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My clang format file. In sample.cpp is sample output
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"Hey! You need to be on the instagram site before you run the code. I'm taking you there now but you're going to have to run the code into the console again.",
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Tampermonkey script to claim all of the items within a Bundle on Itch.io
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Example Configuration for Renovate in TYPO3 Projects (as shown at #T3CRR24)
This Gist provides an example configuration for Renovate, designed to automate updates in TYPO3 projects. Note: This configuration is a starting point and should be adjusted to fit your specific requirements. The update behavior, in particular, likely has room for improvement depending on your workflow.
Key Assumptions
Git Hosting: The example assumes the use of either GitLab or GitHub for managing TYPO3 projects.
Shared Configuration: A shared GitLab project (gitlab/renovate-config) is used to store general configuration files (.json). This setup allows reusing shared rules across all TYPO3 projects.
Project-Specific Configuration: The renovate.json file must reside in the TYPO3 project repository you wish to update. This file references the shared configuration and defines project-specific overrides.
Accelerating Discrete Program Search with SUP Nodes
Fast Discrete Program Search 2
I am investigating how to use Bend (a parallel language) to accelerate Symbolic AI; in special, Discrete Program Search. Basically, think of it as an alternative to LLMs, GPTs, NNs, that is also capable of generating code, but by entirely different means. This kind of approach was never scaled with mass compute before - it wasn't possible! - but Bend changes this. So, my idea was to do it, and see where it goes.
Now, while I was implementing some candidate algorithms on Bend, I realized that, rather than mass parallelism, I could use an entirely different mechanism to speed things up: SUP Nodes. Basically, it is a feature that Bend inherited from its underlying model ("Interaction Combinators") that, in simple terms, allows us to combine multiple functions into a single superposed one, and apply them all to an argument "at the same time". In short, it allows us to call N functions at a fraction of the expected cost. Or, in simple terms: why parallelize when we can share?
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