Ethereum Reviews

Jupyter Reviews

Ethereum mentions (161)

• Navigating the Path to Blockchain Scalability: Emerging Solutions and Innovations

  This post takes a deep dive into the evolving realm of blockchain scalability. It explores both layer-one and layer-two solutions, next-generation innovations, as well as emerging techniques that enhance transaction speed and efficiency. We cover topics ranging from sharding and consensus algorithm improvements to state channels and rollups. In addition, this post provides background context, practical... - Source: dev.to / about 2 months ago

• Unlocking Synergy: The Intersection of Blockchain and AI

  Blockchain is essentially a decentralized digital ledger which records transactions on multiple computers so that the record cannot be altered retroactively. Originally popularized by cryptocurrencies like Bitcoin and Ethereum, blockchain has evolved into a technology that ensures data integrity, transparency, and enhanced security. For those new to this topic, a deep dive on the basics can be found at what is... - Source: dev.to / about 2 months ago

• Arbitrum Sequencer: Transforming Ethereum's Capabilities

  As the DeFi and NFT ecosystems expand, so does the adoption of Layer 2 solutions. The Arbitrum sequencer is expected to see broader adoption, with more dApps migrating to its scalable network. Works like those by Ethereum illustrate the growing enthusiasm for such technologies. - Source: dev.to / about 2 months ago

• Exploring Decentraland: Cyberwar Simulations Transforming Cybersecurity Training

  This post explores how Decentraland—a decentralized virtual world built on the Ethereum blockchain—is revolutionizing cybersecurity training through immersive cyberwar simulations. We discuss the background and context of blockchain-powered virtual environments, detail the core simulation concepts like offensive "red teams" and defensive "blue teams," provide real-world applications and use cases, examine... - Source: dev.to / 2 months ago

• The Intersection of Trump NFTs and Open Source Technology: Bridging Politics and Digital Innovation

  The NFT arena has exploded in popularity since its debut, providing a platform for artists and innovators to offer tangible proof of digital authenticity. NFTs allow the uniqueness of each digital asset to be verified on a blockchain, making them highly sought after by collectors and enthusiasts alike. The recent entry of Trump-themed NFTs into this space marks another milestone as it taps into a politically... - Source: dev.to / 3 months ago

Jupyter mentions (216)

• The 3 Best Python Frameworks To Build UIs for AI Apps

  Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 3 months ago

• LangChain: From Chains to Threads

  LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 4 months ago

• Applied Artificial Intelligence & its role in an AGI World

  Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago

• Jupyter Notebook for Java

  Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago

• JIRA Analytics with Pandas

  One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / about 1 year ago