it Edit: I can see some of you asking questions lol. Way more fun than LinkedIn's 'This random person looked at your profile but what did they want to know? We have no idea'. https://ift.tt/hVgLKP8 November 13, 2023 at 06:14AM

Show HN: LoRA Tune LLM in Lightning on GPU https://ift.tt/oDfcnsy

Show HN: LoRA Tune LLM in Lightning on GPU https://ift.tt/s81aQ7r November 12, 2023 at 09:37PM

Show HN: Docker Swarm Multi Tenant Proxy https://ift.tt/Hy3Danl

Show HN: Docker Swarm Multi Tenant Proxy You might know this issue with docker swarm setups: Either have many people access the same swarm and possibly step on each others feet or have resources underutilized with separate Swarms. This project aims to alleviate this. We implement a Docker Socket Proxy which is intended to give a tenant specific view onto a Docker Swarm. It exposes all necessary operations to deploy stacks with all features to Docker Swarm as well as management endpoints for volumes, secrets, configs, networks. Every proxy can be configured with a unique label to give a tenant specific view onto the swarm. This way you can have multiple teams of people collaborate on the same cluster. This is done by filtering all requests on resource labels to check whether the resources are "owned" by the proxy/tenant. This projects uses Node.js and Express for the server, along with the dockerode (and docker-modem) library to interact with Docker. https://ift.tt/lWuDb1x November 13, 2023 at 12:45AM

Show HN: AllGPTs.co – world largest custom GPT directory https://ift.tt/Na07ipj

Show HN: AllGPTs.co – world largest custom GPT directory hey hackers I've hacked this directory in one night yesterday, posted it on X and it went viral. Got 4600 custom GPTs sumbitted by the makers. I've reviewed and approved 310 GPTs, adding more as we speak. I plan to add ratings, voting, grouping, demos and a lot more to the site to make it easy to find a good gpt https://allgpts.co/ November 12, 2023 at 01:56AM