About itinai.com Team
Our teams are a diverse group of talented individuals working remotely from different corners of the world. With members proficient in seven languages, we value and embrace diversity. However, what truly unites us is our shared passion for the language of modern technology. We come together to collaborate, innovate, and harness the power of cutting-edge technology to create exceptional solutions.
Our Mission
itinai.com is a global AI lab, product incubator. We make artificial intelligence accessible, applicable, and transparent for professionals across industries. Every article, tool, and product is driven by our belief that AI should be practical, verifiable, and human-centered.
Our Global AI Teams
At itinai.com, we build AI products and launch innovation programs in collaboration with expert teams across 12 countries.
- 🇷🇺 Russia
- 🇺🇦 Ukraine
- 🇰🇿 Kazakhstan
- 🇬🇪 Georgia
- 🇦🇪 UAE
- 🇺🇸 United States
- 🇵🇭 Philippines
- 🇻🇳 Vietnam
- 🇦🇷 Argentina
- 🇪🇪 Estonia
- 🇹🇭 Thailand
- 🇩🇪 Germany
Community of AI Builders
We are not just a tech company — we’re a decentralized network of creators, researchers, and entrepreneurs. Each team contributes to building AI-driven tools, bots, content engines, and monetization models tailored to local markets.
Editorial Principles
- Trustworthiness – We cite sources, check facts, and avoid hype.
- Experience-first – Written and reviewed by domain experts.
- Human in the Loop – AI is a tool, not a replacement for judgment.
- Transparency – Author names, background, and intent are disclosed.
AI Accelerators & Product Labs
In every region, we run AI Product Accelerators — programs that help local talent and businesses turn ideas into profitable, autonomous AI-powered businesses in just weeks. We provide infrastructure, AI models, training, and monetization pipelines.
Your Global AI Accelerator Partner. Ask me, I will help you
Get Involved
Follow us, contribute insights, or propose partnerships. We welcome collaboration from researchers, writers, and product leaders passionate about building ethical, usable AI.
Our Team’s the Most Interesting Articles Picks
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FastV: A Plug-and-Play Inference Acceleration AI Method for Large Vision Language Models Relying on Visual Tokens
Peking University and Alibaba Group developed FastV to tackle inefficiencies in Large Vision-Language Models’ attention computation. FastV dynamically prunes less relevant visual tokens, significantly reducing computational costs without compromising performance. This improves the computational efficiency and…
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What is Prompt Architecture in LLMs?
The article discusses prompt engineering techniques and introduces the concept of prompt architecture for interacting with Large Language Models (LLMs). It highlights the importance of specific prompts and explores different prompt architectures such as role prompting,…
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Sam Altman: Future AIs might enable internal monologue visualization
OpenAI CEO Sam Altman envisions a future where neural devices, combined with advanced AI like GPT-5 or 6, could potentially visualize a person’s inner monologue. These devices would display words in a user’s field of vision,…
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Agentic AI: The Foundations Based on Perception Layer, Knowledge Representation and Memory Systems
Understanding Agentic AI Agentic AI combines autonomy, intelligence, and adaptability to create systems that can sense, reason, and act with minimal human intervention. These systems observe their environment, process information, make decisions, and take actions in…
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IBM Watson TTS vs Azure TTS: Which Enterprise Platform Offers More Control and Clarity?
Comparing IBM Watson Text to Speech (TTS) vs. Azure Text to Speech: A Control & Clarity Focus Purpose of Comparison: Businesses increasingly rely on text-to-speech for applications like IVR systems, voice assistants, content creation, and accessibility.…
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Parameter-Efficient Fine-Tuning for Optimized LLM Performance: LoRA, QLoRA, and Test-Time Scaling
Introduction to Large Language Models (LLMs) Large Language Models (LLMs) play a crucial role in areas that require understanding context and making decisions. However, their high computational costs limit their scalability and accessibility. Researchers are working…
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DeepSim: AI-Accelerated 3D Physics Simulator for Engineers
DeepSim: AI-Accelerated 3D Physics Simulator for Engineers Practical Solutions and Value DeepSim is a groundbreaking AI simulation platform that automates physics setup, enabling 1000X faster design simulations without compromising accuracy. By combining a powerful GPU-accelerated solver…
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Lagent: A Lightweight Open-Source Python Framework that Allows Users to Efficiently Build Large Language Model (LLM)-Based Agents
Practical AI Solutions for Building Language Model-Based Agents Developing language model-based agents for virtual assistants and customer service requires efficient and resource-effective solutions. However, existing frameworks often lack flexibility and comprehensive documentation, leading to complexities in…
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Build Modular AI Workflows with Anthropic’s Claude Sonnet 3.7 and LangGraph
Building Modular AI Workflows with Anthropic’s Claude and LangGraph This guide offers a straightforward approach to implementing LangGraph, a user-friendly framework for creating AI workflows integrated with Anthropic’s Claude API. By following this tutorial, developers will…
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20 GitHub Repositories to Master Natural Language Processing (NLP)
Natural Language Processing (NLP) NLP is a fast-growing area focused on how computers understand human language. As NLP technology improves, there is a rising demand for skilled professionals to create solutions like chatbots, sentiment analysis tools,…
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Logic-of-Thought: Enhancing Logical Reasoning in Large Language Models through Propositional Logic Augmentation
Practical Solutions to Enhance Logical Reasoning in Large Language Models Overview: Large Language Models (LLMs) excel in NLP tasks but struggle with math and logic. The Logic-of-Thought (LoT) method overcomes this by integrating symbolic reasoning with…
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Meta AI Releases ‘NATURAL REASONING’: A Multi-Domain Dataset with 2.8 Million Questions To Enhance LLMs’ Reasoning Capabilities
“`html Enhancing Business Solutions with Advanced AI Introduction to Large Language Models Large language models (LLMs) have made significant strides in their reasoning abilities, particularly in tackling complex tasks. However, there are still challenges in accurately…
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Comprehensive Guide: Supporting Customers on Social Media
Summary: Supporting customers on social media has become crucial for businesses. Social media platforms provide a convenient and direct way for customers to seek help and voice concerns. It allows for real-time problem-solving and provides opportunities…
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Introducing three new NVIDIA GPU-based Amazon EC2 instances
Amazon announces the expansion of its EC2 accelerated computing portfolio with three new instances powered by NVIDIA GPUs: P5e instances with H200 GPUs, G6 instances with L4 GPUs, and G6e instances with L40S GPUs. These instances…
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LLM Reasoning Benchmarks: Study Reveals Statistical Fragility in RL Gains
Understanding the Fragility of LLM Reasoning Benchmarks Recent research has highlighted significant weaknesses in the evaluation of reasoning capabilities in large language models (LLMs). These weaknesses can lead to misleading assessments that may distort scientific understanding…
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MBZUAI Researchers Release Atlas-Chat (2B, 9B, and 27B): A Family of Open Models Instruction-Tuned for Darija (Moroccan Arabic)
Understanding the Importance of Natural Language Processing for Darija Natural Language Processing (NLP) has advanced significantly, but many languages, especially dialects like Moroccan Arabic (Darija), have been overlooked. Darija is spoken by over 40 million people,…