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Products that do what A batched, concurrent wrapper for OpenAI gym library does

Hello HN, new user here, so please let me know if I break some rules. Currently I've been working on training reinforcement learning agents, and OpenAI gym, while is great, runs only one agent at a time. Hence I decided to extend it. I built a wrapper around OpenAI gym, such that it now runs several environments concurrently. All while (mostly) having the same call signature as OpenAI gym. And it is published to PyPI for anyone interested. For more details, please visit: https://github.com/Chimpan-Z/agymc Feedback really appreciated! Have a good day everyone!

  1. 1

    Open source interface to reinforcement learning tasks

    2016

  2. 2

    Platform for measuring and training AI agents

    2016

  3. 3MG
  4. 42C

    Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…

    Feb 2026 · github.com

  5. 5AO

    Hey HN, My workflow for any complex queries is to ask it in multiple AI chats (Gemini, Claude, o3,..) in parallel and then continue the conversation with the chat response that I found the most useful. I built a simple open source app that queries 10+ AI models at once and summarizes their answers with a selected combiner AI model. There's a GIF in the github repo that shows it in action. You can try it on your local machine: https://github.com/Nexarithm/multi_model_chat If you are interested, I also made a detailed blog post on technical details, feature of the personal…

    2025 · github.com

  6. 6WB

    Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…

    Mar 2026 · enlidea.com

  7. 7

    An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.

    22d ago · pinglin.tw

  8. 8RA

    Hi there, looking for feedback on my new project "Featherless.AI" The idea is to allow users to run all the models on hugging face instantly. Via the OpenAI API compatible endpoint. Why? Because its a real chore to download models and spin up GPUs, especially if you want to test multiple models. Not to mention GPUs cost multiple dollars an hour to rent. And if we want more people to use open source AI, we got to make it easier for them to try and play with all of them. So what if instead of spinning up dedicated GPUs per model (which is what every provider is doing) We can startup a LLM…

    2024 · featherless.ai

  9. 9IM

    I got tired of posting on r/fitness and getting unclear feedback, so I built an app that analyzes physique photos with AI. It started as a weekend project after I couldn't get good advice on my lifting progress. Upload a few pics and the AI tells you: Where you rank compared to others (using a gaming-style tier system) Scores for each muscle group with actual numbers Body proportions and balance issues Body fat estimates and body type I also added some fun stuff like matching you to a "warrior type" based on your build (turns out I'm built like a Viking, not the Spartan I was hoping…

    2025 · aestheticrank.com

  10. 10HG

    Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at…

    2025 · youtube.com

  11. 11PS

    I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.

    28d ago · pacslate.com

  12. 12SR

    Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…

    Jul 2026 · shikigami.dev

  13. 13IA
  14. 14GA

    Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the…

    2023 · github.com

  15. 15

    We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early

    9d ago · twing.dev

  16. 16IC

    I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.

    2024 · bell-boy.github.io

  17. 17OS

    Hi HN, We're a small team building AI tutors out of India, and as you might guess, this means we spend a ton of time writing, testing, and refining prompts for LLMs. When we started out, we were using the OpenAI playground but things became tedious when we wanted to compare responses from different models. We tried a bunch of other playgrounds but found them lacking in some features so we built our own. Quick Links: Github: https://github.com/supernova-app/ai-playground Hosted demo: http://playground.getsupernova.ai Demo video:…

    2025 · playground.getsupernova.ai

  18. 18IB

    I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…

    2025 · codii.dev

  19. 19AP

    I've been working on this internal project initially both to learn more Vibe-Coding but also to help our teams and projects to use AI more efficiently. As more people used it, it grew to support multiple teams/projects to analyze their Claude Code conversation and optimize them over time (understanding how to write better conversation with Claude Code and share knowledge between them) With time we added support for multiple Claude account management and monitor usage/rate limit. This is a simple project but has proved to be quite useful for our company. We have reached 5000+…

    2025 · github.com

  20. 20IM

    I’m Hayden, a 13-year-old developer based in Australia, and I’ve built a CoT logical thinking and reasoning AI model similar to OpenAI o1. It's powered by open source small models like Llama 3.1 and 3.2 and I would love for you to try it and share your feedback with me. You can try it here: https://ai.pixelverse.tech/app/cortexchat I built it just for fun and launched it a day after the o1 release. It's not perfect yet but its still amazing to see how a detailed prompt can have such a difference on the quality of the LLM response! Please let me know any feedback or…

    2024 · ai.pixelverse.tech

  21. 21IB

    Hey HN! I just released a suite of AI models for deployment on UAV and other "overhead" devices to provide some understanding of the world below. The objective is to empower all sorts of open-source use cases around search and rescue, wildfire prevention, ground risk mitigation for flight over populated areas etc... The neural networks are trained for a bunch of different devices from big GPUs to tiny edge AI cameras like the Luxonis OAK, with some optimised ones for Nvidia TensorRT and other cool bits and pieces too. The main release package also includes some boilerplate code for running…

    2023 · github.com

  22. 22IB

    Hey HN. I built an AI agent harness over the past few months and I'm open sourcing it today. Some context on why. I've been building with Claude Code daily using this harness. It orchestrates multiple AI agents as a team, with a dashboard, chat, kanban board, the works. I used it to build a full SaaS product (MyUpMonitor, https://myupmonitor.com) in about 24 hours of focused coding. Then yesterday Anthropic announced Mythos and decided to keep it behind closed doors. Meanwhile I'm paying for Claude and I can't access their best model. I don't think that is nice at all... So I'm…

    Apr 2026 · github.com

  23. 23MD

    We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…

    2025 · github.com

  24. 24YA

    I was randomly browsing claude codes ralph wiggum plugin[1] and was surprised to see my toy project referenced. Since it appears people are getting some value out of it, thought I'd share it here... Full disclosure, this repo itself was built with a primitive ralph wiggum loop so expect AI slop. It works though. ¯\_(ツ)_/¯ https://mikeyobrien.github.io/ralph-orchestrator/ [1] https://github.com/anthropics/claude-code/tree/main/plugins/...

    Dec 2025 · github.com

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