#al

With respect to memory, while I can’t remember everything I used to be able to, I can understand things again very quickly with appropriate, er, prompting. AI is very good at helping me with this, and it lets me switch between open source projects and work projects much more efficiently than if I didn’t have it. With respect to the long hours, AI is able to grind in a way that, even as a young developer, I would have had a difficult time keeping up with. This means, for example, I can have a much more extensive test suite for my projects than I would have otherwise. Looking at the tests that Claude generated in this case, they are more extensive than what I probably could have mustered the energy to do myself. So AI has addressed two fundamental (relative) weaknesses I have developed as an older developer.

Text With Jesus offers users an interactive experience with a religious deity. In other words, users can text questions to Jesus and get a response. (Premium users can also converse with Satan.)

The compounding comes from a “build a tool for making a tool” recursive mindset. These teams will automate everything they can, and will often tell the coding system “you’ll need a tool for that, go ahead and build and use it” - and then the system will do just that, checking it in to git and making it a permanent improvement.

My point is that if all knowledge were stored in a structured way with rich semantic linking, then very primitive natural language processing algorithms could parse question like the example at the beginning of the article, and could find the answer using orders of magnitude fewer computational resources. And most importantly: the knowledge and the connections would remain accessible and comprehensible, not hidden within impenetrable AI models.

Transformer Lab is a free, open-source LLM workspace that you can run on your own computer. It is designed to go beyond what most modern open LLM applications allow. Using Transformer Lab you can easily finetune, evaluate, export and test LLMs across different inference engines and platforms.

my attempt to build such a capable AI computer without spending too much. I ended up with a workstation with 48GB of VRAM that cost me around 1700 euros

Highlight:The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. We’re on a journey to advance and democratize artificial intelligence through open source and open science.