Crypto-Randy

📢 DYNEX STRATEGIC PLAN 2024 & UPDATE Such a productive couple of days! As the announcements and updates would be exceeding the scope of simple posts, we decided to host a press conference / AMA with the core leadership team instead - our agency is coordinating, details will follow $DNX #Dynex #DePIN #PoUW #announcement #strategy2024

📢 Computing on Dynex just became even more easy ⚡️🔥🔥 Now, any optimization problem can be elegantly described within a standard Python function. With just one line, it can be seamlessly converted and computed on the Dynex platform: github.com/dynexcoin/autoqub… $DNX #Dynex #PoUW #DePIN @fujitsulabs @ThePSF

Big Kudos to @SamGR55 en.wikipedia.org/wiki/Intege… $DNX #Dynex #DePIN #PoUW #record

*** MALLOBLESS MINING (BETA) ROLLING OUT *** We can announce that the following pools are now supporting DynexSolve 2.3.5 (beta) in production: - Deepminerz - Neuropool - Minenow Commercial miners will announce compatible versions when available separately. $DNX #Dynex #PoUW #Mining

Always nice to receive such customer feedback. Want to experience the neuromorphic power of the Dynex SDK (beta) as well? Apply by sending ‚subscribe‘ to [email protected] „Dear Dynex Team, I am Wei He from Harmony Investment Group, Hong Kong (hkharmony.io). We are immensely grateful for the opportunity to use the Dynex SDK trial. Currently, we are utilizing Dynex for the training and testing of large-scale language models, with the hope that the impressive GPU capabilities and efficient computations provided by Dynex will further enhance our research and development efficiency and model performance in the field of Natural Language Processing (NLP). Our admiration for Dynex stems from two key aspects: - Nearly boundless GPU capabilities: The nearly limitless GPU capabilities provided by Dynex allows us to easily cope with the training needs of large models, greatly improving our work efficiency. - Efficiency: The use of Dynex SDK is extremely simple and efficient, enabling our developers to focus more on the development and optimization of models, rather than wasting time on environment configuration and hardware issues.“ $DNX #Dynex #PoUW #MachineLearning

Dynex Machine Learning Released ++ New Miner Version Released ++ Pools Upgraded ++ Ceasing Support for Old Miner Versions ++ Dynex SDK Today marks a major milestone in the development of Dynex. We are thrilled to announce that the Dynex Platform now supports machine learning tasks (DynexSolveML). Thousands of hours went into our massive platform upgrade. In a coordinated, joint effort over the last weeks, our science, research & development team was working tirelessly on making the Dynex SDK (beta) compatible with existing Quantum computing frameworks - making it possible to run originally developed algorithms for Quantum machines on our platform by simply using Python code. Without the limitations of scaling, error-correction and accessibility. The miner developers didn't spare any effort to incorporate and optimise the Dynex Ising/QUBO sampler into their miners and our pool operators upgraded their infrastructure to support the new miner versions. A big thank you at this point to everyone who contributed! $DNX #Dynex #Quantumcomputing #MachineLearning #ArtificialIntelligence #PoUW

Breast Cancer Prediction on the Dynex Platform🧬 By applying a quantum feature selection algorithm on deep learning models, the feature space is being condensed and better model accuracy achieved. Check out our latest Dynex SDK example: github.com/dynexcoin/DynexSD… $DNX #Dynex #PoUW #ArtificialIntelligence #QuantumComputing

Dynex & Python 🖥 As we are currently busy working on "Dynex Machine Learning", a full integration to compute on the Dynex Platform using Python is part of the release. The following example shows how to use Dynex to solve a Binary Quadratic Model. The Dynex Python SDK allows users to interact with the Dynex Annealer through Python programs. This python program introduces a Quadratic Model (QM) and submits it to the annealer to find the minimum energy value for that model. You will find these functions also being part of Machine Learning. $DNX #Dynex #machinelearning #artificalintelligence

The Dynex Annealer is at the core of #Dynex #Machine #Learning. It is utilising Dynex' decentralised network of GPUs. Similar to #quantum #annealing, it advances state-of-the-art machine learning classifiers through improvement in speed and performance by offloading various aspects of training to the Dynex platform. $DNX #pouw