Code Conversations

MCP or API: Which transforms AI integration? Martin Keen explains how the Model Context Protocol (MCP) revolutionizes AI agents by enabling dynamic discovery, tool execution, and seamless external data retrieval. Discover how MCP simplifies LLM workflows and outpaces traditional APIs. Ref: https://www.youtube.com/watch?v=7j1t3UZA1TY

Tim Berglund is back at the lightboard with MCP (Model Context Protocol). MCP really is a big deal, but most people are missing the point. It's not just about enhancing desktop applications with agentic functionality—it's about exposing reusable tools and resources (including real-time data from sources like Apache Kafka) to build agentic microservices. Doing this takes us beyond basic LLM chatbots to dynamic, problem-solving systems that deliver real value in real professional settings.

With the rise of AI and automation, how do we as humans find our value in the workplace? How do we work with these new technologies? How do we build resilience to changes? What skills are needed for us to thrive in this new world?People have often felt apprehensive about change, especially big changes to the way we work. It happened in the industrial revolution and now it's happening with the rise of artificial intelligence. Five years ago these things existed, but now they are accessible to almost everyone.Ref: https://www.youtube.com/watch?v=IJjl4X1Lr8w

Learn how AI can change the game in an important scenario. The age-old battle between Product Owners and Developers rages on: POs push for speed, while devs demand clarity. When specs are too vague, developers waste time making assumptions. When specs are too detailed, POs get bogged down in documentation.The result? Context switching, frustration, and a backlog filled with half-baked work items.In this talk, Adam Cogan will show how AI powered tools like YakShaver (for GitHub and Azure DevOps) and recently Loom (for Jira) can act as the ultimate peacemaker.These tools capture discussions, structuring work items, and ensuring that every backlog item is ready… and assigned to the right backlog—all automatically.Ref: https://www.youtube.com/watch?v=u_JSpT3i1Z4&list=TLGGLRPrrPRtEmAyMDAzMjAyNg

Copilots are everywhere these days, and… rightfully so! Let's face it: these tools are incredible at getting things done. They have the potential to turn any one of us into a 20x developer. Need a new feature? Bam, there you have it! Refactor that function? Sure, it'll be done before you grind the coffee for your next cup. These tools do a very good job of generating well-designed, tested, and performant code. Before you know it, you're not just building a feature—you're building 17 slightly different features simultaneously because why not? After all, the code writes itself!But guess what? Just because you can build it doesn't mean you should. Without a clear vision, our solutions risk becoming soulless Franken-software, a mishmash of best intentions and uncontrolled enthusiasm that don't make the user's life any easier. That’s why we need to remind ourselves that the true art of building great systems is more about what you choose not to do. More than ever, our mission needs to remain crystal clear: crafting lasting, impactful solutions that our users love.Ref: https://youtu.be/6UGgP8l7TA4?list=TLGGCR2Fp6AOZlMxNDAzMjAyNg

Explore how AI and continual learning can revolutionize MRI diagnostics, using our real-world case study in detecting Focal Cortical Dysplasias (FCD)—a crucial factor in epilepsy treatment. In this session, we’ll dive into how continual learning techniques, inspired by human adaptability, help AI models improve diagnostic accuracy, minimize false positives, and handle evolving data in medical settings. We’ll discuss challenges like catastrophic forgetting, maintaining model performance in dynamic environments, and practical strategies that developers can apply to other domains. Gain hands-on insights into building resilient AI systems that evolve and adapt to new data, ensuring long-term reliability in critical applications.Ref: https://www.youtube.com/watch?v=WsHfnJLXP-U&list=TLGGrM3vbzrnOnoxNDAzMjAyNg

Ready to give your old code a makeover? Step into the world of AI-powered code refactoring, where smart algorithms take on the challenge of sprucing up cluttered codebases. See how AI deciphers code DNA, performs digital reconstructive surgery, and scales from small scripts to sprawling systems. Explore real-world success stories, understand the current limits, and discover tools that'll make your development process smoother than a freshly refactored function.Ref: https://www.youtube.com/watch?v=u8tvVxUOwvY&list=TLGGNgLFYxiYuL8xNDAzMjAyNg

So we all know AI is changing the software industry right now. Whether you build backend systems, web or native UIs, or embedded devices, you keep hearing it: the next generation of users will simply expect your software to carry out their tasks intelligently.Let's start with how we got here. I'll demo AI systems from the 1960s primordial soup up to the emergence of large language models (LLMs). We'll see a GPT-2 implementation in C code, and train it from scratch on nothing but NDC talks (what will it say??). We'll then build up to modern chat/assistant/agent systems. The point of this is to give you a deep intuition for the capabilities and limitations of LLM-based systems.Ref: https://www.youtube.com/watch?v=Xyios5mdkIM&list=TLGGIrtOt4nkon8xMjAzMjAyNg

It's 2025 and we're all adding AI features to our apps. But the tech moves so fast - what solid ground can you actually build on?This talk will focus on one of the best established patterns: building chat UIs that accept natural-language instructions. This includes:Getting started with Microsoft.Extensions.AI and Aspire (without needing any paid service) Ingesting, indexing, citing, and visualizing your custom business data sources for retrieval-augmented generation (RAG) Teaching AI how to retrieve data from your APIs and invoke your app's actions on behalf of a user Consuming and building Model-Context Protocol (MCP) servers for easy interop across AI systems A peek into potential future tooling that simplifies the development processThis is a fast-paced deep dive into how to do it - it's coding demos all the way through! By the end you'll hopefully have a strong sense of which parts are relevant to your scenarios and how to wire them together into something genuinely useful for your users.Ref: https://www.youtube.com/watch?v=WzF5XpzkZeg&list=TLGG7qyJwEWYNdExMjAzMjAyNg

Large language models (LLMs) exploded into mainstream awareness in 2022, and have continued to fascinate us since. But what is it about LLMs, compared to other, similarly complex algorithms, that have so captured our imagination? And why is it that we are so ready to believe that these models have started to show signs of human behavior?Ref: https://www.youtube.com/watch?v=RSS1a8ngGRU&list=TLGGxdryY_1A4T4yMDAxMjAyNg

Generative AI has leapt from clever chatbots to self-directed digital coworkers, but most organisations still treat it as a plug-in for their existing processes. This session maps the journey from rule-based bots to AI-enabled workflows, then shows why 2025 belongs to true agents: systems that perceive, plan, and act with minimal hand-holding.Ref: https://www.youtube.com/watch?v=zaQOlw0-jQ4

Generative AI, exemplified by tools like ChatGPT, marks a significant shift in computing, enabling machines to perform creative and intellectual tasks once exclusive to humans. This talk will explore the evolution of generative AI, its applications across various fields, and its potential to revolutionize industries. This session will examine the roles AI can assume, from doctor, to creator to security analyst. We will explore the impact on medicine, farming, food security, water, environmental sustainability, and biodiversity.Ref: https://www.youtube.com/watch?v=AovbXg_R3Pw&list=TLGGZE23od5J70MyMDAxMjAyNg

The whole world is excited about generative AI, but how do we start to build with it? Do we need to learn linear algebra, machine learning, or even python?It turns out that our existing knowledge and skills are still very much in demand. There are some terms and tools you need to understand, but it's not as big a jump as you think.This talk is a roadmap for understanding GenAI as a developer and how to start building with it, from interacting with large language models to rendering output to the browser and everything you need to know in between.Ref: https://youtu.be/XhdOFJX6byM?list=TLGGr6MONZks9QoyMDAxMjAyNg

Every enterprise is under pressure to implement AI - from board mandates to competitive necessity. Yet the path from aspiration to successful implementation is filled with misconceptions, unrealistic expectations, and poorly chosen use cases that waste time and resources before we even tackle technical implementation decisions in this rapidly evolving field..I've spent the past few years implementing AI solutions that deliver measurable business value across government agencies, educational institutions and healthcare organizations.Ref: https://www.youtube.com/watch?v=KTbeNNUOmVI

2025 was the year AI stopped feeling chaotic and started feeling buildable. In this Lightcone episode, the YC partners break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started.

What will define AI in 2026? 🚀 Martin Keen & Aaron Baughman explore groundbreaking trends like Agentic AI, cloud computing, automation, and quantum computing, plus innovations like Physical AI. Discover how these technologies will transform industries and drive the next wave of intelligence.

Large language models (LLMs) exploded into mainstream awareness in 2022, and have continued to fascinate us since. But what is it about LLMs, compared to other, similarly complex algorithms, that have so captured our imagination? And why is it that we are so ready to believe that these models have started to show signs of human behavior?In this talk, we’ll delve into some of the more extraordinary claims that have been made about LLMs in the past few years, including that these models are showing signs of sentience or intelligence. We’ll discuss why humans have a tendency to see such traits in these models, due to the way they mirror back a “lossy compression” of our humanity. And we’ll talk about how dispelling myths about LLMs being anything more than language models can help us apply them to their best current uses.Ref: https://www.youtube.com/watch?v=RSS1a8ngGRU&list=TLGGxdryY_1A4T4xODAxMjAyNg

The roles of programmers and designers are evolving. The convergence of design and code signals a narrowing gap, prompting us to question the future landscape of design. As we enter the age of AI, will this lead us to chart a new course, or will it see us walking down familiar paths?Drawing from her experience leading Design at GitHub, she’ll delve into her journey starting out as a designer who codes, to building and leading teams of hybrid designer-developers. She'll examine how blurring the traditional boundaries between design and engineering has shaped the role of Design Engineering in the future of software design. Join her as she explores the dynamic interplay between AI, design, and programming, and consider the exciting possibilities that lie ahead.Ref: https://www.youtube.com/watch?v=3fu3nvOYs8Q&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=49

Retrieval augmented generation (RAG) provides large language models with up to date information and helps them hallucinate less. But how does it all work beneath the covers?In this live coding session we'll build the components of a RAG system from scratch. (Aside from the LLM, there probably isn't time for that!) By building our own, we'll understand vectorisation, similarity search, and the role of embedding models and vector databases. We'll then plug it all together to see our augmented bot in action.You'll get a good grounding in the components of successful chatbots and why they work.Ref: https://www.youtube.com/watch?v=wkvGcbsZmPo&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=48

AI is transforming the way Security Operations Centers (SOCs) work, and as a SOC engineer, your role is evolving fast.Ref: https://www.youtube.com/watch?v=CaZbsbKnOho&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=47

Cybersecurity is rapidly evolving, shaped by artificial intelligence (AI) and the emergent potential of Quantum Computing.AI enhances security through automated detection and analysis, swiftly processing vast amounts of data to spot and predict threats, and Quantum Computing holds the promise to revolutionize various industries by offering unparalleled computational speed and efficiency, enabling it to tackle complex problems far beyond the reach of classical computers.Yet, these benefits also come with risks: AI's capabilities can be exploited for advanced phishing, vulnerability discovery, and creating adaptive malware, complicating the cybersecurity landscape, while Quantum Computing further challenges digital security by threatening to undermine traditional encryption, making existing protections potentially obsolete.This session will explore the mixed impact of AI and Quantum Computing on Cybersecurity, highlighting both the advancements and vulnerabilities they introduce. We'll discuss current threats like supply chain attacks and ransomware, alongside the integration of Privacy Enhancing Technologies (PETs) with AI and quantum defenses, offering a strategic viewpoint on safeguarding against the future of cyber threats.Ref: https://www.youtube.com/watch?v=C9IgEfYCwFo&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=46

With GenAI, developers are shifting from traditional code reuse to generating new code snippets by prompting GenAI, leading to a significant change in the ways software gets developed.Several academic studies show that AI generated code based on LLM's that are trained on vulnerable OSS implementations lead to vulnerable generated code. Another study showed that developers tend to trust GenAI created code more than human created code. Combining that with the higher code velocity it will result in more vulnerabilities in it's output.Using an AI system that runs an LLM also has additional risks tied to it, related to jailbreaks, data poisoning and malicious agents, recursive learning and IP infringements.In this presentation, we will examine real-world data from several academic studies to understand how GenAI is changing software security, the risks it introduces, and possible strategies to address these emerging issues.Ref: https://www.youtube.com/watch?v=krDJlrw5mM0&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=44

In the past year, ChatGPT and the OpenAI API have gone from 0 to 100 faster than a Tesla. No one wants to be left behind. Businesses are automating tasks and having content written instantly.Some companies are suddenly ✅ 10x more productive, and some companies ❌ struggle.SSW consultants have been leveraging AI to improve client’s products…. And solving problems inside SSW too. Let’s take a look at the best ones!The advent of Custom GPTs has meant problems that would have taken weeks to solve before, can have production ready solutions in hours.Semantic Kernel then allows you to build enterprise solutions that take advantage of LLMs in a structured and scalable way, enabling the integration of AI into various business processes and systems.Ref: https://www.youtube.com/watch?v=upUZAZljueI&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=28

Web developers: you have a fantastic opportunity to make your web UIs more intelligent and productive than before. But don’t just throw on a chat pane and call it done, as people may not even use or like it. Let's explore how language models can integrate into your existing web UIs, anticipating your users' needs and completing their tasks faster.This is a technical, demo-centric talk with examples in Blazor, MVC/Razor Pages, and plain old C#, but the concepts would apply in other stacks too. We’re not going to get into the depths of how large language models (LLMs) work internally, nor will we focus on specifics of OpenAI APIs. Instead, we’ll focus on practical, complete, end-to-end usage patterns for AI in web UIs, making your users happier and more productive.Ref: https://www.youtube.com/watch?v=TSNAvFJoP4M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=29

In this session, we will explore what this model can do, and rather than just showing a perfect polished final demo, I will walk you through my entire journey of trying to use the model to solve Wordle puzzles, starting with "Hello World". Along the way, you will gain a good understanding of the model's capabilities, along with learning some prompt engineering techniques that drove progress in this journey (along with what didn't work!). We'll close with a live demo to attempt to solve today's Wordle! This session will tackle a fun problem, but the underlying prompt engineering techniques for image understanding that you will learn are applicable to a wide variety of business problems.Ref: https://www.youtube.com/watch?v=sfpk4wQeS_g&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=37

The focus upon AI continues to be the predominant technology subject of the day; it’s the must-have feature of any new product or service; it’s at the forefront of many discussions about ethics, attribution and indeed our own future employment prospects.But increasingly, the term “AI” has become synonymous with only one flavour of artificial intelligence - that being “machine learning” (ML) - e.g. generative AI, applied AI, large language models (LLMs) and the such.However, there are many other types of AI, which until recently, were the mainstay algorithms found behind automated decision making solutions.Many of these concepts can be found in the video games we know and love.Are these other types of AI still relevant? Do they risk being drowned out, or forgotten, in the rush to embrace machine learning solutions?In this session, intended for the enterprise/business application developer, we’ll open a window into the world of the video game development.We’ll explore the type of algorithms that are a staple of game development: pathfinding, state machines, decision trees, and goal-oriented action planning.We’ll delve into some of the performance considerations necessary to keep these algorithms running efficiently.We’ll circle back to how the business application developer can use this type of AI in applications, and how the lessons learnt making video games can help us write better software.Ref: https://www.youtube.com/watch?v=w30cK2ga42M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=36

AI CoPilots are all the rage - but none quite offer that personalised butler service SciFi told us we might one day have.To understand what it takes to train a CoPilot, we will see how training a model works under-the-hood; discuss the importance of quality training data to craft a truly powerful and personalised experience, and safety or security concerns to consider when training a model on a public service.Moving beyond the (chat) box, we will leverage Azure's OpenAI Service and Semantic Kernel in .NET to create a custom AI CoPilot for internal applications or data. We will see how to train our own custom Codex model, for generating code and commands to perform bespoke tasks against a non-public API, plus some innovative ways to glue this together with a nice user experience.You will leave feeling excited about the power of custom CoPilots, and armed with the knowledge to build your own!Ref: https://www.youtube.com/watch?v=QXPlIYd408A&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=35

As we enter the age of AI, the roles of programmers and designers are evolving. The convergence of design and code signals a narrowing gap, prompting us to question the future landscape of design. Will AI-driven innovation lead us to chart a new course, or will it see us walking down familiar paths?Drawing from my experience leading Design at GitHub, I’ll delve into my journey starting out as a designer who codes, to building and leading teams of hybrid designer-developers. I'll examine how blurring the traditional boundaries between design and engineering has shaped the role of Design Engineering in the future of software design. Join me as I explore the dynamic interplay between AI, design, and programming, and consider the exciting possibilities that lie ahead.Ref: https://www.youtube.com/watch?v=c1uEHfmJMTM&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=34

It’s 1998. It’s the year of Britney Spears, The Spice Girls, the first Google Doodle, and the year Titanic dominated the box office.It’s also the year Hasbro gifted us with the Furby, the first successful attempt at an interactive robot pet. It divided the playground, created a generation of spooky sleepover stories and sparked the xmas riots of 99’.Two decades on, creatives and engineers have started to crack the secrets of the Furby, evolving and unlocking its full potential using today’s technology.https://www.youtube.com/watch?v=2cW9KeFkMnE&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=33

It's time you meet your AI pair programmer. Do you find yourself stuck on a chunk of code? Unsure of how best to center a div? GitHub Copilot can help. Get unstuck by seeing suggested lines or code, whole functions, and learn more about your development journey through having code explained, and even translate your code into other languages.Ref: https://www.youtube.com/watch?v=Mlviuph9QX4&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=32

Natural language processing using generative pre-trained transformers (GPT) algorithms is a rapidly evolving field that offers many opportunities and challenges for application developers. But what is a generative pre-trained transformer, and how does it work? How can you leverage the latest advances in GPT algorithms to create engaging and useful applications? Can my business benefit from creating a GPT powered chat bot?In this demo intensive session Alan will take a deep dive into the architecture of GPT algorithms and the inner workings of ChatGPT. The journey will begin by looking at the fundamental concepts of natural language processing, such as word embedding, vectorization and tokenization. He will then demonstrate how you can apply these techniques to train a GPT2 model that can generate song lyrics, showing the internals of how word sequences are predicted.Alan will then shift the focus to larger language models, such as ChatGPT and GPT4, demonstrating their power, capabilities, and limitations. The use of hyperparameters such as temperature and frequency penalty will be explained and their effect on the generated output demonstrated. He will then cover the concepts of prompt engineering and demonstrate how Retrieval Augmented Generation (RAG) patterns can be leveraged to create a ChatGPT experience based on your own textual data.Ref: https://www.youtube.com/watch?v=P2cTtiirPnU&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=31

AI is due to revolutionize the life of a developer, with Microsoft leading the way, combining the public code base of GitHub.com with ChatGPT to product Copilot to speed code generation and increase developer productivity.However, this is just the latest in a set of tools and frameworks that have all had the goal of improving productivity. But have we lost something along the way?As soon as we start using tools, they will directly influence the way we work, and we need to be aware of when they are useful and when we should use them. This is Conway's Law applied to tools - the very tools you use change how you developer, and not necessarily for the better.This includes ORMs for DB access like NHibernate and EF, mocking frameworks, IoC frameworks, refactoring tools like ReSharper all the way to Copilot.Should we always lean so heavily on these tools? Will they be supported in the future? Are we deskilling future generations of programmers? Am I just an old grumpy developer? Will Jon Skeet no longer be required?Some of these questions may be answered in this talk.Ref: https://www.youtube.com/watch?v=rLN9kSvMRXI&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=30

Web developers: you have a fantastic opportunity to make your web UIs more intelligent and productive than before. But don’t just throw on a chat pane and call it done, as people may not even use or like it. Let's explore how language models can integrate into your existing web UIs, anticipating your users' needs and completing their tasks faster.This is a technical, demo-centric talk with examples in Blazor, MVC/Razor Pages, and plain old C#, but the concepts would apply in other stacks too. We’re not going to get into the depths of how large language models (LLMs) work internally, nor will we focus on specifics of OpenAI APIs. Instead, we’ll focus on practical, complete, end-to-end usage patterns for AI in web UIs, making your users happier and more productive.Ref: https://www.youtube.com/watch?v=TSNAvFJoP4M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=29

The journey into AI integration shows that every single person's job—from developers to non-developers—has been impacted by this technology. Adoption starts with the basics: most users overlook critical steps like setting up Custom Instructions in ChatGPT to ensure clear, concise, and direct responses while avoiding unnecessary niceties and garbage. Furthermore, the rise of custom GPTs, built even by non-coders, demonstrates incredible low-code power, enabling the automation of enterprise tasks—like developer booking—that reduced the process time from 8 minutes down to just 30 seconds.Ref: https://www.youtube.com/watch?v=upUZAZljueI&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=27

My talk, "I Connected My Farm To The Internet. Now What?", uses the Llama cam hobby project to explore product development under real-world constraints like a 100 gigabytes of internet data per month limit and zero budget. We integrated Home Assistant and custom AI for llama detection to provide value and continuously iterate. Key takeaways: Listen to your community feedback and remember to freaking build it.Ref: https://www.youtube.com/watch?v=okqngjyTW88&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=26

Microsoft Security Copilot leverages generative AI to help overwhelmed security teams by summarizing complex incidents and generating crucial KQL queries using natural language prompts. This first-of-its-kind security AI operates at machine speed, leveraging Microsoft’s comprehensive global threat intelligence to make defenders more efficient and effective against the rapidly scaling challenges of modern cyberattacks.Ref: https://www.youtube.com/watch?v=cCUMJ9ywfuQ&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=25

Struggling to make an impact or overcome networking anxiety? LinkedIn is a powerful, free tool that can help you shortcut your time to becoming a "Minimum Visible Person" (MVP). By establishing credibility from a distance and influencing by volume, you can use LinkedIn to access all areas, from the factory floor all the way up to the boardroom, positioning yourself for the career that you want.Ref: https://www.youtube.com/watch?v=5Tg9leLR3fE&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=24

Deploying Generative AI applications at production scale demands careful attention to architecture and security, starting with the realization that large language models are entirely stateless and state must be constructed and passed through (e.g., via a database) to avoid losing conversation context and enable proper scaling. To achieve production readiness and control costs, developers should implement basic patterns like rate limiting for tokens and messages, restrict maximum payload size to prevent exhaustion attacks, and proactively utilize message analytics to monitor abuse and understand user behavior.Ref: https://www.youtube.com/watch?v=hn2Dn3fLIfg&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=23

HTML is not just the foundation we build on, its vital in making our websites accessible usable and performant.We'll explore how we can make the most of our HTML elements and attributes to improve the performance and accessibility of our website and applications as well as boosting the efficiency of our development process.All by using a technology we are already use day to day, but just using it better than we were before. At the end of the talk you'll be able to make things more useful and useable, not just for performance and accessibility but for our users and different technologies, likes bots, applications and AI as well.Ref: https://www.youtube.com/watch?v=zA4QzRGIP_w&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=22

Everyone has at some point wished they could clone themselves – to do the dishes, or work more efficiently. With advancements and improved accessibility of AI, this becomes more of a reality...This session will explore how to use Azure Custom Neural Voice to create a synthesised version of our own voice for use in a range of fun and practical applications – telephony, voice overs, or even attending meetings on your behalf...!We'll cover the basics of training and tweaking a model of our own voice, with minimal code, that can be used to turn text into natural-sounding speech.Advancing further, we'll learn some ways to train our model with enhanced training data. As an example we'll take a larger amount of audio, such as a podcast, use widely available tools and APIs to identify the individual speakers and the words being spoken – including highly technical language – to feed into our model.You will leave with the knowledge and inspiration to give "cloning" your voice a try!Ref: https://www.youtube.com/watch?v=XkbvfSLO3yE&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=21

This session covers the ethical use of AI, detailing how to identify, understand, and proactively counter potential risks while sharing examples of impactful solutions built for the nonprofit and humanitarian sectors. The discussion emphasizes the necessity of implementing responsible AI principles, utilizing mitigation strategies like prompt engineering and grounding, and acknowledging that AI will always make mistakes.Ref: https://www.youtube.com/watch?v=odWIkRcqEAU&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=20

Large Language Models (LLMs), including GPT, operate at their simplest level by attempting to produce a reasonable continuation of the text they are given, basing their predictions on patterns observed across a massive corpus of information like billions of web pages. Prompt engineering is an iterative process that employs various techniques—such as role prompting, few-shot learning, and Chain of Thought prompting—to increase the accuracy, reliability, and personalization of the output, which helps minimize uncertainty and build trust in the generative AI technology.Ref: https://www.youtube.com/watch?v=1XrgOK-Ydl8&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=19

Recent advances in generative AI, exemplified by LLMs like Stable Diffusion and ChatGPT, have created significant industry hype. Generative AI involves creating new media (such as text or images) by analyzing massive datasets to deduce and mimic existing patterns, a process driven by probabilistic and stochastic modeling. While models like GPT can produce humanlike text, they operate as language prediction models rather than utilizing true reasoning (AGI), which means they often "stumble over facts," produce inconsistent results, and struggle with basic tasks like multiplication, leading to "hallucinations". To leverage these tools effectively, prompt engineering is necessary—this "subtle art" involves providing clear, specific instructions, setting a system context or persona, and potentially using examples to coax a useful result from the AI. When integrating AI via the stateless Completions API, developers must manually maintain conversation state by sending the entire history with each request, often summarizing older messages to manage token costs. More robust applications can utilize GPT Functions (Tools) to allow the model to intelligently call external functions—avoiding expensive model retraining—to access live or proprietary data. Alternatively, to query custom data using natural language, facts can be converted into high-dimensional vectors called embeddings and compared using cosine similarity against user queries, often managed in a database like Postgress with PG Vector. Finally, the newer Assistants API simplifies the development of domain-specific helpers by automatically managing message history and context compaction, and uniquely, when referencing uploaded knowledge files (like a lease document), it provides specific references or footnotes detailing where the answer was found.Ref: https://www.youtube.com/watch?v=OxHw_u45h7M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=18

Enterprise software development is currently facing immense executive pressure, driven by boards and CEOs demanding rapid innovation, especially utilizing AI, to increase productivity, save costs, and gain a competitive advantage, a significant shift from previous executive disinterest in issues like integration modernizations. Developers frequently encounter frustrations and delays, including the "age old disconnect" where their pursuit of new tools clashes with IT and Security's focus on uptime, reliability, and avoiding security breaches, leading to delays of months in realizing business value after writing working code. This inefficiency is exacerbated by the extensive time spent on environment setup, sometimes weeks, which the speaker suggests "should be illegal" given the cost of developer time. To address these challenges, modern tooling focuses on standardizing environments: GitHub Codespaces provides an ephemeral, standardized development environment (VS Code in the browser connected to backend compute) where mean time to onboard can be reduced to minutes, defined by a devcontainer.json file that specifies necessary dependencies; complementarily, Dev Box offers full, on-demand virtual machines based on team-defined templates, which are highly supported by security teams because they are built on existing tooling (like Windows 365 and InTune) and allow for customized security profiles that can exclude productivity apps (a common attack vector), helping one customer reduce environment setup time for .NET applications from over two weeks to three hours. Further accelerating delivery involves codifying Deployable Architectural Archetypes using the Azure Developer CLI (azd), a principle that dictates repositories contain source code alongside infrastructure-as-code (Terraform or Bicep) and CI/CD pipelines, ensuring critical elements like Key Vault and Azure Monitor are "baked in" from the start. This approach is key to engaging security, architecture, and infrastructure champions early, transforming them into innovation accelerators by incorporating their requirements into the blueprint templates, allowing deployment of full infrastructure and applications with simple commands like azd up. Ultimately, while tools like these, along with GitHub Co-pilot for Business (which securely boosts performance by 30% to 50%), help "grease the wheels," successful acceleration relies on ensuring the people and process are right, including having security champions on the team and deploying applications to secure, compliant Landing Zones.Ref: https://www.youtube.com/watch?v=mTozV_eV4jQ&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=17

This presentation by Mandy Michael, a Staff Software Engineer and Google Developer Expert, makes a compelling case for using HTML meaningfully to improve web performance and accessibility, arguing that these concerns should be addressed from the foundational HTML stage, not left until the end of development. The core message is that HTML elements should be chosen based on the content type—similar to using types in TypeScript—rather than relying excessively on generic div elements, because correct semantic HTML creates a crucial Document Object Model (DOM) and Accessibility Tree that is consumed by assistive technologies and search engines. Michael provides practical HTML tips, such as favoring native element functionality (like using <button> instead of a styled <div>) for built-in features and using attributes like fetchpriority, loading="lazy", and resource hints (preload, preconnect) to effectively optimize resource loading and improve performance metrics like Cumulative Layout Shift (CLS) and Largest Contentful Paint (LCP).https://youtu.be/zA4QzRGIP_w?list=TLGGh5u1cryB-zYwNzEwMjAyNQ

This speech synthesis service allows you to train your own model based on existing base models, utilizing a neural Voder to generate speech from text input. Crucially, Microsoft promotes responsible use: every custom voice must provide a talent statement (consent) that expires after 90 days.https://youtu.be/XkbvfSLO3yE?list=TLGGiewzlim6miMwNzEwMjAyNQ

This talk was recorded at NDC Sydney in Sydney, Australia.Ref: https://www.youtube.com/watch?v=odWIkRcqEAU&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=20

To trust or not to trust? That depends on the quality of your prompts. Trusting Large Language Models (LLMs) is all about reducing uncertainties, and effective prompt design is the key to achieving the desired outputs.Prompt design, often referred to as Prompt Engineering, is a burgeoning field that demands creativity and meticulous attention to detail. It involves carefully selecting the right words, symbols, patterns, and formats to guide the LLMs in generating high-quality and relevant texts for the tasks at hand.Ref: https://www.youtube.com/watch?v=1XrgOK-Ydl8&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=19

People talking about AI is like glitter after a craft project, or azulejos in architecture, it's everywhere! Recent advances in generative AI, like Stable Diffusion and Chat-GPT, have the industry more hyped up than a caffeine-addicted toddler. It can leave one feeling like they're being left behind if they're not incorporating AI into their apps today.In this talk, we'll take a sober look at the topic of Generative AIs. In particular, we'll look at how Large Language Models (LLMs) work and where they break down. We'll consider how to engineer prompts for AI. And finally, we'll look at practical examples of adding AI into into a real-world app based on my experiences introducing AI into https://ab.bot/, an app designed to help customer success teams support their customers. We'll write code that calls Open AI APIs that demonstrate how to sprinkle a little AI into your own app. Join us to learn how to leverage the latest advancements in generative AI and take your applications to the next level.Ref: https://www.youtube.com/watch?v=OxHw_u45h7M&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=18

The landscape of software development is rapidly evolving, and developers are constantly seeking better tools to enhance their productivity and create more efficient workflows. In this talk, I'll show you what many of my enterprise customers are looking to implement - deploying architectural archetypes using the latest advancements in developer toolsets.We will stand up our dev environment on Azure DevBox and GitHub Codespaces, use the Azure Developer CLI to deploy the templates, leverage GitHub CoPilot for AI-powered code generation to deploy an architecture including the Azure OpenAI API for integrating machine learning and natural language processing into applications - and we'll easily do it in 60 minutes.Come along for a hands-on tour of the latest in developer tooling.Ref: https://www.youtube.com/watch?v=mTozV_eV4jQ&list=PL03Lrmd9CiGey6VY_mGu_N8uI10FrTtXZ&index=17