Today I attended and contributed a talk to the Boston Code Camp 38 (yes, impressively, the 38th edition of this event). I made the trip with Maura (she gave a talk combining Cryptocurrency And Agentic AI) and Kevin (he gave a talk on Top 10 AI Security Risks). and we got to hang out with and chat with so many cool people from the Boston technology community.
The description of my Let’s Build a Goal-Oriented AI Agent Using Semantic Kerneltalk follows, followed by a couple of relevant links, then the slide deck.
Imagine an AI not limited to answering individual questions or chatting, but actively working towards a goal you’ve assigned to it.
In this session, we’ll explore the building of an AI Agent – an autonomous actor that can execute tasks and achieve objectives on your behalf.
Along the way we will demystify:
1. 🧠 LLMs – What is a Large Language Model (LLM) 2. 📚 Tokens – What is a token and what are its roles 3. 💡 Embeddings – What are embedding models and vectors and what can they do for us 4. 🎯 Prompts – Beyond the basics 5. ⚙️ Tools – How can these be created and accessed using Semantic Kernel 6. 🤖 Agents – Let’s put all these concepts to work!
The end result will be the core (or perhaps ‘kernel’ 😉) of an AI Agent – your virtual coworker willing to handle tasks on your behalf without. It will be built in C# using the open source, cross-platform Semantic Kernel library.
This talk assumes user-level familiarity with LLMs like ChatGPT or Microsoft Copilot and basic prompting. Anything else will be explained.
Always great to hang out with the greater Boston tech community. Today I attended and contributed a talk to Boston Code Camp 36 (the 36th edition of this event).
I made the trip with Maura (she gave a talk on blockchain). and we met a lot of cool people and had a great time.
I spoke on GitHub Copilot. Much of my talk was demo and discussion – you have to see this in action (or use it) to appreciate what’s happening. I consider this a glimpse into the future – it will surely become then norm to have an AI assistant when programming.
It is fun have one AI 🤖 (GitHub Copilot) help us program another AI 🤖 (Azure OpenAI). 🤖 🤖 🤖 😀 After Copilot Chat was able to explain that Azure OpenAI did not have any notion of “today” we used Copilot to implement a trivial version of RAG to anchor the prompt to the current day.
We saw how the agents like @workspace can explain a body of code and even help us figure out where to implement a new feature (such as the --joke command line param).
Another demo was to get Copilot to write unit tests for me. The demo gods were not helpful 😱 😱 😱 and I ran into an error. I moved on without fixing it since time was short. I diagnosed it later and it turns out I had double-pasted (classic demo failure!) which caused the problem. We did use /tests to create unit tests, which were initially NUnit test, but then we asked Copilot to recast them as xUnit tests, then to more efficiently create test cases using the InlineData attribute to consolidate similar test cases.We didn’t get to run the tests at the end, but hopefully the power of GitHub Copilot in helping to create unit tests came through.
I also had the opportunity to hang out with some smart soon-to-be graduates from my alma mater – University of Massachusetts at Boston (some of them were Rohini Deshmukh, Master’s in Information Technology, Kunal Sahjwani, Master’s in Information Technology, and Shounak Kulkarni, Master’s in Business Analytics). Great to see our profession is in such capable hands from chatting with these very smart and capable technologists, analysts, and future leaders.
Here is the published talk abstract for the talk I delivered – and though much of the session was demos, the PowerPoint deck is attached after the abstract.
Meet GitHub Copilot, Your AI Coding Assistant
Imagine an assistant who anticipates your needs as you code, handling mundane and time-consuming steps, allowing you to focus on more complex challenges (the fun stuff). Allow me to introduce you to GitHub Copilot.
GitHub Copilot is an AI-powered coding assistant that integrates with your developer IDE adding many powerful productivity features. Backed by the same OpenAI Large Language Model (LLM) behind ChatGPT, it has an uncanny ability to suggest code snippets that you were about to type in. But suggesting code snippets is just the beginning.
In this demo-heavy talk, we’ll show usage basics, distinguish scenarios where it excels vs. some it finds challenging, and point out a few common anti-patterns so you can avoid them.
Since it is still early days, big features are still showing up at a fast clip, so we’ll highlight some recent features and some others just emerging. At the end we’ll squeeze in just a bit of prognosticating about what it might mean for the future of software development.
As you’ll learn in more depth during this session, the promise of GitHub Copilot is to help you be more productive – go faster, stay in flow, build even more robust software. We are not fully there but we are on the way. This imperfect tool is still a game changer.
I believe the rise of AI coding assistants is unstoppable at this point – and that’s a good thing. By the end of this session, you might agree. And maybe you’ll join the revolution early.
On 27-June-2023 at our first back-from-the-pandemic in-person regularly scheduled Boston Azure monthly meeting, the main event featured Juan Pablo Garcia Gonzalez from Microsoft talking about Azure OpenAI ChatGPT Prompt Engineering for Developers. Juan Pablo was kind enough to share his slides and code samples.
Keeping with the AI theme, I opened with a talk about GitHub Copilot where I gave a brief demo and explanation, then attempted to put it into context among other AI tools (including other Microsoft Copilot initiatives).
Coding Out Loud 