7 Free AI Tools that You Have to Know as a Developer

Paulina Lewandowska

07 Feb 2023
7 Free AI Tools that You Have to Know as a Developer

Introduction

There has never been a greater need for skilled developers than there is now, as the AI sector continues to grow. It can be difficult to decide which AI development tools are the most helpful given the variety of tools available. In this post, we'll examine 7 of the most crucial tools for developing AI, guaranteeing that you have the resources necessary to generate creative and useful solutions. From computer vision tools to deep learning libraries, we'll emphasize what makes each tool unique and how it might help your development process. These tools will assist you in achieving your objectives more quickly and effectively, whether you are an experienced AI developer or just getting started.

TensorFlow

A well-liked AI development tool among programmers, academics, and data scientists is called TensorFlow. It is favored for a variety of AI applications because to its adaptable architecture and quick computing capabilities. Python, C++, and JavaScript are just a few of the many programming languages that TensorFlow supports, making it useful for developers of all skill levels. For anyone interested in entering the field of AI development, its extensive documentation, vast community, and broad use in both industry and academics make it a great resource. 

Category: Free

Scikit-learn

The library enables developers to quickly interact with data and create machine learning models by offering seamless connectivity with other well-known Python libraries like NumPy and pandas. Additionally, it provides a user-friendly interface that enables developers with little to no machine learning knowledge to get started and create their models right away. 

Category: Free

Keras

You can use TensorFlow, CNTK, or Theano's power with Keras to develop unique and successful deep learning models. It's like having a sophisticated blueprint at your disposal, enabling you to quickly build cutting-edge neural networks. You may create and experiment with a variety of network designs thanks to the intuitive API, which makes the challenging process of training and assessing models simpler. Along with practical tools for visualizing and saving your models, Keras offers a multitude of pre-processing methods for diverse data formats. Keras provides the flexibility and functionality you need to realize your deep learning vision, regardless of your level of AI development experience.

Category: Free

OpenCV 

OpenCV is a complete computer vision library with a huge selection of image and video processing techniques. It is well suited for usage in a range of industries, including robotics, security, and entertainment since it is highly tuned for real-time computer vision applications. Because the library is open-source, developers can easily adapt the algorithms to meet their own requirements and even contribute to the library's development.

Category: Free

NLTK

NLTK offers a huge variety of pre-processed corpora and lexical resources in addition to a user-friendly interface, making it easier for developers to integrate these resources into projects and saving them time and effort. Tools for complex NLP tasks, such as part-of-speech tagging, parsing, semantic analysis, and coreference resolution, are also available in the collection. The NLTK library is a useful tool for developers who want to create robust and effective NLP applications because of its open-source nature and dedication to continual growth. 

Category: Free

PyTorch

The user-friendly, adaptable, and highly modular design of PyTorch makes it simple for developers to create and test out complex models. Additionally, it works nicely with other well-known Python libraries like NumPy, pandas, and Matplotlib, enabling programmers to deal with data and complete visualization jobs with ease. PyTorch is ideal for use in a variety of practical applications, including computer vision, natural language processing, and reinforcement learning. It is also performance-optimized. PyTorch is a promising and potent tool for developers working in the field of AI and machine learning thanks to its expanding community and backing from industry heavyweights.

Category: Free

Watson Studio

Watson Studio offers data scientists a complete platform for creating and scaling up AI models. Data connections, notebooks, and model builders are just a few of the many tools available for data preparation, modeling, and deployment. For data scientists wishing to deploy their models in production, Watson Studio also connects with other IBM Cloud services like Watson Machine Learning and Watson Knowledge Catalog. Additionally, the platform offers a flourishing user community where members can work together, share resources, and get access to a plethora of instructional materials to aid them in their endeavors to develop AI. Data scientists may expedite their work and produce effective AI solutions with the help of the powerful and user-friendly Watson Studio platform. 

Category: Both freemium and premium plans.

Conclusion

In conclusion, the demand for talented engineers is greater than ever since as the adoption of AI is increasing and becoming more visible in many industries. In this quickly changing area, the capacity to develop novel and practical solutions is crucial. Having access to the appropriate tools is essential for success, regardless of your level of expertise in artificial intelligence (AI). You'll be able to streamline your work and reach your full potential as a cutting-edge AI developer with the variety of resources at your disposal. So get ready to enter into the fascinating realm of AI and start developing solutions that have the potential to transform it.

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Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.

source: https://www.canva.com/design/DAFDTNKsIJs/8Ky9EoJJI7p98qKLIu2XNw/view#7

Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics

Summary

The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.

What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Karolina

18 Mar 2024
What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Enter Berachain: a high-performance, EVM-compatible blockchain that is set to redefine the landscape of decentralized applications (dApps) and blockchain services. Built on the innovative Proof-of-Liquidity consensus and leveraging the robust Polaris framework alongside the CometBFT consensus engine, Berachain is poised to offer an unprecedented blend of efficiency, security, and user-centric benefits. Let's dive into what makes it a groundbreaking development in the blockchain ecosystem.

What is Berachain?

Overview

Berachain is an EVM-compatible Layer 1 (L1) blockchain that stands out through its adoption of the Proof-of-Liquidity (PoL) consensus mechanism. Designed to address the critical challenges faced by decentralized networks. It introduces a cutting-edge approach to blockchain governance and operations.

Key Features

  • High-performance Capabilities. Berachain is engineered for speed and scalability, catering to the growing demand for efficient blockchain solutions.
  • EVM Compatibility. It supports all Ethereum tooling, operations, and smart contract languages, making it a seamless transition for developers and projects from the Ethereum ecosystem.
  • Proof-of-Liquidity.This novel consensus mechanism focuses on building liquidity, decentralizing stake, and aligning the interests of validators and protocol developers.

MUST READ: Docs

EVM-Compatible vs EVM-Equivalent

EVM-Compatible

EVM compatibility means a blockchain can interact with Ethereum's ecosystem to some extent. It can interact supporting its smart contracts and tools but not replicating the entire EVM environment.

EVM-Equivalent

An EVM-equivalent blockchain, on the other hand, aims to fully replicate Ethereum's environment. It ensures complete compatibility and a smooth transition for developers and users alike.

Berachain's Position

Berachain can be considered an "EVM-equivalent-plus" blockchain. It supports all Ethereum operations, tooling, and additional functionalities that optimize for its unique Proof-of-Liquidity and abstracted use cases.

Berachain Modular First Approach

At the heart of Berachain's development philosophy is the Polaris EVM framework. It's a testament to the blockchain's commitment to modularity and flexibility. This approach allows for the easy separation of the EVM runtime layer, ensuring that Berachain can adapt and evolve without compromising on performance or security.

Proof Of Liquidity Overview

High-Level Model Objectives

  • Systemically Build Liquidity. By enhancing trading efficiency, price stability, and network growth, Berachain aims to foster a thriving ecosystem of decentralized applications.
  • Solve Stake Centralization. The PoL consensus works to distribute stake more evenly across the network, preventing monopolization and ensuring a decentralized, secure blockchain.
  • Align Protocols and Validators. Berachain encourages a symbiotic relationship between validators and the broader protocol ecosystem.

Proof-of-Liquidity vs Proof-of-Stake

Unlike traditional Proof of Stake (PoS), which often leads to stake centralization and reduced liquidity, Proof of Liquidity (PoL) introduces mechanisms to incentivize liquidity provision and ensure a fairer, more decentralized network. Berachain separates the governance token (BGT) from the chain's gas token (BERA) and incentives liquidity through BEX pools. Berachain's PoL aims to overcome the limitations of PoS, fostering a more secure and user-centric blockchain.

Berachain EVM and Modular Approach

Polaris EVM

Polaris EVM is the cornerstone of Berachain's EVM compatibility, offering developers an enhanced environment for smart contract execution that includes stateful precompiles and custom modules. This framework ensures that Berachain not only meets but exceeds the capabilities of the traditional Ethereum Virtual Machine.

CometBFT

The CometBFT consensus engine underpins Berachain's network, providing a secure and efficient mechanism for transaction verification and block production. By leveraging the principles of Byzantine fault tolerance (BFT), CometBFT ensures the integrity and resilience of the Berachain blockchain.

Conclusion

Berachain represents a significant leap forward in blockchain technology, combining the best of Ethereum's ecosystem with innovative consensus mechanisms and a modular development approach. As the blockchain landscape continues to evolve, Berachain stands out as a promising platform for developers, users, and validators alike, offering a scalable, efficient, and inclusive environment for decentralized applications and services.

Resources

For those interested in exploring further, a wealth of resources is available, including the Berachain documentation, GitHub repository, and community forums. It offers a compelling vision for the future of blockchain technology, marked by efficiency, security, and community-driven innovation.

FAQ

How is Berachain different?

  • It integrates Proof-of-Liquidity to address stake centralization and enhance liquidity, setting it apart from other blockchains.

Is Berachain EVM-compatible?

  • Yes, it supports Ethereum's tooling and smart contract languages, facilitating easy migration of dApps.

Can it handle high transaction volumes?

  • Yes, thanks to the Polaris framework and CometBFT consensus engine, it's built for scalability and high throughput.