Posts

Hi, Four weeks ago, I looked into Temporal.io as a platform for building and orchestration reliable workflows. I looked at it through the lens of distributed systems, combining it with concepts from Martin Kleppmann’s Designing Data-Intensive Applications (for reference, DDIA). In this post, I team up with Claude Code and Codex to rebuild these core architectural ideas using LangGraph and Postgres. LangGraph is built specifically for stateful, multi-agent systems. My goal here is to dig deeper into orchestration patterns and gain a clearer understanding of what it takes to run agentic systems in production. My long-term goal is exploring the fault-tolerant guarantees of Temporal with the agentic flexibility of LangGraph as a combination for modern enterprise workflows. ...

Hi, For more than a year, I have been using coding agents, mostly Claude Code. During that time, I have barely written code myself. Instead, my role has shifted toward planning, reviewing and understanding code. I also use these agents for studying, brainstorming and problem-solving. I definitely struggle with the blurred boundary of my own work and orchestrated LLM work. I feel like a Code DJ by using and mixing ideas and samples from someone else. ...

Hi, In this post I want to outline Agentic AI. I’ve spent the last two years building AI agents and I want to share an overview of what I’ve learned. I originally meant to start this series a year ago, but finding the time was tough. While this field is moving incredibly fast, I’m going to ignore the latest hype and focus strictly on the fundamentals. Nowadays, the term “agent” is used loosely for almost any system involving an LLM. As an introduction, I want to draw a clear distinction between an agent and a workflow. An agent operates in an open environment and autonomously figures out how to solve a problem (e.g. a chatbot for data analysis). In contrast, a workflow is a predefined sequence of steps in which an LLM is used to execute specific tasks (e.g. extracting text to fill out a form). ...

Hi, This post is written by AI as an experiment. I put much effort into guidelines and specified several rounds of improvements, but the result is still not very graspable. It’s not bad, but also not really good. I see this as a proxy for the current state of agentic coding. Everyone uses it, but if you look at the outcome it is often shallow. If you mention your concerns about quality, responsibility and ownership, you will be always ignored because of the productivity gain. ...

Hi, this is the final post in my series about RAG systems. Here I will look into fine-tuning the generative part of a RAG system. Here is the notebook. But mostly this is for me a method to understand LLM fine-tuning in a superficial and general way. For the records, I looked into search, ANN Algorithms, fine-tuning embedding models, fine-tuning a reranker and evaluation of RAG systems in my previous posts. In RAG, the retrieval step provides the facts. The fine-tuning is about model behavior to prevent that a base model hallucinate or ignore the provided data. As a rule of thumb, retrieval gives the model its memory and fine-tuning gives it its instructions. ...

Hi, Based on my previous post, I looked into fine-tuning a bi-encoder via self-supervised learning. As a logical next step, I now want to fine-tune a cross-encoder for my specific task. This is not as easy as fine-tuning a bi-encoder, because cross-encoders are not designed to be trained with contrastive learning. Therefor I looked into four different approaches to fine-tune a cross-encoder for my specific task: BCE loss BCE loss with hard negatives InfoNCE loss Margin MSE loss BCE loss The simplest approach is to use binary cross-entropy loss to train the model. Here we have two labels, 1 for a positive pair and 0 for a negative pair and treat the relevance as a binary classification problem Passage Re-Ranking with BERT by Nogueira and Cho 2020. This can also be called a pointwise approach. For every query-document pair we calculate the logit, then apply a sigmoid activation twhich will represent the probability of relevance. ...

Hi, Based on my previous post, I need to write a correction. I was wrong. I previously suggested using embeddings directly from pre-trained models. That turns out to be a bad idea, because the training objectives are fundamentally different. As an alternative, I have now explored training an embedding model using Contrastive Learning. Please look at my notebook for the full code and details. The main reason you should not use raw embeddings from pre-trained models is the anisotropy problem. The paper How Contextual are Contextualized Word Representations? from Ethayarajh et al. 2019 shows that the embeddings are clustered in a narrow cone of the vector space, making them almost useless for differentiation. So, if you calculate the cosine similarity between two completely different sentences, the result will be around 0.80. ...

Hi, I decided to add another post to this year’s recap. After I described my three key learnings from my failed startup endeavor, I wasn’t feeling finished or satisfied. Overall I’m not satisfied with what I learned this year. It feels very shallow without any hard skills behind it. In every year of my life, I learned a lot, independent of the later usefulness, but at least I can say, that I moved forward. This year, I don’t feel like this. As Confucius said: “By three methods we may learn wisdom: First, by reflection, which is noblest; Second, by imitation, which is easiest; and third by experience, which is the bitterest.” My learnings weren’t helpful at all and they came from experience. To sum it up, the ratio of effort and outcome was very low and I definitely can do better. ...

Hi, This year was an interesting learning experience. I tried to commercialize a product, which in the end was much harder than expected. As a short summary, the first attempt failed, because I couldn’t figure out how to sell the product. The pivot failed, because multiple big corporates released a similar product. Also the goal was to get 1-2 customers to build a MVP before looking for funding and so on. So I never incorporated the idea into a company. Basically, I failed already on the first meter, but this saved a lot of time. So I only invested 7 months. 🥲 ...

Hi, Over the last three months, I’ve been working on Agents and LLM workflows. I had the opportunity to do this as part of a residency program at Merantix. I’m deeply thankful for the opportunity and would highly recommend the program to others. Being surrounded by people with shared goals was incredibly motivating and inspiring. I already miss the discussions and the knowledge exchange. Unlike typical work environments, here the participants were truly aligned, and everyone learned from each other. ...