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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Aethir Tokenomics – Case Study

Kajetan Olas

22 Nov 2024
Aethir Tokenomics – Case Study

Authors of the contents are not affiliated to the reviewed project in any way and none of the information presented should be taken as financial advice.

In this article we analyze tokenomics of Aethir - a project providing on-demand cloud compute resources for the AI, Gaming, and virtualized compute sectors.
Aethir aims to aggregate enterprise-grade GPUs from multiple providers into a DePIN (Decentralized Physical Infrastructure Network). Its competitive edge comes from utlizing the GPUs for very specific use-cases, such as low-latency rendering for online games.
Due to decentralized nature of its infrastructure Aethir can meet the demands of online-gaming in any region. This is especially important for some gamer-abundant regions in Asia with underdeveloped cloud infrastructure that causes high latency ("lags").
We will analyze Aethir's tokenomics, give our opinion on what was done well, and provide specific recommendations on how to improve it.

Evaluation Summary

Aethir Tokenomics Structure

The total supply of ATH tokens is capped at 42 billion ATH. This fixed cap provides a predictable supply environment, and the complete emissions schedule is listed here. As of November 2024 there are approximately 5.2 Billion ATH in circulation. In a year from now (November 2025), the circulating supply will almost triple, and will amount to approximately 15 Billion ATH. By November 2028, today's circulating supply will be diluted by around 86%.

From an investor standpoint the rational decision would be to stake their tokens and hope for rewards that will balance the inflation. Currently the estimated APR for 3-year staking is 195% and for 4-year staking APR is 261%. The rewards are paid out weekly. Furthermore, stakers can expect to get additional rewards from partnered AI projects.

Staking Incentives

Rewards are calculated based on the staking duration and staked amount. These factors are equally important and they linearly influence weekly rewards. This means that someone who stakes 100 ATH for 2 weeks will have the same weekly rewards as someone who stakes 200 ATH for 1 week. This mechanism greatly emphasizes long-term holding. That's because holding a token makes sense only if you go for long-term staking. E.g. a whale staking $200k with 1 week lockup. will have the same weekly rewards as person staking $1k with 4 year lockup. Furthermore the ATH staking rewards are fixed and divided among stakers. Therefore Increase of user base is likely to come with decrease in rewards.
We believe the main weak-point of Aethirs staking is the lack of equivalency between rewards paid out to the users and value generated for the protocol as a result of staking.

Token Distribution

The token distribution of $ATH is well designed and comes with long vesting time-frames. 18-month cliff and 36-moths subsequent linear vesting is applied to team's allocation. This is higher than industry standard and is a sign of long-term commitment.

  • Checkers and Compute Providers: 50%
  • Ecosystem: 15%
  • Team: 12.5%
  • Investors: 11.5%
  • Airdrop: 6%
  • Advisors: 5%

Aethir's airdrop is divided into 3 phases to ensure that only loyal users get rewarded. This mechanism is very-well thought and we rate it highly. It fosters high community engagement within the first months of the project and sets the ground for potentially giving more-control to the DAO.

Governance and Community-Led Development

Aethir’s governance model promotes community-led decision-making in a very practical way. Instead of rushing with creation of a DAO for PR and marketing purposes Aethir is trying to make it the right way. They support projects building on their infrastructure and regularly share updates with their community in the most professional manner.

We believe Aethir would benefit from implementing reputation boosted voting. An example of such system is described here. The core assumption is to abandon the simplistic: 1 token = 1 vote and go towards: Votes = tokens * reputation_based_multiplication_factor.

In the attached example, reputation_based_multiplication_factor rises exponentially with the number of standard deviations above norm, with regard to user's rating. For compute compute providers at Aethir, user's rating could be replaced by provider's uptime.

Perspectives for the future

While it's important to analyze aspects such as supply-side tokenomics, or governance, we must keep in mind that 95% of project's success depends on demand-side. In this regard the outlook for Aethir may be very bright. The project declares $36M annual reccuring revenue. Revenue like this is very rare in the web3 space. Many projects are not able to generate any revenue after succesfull ICO event, due to lack fo product-market-fit.

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.

Quadratic Voting in Web3

Kajetan Olas

04 Dec 2024
Quadratic Voting in Web3

Decentralized systems are reshaping how we interact, conduct transactions, and govern online communities. As Web3 continues to advance, the necessity for effective and fair voting mechanisms becomes apparent. Traditional voting systems, such as the one-token-one-vote model, often fall short in capturing the intensity of individual preferences, which can result in centralization. Quadratic Voting (QV) addresses this challenge by enabling individuals to express not only their choices but also the strength of their preferences.

In QV, voters are allocated a budget of credits that they can spend to cast votes on various issues. The cost of casting multiple votes on a single issue increases quadratically, meaning that each additional vote costs more than the last. This system allows for a more precise expression of preferences, as individuals can invest more heavily in issues they care deeply about while conserving credits on matters of lesser importance.

Understanding Quadratic Voting

Quadratic Voting (QV) is a voting system designed to capture not only the choices of individuals but also the strength of their preferences. In most DAO voting mechanisms, each person typically has one vote per token, which limits the ability to express how strongly they feel about a particular matter. Furthermore, QV limits the power of whales and founding team who typically have large token allocations. These problems are adressed by making the cost of each additional vote increase quadratically.

In QV, each voter is given a budget of credits or tokens that they can spend to cast votes on various issues. The key principle is that the cost to cast n votes on a single issue is proportional to the square of n. This quadratic cost function ensures that while voters can express stronger preferences, doing so requires a disproportionately higher expenditure of their voting credits. This mechanism discourages voters from concentrating all their influence on a single issue unless they feel very strongly about it. In the context of DAOs, it means that large holders will have a hard-time pushing through with a proposal if they'll try to do it on their own.

Practical Example

Consider a voter who has been allocated 25 voting credits to spend on several proposals. The voter has varying degrees of interest in three proposals: Proposal A, Proposal B, and Proposal C.

  • Proposal A: High interest.
  • Proposal B: Moderate interest.
  • Proposal C: Low interest.

The voter might allocate their credits as follows:

Proposal A:

  • Votes cast: 3
  • Cost: 9 delegated tokens

Proposal B:

  • Votes cast: 2
  • Cost: 4 delegated tokens

Proposal C:

  • Votes cast: 1
  • Cost: 1 delegated token

Total delegated tokens: 14
Remaining tokens: 11

With the remaining tokens, the voter can choose to allocate additional votes to the proposals based on their preferences or save for future proposals. If they feel particularly strong about Proposal A, they might decide to cast one more vote:

Additional vote on Proposal A:

  • New total votes: 4
  • New cost: 16 delegated tokens
  • Additional cost: 16−9 = 7 delegated tokens

Updated total delegated tokens: 14+7 = 21

Updated remaining tokens: 25−21 = 425 - 21 = 4

This additional vote on Proposal A costs 7 credits, significantly more than the previous vote, illustrating how the quadratic cost discourages excessive influence on a single issue without strong conviction.

Benefits of Implementing Quadratic Voting

Key Characteristics of the Quadratic Cost Function

  • Marginal Cost Increases Linearly: The marginal cost of each additional vote increases linearly. The cost difference between casting n and n−1 votes is 2n−1.
  • Total Cost Increases Quadratically: The total cost to cast multiple votes rises steeply, discouraging voters from concentrating too many votes on a single issue without significant reason.
  • Promotes Egalitarian Voting: Small voters are encouraged to participate, because relatively they have a much higher impact.

Advantages Over Traditional Voting Systems

Quadratic Voting offers several benefits compared to traditional one-person-one-vote systems:

  • Captures Preference Intensity: By allowing voters to express how strongly they feel about an issue, QV leads to outcomes that better reflect the collective welfare.
  • Reduces Majority Domination: The quadratic cost makes it costly for majority groups to overpower minority interests on every issue.
  • Encourages Honest Voting: Voters are incentivized to allocate votes in proportion to their true preferences, reducing manipulation.

By understanding the foundation of Quadratic Voting, stakeholders in Web3 communities can appreciate how this system supports more representative governance.

Conclusion

Quadratic voting is a novel voting system that may be used within DAOs to foster decentralization. The key idea is to make the cost of voting on a certain issue increase quadratically. The leading player that makes use of this mechanism is Optimism. If you're pondering about the design of your DAO, we highly recommend taking a look at their research on quadratic funding.

If you're looking to create a robust governance 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 that your DAO will stand out as a beacon of innovation and resilience in the long term.