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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Blockchain for Creators: Secure and Sustainable Infrastructure

Miłosz Mach

07 Nov 2025
Blockchain for Creators: Secure and Sustainable Infrastructure

In today’s digital creative space, where the lines between art and technology are constantly blurring, projects like MARMALADE mark the beginning of a new era - one where creators can protect their work and maintain ownership through blockchain technology.

For Nextrope, being part of MARMALADE goes far beyond implementing features like screenshot blocking or digital watermarking. It’s about building trust infrastructure - systems that empower creators to thrive in the digital world safely and sustainably.

A new kind of blockchain challenge

Cultural and educational projects come with a completely different set of challenges than typical DeFi systems. Here, the focus isn’t on returns or complex smart contracts - it’s on people: artists, illustrators, educators.

That’s why our biggest task was to design secure yet intuitive infrastructure - lightweight, energy-efficient, and accessible for non-technical users exploring Web3 for the first time.

“Our mission wasn’t to build another financial protocol. It was to create a layer of trust for digital creators.”
— Nextrope Team

Security that stays invisible

The best security is the kind you don’t notice.
Within MARMALADE, we focused on making creators' protection seamless:

  • Screenshot blocking safeguards artworks viewed in browsers.
  • Dynamic watermarking helps identify unauthorized copies.
  • Blockchain registry ensures every proof of ownership remains transparent and immutable

“Creators shouldn’t have to think about encryption or private keys - our job is to make security invisible.”

Sustainability by design

MARMALADE also answers a bigger question - how to innovate responsibly.
Nextrope’s infrastructure relies on low-emission blockchain networks and modular architecture that can easily be adapted for other creative or cultural initiatives.

This means the technology built here can support not only artists but also institutions, universities, and educators seeking to integrate blockchain in meaningful ways.

Beyond technology

For Nextrope, MARMALADE is more than a project — it’s proof that blockchain can empower culture and creators, not just finance. By building tools for digital artists, we’re helping them protect their creativity and discover how technology can amplify human expression.

Plasma blockchain. Architecture, Key Features & Why It Matters

Miłosz Mach

21 Oct 2025
Plasma blockchain. Architecture, Key Features & Why It Matters

What is Plasma?

Plasma is a Layer-1 blockchain built specifically for stablecoin infrastructure combining Bitcoin-level security with EVM compatibility and ultra-low fees for stablecoin transfers.

Why Plasma Blockchain Was Created?

Existing blockchains (Ethereum, L2s, etc.) weren’t originally designed around stablecoin payments at scale. As stablecoins grow, issues like congestion, gas cost, latency, and interoperability become constraints. Plasma addresses these by being purpose-built for stablecoin transfers, offering features not found elsewhere.

  • Zero-fee transfers (especially for USDT)
  • Custom gas tokens (separate from XPL, to reduce friction)
  • Trust-minimized Bitcoin bridge (to allow BTC collateral use)
  • Full EVM compatibility smart contracts can work with minimal modifications

Plasma’s Architecture & Core Mechanisms

EVM Compatibility + Smart Contracts

Developers familiar with Ethereum tooling (Solidity, Hardhat, etc.) can deploy contracts on Plasma with limited changes making it easy to port existing dApps or DeFi, similar to other EVM-compatible infrastructures discussed in the article „The Ultimate Web3 Backend Guide: Supercharge dApps with APIs".

Gas Model & Token Mechanism

Instead of forcing users always to hold XPL for gas, Plasma supports custom gas tokens. For stablecoin-native flows (e.g. USDT transfers), there is often zero fee usage, lowering UX friction.

Bitcoin Bridge & Collateral

Plasma supports a Bitcoin bridge that lets BTC become collateral inside smart contracts (like pBTC). This bridges the security of Bitcoin with DeFi use cases within Plasma.
This makes Plasma a “Bitcoin-secured blockchain for stablecoins".

Security & Finality

Plasma emphasizes finality and security, tuned to payment workloads. Its consensus and architecture aim for strong protection against reorgs and double spends while maintaining high throughput.
The network launched mainnet beta holding over $2B in stablecoin liquidity shortly after opening.

Plasma Blockchain vs Alternatives: What Makes It Stand Out?

FeaturePlasma (XPL)Other L1 / L2
Stablecoin native designusually second-class
Zero fees for stablecoin transfersrare, or subsidized
BTC bridge (collateral)only some chains
EVM compatibilityyes in many, but with trade-offs
High liquidity early✅ (>$2B TVL)many chains struggle to bootstrap

These distinctions make Plasma especially compelling for institutions, stablecoin issuers, and DeFi innovators looking for scalable, low-cost, secure payments infrastructure.

Use Cases: What You Can Build with Plasma Blockchain

  • Stablecoin native vaults / money markets
  • Payment rails & cross-border settlement
  • Treasury and cash management flows
  • Bridged BTC-backed stablecoin services
  • DeFi primitives (DEX, staking, yield aggregation) optimized for stablecoins

If you’re building any product reliant on stablecoin transfers or needing strong collateral backing from BTC, Plasma offers a compelling infrastructure foundation.

Get Started with Plasma Blockchain: Key Steps & Considerations

  1. Smart contract migration: assess if existing contracts can port with minimal changes.
  2. Gas token planning: decide whether to use USDT, separate gas tokens, or hybrid models.
  3. Security & audit: focus on bridge logic, reentrancy, oracle risks.
  4. Liquidity onboarding & market making: bootstrap stablecoin liquidity, incentives.
  5. Regulation & compliance: stablecoin issuance may attract legal scrutiny.
  6. Deploy MVP & scale: iterate fast, measure gas, slippage, UX, security.