Code Conversations

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