Recently I was asked to come up with a solution to classify a large list of tens of thousands of email addresses based on their details. Although the original request was a bit different, for this blog post I will make the example of classifying the addresses as being from a personal email provider (like Gmail, Yahoo, etc.) or from a business or other institution. I will demonstrate an approach how to use a self-hosted Zephyr-7B model using the Litserve package to perform this task very efficiently.
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Nowadays it feels that the TrueNAS CORE community is shrinking every month. I have been a user of this solution for more than 8 years now, but some aspects of the system like plugins have never matured to a degree where they are really usable. I have been using the Nextcloud plugin for years now (after having upgraded from ownCloud), but updating the plugin from either the designated plugin architecture of from the Nextcloud management screens is quite often painful and tends to break the jail if there are major upgrades on key components like PHP.
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Last November ChatGPT has held its first anniversary, after having reached a user base of 180 million active users, making this the fastest adopted technology with an internet user base ever. Now we are a year in, I’d like to explore how the open-source world has caught up with ChatGPT’s success. What is the current state of affairs when comparing open source LLMs to the closed source and proprietary models from OpenAI and others? Is open-source technology able to match or surpass the technology brought by OpenAI in 2022?
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Retrieval Augmented Generation (RAG) allows for adding knowledge from external sources into a Large Language Model (LLM) prompt. In this blog post I will be using this approach for using an LLM to retrieve data from an SAP S4/HANA system and visualizing the data using Streamlit. You will learn how to build a dashboard converting natural language questions into data visualizations using only a minimal amount of code!
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With the Tour de France 2023 having started last weekend there will be lots of media coverage of the world’s toughest cycle race in the next weeks. If you are a regular viewer of these races you will probably recognize a few situations where you found it difficult to exactly determine who you are watching on the screen. In this blog post I am presenting a way to do augment the race with rider and team recognition capabilities to make it easier to detect riders and follow what is happening during the race.
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