#OpenLedger is the "AI version of Ethereum + GitHub", making AI open-source, credible, and traceable, and everyone can participate and benefit. Recently, after reading @OpenledgerHQ's "Proof of Attribution" white paper, I feel more and more that in the second half of #AI development, the issue of confirming rights in the whole process of #AI contribution will be the biggest pain point of traditional AI. #OpenLedger combines the current popular #AI + #Blockchain program to effectively solve the above pain points, and according to the @MessariCrypto research report, the #AI track will reach a market value of more than $2 trillion by 2030, of which the potential is self-evident, today we will analyze the #AI new dark horse #OpenLedger and the early 3 free participation opportunities.

Currently, #AI is mostly controlled by large companies (OpenAI, Google, Meta). How the models are trained, whose data is used, and how the profits are distributed—it's all a black box operation. Ordinary people cannot participate or benefit from it. However, #OpenLedger utilizes Proof of Attribution technology to ensure that content generated by #AI (such as images, articles, music) can be traced back to its source, and guarantees that all data providers who contribute to building professional AI models will receive recognition or incentives. • How the model is trained → Publicly accessible • Whose data is used → There are records and evidence • Who contributed data → Can all be tracked and rewarded This is a "counter-monopoly" underlying architecture that directly addresses the core pain points of the current unfairness in #AI.

#OpenLedger (@OpenledgerHQ) is a decentralized, blockchain-based artificial intelligence platform aimed at achieving transparency, community governance, and open access in AI. Unlike traditional AI models controlled by large tech companies, #OpenLedger allows the community to train, validate, and create specialized AI models. This builds a fairer system where individuals providing data and models receive the recognition and rewards they deserve.

#OpenLedger architecture consists of 5 core layers: 🔵 Consensus Layer (based on EigenLayer secure sharing) 🔵 Model Execution Layer (OpenLoRA) 🔵 Data Rights Layer (Datanet + Proof of Attribution) 🔵 Task Execution Layer (task validation and incentives) 🔵 User Participation Layer (plugin-based, low-threshold participation entry) There are too many technical aspects to elaborate on here. Interested parties can check the pinned white paper on @OpenledgerHQ's homepage. For us ordinary users, we might be more concerned about the methods in the user participation layer, with the main strategies being (detailed guides follow): • Chrome Plugin: can be used as a data collector (contributing prompts, web data) • Local Runner: run OpenLoRA nodes to earn points, supporting CPU and GPU • Contribution-based Interaction: upload datasets, train models, validate others' results, forming an #AI crowdsourcing community.

#OpenLedger Core Advantages: 1️⃣OpenLoRA: Run thousands of models with one GPU The most hardcore product of #OpenLedger is OpenLoRA, which is an infrastructure for model deployment. It might be a bit difficult to understand, so let me give you an example. 👉 For instance: You currently have an LLM model and have fine-tuned it with LoRA to create 1000 "skill-based plugins" (like lawyer, doctor, fitness coach, teacher, etc.). If you were to run these models in a traditional way, you would need to equip 1000 GPUs, which would be outrageously expensive. #OpenLoRA's approach: • Only load one base model (like Mistral) • Dynamically load LoRA plugins only when needed • No memory overload, millisecond switching, and even faster speeds • Save over 90% of server costs 📌 This technology is simply the best #AI privatization boon for small and medium-sized enterprises and individuals. It allows for one LoRA per person, enabling multi-user Copilot. Additionally, it creates a strong complementary cooperation relationship with large model platforms (like HuggingFace, Bittensor-type projects). So, #OpenLoRA is not just a concept; it is a real product that addresses the cost and scalability issues of AI infrastructure, possessing strong commercialization capabilities, especially suitable for localized private deployment for small and medium-sized enterprises or individuals.