Artificial intelligence has made huge strides, and large language models now churn out text that feels human. A big part of this leap comes from reinforcement learning, a training method that pushes these models to keep generating tokens—tiny text chunks—until they resemble a thinking process. This article digs into how RL shapes LLMs, with a focus on the tech behind it.

How Reinforcement Learning Boosts AI Thinking in Language Models

Deep learning is changing the way we build humanoids, making them smarter, more adaptable, and closer to human-like behavior than ever before. This branch of artificial intelligence uses neural networks to process vast amounts of data, enabling machines to learn and improve on their own. As a result, the latest generation of humanoids is stepping out of science fiction and into reality, with abilities that surprise even their creators. Let’s explore how deep learning is shaping these advanced robots.

Deep Learning Fuels Next-Gen Humanoids

Counting unique occurrences of items in a data set is a common task in software development. This concept often surfaces in technical interviews, where candidates are asked to find the number of distinct values or the frequency of occurrences for each item in a list. Understanding this topic can help demonstrate your problem-solving skills and your ability to work with data structures.

How Can You Count Unique Number of Occurrences?

Removing specific characters, like stars, from a string is a common task in programming. Stars are often used as placeholders or for formatting in strings. In developer interviews, questions regarding string manipulation, including removing characters, are popular because they test problem-solving skills and knowledge of different programming languages.

How Can You Remove Stars from a String?

Reversing words in a string is a common task in programming. This question often appears in technical interviews to assess a candidate's problem-solving skills, understanding of strings, and ability to manipulate data.

How Can You Reverse Words in a String?

Traditional web search involves typing questions into a search box and browsing through multiple links for answers. Today, more people find that asking AI directly for information is easier and quicker. This convenience raises an important question: Will AI-generated answers eventually replace traditional search methods altogether?

Can AI Answers Replace Traditional Web Searches?

Spring is the perfect time to refresh your home and clear out the clutter. A deep clean can make your space feel brighter, healthier, and more organized. If you’re ready to tackle the dust and mess, these tips will help you get the job done efficiently.

10 Tips for a Fresh and Tidy Spring Cleaning

Fine-tuning a Llama 3 model can significantly enhance its performance for specific tasks or datasets. This guide provides a straightforward approach to customizing this powerful AI tool, ensuring you achieve the best results without requiring extensive technical knowledge. Let’s explore how to fine-tune this model with easy steps and clear explanations.

A Simple Guide to Fine-Tune a Llama 3 Model

Determining whether two strings are close involves comparing their characters and the frequency of those characters. Two strings are considered close if you can transform one into the other using a series of operations like rearranging characters or swapping their frequencies. This concept is often discussed in coding interviews, particularly for roles that involve problem-solving skills.

How Can We Determine If Two Strings Are Close?

Reversing a linked list is a classic problem that frequently comes up in technical interviews for software developers. This task tests a developer's knowledge of data structures, algorithms, and their ability to manipulate pointers effectively.

How Do You Reverse a Linked List?

Artificial intelligence is the heartbeat of autonomous driving, turning regular cars into smart machines that roll down roads without a human at the wheel. It’s an exciting shift that’s making travel safer, smoother, and more efficient. From spotting a pedestrian to picking the fastest route, AI handles it all with precision.

How Is AI Powering Self-Driving Cars?

International Women’s Day is a celebration of the incredible women in our lives, honoring their contributions, strength, and resilience. Finding the perfect gift can be a challenge, but it's a great opportunity to show appreciation and care. Here are 10 thoughtful and stylish gift ideas to make this day special.

Gift Ideas for International Women’s Day 2025

Merging k sorted lists is a common problem encountered in technical interviews. This task involves taking multiple sorted lists and combining them into a single sorted list. This problem can seem complex at first, but it can be solved efficiently with the right approach.

How Can You Efficiently Merge K Sorted Lists?

Removing duplicates from a sorted array is a common problem that many encounter while preparing for tech interviews. This task is typically straightforward due to the nature of sorted arrays, which makes it easier to identify duplicates. Let’s break down how to approach this problem and offer a clear solution.

How Can You Remove Duplicates from a Sorted Array?

Converting Roman numerals to integers is a common problem posed in technical interviews, especially for software engineering roles. Understanding both Roman numeral conventions and the implementation of conversion algorithms can greatly improve your problem-solving skills. Let's explore how to accomplish this task using a straightforward approach.

How Can You Convert Roman Numerals to Integer?

Reversing an integer is a common problem encountered in technical interviews. It involves taking a number and flipping its digits. For example, reversing the integer 123 would yield 321. While this may sound straightforward, complications arise, especially with negative numbers and integer overflows.

How Can You Reverse an Integer in Programming?

Large language models (LLMs) like GPT are amazing! They can write stories, summarize information, and even chat with you. But, out of the box, they aren't perfect for everything. If you want an LLM to be a super-smart question answering (QA) assistant, you need to give it some extra training. This extra training is called post-training.

How Post-Training Creates Amazing Question Answering LLMs

In many tech interviews, candidates are often asked about manipulating arrays, particularly the task of removing an element. This problem tests not only your understanding of arrays but also your ability to manipulate data efficiently. Let's explore how to remove an element from an array in different programming contexts, focusing on JavaScript and Python.

How Can You Remove an Element from an Array?

ChatGPT has become a powerful tool for various tasks, from brainstorming ideas to drafting emails. To make the most out of this AI, here are ten practical tips that can help improve your interactions and get better results.

10 Tips to Enhance Your ChatGPT Experience

Validating parentheses is a common question that tech interviewers like to ask, primarily because it tests your understanding of data structures and algorithms. The challenge usually revolves around determining if a string consisting of parentheses is valid, meaning every opening parenthesis has a corresponding closing parenthesis, and they are correctly nested. This problem can be tackled efficiently using a stack data structure.

Are Your Parentheses Balanced?

A parameter in a large language model (LLM) refers to the weights and biases within the model that control how it processes and generates text. These parameters define the behavior of the model, allowing it to map inputs (like a question or prompt) to outputs (such as a response). The parameters are adjusted during training to improve the model’s performance.

How Are Parameters Initialized and Utilized in Large Language Models?

Generating all valid combinations of parentheses is a classic problem in computer science and often appears in coding interviews. The challenge is to create combinations of parentheses that are balanced — meaning that for every opening parenthesis, there is a closing parenthesis that follows and there are no unmatched closing parentheses.

How Can We Generate All Valid Parentheses Combinations?

Chatbots have become a common feature in our digital interactions. They assist with customer service, provide information, and even engage users in casual conversation. But the question arises: are chatbots truly applications of artificial intelligence?

Are Chatbots Considered AI Applications?

When faced with a technical interview question that involves dividing two integers without using division operators, it can seem tricky at first. This question tests not only your programming skills but also your ability to think logically and implement basic algorithms. The goal is to arrive at a solution that correctly divides the integers while adhering to the constraints of the problem.

How Can You Divide Two Integers Without Using Division Operators?

The 4Sum problem is a popular question asked in technical interviews, especially for software engineering positions. It involves finding all unique quadruplets in an array that sum up to a given target. This problem can be a bit tricky, but with a structured approach, it becomes manageable.

How Can You Solve the 4Sum Problem?

Merging two sorted lists is a common problem in computer science and is often asked in technical interviews. The goal is to create a new list that contains all elements from the two given lists, maintaining the sorted order. This task can be tackled efficiently, and understanding the approach will help improve your algorithmic skills.

How do you merge two sorted lists?

When discussing the concept of swapping nodes in a linked list, one common interview problem arises: the swapping of nodes positioned at certain intervals. A classic illustration of this problem can be framed using a fictitious scenario involving nodes in Paris. Let's break this down clearly.

Can You Swap Nodes in Paris?

When you think of a phone number, you likely think of digits, but there’s a fascinating connection to the alphabet that many people might overlook. Traditional phone keypads associate numbers with letters. For example, the number 2 corresponds to the letters A, B, and C, while 3 corresponds to D, E, and F. This association allows us to generate letter combinations from the digits of a phone number, and it’s a common question in technical interviews, particularly in programming roles.

How Can We Generate Letter Combinations from a Phone Number?

Adding two numbers is one of the most fundamental operations in programming, and it often serves as a warm-up question in tech interviews. Whether you work in front-end development, back-end development, or data science, understanding how to perform this simple task is crucial. In this article, we will explore various ways to add two numbers in different programming languages.

How Can You Add Two Numbers in Programming?

Artificial Intelligence (AI) has transformed how we interact with technology and the world around us. From smartphones to smart homes, AI is increasingly integrated into our daily lives. This article explores ten key elements that define AI and its functionality.

10 Key Elements of Artificial Intelligence

Ollama is an innovative platform designed to enhance the experience of working with AI models. Targeting developers and tech enthusiasts, it simplifies the process of integrating and deploying machine learning models. With a focus on usability and flexibility, Ollama stands out in a crowded market of AI tools.

Exploring Ollama: A New Tool for AI Enthusiasts

Artificial Intelligence (AI) is transforming various fields, from healthcare to entertainment. For beginners, learning how to use AI can feel overwhelming. Fortunately, there are many accessible ways to start this journey.

Beginner's Guide to Learning AI

Azure OpenAI is an innovative platform that brings advanced artificial intelligence capabilities to businesses and developers. It combines the power of OpenAI's language models with the robustness of Microsoft Azure’s cloud computing services. This partnership allows users to build, test, and deploy AI applications that can enhance productivity and creativity across various industries.

Exploring Azure OpenAI: A Powerful AI Solution

Improving your credit score is a crucial step in enhancing your financial health and opening up better financial opportunities. Here are 10 actionable tips to help you raise your credit score and get on the path to financial stability.

10 Tips to Raise Your Credit Score

Creating distributed systems can seem daunting, but with Node.js, the task becomes not only manageable but also enjoyable. This article guides you through the basics of building distributed systems using Node.js, highlighting its benefits and key concepts to get you started.

Building Distributed Systems with Node.js

Humanoid robots are stepping out of movies and into reality, and a big part of what makes them tick is a powerful large language model. These advanced AI systems don’t just help robots chat—they give them the ability to think through tasks and act in ways that feel human. Let’s see how this works.

How a Mighty LLM Powers Humanoid Thinking?

Humanoid robots—machines built to look and act like us—are no longer just a sci-fi dream. They’re stepping into the real world, and factories might be their first big stage. But when can we expect these robots to handle actual jobs on the factory floor? Let’s break it down.

When Will Humanoid Robots Take Over Factory Jobs?

AI models are powerful tools that can perform tasks like recognizing images, understanding text, or even generating creative content. But how do these models become so good at specific tasks? The answer lies in a process called fine-tuning. Fine-tuning is a technique used to adapt a pre-trained AI model to perform better on a specific task or dataset. Let’s explore what fine-tuning is, why it’s important, and how it works.

What is AI Model Fine-Tuning?

Large language models (LLMs) like ChatGPT, GPT-4, and other generative AI tools have transformed the way people communicate, write, and get information. Despite their impressive capabilities, these models often struggle with seemingly basic tasks such as accurate counting and consistent spelling. The reasons behind these shortcomings reveal a lot about how these models work—and their limitations.

Why Language Models Struggle with Counting and Spelling?

Inference in AI is the process where a trained model makes predictions or decisions based on new data. It is what happens when AI applies what it has learned during training to real-world problems. Every time a chatbot responds, a self-driving car recognizes a stop sign, or a recommendation engine suggests a movie, inference is at work.

What is Inference in AI?

Retrieval-Augmented Generation, or RAG, stands out as a fascinating approach in artificial intelligence that blends two powerful techniques to create smarter, more informed systems. This article explains RAG in detail, breaking it down into its key components and showing how it enhances AI capabilities.

What Is RAG in AI?

Training a large language model is a wild ride, and at the heart of it all is the GPU—short for graphics processing unit. These little powerhouses crunch numbers at lightning speed to make smart AI systems come to life. Let’s break down what a GPU actually calculates during the training phase and explain why having a ton of high-speed GPUs is a big deal for building a powerful AI model. This article will keep things simple and clear, walking you through the process step by step.

What a GPU Does in AI Training and Why Speedy GPUs Matter?

To get the best results from a large language model, your prompts need to be sharp, clear, and purposeful. Weak prompts lead to generic answers, while well-crafted ones unlock precise, creative, and useful outputs. Below are ten strategies to help you write prompts that push AI to perform at its peak.

How to Write Prompts That Supercharge AI Performance?

Large language models and the broader AI field don’t grow on their own—they need developers and their communities to push them ahead. These folks aren’t just coding; they’re the heartbeat of progress, turning raw tech into tools we can actually use. Here’s why their involvement matters so much and why we need them to keep dreaming up fresh ideas.

Why Developers Drive AI Forward

Artificial intelligence is moving fast, and keeping up can feel like chasing a speeding train. The good news? You don’t need to be a tech wizard to ride the wave. With some practical steps, you can weave AI into your daily work and stay in the loop. Here’s how to catch up and make AI a natural part of your routine.

Stay Ahead of the AI Wave

When discussing large language models (LLMs), you may often come across the term “base model.” But what does it really mean, and why is it so important in the process of training AI systems? In this article, we’ll explore the concept of a base model, what it does during the pre-training phase, and how it serves as the foundation for more specialized models.

What is a Base Model in Large Language Models?

AI research has rapidly become a priority for governments and corporations globally, with billions invested into research and development each year. The magnitude of this investment prompts a key question: why is AI development so expensive? The answer is a combination of advanced technology, specialized talent, and significant infrastructure required to push the boundaries of innovation in this field.

Why AI Research Demands Massive Investment?

When preparing for a developer interview, it's important to focus not only on technical skills but also on how well you communicate your solutions and approach. Interviewers are likely to ask a variety of questions to gauge your problem-solving skills, experience, and ability to fit within their team. To help you prepare, here are the top 10 most common interview questions for developers.

What Are the 20 Key Terms You Need to Know to Master AI?

Large language models (LLMs) are rapidly changing how we interact with technology. Recent developments have focused not only on creating even more powerful models, but also on making them openly available. This openness carries significant implications for innovation, research, and the future direction of artificial intelligence. But when we say open-source, what does it really mean?

What Is an Open-Sourced Large Language Model?

Artificial Intelligence (AI) encompasses a wide array of technologies designed to replicate human-like intelligence. Among these technologies, machine learning (ML) plays a crucial role. This article will explain how machine learning fits within the broader framework of artificial intelligence and its significance.

Is Machine Learning Part of AI?

WebRTC (Web Real-Time Communication) is an open-source technology that allows web applications and websites to capture, share, and exchange multimedia (video, audio, and data) directly between browsers, without the need for third-party plugins or software. In simple terms, WebRTC enables real-time communication directly in your browser, making it easier for developers to create video chat applications, file-sharing tools, and other interactive communication services.

What is WebRTC and Why is it So Useful?

The development of large language models (LLMs) has created a variety of options for users. Each model has its strengths and weaknesses, which can make the choice overwhelming. Yet, embracing a single unified model offers significant advantages that can enhance efficiency, coherence, and overall performance in various applications.

Why Choose a Unified Model Over Multiple LLMs?

Reasoning plays a critical role in how large language models (LLMs) interact and provide value to users. These sophisticated systems have transformed the way we engage with artificial intelligence, offering insights, suggestions, and information across various domains. This article explores what reasoning means for LLMs and how it affects their functionality and effectiveness.

The Meaning of Reasoning for a Large Language Model

Running a product website means constantly looking for ways to improve user experience and streamline information access. One innovative solution gaining traction is using generative AI as a search engine. Instead of relying on traditional keyword-based search methods, you can have an AI that understands the specific details of your products and provides direct, accurate answers to user queries. Here's how you can achieve this with tools like AskHandle.

Use Generative AI as Your Product Website Search Engine

In a world increasingly filled with technology, questions about artificial intelligence and its capabilities continue to grow. One such curiosity is whether ChatGPT qualifies as an AI chat service. This article will explore what ChatGPT is and how it functions as a chatbot powered by artificial intelligence.

Is ChatGPT an AI Chat?

In the modern business environment, providing top-notch customer support is crucial for maintaining customer satisfaction and loyalty. However, as businesses grow, managing the volume of customer inquiries can become increasingly challenging. This is where AI agents come into play, offering a robust solution to scale your customer support team efficiently.

Scaling Customer Support with AI Agents

AI is reshaping the world in ways we never thought possible. It’s more than just a tool for automation; it’s a catalyst for unlocking new levels of productivity, economic growth, and personal freedom. Imagine getting more done in less time, having access to opportunities that were once out of reach, and freeing up your time for what truly matters. That’s the promise of AI, and it’s already starting to deliver.

Why Is AI the Key to Boosting Productivity, Creating Jobs, and Giving Us More Freedom?

Large language models have greatly improved due to reinforcement learning (RL). RL allows LLMs to learn from feedback, improving their ability to generate relevant, coherent, and helpful text. This article explains how the RL process works in training LLMs, with simple examples.

How Does Reinforcement Learning Improve LLM Performance During Training?

AI and machine learning are rapidly changing how we solve problems, with various techniques offering different solutions. Among the most talked about methods are reinforcement learning and supervised fine-tuning. Both are widely used in AI development but differ significantly in how they approach learning, adaptation, and optimization. In this article, we’ll explore how these two techniques work, where they shine, and what sets them apart.

Reinforcement Learning vs Supervised Fine-Tuning: Key Differences

A large language model (LLM) is a type of artificial intelligence that processes and generates human-like text. These models are trained on vast amounts of text data and are designed to predict and create coherent sentences. LLMs, like ChatGPT, can understand context, provide detailed answers, and carry conversations almost as fluently as a human. Their capabilities make them valuable tools across industries, enhancing productivity and improving services.

What is an LLM and What Can It Do?

In artificial intelligence, especially within large language models (LLMs) like GPT, the concept of a token plays a key role. These tokens act as the building blocks of the language processing system. Without tokens, these models wouldn't know how to analyze or generate text effectively.

The AskHandle Blog

How a Mighty LLM Powers Humanoid Thinking?