framework-internals
0
avoiding-non-null-assertions
Avoid non-null assertion operator (!) and use type-safe alternatives instead
djankies
development
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framework-internals
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debugpytorch
Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows.
SnakeO
development
framework-internals
0
reference-chimerax-dev
ChimeraX bundle/extension development reference
N283T
development
framework-internals
0
modern-python-stack
Astral็คพ่ฃฝใใผใซ๏ผuv, Ruff, ty๏ผใงPython้็บ็ฐๅขใๆง็ฏใใใญใธใงใฏใไฝๆใไพๅญ้ขไฟ็ฎก็ใใณใผใๅ่ณชๆนๅใCI/CD่จญๅฎๆใซไฝฟ็จใ
YunosukeYoshino
development
framework-internals
0
pytorch-common-pitfalls
Fix common PyTorch bugs: percentile calculations, LayerNorm for Conv1d, buffer edge cases. Trigger when writing PyTorch code for RL or neural networks.
smith6jt-cop
development
framework-internals
0
refactoring-04-data-io-validation
Use when improving data loading, validation, and IO boundaries in Python research code.
Silviase
development
framework-internals
0
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use for ML training, analytics tools, performance profiling, or any Python heavy lifting.
ak-eyther
development
framework-internals
0
numpy-ufuncs
Universal functions (ufuncs) for vectorization, including reductions, in-place operations, and custom Python-function wrapping. Triggers: ufunc, vectorize, reduce, accumulate, frompyfunc, in-place.
cuba6112
development
framework-internals
0
precision-protocol
Protocol untuk meningkatkan kecerdasan, presisi, dan keamanan dalam coding dan debugging.
RaiKanaeru
development
framework-internals
0
pytorch-onnx
Exporting PyTorch models to ONNX format for cross-platform deployment. Includes handling dynamic axes, graph optimization in ONNX Runtime, and INT8 model quantization. (onnx, onnxruntime, torch.onnx.export, dynamic_axes, constant-folding, edge-deployment)
cuba6112
development
framework-internals
0
cpu-mode-assertions-torch
Assert CPU-only runtime inside a container using PyTorch (torch.cuda.is_available()==False) and optional env var checks. Use to prevent accidental GPU execution during CPU smoke tests.
jfriisj
development
framework-internals
0
webgpu-canvas
WebGPU fundamentals for high-performance canvas rendering. Covers device initialization, buffer management, WGSL shaders, render pipelines, compute shaders, and web component integration. Use when building GPU-accelerated graphics, particle systems, or compute-intensive visualizations.
matthewharwood
development
framework-internals
0
superpowers-factory-bridge
็ปไธๅผๅ็ผๆๅจ๏ผๅฐ Superpowers ๆนๆณ่ฎบๆกๆถไธ AI ไปฃ็ ๅทฅๅๆทฑๅบฆๆดๅ๏ผ ๅๅปบ็ปไธ็ๅผๅๅทฅไฝๆต๏ผ็ปๅ TDD ็บชๅพใSoT ๅ่งๅ่ชๅจๅไปฃ็ ็ๆใ
wade56754
development
framework-internals
0
lean-hit-development
Guides adding new Higher Inductive Types to the ComputationalPaths library. Use when creating new HITs, defining fundamental group (pi1) calculations, implementing encode-decode proofs, or adding new topological spaces.
Arthur742Ramos
development
framework-internals
0
ml-optimization
Optimizes TensorFlow.js WebGPU backend, WebLLM offline inference, and implements memory safety for heavy ML models
do-ops885
development
framework-internals
0
complexity-killer
Audit and remove unnecessary abstractions from the codebase. Use when user wants to simplify, reduce dependencies, or eliminate complexity that slows down AI agents.
DennisToma
development
framework-internals
0
collimator-optics
Use profunctor optics from the Collimator library for Lean 4. Use when working with lenses, prisms, traversals, or nested data access.
nathanial
development
framework-internals
0
optimizing-truffle-performance
[ORCHESTRATOR] Runs full performance optimization loop for Truffle languages. Entry: User wants to optimize performance. Process: Determines current state, executes appropriate phase (1โ2โ3โ4), loops until performance meets expectations. IMPORTANT: Single entry point for all optimization work.
antonykamp
development
framework-internals
0
pytorch-distributed
Distributed training strategies including DistributedDataParallel (DDP) and Fully Sharded Data Parallel (FSDP). Covers multi-node setup, checkpointing, and process management using torchrun. (ddp, fsdp, distributeddataparallel, torchrun, nccl, rank, process-group)
cuba6112
development
framework-internals
0
type-hints-for-ml-code
Apply appropriate type hints for ML/PyTorch code. Use when adding type annotations to ML code or addressing mypy errors.
apassuello
development
framework-internals
0
embedded-systems
Expert embedded systems engineer specializing in microcontroller programming, RTOS development, and hardware optimization. Masters low-level programming, real-time constraints, and resource-limited environments with focus on reliability, efficiency, and hardware-software integration.
vx-daniel
development