Problem #1:

 A stream of arbitrary integers appears in no particular order and without duplicates

  the rank of each integer is determined by the number of smaller integers before and after it up to the current position

  Write a method to get the rank of the current integer in an efficient way.

  eg: 7, 1, 3, 9, 5, 8

Solution:

A max heap is used to keep track of the elements we have seen and to count those that are smaller

using System;

using System.Collections.Generic;

using System.Linq;

public class Test

{

 private static SortedList<int, int> itemRankPair = new SortedList<int, int>();

 public static void Main()

 {

      var items = new List<int>(){7, 1, 3, 9, 5, 8};

      for (int i = 0; i < items.Count; i++)

      {

       var item = items[i];

       Console.WriteLine("Item={0}", item);

       if (itemRankPair.ContainsKey(item) == false)

       {

           itemRankPair.Add(item, GetRank(item));

       }

       Console.WriteLine();

       for (int j = 0; j < i; j++)

       {

           int k = items[j];

           if (k >= item)

           {

            itemRankPair[k] += 1;

            Console.WriteLine("item={0}, Rank={1}", k, itemRankPair[k]);

           }

       }

       Console.WriteLine();

      }

      foreach (var k in itemRankPair.Keys.ToList()){

  Console.WriteLine("item={0}, Rank={1}", k, itemRankPair[k]);

      }

 }

 private static int GetRank(int n)

 {

  int rank = 0;

  foreach( var key in itemRankPair.Keys.ToList())

  {

  if (key < n)

  {

      rank++;

  }

  }

  return rank;

 }

}

 These are the steps to create an AI agent. As discussed earlier, they came in various types and forms, but they can be quite capable. From design, implementation, test to deployment, it must be specific to its requirements, make the right selections of model and knowledge base, and remain pertinent and accurate, while avoiding the pitfalls such as bias, hallucinations, and concerns against safety, security and privacy.

The first step is to draw the requirements, which involves 1. identifying the problem, 2. prompt engineering, and 3. determining user interaction. For example, scheduling meetings, answering common questions, and generating creative content requires have different approaches. Clear instructions for guiding the agents’ behavior, outlining what the agent should do in different scenarios and providing specific instructions while allowing flexibility to handle parameters will help with prompts. The way the user interacts with the agent such as a chat is also important.

The second step is to choose the right model. This is a critical step in building an effective AI agent. The models have their advantages such as GPT-4 from OpenAI for advanced NLP, LLaMA 3 by Meta for its efficiency and adaptability, and Google’s PaLM 2 for handling multilingual tasks. Model’s like Meta’s LLaMA are open and offer customization options while OpenAI’s closed/hosted model GPT-4 come with support, maintenance and ease of use. Also, a less complex model such as GPT-3 might suffice for the requirements.

The performance metrics of different models such as accuracy, response time, scalability, and the ability to handle concurrent requests are important to align with the requirements. Consider customization options when you must fine-tune them for your specific tasks, especially when there is a domain-specific language.

Check if the model can easily be integrated with existing systems and tools especially for API support, environments and external databases, web services or interfaces. There might be some cost implications of various models, and some effort required for experimentation and iteration.

The third step involves enabling tools, for say information retrieval, web browsing and function calling, along with the configuration of specific settings for each tool, the testing of its integration, and adherence to security best practices and observability.

The fourth step is the extension of capabilities via custom functions, such as for summarization or reports, even if it involves writing code, testing the implementation and integrating via endpoints or webhooks, defining parameters and configuring invocations, testing and optimization

As with all software, some best practices are upheld. AI agents are only as good as their data which must be comprehensive and free of bias. They could provide transparency in terms of references or statistics or enumeration of thought,action and observations, enhance security with robust access control with defense-in-depth strategy, avoiding complexity that involves common sense, reasoning, or understanding context and not require a whole lot of or absence of human supervision.

#codingexercise: 

https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNYlIwEAAAABD8nSsN--hM7kfA-W_mzuWw?e=f29Tjt

 This article is all about AI agents. There are many types, development processes and real-world implementations. There has always been automation for complex workflows with curated artifacts, they have been boutique and never really intended for Large Language Models. While information can be tapped from multiple data sources or a knowledge base, enhanced decision-making processes needed to leverage AI agents. The operational framework of AI agents and the ways they augment LLMs is described here.

AI agents are software entities that complete a task autonomously on behalf of a user, including making requests to other services to improve the reach of standalone LLMs. They can retrieve real-time data from external databases, and APIs, manage interactive sessions with users and automate routine tasks that can be invoked dynamically or on schedule and with different parameters. An agent framework provides the tools and structures necessary to a developer to build robust, scaleable and efficient agent-based systems. This agent framework is an evolution of Reason-Action (ReAct) framework where an LLM is prompted to follow Thought/Action/Observation sequences. The Agent framework extends this by including external tools into the action step. The tools can range from simple calculators and database calls to python code generation and execution and even interactions with other agents. The calling program typically parses the output of the LLM at each step to determine the next steps. As an example, a prompt to find the weather in Seattle, WA involves a thought for needing to access a weather API to get the information, an action to call the weather API with location information and an observation on the response, followed by a thought that the relevant information is 65 degrees Fahrenheit and sunny, an action to report it to the user and an observation that the user is informed. By increasing iterations, articulation of granularity, dynamic adaptability and interoperability, the decision-making process can be arbitrarily enhanced. Compared with traditional software agents, there is very little distraction from syntax and format and more emphasis on semantics and latent meaning by virtue of LLMs and vector databases. This helps them to provide more dynamic, context-aware responses.

LLM agents can be diverse with each tailored to address specific challenges in information processing, decision support, and task automation. The task-specific agents are designed to perform specific, well-defined tasks. The conversational agents leverage natural language not a query language to interact with users. The decision support agents analyze complex data and provide insights. The workflow-automation agents co-ordinate and execute multi-step processes across different systems. The information retrieval agents can search and extract relevant information from large datasets or document repositories. The collaborative agents are creative and work with humans to accomplish complex tasks. The predictive agents use historical data and current trends to forecast future outcomes. The adaptive learning agents improve performance over time by learning from interactions and feedback. By categorizing different types of agents, an organization can streamline their operations, improve customer experiences and gain valuable insights.

 This is a summary of the book titled “Win the inside game” written by Steve Magness and published by HarperOne in 2025. The author is a performance coach who argues for a cognitive and psychological strategies to start living your full potential especially when there are increasing numbers of burnouts and for a many, a crisis of meaning. As we immerse ourselves in workplace and social media, Steve suggests developing a healthy sense of self-worth and intrinsic motivation. It’s just that we are in survival mode with the pressures of the modern life that we are merciless on ourselves and what it means for as growth and purpose. Hard work is not always virtuous. Intrinsic motivation and playful exploration will foster a sense of belonging and growth. By accepting ourselves and showing self-compassion, we can embrace the messiness of life. We learn to recast failures and losses as opportunities for learning and growth. We must proactively surround ourselves with people, objects and environments that support our growth. We will find more freedom and authenticity by disrupting the state of fear.

Many people live in "survival mode," feeling trapped in a fight for survival due to the pressures of modern life. This mode, which involves avoiding or shutting down, fighting and defending, narrowing and clinging, or accepting and exploring threats, can hinder growth and undermine a sense of life's meaning. Existential psychologist Tatjana Schnell suggests four qualities essential for a meaningful life: coherence, significance, purpose, and belonging. However, these qualities can be elusive in the modern world, with social media platforms encouraging inauthentic self-presentation, productivity-obsessed work culture, and superficial online interactions failing to foster a deep sense of belonging. Recognizing and addressing these needs can help individuals thrive in a world that is too big for their minds to handle.

The belief that hard work is virtuous can hinder happiness in the modern world. The Protestant notion that hard work is a virtue has led to an unhealthy fixation on external indicators of success, leading to poorer performance, stress, anxiety, and burnout. To thrive, prioritize intrinsic motivations and do what matters to you rather than competing with others.

High performance comes from intense work and commitment, which can grow only from an internal drive that manifests at the intersection of interest, motivation, and talent. Children naturally explore various interests, often becoming obsessed for periods of time. As people grow older, they often feel a need to choose a particular path, but rigid attachment to a narrow identity can lead to feelings of missing out and a crisis of meaning.

To achieve sustainable excellence, bring childlike exploration back into your life, seek a balance between exploration and commitment, alternate between narrowing and broadening focus, and be wary of success that might lead to cementing a commitment for the wrong reasons.

To embrace the messiness of life, cultivate self-compassion, "be someone," and "integrate the messiness." Accept your inner critic and focus on wisdom and courage to alleviate suffering. Hold onto a core sense of yourself that endures even in failures and setbacks. Seek meaning from diverse sources, such as hobbies or volunteering. Craft an empowering narrative of your journey to increase resilience and stress management. Recast failures and losses as opportunities for learning and growth.

In today's world, losing well is essential. Learning to lose well means accepting a loss and learning from it, rather than throwing a tantrum or shutting down. Failure brings clarity and helps you see yourself and pursuits as they are. Learning to lose well also helps you win better, as emotional outbursts or avoidance after a loss can lead to retreat and self-protection. Reframe your performance and view success and failure as part of your learning and growth journey.

To exit survival mode, create an environment that supports growth and downregulates the nervous system. Research shows that a person's physical environment can significantly impact their performance, with studies showing that making an office feel more like home can improve performance by up to 160%. This creates psychological ownership, which supports emotional needs for identity, belonging, and safety. Surround yourself with people who inspire you and serve as role models and cultivate relationships that feel expansive.

To find more freedom and authenticity, disrupt the state of fear by using physiological techniques to reset the nervous system. If your fears aren't life-threatening, confront them deliberately, such as dressing in a ridiculous outfit and going out in public.

Reducing attachment to specific outcomes and approaching life with more openness can help you stop living in a fearful, protective, and defensive state and start thriving. By embracing change, you can grow, adapt, form genuine relationships, and achieve goals that align with your authentic self.

 This is a summary of the book titled “Energy - a human history” written by Richard Rhodes and published by Simon and Schuster in 2018. The author is a prolific and acclaimed writer who covers major innovations in energy in the last 400 years. As a journal of scientific history, this book covers breakthroughs such as turning coal into steam, building railroads, electric grid and automobiles and eventually to harness the power of atom. His lessons on the benefits and risks of each source of energy holds value as we evaluate the challenges of climate change. Cheap abundant energy has driven prosperity for society. Wood was scarce and that prompted finding coal, but mining and transportation was difficult. Canals were built and steam drove the railway beginning with freight in 1831. The search for oil began because kerosene could be distilled from bitumen. Electricity was a breakthrough for the industrial age and the invention of the internal combustion engine propelled transportation. Oil became a global hunt with Middle East becoming a huge producer. World war II spurred the development of nuclear power, and its aftermath highlighted the pollution from power generation. Understanding the benefits and risks of each source of energy is crucial to managing environmental impact.

Over the last 400 years, western societies have demonstrated remarkable innovation in finding and exploiting new energy sources. Wood gave way to coal, and coal made room for oil, as coal and oil now make way for natural gas, nuclear power, and renewables. Obscure inventors and scientists made great advances motivated by the scarcity, cost, or other shortcomings of existing energy sources, delivering more efficient sources of heat, light, and transportation.

Elizabethan England's scarcity of wood led to the search for alternatives, such as coal, which provided energy but was difficult to mine. As coal mining expanded, miners faced the problem of flooding, leading to the development of steam engines. James Watt patented a better steam engine in 1769, which was sold to coal miners and other industrialists under an exclusive patent until 1800.

Mine owners built canals to reduce the cost of transporting coal, such as the Bridgewater Canal, which reduced the price of coal in Manchester by 50%. Improved smelting allowed for the use of iron rails for efficient coal hauling.

The Liverpool and Manchester Railway, the first commercial passenger and freight railway, opened in 1831, powered by steam. Cornish inventor Richard Trevithick developed a high-pressure steam engine, allowing it to be smaller than earlier models. George Stephenson won a competition to demonstrate the safety and speed of steam-powered rail, leading to the development of the first railway in the world, the Liverpool and Manchester Railway. The first gas lights were installed in London in 1807. The search for oil began with kerosene, a fuel source for lighting, invented by Canadian physician Abraham Gesner. The US Civil War boosted the market for oil, with production reaching 4.8 million barrels by 1870. However, the environmental costs of drilling, transporting, and distilling oil became apparent, making the process messy.

Electricity was a significant energy source that fueled economic growth. Despite the existence of electricity, scientists were unsure of how to use it. Hans Christian Oersted discovered electromagnetism, which enabled the generation of electricity in sufficient quantities for practical use. Early developers recognized Niagara Falls as a potential power source and worked to harness its potential effectively. William Stanley Jr. developed alternating current (AC), allowing transmission over long distances. Westinghouse built generators and transmission lines to harness Niagara Falls' power, making Buffalo, New York, the first electrified city. The introduction of electric streetcars in the 1880s reduced transportation costs and accelerated city growth. Henry Ford developed his first automobile in 1896, using a gasoline-powered internal combustion engine.

The need for oil became international, leading to exploration in the Middle East. In 1933, Standard Oil of California signed a 60-year deal with Saudi Arabia, leading to a significant discovery in 1938. The development of oil fields required the construction of oil and gas pipelines, which were later used to deliver natural gas, a by-product of oil production. The outbreak of World War II boosted the demand for oil, leading to the construction of the world's largest and longest oil pipeline, the Big Inch. The Atomic Energy Act granted the US government a monopoly on nuclear power, but the Atomic Energy Commission created a joint venture to build a nuclear reactor near Pittsburgh in 1953. By the 1950s, the problem of pollution from power generation became evident, and the connection between air pollution and health was poorly understood until the mid-20th century.

In the 1950s, a chemist at the California Institute of Technology discovered that smog in Los Angeles was caused by automobile and factory emissions interacting with sunlight and ozone. This led to the 1970 US Clean Air Act. Wealth has been linked to environmental regulation, with wealthier societies becoming cleaner and healthier. Understanding the benefits and risks of energy sources is crucial for managing environmental impact and addressing climate change. Climate change has increased public awareness, leading to research on renewable energy sources like wind and solar power.

 The vectors generated by embedding models are often stored in a specialized vector database. Vector databases are optimized for storing and retrieving vector data efficiently. Like traditional databases, vector databases can be used to manage permissions, metadata and data integrity, ensuring secure and organized access to information. They also tend to include update mechanisms so newly added texts are indexed and ready to use quickly.

The difference that a vector database and Retrieval Augmented Generation makes might be easier to explain with an example. When a chatbot powered by LLama2 LLM is asked about an acronym that was not part of its training text, it tends to guess and respond with an incorrect expansion and elaborating on what that might be. It does not even hint that it might be making things up. This is often referred to as hallucination. But if an RAG system is setup with access to documentation that explains what the acronym stands for, the relevant information is indexed and becomes part of the vector database, and the same prompt will now give a more pertinent information. With RAG, the LLM provides correct answers.

If the prompt was provided with the relevant documents that contain an answer, which is referred to as augmenting the prompt, the LLM can leverage that against the vector database and provide more compelling and coherent answers that would turn out to be knowledgeable as well as opposed to the hallucination referred above. By automating this process, we can make the chat responses to be more satisfactory every time. This might require additional steps of building a retrieval system backed by a vector database. It might also involve extra steps of data processing and managing the generated vectors. RAG also has added benefits for the LLM to consolidate multiple sources of data into a readable output tailored to the user's prompt. RAG applications can also incorporate proprietary data which makes it different from the public data that most LLM are trained on. The data can be up to date so that the LLM is not restricted to the point-in-time that it was trained on. RAG reduces hallucinations and allows the LLM to provide citations and query statistics to make the processing more transparent to the users. As with all retrieval systems, fine-grained data access control also brings about its own advantages.

There are four steps for building Retrieval-Augmented Generation (RAG):

1. Data Augmentation

a. Objective: Prepare data for a real-time knowledge base and contextualization in LLM queries by populating a vector database.

b. Process: Integrate disparate data using connectors, transform and refine raw data streams, and create vector embeddings from unstructured data. This step ensures that the latest version of proprietary data is instantly accessible for GenAI applications.

2. Inference

a. Objective: Connect relevant information with each prompt, contextualizing user queries and ensuring GenAI applications handle responses accurately.

b. Process: Continuously update the vector store with fresh sensor data. When a user prompt comes in, enrich and contextualize it in real-time with private data and data retrieved from the vector store. Stream this information to an LLM service and pass the generated response back to the web application.

3. Workflows

a. Objective: Parse natural language, synthesize necessary information, and use reasoning agents to determine the next steps to optimize performance.

b. Process: Break down complex queries into simpler steps using reasoning agents, which interact with external tools and resources. This involves multiple calls to different systems and APIs, processed by the LLM to give a coherent response. Stream Governance ensures data quality and compliance throughout the workflow.

4. Post-Processing

a. Objective: Validate LLM outputs and enforce business logic and compliance requirements to detect hallucinations and ensure trustworthy answers.

b. Process: Use frameworks like BPML or Morphir to perform sanity checks and other safeguards on data and queries associated with domain data. Decouple post-processing from the main application to allow different teams to develop independently. Apply complex business rules in real-time to ensure accuracy and compliance and use querying for deeper logic checks.

These steps collectively ensure that RAG systems provide accurate, relevant, and trustworthy responses by leveraging real-time data and domain-specific context

#codingexercise: CodingExercise-03-31-2025.docx

 This is a summary of the book titled “Prompt Engineering for Generative AI - Future Proof inputs for reliable AI outputs” written by James Phoenix and Mike Taylor and published by O’Reilly in 2024. The authors are data scientists who explain that the varying results from queries to Large Language Models such as ChatGPT can be made more accurate, relevant and consistent with prompt engineering which focuses on how the inputs are worded so that users can truly harness the power of AI. With several examples, they teach the ins and outs of crafting text- and image-based prompts that will yield desirable outputs. LLMs, particularly those that are used in chatbots are trained on large datasets to output human like text and there are some principles to optimize the responses, namely, set clear expectations, structure your request, give specific examples, and assess the quality of responses. Specify context and experiment with different output formats to maximize the results. “LangChain” and “Autonomous agents” are two features of LLMs that can be tapped to get high quality responses. Diffusion models are effective for generating images from text. Image outputs for creative prompts can be further enhanced by training the model on specific tasks. Using the prompting principles mentioned in this book we can build an exhaustive content-writing AI.

Prompt engineering is a technique used by users to create prompts that guide AI models like ChatGPT to generate desired outputs. These prompts provide instructions in text, either to large language models (LLMs) or image-related diffusion AIs like Midjourney. Proper prompt engineering ensures valuable outputs, as generic inputs create varying outputs. Prompt engineering follows basic principles, such as providing clarity about the type of response, defining the general format, giving specific examples, and assessing the quality of responses. Large language models (LLMs) can learn from vast amounts of data, enabling the generation of coherent, context-sensitive, and human-sounding text. These models use advanced algorithms to understand the meaning in text and produce outputs that are often indistinguishable from human work. Tokens, created by Byte-pair Encoding (BPE), are used to compress linguistic units into tokens, which can be assigned numbers or vectors. LLM models are initially trained on massive amounts of data to instill a broad, flexible understanding of language, then fine-tuned to adapt to more specialized areas and tasks.

ChatGPT is a machine learning model that can generate text in various formats, such as lists, hierarchical structures, and more. To optimize its results, specify context and experiment with different output formats. To avoid issues with LLM outputs, try using alternative formats like JSON or YAML. Advanced LLMs like ChatGPT-4 can also make recommendations if the model's response is inadequate. Users can provide more context to the model to generate more accurate outputs.

LangChain, an open-source framework, can help address complex generative AI issues such as incorrect responses or hallucinations. It integrates LLMs into other applications and enables fluid interactions between models and data sources with retrieval, augmentation and generation enhancements. It allows developers to build applications like conversational agents, knowledge retrieval systems, and automated pipelines. As LLM applications grow, it's beneficial to use LangChain's prompt templates, which allow for validation, combination, and customization of prompts.

Large language models (LLMs) play a crucial role in AI evolution by addressing complex problems autonomously. They can use chain-of-thought reasoning (CoT) to break down complex problems into smaller parts, allowing for more efficient problem-solving. Agent-based architectures, where agents perceive their environment and act in pursuit of specific goals, are essential for creating useful applications. Diffusion models, such as DALL-E 3, Stable Diffusion, and Midjourney, are particularly effective in generating high-quality images from text inputs. These models are trained on massive internet data sets, allowing them to imitate most artistic styles. However, concerns about copyright infringement have been raised, but the images are not literal imitations of images or styles but are derived from patterns detected among a vast array of images. As AI image generation shifts, the focus will likely shift towards text-to-video and image-to-video generation.

AI image generation can be a creative process, with each model having its own unique idiosyncrasies. The first step is to specify the desired image format, which can be stock press photos or traditional oil paintings. AI models can replicate any known art style, but copyright issues should be considered. Midcentury allows users to reverse engineer a prompt from an image, allowing them to craft another image in the sample's style.

Stable Diffusion, an open-source image generation model, can be run for free and customized to suit specific needs. However, customization can be complicated and best done by advanced users. The web user interface AUTOMATIC1111 is particularly appealing for serious users, as it allows for higher resolution images with significant controls. DreamBooth can be used to fine-tune the model to understand unfamiliar ideas in training data.

To create an exhaustive content-writing AI, users should specify the appropriate writing tone and provide keywords. Blind prompting can make it difficult for the model to evaluate its own quality but providing at least one example can significantly improve the response quality.