The library

optimizing-attention-flash · Long-context LLM inference and training where attention is the bottleneck and 2-4x speedup with 10-20x memory savings is critical

migrate-to-shoehorn · Test suites with large mock objects where only a few properties matter and traditional `as` is heavy-handed

token-budget · Multi-agent Gastown convoys needing hard token gates to prevent runaway spend and cascade failures

gguf-quantization · Deploying LLMs on consumer hardware (MacBook M1+) or servers without NVIDIA GPU when universal hardware support is required

bayesian-methods · Incorporating prior domain knowledge into inference and updating beliefs with small sample sizes where frequentist confidence intervals fail

gptq · Deploying 70B+ models on A100/H100 when 4× compression and <2% accuracy loss is acceptable

k-fencore · WoW addon authors building complex addon logic without reimplementing common utilities

descriptive-statistics · Data exploration when hypothesis testing is premature and you need to understand raw data distribution first

hqq-quantization · Fast model quantization when calibration data unavailable and extreme compression (2-bit) is acceptable

webapp-testing · End-to-end regression testing when unit tests miss user-facing behavior

inferential-statistics · Determining whether a sample observation generalizes to the population when descriptive stats alone insufficient

ml-training-recipes · Starting model training quickly with expert-vetted defaults instead of tuning from scratch

commands-gizix-cc-projects · Extending Claude Code project templates with custom agent behaviors

probability-theory · Building rigorous statistical reasoning from first principles when intuitive probability reasoning fails

evaluating-code-models · Comparing code model performance across standard benchmarks when new architecture or training method is evaluated

init-pablolion-bmad-plugin · Starting a new project using BMAD methodology with pre-built scaffolding

regression-modeling · Quantifying how variables influence outcomes when you need interpretable coefficients rather than pure prediction.

evaluating-llms-harness · Standardized model comparison using industry-standard benchmarks when you need reproducible academic metrics.

api-design · Creating APIs that agents and humans consume reliably when predictability beats cleverness.

statistical-computing · Deriving confidence intervals and p-values when analytical formulas are unavailable or standard assumptions violated.

nemo-evaluator-sdk · Enterprise benchmarking of multiple models at scale when reproducible containerized evaluation is required.

cybersecurity-basics · Teaching security fundamentals when professional penetration testing is not yet required.

llama-cpp · Edge deployment and local inference on Apple Silicon and non-NVIDIA hardware without Docker complexity.

detecting-command-injection · Finding command injection during code review when automated scanners miss indirect and complex paths.

design-thinking · Designing solutions that solve real problems for real people, not just technically elegant systems.

sglang · Agentic workflows with repeated prefixes (system prompts, tools) where 5× speedup via caching outweighs setup.

digital-systems · Building systems where correctness cannot be patched after deployment (aerospace, medical, critical infrastructure).

code-health-check · Regular codebase audits to catch health degradation before it blocks features.

emerging-tech · Evaluating novel tech claims when you want to separate signal from hype and zeitgeist.

serving-llms-vllm · Production serving of open models on NVIDIA hardware when high throughput and predictable latency required.

human-computer-interaction · Creating interfaces where users don't get stuck because system behavior matches their expectations.

mlflow · Managing dozens of experiments with repeatable comparison and governance when you need audit trails.

idiomatic-rust · Systems code where memory safety must hold at compile-time without runtime GC overhead.

experiment-tracking-swanlab · Real-time experiment monitoring during training when live curves beat post-hoc analysis.

forderungen-interessen-matrix · Navigating multi-stakeholder projects when you need to identify whose buy-in actually matters.

comparative-religion · Understanding religious frameworks systematically when you need historical and comparative analysis, not apologetics.

tensorboard · When debugging deep learning models with real-time metric visualization and experiment comparison across runs.

ethics-and-practice · When comparing how religious traditions ground moral claims in sources, virtue, law, and contemplative practice.

weights-and-biases · When managing production ML experiments that need team collaboration, automatic metric logging, and hyperparameter optimization.

commit · When creating git commits that reflect user intent and preserve reasoning for future context.

mysticism-and-contemplation · When analyzing contemplative experiences and mapping apophatic vs kataphatic approaches across traditions.

evolving-ai-agents · When optimizing agent performance through iterative evolution cycles that automatically mutate prompts, skills, and memory.

aktenstruktur-und-dokumentenintake · When organizing German legal personnel files and identifying missing documentation in administrative disputes.

philosophical-theology · When evaluating whether theological claims survive rational scrutiny or examining how philosophy engages divine attributes.

autogpt-agents · When building persistent autonomous agents with visual workflows and external triggers without code.

jakobs-law · When deciding whether to follow established UI conventions or justify innovation against user mental models.

scripture-and-interpretation · When using scripture-and-interpretation is more effective than generic alternatives.

crewai-multi-agent · When using crewai-multi-agent is more effective than generic alternatives.

sql-patterns · When using sql-patterns is more effective than generic alternatives.

systematic-theology · When using systematic-theology is more effective than generic alternatives.