Can you imagine a piece of code that takes the 3D model data, transforms/processes it, and then passes it on to the next Image-To-Image step? Let's call this the full program, because the word pipeline seems to be difficult.
@alexjc
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3D Model Processing and Geometric Transformations in AI Pipelines
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Hypothetically, if the full pipeline can be shown to do rotations/transformations, then by your explanation of geometric transformations above, then it *must* be processing 3D model data?
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Rendering Pipeline Logic: Generative Processing Inclusion
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If the rendering pipeline includes both the processing of the 3D model as well as the generative part, then the answer must be "Yes" too because the first part does it and it's included? (This is basic logic, not a trick either.)
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Frontier Models: Cleaning Up Lower-Generation AI Output
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The biggest usecase for advancing the frontier models is to clean up slop written by frontier(n-1) models! Arguably, skilled programmers writing building on quality code could manage just fine on prior generations of models by just improving tools, harnesses, skills, etc. With
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Project Migration to Codeberg with Improved Action Runners
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REMINDER: The project and releases are now hosted on Codeberg. Enjoying it very much, easier to customize the Action Runners.
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joyfl v0.7: Stack Manipulation Puzzle Solver Framework
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joyfl — v0.7: Quotation Solver Newest release last week uses the existing search framework to find matching program fragments. It's like using a sudoku solver to discover solutions for stack manipulation puzzles, based on user-provided tests.
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GLM-5 Regression in Interactive Python Coding Performance
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I think GLM-5 is a regression on interactive Python coding though, been using it almost daily and GLM 4.7 before that. The most likely culprit is DSA — and I conclude it's not straightforward to apply. Likely V4 manages better, but there will be tradeoffs.
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GLM-5 Regression for Python Coding Tasks Compared to GLM 4.7
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Alright, I'm calling it: GLM-5 is a regression from GLM 4.7 for Python coding. Subscribed to Z(.)ai on the basis of 4.7 as it reliably took over all my devops too, and been using GLM 5 since launch. But with multiple turns of Python writing/editing 5 regularly gets confused
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LLM Performance Degradation Over Extended Context Windows
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Performance degrades over the course of 100k tokens even, let alone the whole currently supported window… After a few turns of coding Python, it just can't reliably use its tools anymore. Requires constant jumping back and/or offloading.
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Tool calling conventions bug fixed at launch
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At launch it would screw up the tool calling conventions, is that better now? Maybe it was a launch glitch…