Knowledgator’s Post

Check out my latest article at ParametricArchitecture delving into generative architecture and the use of AI in design

Grok is open source now github.com/xai-org/grok Grok-1 is a large-scale language model with 314 billion parameters, utilizing a Mixture of Experts (MoE) architecture

MVI is an achitecture pattern becoming increasingly common on newer Android projects. Here is an article by Mohammed khudair on how the MVI architecture works: https://lnkd.in/dcKVhXDX

Implemented the Swin Transformer architecture from https://lnkd.in/eMqWFNBX. This architecture makes use of a hierarchical way of processing features in a way similar to CNNs which captures both the local and global features. Additionally, it introduces relative positional embeddings, enhancing its ability to model spatial relationships in images. It sparks the potential of Transformer-based models as vision backbones. Traditional vision transformers face challenges in scaling to high-resolution images due to their quadratic complexity with respect to input size. This limits their applicability to tasks such as high-resolution image classification and object detection. The shifted window attention in the Swin Transformer leads to greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. Thus, it scales linearly instead of quadratically like the Vision Transformer with respect to image size. These properties enable the Swin Transformer to excel for a broad range of vision tasks, not only image classification but also dense prediction tasks such as object detection and semantic segmentation. Throughout the implementation process, I learned about relative positional embeddings, shifted window attention and patch merging. Here's the repository : https://lnkd.in/eWpBRQUC

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Last december I did a very extensive analysis with NDepend. That write-up was quite an endeavour so I decided to try another approach. Last Monday I sat down with Kamil Baczek who co-authored Evolutionary Architecture by Example with his colleague Maciej Jedrzejewski. My initial reason to do this analysis was to visualize the structural evolution but also see what other insights NDepend would add to improve the examplary nature of the project. I was a first for me and Kamil to do a public video. I liked having a dialog instead of my writing down a monologue. So here’s a raw and unedited recording of that session. If you’re interested in the project and/or what NDepend can do, give it a look. 𝗔𝗯𝗼𝘂𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗯𝘆 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 What I like about the project is that it shows various technical implementations depending on the complexity and stage. Most examples show a single solution direction which if applied to real world scenario’s might not always fit. They not only show the code but take you along the journey of the analysis and construction by providing explanation, methods and Architecture Decision Records. In the video you can see how NDepend can quickly show proper layering of the codebase. The few places where dependency cycles are fairly low and relatively benign. It also shows that sometimes conflicting nature of various NDepend rules where solving one might result in a low severity violation of another rule. Even on such small projects like this which shows that implementing NDepend in production apps usually requires incremental tweaking and adding of rules. Looking forward to Chapter 4 where they intend to evolve the most complex part. My recommendation would be to consider merging (certain) subdomain assemblies to show how you then need to replace project dependencies with architectural tests to prevent undesired coupling. I believe this is also an interesting debate of trade-offs between various dimensions of simplicity and productivity. https://lnkd.in/epK7A29Z Check the comments for a link to the repo.

The article covers Hexagonal Architecture aka Ports and Adapters and the derived Model-View-Controller pattern https://lnkd.in/daj2tSzz

Here's how one can combine LLM and non-LLM components in a blackboard architecture. https://lnkd.in/gK5W7B4t