Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

RAG won't work in 2026 if you're still using old approaches.

Yes, many companies are still failing with RAG — not because they're doing it wrong, but because they're stuck on outdated techniques.

Here's what usually happens: most companies start with a chatbot / chat app when talking about AI implementation. And here RAG becomes key — to connect their data via a database and enable the chat app to retrieve relevant documents.

But today, RAG is no longer limited to just chats. The applications of RAG are practically limitless, and that's a good thing.

RAG still remains the foundation for everything you build on LLMs and AI agents. The only thing that's changed is the RAG techniques themselves. The old approach no longer works — more advanced techniques are needed, what's now called advanced RAG.

The essence of RAG is to enrich the system with your data via a database so it can find relevant documents or their parts. The results are simple and often "okay", especially if the documents are well-structured and there aren't many of them.

But when the documents are unstructured and it's important to get not just accurate documents but also the right context, advanced techniques come into play:

- query decomposition
- metadata enrichment
- hybrid indexing
- reranking
- context fusion

These approaches allow the RAG system to find and generate more accurate and contextually relevant answers.

Therefore, advanced RAG is important. RAG isn't dead and can't die. Just use smarter techniques.

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When have you ever needed to add a mathematical description for your function in Python, but found that it takes too much time?

Non-programmers can't easily read Python's logic. However, manually converting it to LaTeX is slow and quickly becomes outdated as the code changes.

latexify_py solves this problem with a single decorator, generating LaTeX directly from your function, so that the mathematics remains readable and always synchronized with the code.

Main features:
• Three decorators for different outputs: expressions, full equations, or pseudocode
• Displays the rendered LaTeX directly in Jupyter cells
• Functions continue to work normally when called

In addition, latexify_py is open source. Install it using pip install latexify-py

An article about 3 tools that convert Python code to LaTeX: https://bit.ly/3Pw89yP
Run this code: https://bit.ly/4bW2ycE

https://t.me/CodeProgrammer

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