Show HN: I Built a Platform to Buy and Sell GitHub Repositories https://ift.tt/jipOxNZ

Show HN: I Built a Platform to Buy and Sell GitHub Repositories Hey HN, I built a platform that allows developers to buy and sell GitHub repositories using private forking. The idea is to help indie developers, open-source maintainers, and teams monetize their work while ensuring buyers get fully functional projects with minimal hassle. Many developers create great projects but lack the time or resources to maintain them. Instead of letting them fade away, why not sell them to someone who wants to continue the work? Here is how it works: - Sellers list theis GitHub repos in the platform - Buyers purchase repos - Buyers automatically added as collaborators and can fork the repo Check it out here: https://gittrader.com https://ift.tt/dKkTi7g February 3, 2025 at 04:37AM

Show HN: Random Art Generator in Haskell https://ift.tt/XBM0Rhm

Show HN: Random Art Generator in Haskell https://ift.tt/wNne1zG February 3, 2025 at 12:41AM

Show HN: Modest – musical harmony library for Lua https://ift.tt/Qx4hTMW

Show HN: Modest – musical harmony library for Lua This is a project I've been building in my spare time over the past few months. It's a library that provides methods for working with musical harmony ‒ intervals, notes, chords. For example, it can parse almost any chord symbol (Fmaj7, CminMaj9, etc) and turn it into notes, or it can identify a chord from a given set of notes. I started this project with the idea of using formal grammar to parse chord symbols. I wanted to use it instead of a hand-written parser, which is the common approach among similar libraries. Lua caught my attention because of Lpeg, a Parsing Expression Grammar library that is both fast and easy to use. An additional motivation for using Lua was the lack of comparable libraries for it, even though the language is commonly used in audio programming. However, despite being a Lua library, the project itself is written in Fennel — a "lispy" language that transpiles to Lua. Fennel has features that make writing code for the Lua platform much more pleasant: a concise syntax, macros, and destructuring — a feature Lua sorely lacks! In the process, I definitely learned a lot about music theory, although my new knowledge is quite one-sided. By working on this library, I know a thing or two about types and structure of chords, but I learned almost nothing about their composition and transformation. Perhaps these will be the directions I explore next in the project. https://ift.tt/SkgyJ4t February 2, 2025 at 02:32PM

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/S4aLKpV

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/teSX9n8 February 2, 2025 at 11:14AM

Show HN: ESP32 RC Cars https://ift.tt/vmJysuj

Show HN: ESP32 RC Cars This is a projected I started that blends both the fun of playing a split screen multiplayer driving game and controlling real rc cars. The cars can also be controlled via bluetooth gamepads and is meant to be easily hackable. https://ift.tt/vxCEMU4 February 1, 2025 at 10:51PM

Show HN: I hacked LLMs to work like scikit-learn https://ift.tt/MbmsRY7

Show HN: I hacked LLMs to work like scikit-learn Working with LLMs in existing pipelines can often be bloated, complex, and slow. That's why I created FlashLearn , a streamlined library that mirrors the user experience of scikit-learn. It follows a pipeline-like structure allowing you to "fit" (learn) skills from sample data or instructions, and "predict" (apply) these skills to new data, returning structured results. High-Level Concept Flow: Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps Installation: pip install flashlearn Learning a New "Skill" from Sample Data Just like a fit/predict pattern in scikit-learn, you can quickly "learn" a custom skill from minimal (or no!) data. Here's an example where we create a skill to evaluate the likelihood of purchasing a product based on user comments: from flashlearn.skills.learn_skill import LearnSkill from flashlearn.client import OpenAI # Instantiate your pipeline "estimator" or "transformer", similar to a scikit-learn model learner = LearnSkill(model_name="gpt-4o-mini", client=OpenAI()) data = [ {"comment_text": "I love this product, it's everything I wanted!"}, {"comment_text": "Not impressed... wouldn't consider buying this."}, # ... ] # Provide instructions and sample data for the new skill skill = learner.learn_skill( data, task=( "Evaluate how likely the user is to buy my product based on the sentiment in their comment, " "return an integer 1-100 on key 'likely_to_buy', " "and a short explanation on key 'reason'." ), ) # Save skill to use in pipelines skill.save("evaluate_buy_comments_skill.json") Input Is a List of Dictionaries Simply wrap each record into a dictionary, much like feature dictionaries in typical ML workflows: user_inputs = [ {"comment_text": "I love this product, it's everything I wanted!"}, {"comment_text": "Not impressed... wouldn't consider buying this."}, # ... ] Run in 3 Lines of Code - Concurrency Built-in up to 1000 calls/min # Suppose we previously saved a learned skill to "evaluate_buy_comments_skill.json". skill = GeneralSkill.load_skill("evaluate_buy_comments_skill.json") tasks = skill.create_tasks(user_inputs) results = skill.run_tasks_in_parallel(tasks) print(results) Get Structured Results Here's an example of structured outputs mapped to indexes of your original list: { "0": { "likely_to_buy": 90, "reason": "Comment shows strong enthusiasm and positive sentiment." }, "1": { "likely_to_buy": 25, "reason": "Expressed disappointment and reluctance to purchase." } } Pass on to the Next Steps You can use each record’s output for downstream tasks such as storing results in a database or filtering high-likelihood leads: # Suppose 'flash_results' is the dictionary with structured LLM outputs for idx, result in flash_results.items(): desired_score = result["likely_to_buy"] reason_text = result["reason"] # Now do something with the score and reason, e.g., store in DB or pass to next step print(f"Comment #{idx} => Score: {desired_score}, Reason: {reason_text}") https://ift.tt/UyoDaKf February 1, 2025 at 08:39PM

Show HN: Val Town Projects https://ift.tt/Wvi9zXw

Show HN: Val Town Projects Hello! We at Val Town have spent the last couple months redesigning our product around a new core primitive: Val Town Projects. Why: Our prior core primitive, a "val", was too small. A val is just a single hosted JavaScript file. Users kept bumping up against rough edges managing lots of disparate vals. What: A Val Town Project is a group of vals, files, and folders, versioned collectively. They support branches, forks, and merges. How: We made the sacrilegious decision to not build on git. We instead built a simplified system that works directly in our postgres database. Our dream is that Val Town Projects will unlock a new kind of collaboration, and we hope you all make amazing things with it! https://ift.tt/lB8sChr January 31, 2025 at 10:55PM