Uploaded February 2026 | Updated September 2026, 2 weeks ago
Imagine asking a computer to build an entire app from scratch and it does, instantly, just by understanding your words. That is not science fiction anymore. Large Language Models (LLMs) are reshaping how we write software, aiding developers and even non-developers alike in crafting code with unprecedented ease.
But there is a catch. These models are fluent in the world's most popular programming languages, like Python or Java, and struggle when they face the dialects of industry: the in-house programming languages companies build for their own unique problems. LLMs often have little to no training knowledge of these languages, which makes it difficult for them to assist in these specialized domains. For this reason, in-house programming languages are perceived as exotic by LLMs.
In this talk, I will share my experience in teaching new and exotic programming languages to LLMs.
I will show the challenges we faced during the process and how current techniques like retrieval-augmented generation, few-shot learning, and fine-tuning open-weight models performed in this context. Finally, I will share the "secret recipe" we used to successfully teach these languages to LLMs, enabling them to assist in coding tasks that were previously out of reach.
Imagine asking a computer to build an entire app from scratch and it does, instantly, just by understanding your words. That is not science fiction anymore. Large Language Models (LLMs) are reshaping how we write software, aiding developers and even non-developers alike in crafting code with unprecedented ease.
But there is a catch. These models are fluent in the world's most popular programming languages, like Python or Java, and struggle when they face the dialects of industry: the in-house programming languages companies build for their own unique problems. LLMs often have little to no training knowledge of these languages, which makes it difficult for them to assist in these specialized domains. For this reason, in-house programming languages are perceived as exotic by LLMs.
In this talk, I will share my experience in teaching new and exotic programming languages to LLMs.
I will show the challenges we faced during the process and how current techniques like retrieval-augmented generation, few-shot learning, and fine-tuning open-weight models performed in this context. Finally, I will share the "secret recipe" we used to successfully teach these languages to LLMs, enabling them to assist in coding tasks that were previously out of reach.
![[VDBUH2026] Magda Miu & Alin Miu - Engineering Leadership in the Age of AI](https://i.ytimg.com/vi/DAgVdXO4NFM/mqdefault.jpg "[VDBUH2026] Magda Miu & Alin Miu - Engineering Leadership in the Age of AI
Engineering management isn’t a promotion, it’s a career shift from solving technical problems to navigating human complexity. In the AI era, this shift becomes even more challenging as leaders must make high-stakes decisions under uncertainty and guide teams through rapid change.
This talk is a condensed “management lab” for senior engineers and new managers, offering practical frameworks for building psychological safety, running effective feedback systems, coaching for autonomy, and making AI-aware decisions.
You will leave with actionable tools, not theory, to help you lead with clarity, confidence, and strategic impact from day one.")
![[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us](https://i.ytimg.com/vi/G58PbxBXC8c/mqdefault.jpg "[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us
Not every organization operates with the hyperscale resources of Anthropic, Google, or OpenAI. For the majority of businesses integrating Large Language Models (LLMs) into their critical paths, the high costs and scarcity of GPU/TPU accelerators present a significant challenge. Striking the balance between performance, availability, scalability, and cost-efficiency is a must.
While Kubernetes is a ubiquitous runtime for modern workloads, deploying LLM inference effectively demands a specialized approach. This session dives deep into practical strategies for optimizing your Kubernetes clusters and LLM Inference workloads to run efficiently and cost effectively. We will explore:
– Container and Model Optimization
– Accelerator Management
– Data & Storage
– Network & Load Balancing
– Observability
Attendees will leave with practical techniques for maximizing cost/performance for LLM inference for their AI-powered applications on Kubernetes.")
