Hot on the heels of the release of Gemini, I’d like to share a couple of resources I created to get your hands on large language models, using LangChain4J, and the PaLM 2 model. Later on, I’ll also share with you articles and codelabs that take advantage of Gemini, of course. The PaLM 2 model supports 2 modes: text generation, and chat. In the 2 codelabs, you’ll need to have created an account on Google Cloud, and created a project. Read more...

Google announced today the availability of Gemini, its latest and more powerful Large Language Model. Gemini is multimodal, which means it’s able to consume not only text, but also images or videos. I had the pleasure of working on the Java samples and help with the Java SDK, with wonderful engineer colleagues, and I’d like to share some examples of what you can do with Gemini, using Java! First of all, you’ll need to have an account on Google Cloud and created a project. Read more...

Large Language Models, available through easy to use APIs, bring powerful machine learning tools in the hands of developers. Although Python is usually seen as the lingua franca of everything ML, with LLM APIs and LLM orchestration frameworks, complex tasks become easier to implement for enterprise developers. Abstract Large language models (LLMs) are a powerful new technology that can be used for a variety of tasks, including generating text, translating languages, and writing different kinds of creative content. Read more...

Some friends shared this article from Uwe Friedrichsen, tilted back to the future, that talks about this feeling of “déjà-vu”, this impression that in IT we keep on reinventing the wheel. With references to mainframes, Uwe compared CICS to Lambda function scheduling, JCL to step functions, mainframe software development environments to the trendy platform engineering. There are two things I like about this article. First of all, it rings a bell with me, as we’ve seen the pendulum swing as we keep reinventing some patterns or rediscovering certain best practices, sometimes favoring an approach one day, and coming back to another approach the next day. Read more...

The State of AI report is pretty interesting to read (even if long!). Among the major sections: research, industry, but also politics, safety, and some predictions. You’ll find an executive summary in one slide, on slide #8. On #22, emergent capabilities of LLMs is covered and mentions Stanford’s research that talks about the importance of more linear and continuous measures as otherwise capabilities sound like they emerge out of the blue. Read more...

Stop Using char in Java. And Code Points It’s a can of worms, when you start messing with chars, code points, and you’re likely going to get it wrong in the end. As much as possible, stay away from chars and code points, and instead, use as much as possible the String methods like indexOf() / substring(), and some regex when you really need to find grapheme clusters. Paul King shared his presentations on Why use Groovy in 2023 and an update on the Groovy 5 roadmapIt’s interesting to see how and where Groovy goes beyond what is offered by Java, sometimes thanks to its dynamic nature, sometimes because of its compile-time transformation capabilities. Read more...

Generative AI exists because of the transformer I confess I rarely read the Financial Times, but they have a really neat articles with animations on how large language models work, thanks to the transformer neural network architecture, an architecture invented by Google in 2017. They talk about text vector embeddings, how the self-attention makes LLM understand the relationship between words and the surrounding context, and also doesn’t forget to mention hallucinations, how “grounding” and RLHF (Reinforcement Learning with Human Feedback) can help mitigate them to some extent. Read more...

Inspired my by super boss Richard Seroter with his regular daily reading list, I decided to record and share my tech watch, every week (or so). I always take notes of interesting articles I read for my own curiosity and to remember them when I need those references later on. But also to share them with Les Cast Codeurs podcast! So I hope it’ll be interesting to my readers too! Read more...

As I started my journey with Generative AI and Large Language Models, I’ve been overwhelmed with the omnipresence of Python. Tons of resources are available with Python front and center. However, I’m a Java developer (with a penchant for Apache Groovy, of course). So what is there for me to create cool new Generative AI projects? When I built my first experiment with the PaLM API, using the integration within the Google Cloud’s Vertex AI offering, I called the available REST API, from my Micronaut application. Read more...

Last week, I wrote about how to get started with the PaLM API in the Java ecosystem, and particularly, how to overcome the lack of Java client libraries (at least for now) for the PaLM API, and how to properly authenticate. However, what I didn’t explain was what I was building! Let’s fix that today, by telling you a story, a kid story! Yes, I was using the trendy Generative AI approach to generate bedtime stories for kids. Read more...