Show HN: I Built a Platform to Buy and Sell GitHub Repositories https://ift.tt/Ys3AjQ0

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/nrQkBNf February 3, 2025 at 06:07AM

Show HN: Random Art Generator in Haskell https://ift.tt/W81oZBJ

Show HN: Random Art Generator in Haskell https://ift.tt/vecXmun February 3, 2025 at 02:11AM

Show HN: Groundhog AI Spring API https://ift.tt/XOwrqgG

Show HN: Groundhog AI Spring API For anyone building weather-related AI apps, I am releasing an exciting iteration on last year’s model. My Groundhog API is trained on 130 years of data and makes use of 82 separate data sources. Similar to DeepSeek, it is completely open source and free to use. The primary use case is to make inferences about whether spring will come early or not, using a Mixture of Exports (MoE) approach, but surely others can be found if you are creative. Other use cases: - All predicting groundhogs - Where they all live - Whether they are “real” groundhogs or imposters Excited to see what people do with it! https://ift.tt/G8NktyU February 2, 2025 at 10:59PM

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/krvEjK6

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/neNofSE February 2, 2025 at 12:44PM

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

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/l1dnjia February 2, 2025 at 12:21AM

Show HN: I hacked LLMs to work like scikit-learn https://ift.tt/V32PtTe

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/cKAPhvk February 1, 2025 at 10:09PM

Show HN: I re-designed interface for HN https://ift.tt/e0svRUk

Show HN: I re-designed interface for HN Any suggestions are appreciated, stack utilizes Gluestack UI ,Expo, React Native, and Cloudflare Pages. There is a known bug via touch scroll ability on Android, external keyboard's spacebar or mouse works correctly though, currently. If you know about a solution, let me know. Please note, this is just a prototype, it still has a lot of features to be included. I'd like to learn more about how people use HN and how Hacked could help, where other HN clients failed. https://ift.tt/6YxKdCw February 1, 2025 at 10:41PM